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"abstract": "Research has emerged over the past decade showing gender biases in software. Although a few methods and prototype systems have emerged to help address this issue, none have been reported to have an impact on the people who actually build software. In this paper, we summarize a few highlights from a year-long field study investigating how Gender HCI methods to address gender biases in software can make impacts on a large software company.",
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"abstract": "This study aims to improve students' argumentative capability and enable them to produce better argumentative essays. The \"digital argument-map writing system\" was designed for this study to help students build a complete structure of argumentation step by step so they can produce better argumentative essays with the structure as a thinking frame. We conducted an experiment to the students in an elementary school in New Taipei City for one semester. The experimental result showed that the system provided by this study could improve the students' argumentative capability. From the result of regression analysis, we found that the students using the digital argument-map writing system had a more thorough thinking on the forming of argumentation. Their evidence accords with their reasons and their reasons accord with their claims. The argumentation in their argumentative essays is thus highly connective and more integrated.",
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"abstract": "Empathy is commonly defined as the capacity to stand in another's shoes and can be generally divided into two major components: affective and cognitive empathy. The role and importance of empathy in clinical practice have been widely discussed in the research community at large and there have been numerous initiatives to train physicians and nurses in empathic communication. Qualitative and quantitative investigations of clinical practice settings involving empathy have primarily focused on patient experience, yet little has been done on the quantitative analysis and modeling of clinical empathy themselves. In this paper, we address two important aspects of clinical empathy modeling: the design and implementation of a new annotation protocol, and an automatic classification system trained on a new corpus of narrative essays written by pre-med students. To develop the annotation protocol, we built MedicalCare, a corpus of essays simulating a doctor's delivery of bad news to a hypothetical patient. We compared our results with state-of-the-art research on empathy prediction using self-rated empathy scores. Our research suggests that the classification system we built is effective in modeling clinical empathy and that our annotation protocol is more reliable when compared with self-assessment approaches to empathy prediction.",
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"abstract": "The traditional zero moment point (ZMP) control method is not suitable for underactuated biped robots. In this article, based on the computed torque method, the virtual constraint method and feedback linearization method are introduced into robot control to develop a joint control strategy for a biped robot with flexible ankle joints. Meanwhile, the traditional spline curve planning method of ankle joint and hip joint trajectory is no longer applicable to the biped robot. In this article, the time-invariant gait planning and gait stability of the robot are studied based on the principle of periodic walking. The effectiveness of the control strategy is verified by an experiment on the biped walking robot with flexible ankle joints.",
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"abstract": "Presents a compliant ankle mechanism and its implementation for a biped robot without actuators for the ankle joints. Ankle joints have been built using springs and mechanical constraints, which give a flexibility of joint within a predefined range and stiffness beyond the range. The biped with compliant ankles proposed makes foot landing easy, and weight and cost of legs are also reduced. The cost of the advantages is that the control problem becomes more difficult because the control torque of the ankle joint to put the biped in a desired walking gait can not be provided from the compliant ankle joint. To overcome this problem, we propose a pseudo static walking gait with dynamic gait modification method by adjusting the position of a hip joint. Experimental results with the biped KUBCA are given to show the validity of the proposed controller.",
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"content": "Data mining can be used to model crime detection problems. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. About 10% of the criminals commit about 50% of the crimes. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. We applied these techniques to real crime data from a sheriff?s office and validated our results. We also use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy. We also developed a weighting scheme for attributes here to deal with limitations of various out of the box clustering tools and techniques. This easy to implement data mining framework works with the geo-spatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for homeland security.",
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"proceeding": {
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"abstract": "Law enforcement agencies strive to link serial crimes, most preferably based on physical evidence, such as DNA or fingerprints, in order to solve criminal cases more efficiently. However, physical evidence is more common at crime scenes in some crime categories than others. For crime categories with relative low occurrence of physical evidence it could instead be possible to link related crimes using soft evidence based on the perpetrators' modus operandi (MO). However, crime linkage based on soft evidence is associated with considerably higher error-rates, i.e. crimes being incorrectly linked. In this study, we investigate the possibility of filtering erroneous crime links based on travel time between crimes using web-based direction services, more specifically Google maps. A filtering method has been designed, implemented and evaluated using two data sets of residential burglaries, one with known links between crimes, and one with estimated links based on soft evidence. The results show that the proposed route-based filtering method removed 79 % more erroneous crimes than the state-of-the-art method relying on Euclidean straight-line routes. Further, by analyzing travel times between crimes in known series it is indicated that burglars on average have up to 15 minutes for carrying out the actual burglary event.",
"abstracts": [
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"content": "Law enforcement agencies strive to link serial crimes, most preferably based on physical evidence, such as DNA or fingerprints, in order to solve criminal cases more efficiently. However, physical evidence is more common at crime scenes in some crime categories than others. For crime categories with relative low occurrence of physical evidence it could instead be possible to link related crimes using soft evidence based on the perpetrators' modus operandi (MO). However, crime linkage based on soft evidence is associated with considerably higher error-rates, i.e. crimes being incorrectly linked. In this study, we investigate the possibility of filtering erroneous crime links based on travel time between crimes using web-based direction services, more specifically Google maps. A filtering method has been designed, implemented and evaluated using two data sets of residential burglaries, one with known links between crimes, and one with estimated links based on soft evidence. The results show that the proposed route-based filtering method removed 79 % more erroneous crimes than the state-of-the-art method relying on Euclidean straight-line routes. Further, by analyzing travel times between crimes in known series it is indicated that burglars on average have up to 15 minutes for carrying out the actual burglary event.",
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"normalizedAbstract": "Law enforcement agencies strive to link serial crimes, most preferably based on physical evidence, such as DNA or fingerprints, in order to solve criminal cases more efficiently. However, physical evidence is more common at crime scenes in some crime categories than others. For crime categories with relative low occurrence of physical evidence it could instead be possible to link related crimes using soft evidence based on the perpetrators' modus operandi (MO). However, crime linkage based on soft evidence is associated with considerably higher error-rates, i.e. crimes being incorrectly linked. In this study, we investigate the possibility of filtering erroneous crime links based on travel time between crimes using web-based direction services, more specifically Google maps. A filtering method has been designed, implemented and evaluated using two data sets of residential burglaries, one with known links between crimes, and one with estimated links based on soft evidence. The results show that the proposed route-based filtering method removed 79 % more erroneous crimes than the state-of-the-art method relying on Euclidean straight-line routes. Further, by analyzing travel times between crimes in known series it is indicated that burglars on average have up to 15 minutes for carrying out the actual burglary event.",
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"title": "An Ontology for Property Crime Based on Events from UFO-B Foundational Ontology",
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"abstract": "Property Crime has been highlighted as one of the major criminal offenses within the Brazilian Legal System. Moreover, it is common that there is some misunderstanding amongst the subtypes of this crime, such as Theft, Robbery, Misappropriation, and Extortion. We still emphasize the verynuance of legal literature that makes this domain as hostile as challenging: a weakly formalized knowledge, the presence of conflicts and ambiguities between norms, the heterogeneity of legal literature, as well as the diversity in reasoning models. Therefore, this article presents, inspired by UFO-B foundational ontology, a conceptual model for the representation of crimes against property in the Brazilian Criminal Code, in order to support some decision-making process, as the agents behavior classification and the inference of punishments. Thus, we present throughout this article, an ontological formalization for the Theory of Crime from Brazilian Penal Code, as well as for Property Crimes applications.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Property Crime has been highlighted as one of the major criminal offenses within the Brazilian Legal System. Moreover, it is common that there is some misunderstanding amongst the subtypes of this crime, such as Theft, Robbery, Misappropriation, and Extortion. We still emphasize the verynuance of legal literature that makes this domain as hostile as challenging: a weakly formalized knowledge, the presence of conflicts and ambiguities between norms, the heterogeneity of legal literature, as well as the diversity in reasoning models. Therefore, this article presents, inspired by UFO-B foundational ontology, a conceptual model for the representation of crimes against property in the Brazilian Criminal Code, in order to support some decision-making process, as the agents behavior classification and the inference of punishments. Thus, we present throughout this article, an ontological formalization for the Theory of Crime from Brazilian Penal Code, as well as for Property Crimes applications.",
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"normalizedAbstract": "Property Crime has been highlighted as one of the major criminal offenses within the Brazilian Legal System. Moreover, it is common that there is some misunderstanding amongst the subtypes of this crime, such as Theft, Robbery, Misappropriation, and Extortion. We still emphasize the verynuance of legal literature that makes this domain as hostile as challenging: a weakly formalized knowledge, the presence of conflicts and ambiguities between norms, the heterogeneity of legal literature, as well as the diversity in reasoning models. Therefore, this article presents, inspired by UFO-B foundational ontology, a conceptual model for the representation of crimes against property in the Brazilian Criminal Code, in order to support some decision-making process, as the agents behavior classification and the inference of punishments. Thus, we present throughout this article, an ontological formalization for the Theory of Crime from Brazilian Penal Code, as well as for Property Crimes applications.",
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"Legal Computing",
"Ontologies",
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"affiliation": "Center of Inf., Fed. Univ. of Pernambuco, Recife, Brazil",
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"fullName": "Frederico Luiz Gonçalves de Freitas",
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"surname": "Gonçalves de Freitas",
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{
"affiliation": "Center of Inf., Fed. Univ. of Pernambuco, Recife, Brazil",
"fullName": "Ryan Ribeiro De Azevedo",
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"surname": "Ribeiro De Azevedo",
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"abstract": "A majority of crimes are committed by a minority of offenders. Previous research has provided some support for the theory that serial offenders leave behavioral traces on the crime scene which could be used to link crimes to serial offenders. The aim of this work is to investigate to what extent it is possible to use geographic route estimations and behavioral data to detect serial offenders. Experiments were conducted using behavioral data from authentic burglary reports to investigate if it was possible to find crime routes with high similarity. Further, the use of burglary reports from serial offenders to investigate to what extent it was possible to detect serial offender crime routes. The result show that crime series with the same offender on average had a higher behavioral similarity than random crime series. Sets of crimes with high similarity, but without a known offender would be interesting for law enforcement to investigate further. The algorithm is also evaluated on 9 crime series containing a maximum of 20 crimes per series. The results suggest that it is possible to detect crime series with high similarity using analysis of both geographic routes and behavioral data recorded at crime scenes.",
"abstracts": [
{
"abstractType": "Regular",
"content": "A majority of crimes are committed by a minority of offenders. Previous research has provided some support for the theory that serial offenders leave behavioral traces on the crime scene which could be used to link crimes to serial offenders. The aim of this work is to investigate to what extent it is possible to use geographic route estimations and behavioral data to detect serial offenders. Experiments were conducted using behavioral data from authentic burglary reports to investigate if it was possible to find crime routes with high similarity. Further, the use of burglary reports from serial offenders to investigate to what extent it was possible to detect serial offender crime routes. The result show that crime series with the same offender on average had a higher behavioral similarity than random crime series. Sets of crimes with high similarity, but without a known offender would be interesting for law enforcement to investigate further. The algorithm is also evaluated on 9 crime series containing a maximum of 20 crimes per series. The results suggest that it is possible to detect crime series with high similarity using analysis of both geographic routes and behavioral data recorded at crime scenes.",
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"normalizedAbstract": "A majority of crimes are committed by a minority of offenders. Previous research has provided some support for the theory that serial offenders leave behavioral traces on the crime scene which could be used to link crimes to serial offenders. The aim of this work is to investigate to what extent it is possible to use geographic route estimations and behavioral data to detect serial offenders. Experiments were conducted using behavioral data from authentic burglary reports to investigate if it was possible to find crime routes with high similarity. Further, the use of burglary reports from serial offenders to investigate to what extent it was possible to detect serial offender crime routes. The result show that crime series with the same offender on average had a higher behavioral similarity than random crime series. Sets of crimes with high similarity, but without a known offender would be interesting for law enforcement to investigate further. The algorithm is also evaluated on 9 crime series containing a maximum of 20 crimes per series. The results suggest that it is possible to detect crime series with high similarity using analysis of both geographic routes and behavioral data recorded at crime scenes.",
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"title": "Finding Criminal Attractors Based on Offenders' Directionality of Crimes",
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"abstract": "According to Crime Pattern Theory, individuals all have routine daily activities which require frequent travel between several nodes, with each being used for a different purpose, such as home, work or shopping. As people move between these nodes, their familiarity with the spatial area around the nodes, as well as between nodes, increases. Offenders have the same spatial movement patterns and Awareness Spaces as regular people, hence according to theory an offender will commit the crimes in their own Awareness Space. This idea is used to predict the location of the nodes within the Awareness Space of offenders. The activities of 57,962 offenders who were charged or charges were recommended against them were used to test this idea by mapping their offense locations with respect to their home locations to determine the directions they move. Once directionality to crime was established for each offender, a unique clustering technique, based on K-Means, was used to calculate their Cardinal Directions through which the awareness nodes for all offenders were calculated. It was found that, by looking at the results of various clustering parameters, offenders tend to move towards central shopping areas in a city, and commit crimes along the way. Almost all cluster centers were within one kilometer of a shopping center. This technique of finding Criminal Attractors allows for the reconstruction of the spatial profile of offenders, which allows for narrowing the possible suspects for new crimes.",
"abstracts": [
{
"abstractType": "Regular",
"content": "According to Crime Pattern Theory, individuals all have routine daily activities which require frequent travel between several nodes, with each being used for a different purpose, such as home, work or shopping. As people move between these nodes, their familiarity with the spatial area around the nodes, as well as between nodes, increases. Offenders have the same spatial movement patterns and Awareness Spaces as regular people, hence according to theory an offender will commit the crimes in their own Awareness Space. This idea is used to predict the location of the nodes within the Awareness Space of offenders. The activities of 57,962 offenders who were charged or charges were recommended against them were used to test this idea by mapping their offense locations with respect to their home locations to determine the directions they move. Once directionality to crime was established for each offender, a unique clustering technique, based on K-Means, was used to calculate their Cardinal Directions through which the awareness nodes for all offenders were calculated. It was found that, by looking at the results of various clustering parameters, offenders tend to move towards central shopping areas in a city, and commit crimes along the way. Almost all cluster centers were within one kilometer of a shopping center. This technique of finding Criminal Attractors allows for the reconstruction of the spatial profile of offenders, which allows for narrowing the possible suspects for new crimes.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "According to Crime Pattern Theory, individuals all have routine daily activities which require frequent travel between several nodes, with each being used for a different purpose, such as home, work or shopping. As people move between these nodes, their familiarity with the spatial area around the nodes, as well as between nodes, increases. Offenders have the same spatial movement patterns and Awareness Spaces as regular people, hence according to theory an offender will commit the crimes in their own Awareness Space. This idea is used to predict the location of the nodes within the Awareness Space of offenders. The activities of 57,962 offenders who were charged or charges were recommended against them were used to test this idea by mapping their offense locations with respect to their home locations to determine the directions they move. Once directionality to crime was established for each offender, a unique clustering technique, based on K-Means, was used to calculate their Cardinal Directions through which the awareness nodes for all offenders were calculated. It was found that, by looking at the results of various clustering parameters, offenders tend to move towards central shopping areas in a city, and commit crimes along the way. Almost all cluster centers were within one kilometer of a shopping center. This technique of finding Criminal Attractors allows for the reconstruction of the spatial profile of offenders, which allows for narrowing the possible suspects for new crimes.",
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"abstract": "Data mining can be used to model crime detection problems. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. About 10% of the criminals commit about 50% of the crimes. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. We applied these techniques to real crime data from a sheriff's office and validated our results. We also use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy. We also developed a weighting scheme for attributes here to deal with limitations of various out of the box clustering tools and techniques. This easy to implement data mining framework works with the geospatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for homeland security",
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"content": "Data mining can be used to model crime detection problems. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. About 10% of the criminals commit about 50% of the crimes. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. We applied these techniques to real crime data from a sheriff's office and validated our results. We also use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy. We also developed a weighting scheme for attributes here to deal with limitations of various out of the box clustering tools and techniques. This easy to implement data mining framework works with the geospatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for homeland security",
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"abstract": "Lack of adequate streetlights likely affect public safety, particularly in neighborhoods with higher crime rates. Several researchers have studied the influence of streetlights on crime. However, those studies compare the crime rate during the day and not night or explore crime patterns in socially disorganized communities. This study focuses on detecting the pattern of nighttime street crime near a broken or due-for-repair streetlights. Historical crime data and data on city streetlight service requests studied in this project. Analytical approaches for this projects include the least squares linear regression model applied to determine the relationship between streetlight and crime data and Ripley's K function is used to detect crime clusters near broken streetlights. The Moran's I index is used to measuring the spatial correlation between broken streetlights and crime rates. Optimized hotspot analysis is used to predict crime locations. This study found that broken streetlights cause increasing trends of crime near them The Moran's I index's large positive value underscored the statistically-significant clustering of street crimes around broken streetlights.",
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"content": "Lack of adequate streetlights likely affect public safety, particularly in neighborhoods with higher crime rates. Several researchers have studied the influence of streetlights on crime. However, those studies compare the crime rate during the day and not night or explore crime patterns in socially disorganized communities. This study focuses on detecting the pattern of nighttime street crime near a broken or due-for-repair streetlights. Historical crime data and data on city streetlight service requests studied in this project. Analytical approaches for this projects include the least squares linear regression model applied to determine the relationship between streetlight and crime data and Ripley's K function is used to detect crime clusters near broken streetlights. The Moran's I index is used to measuring the spatial correlation between broken streetlights and crime rates. Optimized hotspot analysis is used to predict crime locations. This study found that broken streetlights cause increasing trends of crime near them The Moran's I index's large positive value underscored the statistically-significant clustering of street crimes around broken streetlights.",
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"proceeding": {
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"title": "Bangladesh Crime Reports Analysis and Prediction",
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"abstract": "The systematic method of detecting crime, evaluating crime patterns, and anticipating crime trends is known as crime analysis and prediction. Crime is inherently unexpected and causes societal disruption. As Bangladesh’s population grows, so does the prevalence of crime, which is wreaking havoc on our society in various ways. As a result, analyzing crime data has become crucial for a better understanding of future crime patterns. Machine learning and data mining techniques can be quite useful in predicting future crime trends and patterns in this situation. Various machine learning algorithms are utilized in this study to predict future crime patterns in Bangladesh. The crime statistics are gathered from the Bangladesh Police website to analyse and predict dacoity, robbery, murder, women and child repression, kidnapping, burglary, theft, and other crimes in Bangladesh’s various regions. Another dataset from ACLED has been used to predict different kinds of events such as battles, explosions, protests, riots, strategic developments, violence against civilians with geolocations of the events. This research might assist Bangladesh police and law enforcement authorities to predict, prevent, and solve future crimes. The performance and success rate of the project are highly satisfactory. All resources of the project can be found at https://tinyurl.com/297yykmu",
"abstracts": [
{
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"content": "The systematic method of detecting crime, evaluating crime patterns, and anticipating crime trends is known as crime analysis and prediction. Crime is inherently unexpected and causes societal disruption. As Bangladesh’s population grows, so does the prevalence of crime, which is wreaking havoc on our society in various ways. As a result, analyzing crime data has become crucial for a better understanding of future crime patterns. Machine learning and data mining techniques can be quite useful in predicting future crime trends and patterns in this situation. Various machine learning algorithms are utilized in this study to predict future crime patterns in Bangladesh. The crime statistics are gathered from the Bangladesh Police website to analyse and predict dacoity, robbery, murder, women and child repression, kidnapping, burglary, theft, and other crimes in Bangladesh’s various regions. Another dataset from ACLED has been used to predict different kinds of events such as battles, explosions, protests, riots, strategic developments, violence against civilians with geolocations of the events. This research might assist Bangladesh police and law enforcement authorities to predict, prevent, and solve future crimes. The performance and success rate of the project are highly satisfactory. All resources of the project can be found at https://tinyurl.com/297yykmu",
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"normalizedAbstract": "The systematic method of detecting crime, evaluating crime patterns, and anticipating crime trends is known as crime analysis and prediction. Crime is inherently unexpected and causes societal disruption. As Bangladesh’s population grows, so does the prevalence of crime, which is wreaking havoc on our society in various ways. As a result, analyzing crime data has become crucial for a better understanding of future crime patterns. Machine learning and data mining techniques can be quite useful in predicting future crime trends and patterns in this situation. Various machine learning algorithms are utilized in this study to predict future crime patterns in Bangladesh. The crime statistics are gathered from the Bangladesh Police website to analyse and predict dacoity, robbery, murder, women and child repression, kidnapping, burglary, theft, and other crimes in Bangladesh’s various regions. Another dataset from ACLED has been used to predict different kinds of events such as battles, explosions, protests, riots, strategic developments, violence against civilians with geolocations of the events. This research might assist Bangladesh police and law enforcement authorities to predict, prevent, and solve future crimes. The performance and success rate of the project are highly satisfactory. All resources of the project can be found at https://tinyurl.com/297yykmu",
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"authors": [
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"abstract": "This work presents the construction and implementation of a virtual simulator for the ergonomic study of the firemen' performance at the time of removal of firemen equipment from fire truck. The problem is discussed in order to achieve the best value of ergonomic indicators for firemen during anti-fire action. The equipment can be located on the fore truck in a different ways. We are looking for the best way to do it from the point of view of ergonomics. Firefighters must equipment in a short time, causing high physical strain on their bodies. Virtual simulation will allow us to measure the physical loads of firemen' bodies in different types of actions without real firemen activity. It is possible to receive several permissible solutions and then evaluate them in a virtual simulation environment. Then it can then be taken into account a number of additional factors that are important for fireman and ergonomics. The basic modules of the simulator and their properties are shown. Selected sequence views of simulation experiments were also illustrated.",
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"content": "This work presents the construction and implementation of a virtual simulator for the ergonomic study of the firemen' performance at the time of removal of firemen equipment from fire truck. The problem is discussed in order to achieve the best value of ergonomic indicators for firemen during anti-fire action. The equipment can be located on the fore truck in a different ways. We are looking for the best way to do it from the point of view of ergonomics. Firefighters must equipment in a short time, causing high physical strain on their bodies. Virtual simulation will allow us to measure the physical loads of firemen' bodies in different types of actions without real firemen activity. It is possible to receive several permissible solutions and then evaluate them in a virtual simulation environment. Then it can then be taken into account a number of additional factors that are important for fireman and ergonomics. The basic modules of the simulator and their properties are shown. Selected sequence views of simulation experiments were also illustrated.",
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"abstract": "Children suffering from cerebral palsy (CP) tend to have difficulties in moving around, as the condition would affect their walking gait posture. A suitable walking aid would help CP children to maintain balance and body posture during standing and walking. Due to their condition, CP children often have different anthropometric measurement or body sizes compared to children without disabilities. This is one of the biggest challenges as designing assistive devices such as the walking aid, would require suitable dimensions, otherwise it would not fit and cause discomfort, or might further deteriorate the condition of the CP child. This paper describes the work done to acquire anthropometric measurement from CP children in Malaysia and utilizing these measurements in designing the wearable walking aid.",
"abstracts": [
{
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"content": "Children suffering from cerebral palsy (CP) tend to have difficulties in moving around, as the condition would affect their walking gait posture. A suitable walking aid would help CP children to maintain balance and body posture during standing and walking. Due to their condition, CP children often have different anthropometric measurement or body sizes compared to children without disabilities. This is one of the biggest challenges as designing assistive devices such as the walking aid, would require suitable dimensions, otherwise it would not fit and cause discomfort, or might further deteriorate the condition of the CP child. This paper describes the work done to acquire anthropometric measurement from CP children in Malaysia and utilizing these measurements in designing the wearable walking aid.",
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"normalizedAbstract": "Children suffering from cerebral palsy (CP) tend to have difficulties in moving around, as the condition would affect their walking gait posture. A suitable walking aid would help CP children to maintain balance and body posture during standing and walking. Due to their condition, CP children often have different anthropometric measurement or body sizes compared to children without disabilities. This is one of the biggest challenges as designing assistive devices such as the walking aid, would require suitable dimensions, otherwise it would not fit and cause discomfort, or might further deteriorate the condition of the CP child. This paper describes the work done to acquire anthropometric measurement from CP children in Malaysia and utilizing these measurements in designing the wearable walking aid.",
"fno": "773800a057",
"keywords": [
"Anthropometry",
"Ergonomics",
"Gait Analysis",
"Handicapped Aids",
"Medical Disorders",
"Paediatrics",
"Walking Aid",
"Anthropometric Measurement",
"Standing Walking",
"Body Posture",
"CP Children",
"Walking Gait Posture",
"Cerebral Palsy Children",
"Wearable Walking Aid",
"Legged Locomotion",
"Weight Measurement",
"Size Measurement",
"Length Measurement",
"Prototypes",
"Assistive Devices",
"Shape Measurement",
"Cerebral Palsy",
"Anthropometric Measurement",
"Assistive Device"
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"affiliation": "Universiti Tenaga Nasional",
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"fullName": "Farid Mahamud",
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{
"affiliation": "Universiti Tenaga Nasional",
"fullName": "Juniza Md Saad",
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{
"affiliation": "Universiti Tenaga Nasional",
"fullName": "Kesavan Rana Singam",
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"abstract": "Synchronicity between user’s movements and that of their self-avatar leads to a subjective illusion of embodiment over the virtual avatar. Tendon vibrations can be used to produce a perception of movement, in the absence of any voluntary movements. Such stimulations have been combined with virtual reality to induce the ownership illusion. In this study we investigate the ability of using a virtual self-avatar combined with tendon vibrations to give standing users the impression of walking physically. The vibrations, applied on 24 participants, had different levels of complexity and congruency with the avatar’s movements. Overall, results suggest that the pattern of vibrations is not crucial to produce ownership illusion.",
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"abstract": "Federated Learning (FL) enables multiple partici-pants to collaboratively train a model in a privacy-preserving way. The performance of the FL model heavily depends on the quality of participants' local data, which makes measuring the contributions of participants an essential task for various purposes, e.g., participant selection and reward allocation. The Shapley value is widely adopted by previous work for contribution assessment, which, however, requires repeatedly leave-one-out retraining and thus incurs the prohibitive cost for FL. In this paper, we propose a highly efficient approach, named DIG-FL, to estimate the Shapley value of each participant without any model retraining. It's worth noting that our approach is applicable to both vertical federated learning (VFL) and horizontal federated learning (HFL), and we provide concrete design for VFL and HFL. In addition, we propose a DIG-FL based reweight mechanism to improve the model training in terms of accuracy and convergence speed by dynamically adjusting the weights of participants according to their per-epoch contributions, and theoretically analyze the convergence speed. Our extensive evaluations on 14 public datasets show that the estimated Shapley value is very close to the actual Shapley value with Pearson's correlation coefficient up to 0.987, while the cost is orders of magnitude smaller than state-of-the-art methods. When there are more than 80% participants holding low-quality data, by dynamically adjusting the weights, DIG-FL can effectively accelerate the convergence and improve the model accuracy.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Federated Learning (FL) enables multiple partici-pants to collaboratively train a model in a privacy-preserving way. The performance of the FL model heavily depends on the quality of participants' local data, which makes measuring the contributions of participants an essential task for various purposes, e.g., participant selection and reward allocation. The Shapley value is widely adopted by previous work for contribution assessment, which, however, requires repeatedly leave-one-out retraining and thus incurs the prohibitive cost for FL. In this paper, we propose a highly efficient approach, named DIG-FL, to estimate the Shapley value of each participant without any model retraining. It's worth noting that our approach is applicable to both vertical federated learning (VFL) and horizontal federated learning (HFL), and we provide concrete design for VFL and HFL. In addition, we propose a DIG-FL based reweight mechanism to improve the model training in terms of accuracy and convergence speed by dynamically adjusting the weights of participants according to their per-epoch contributions, and theoretically analyze the convergence speed. Our extensive evaluations on 14 public datasets show that the estimated Shapley value is very close to the actual Shapley value with Pearson's correlation coefficient up to 0.987, while the cost is orders of magnitude smaller than state-of-the-art methods. When there are more than 80% participants holding low-quality data, by dynamically adjusting the weights, DIG-FL can effectively accelerate the convergence and improve the model accuracy.",
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"affiliation": "University of Science and Technology of China,School of Computer Science and Technology,Hefei,China",
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"affiliation": "University of Science and Technology of China,School of Computer Science and Technology,Hefei,China",
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"abstract": "As a graphical language, the Business Process Modelling Notation (BPMN) plays an important role in enterprise information systems. BPMN is a standard notation for capturing business processes, while Business Process Execution Language (BPEL) provides a standard executable language for specifying business process compositions and interactions. This paper proposes a new method based on graph grammars to map from BPMN to BPEL codes. In the first place, the existing Edge-based Graph Grammar (EGG) is introduced, which is a context-sensitive grammar and a new formal method for simply specifying graph grammars. Secondly, some aspects of EGGs are extended to conveniently map from BPMN to BPEL. Finally, new mapping steps and a parsing algorithm are put forward, which achieve a goal of this paper.",
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"abstract": "Grammar Induction is a machine learning process for learning grammar from corpora. This paper will discuss the process of grammar induction for Indonesian language corpora using genetic algorithm. The Grammar production rules will be modeled in the form of chromosomes. The fitness function is used to count how many sentences can be parsed. The data used are Indonesian fairy tales stories such as \"Bawang Merah Bawang Putih\" and \"Malin Kundang\". This paper describes the detailed explanations about the steps of each process carried out for natural language grammar problems.",
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"abstract": "The optimal path planning for robot patrol is a combinatorial optimization problem that aims to solve the smallest Hamiltonian circle of a complete graph. Such problems are typical NP-hard problems, and the computational complexity of the existing precise algorithm increases exponentially with the expansion of the problem scale. Even if a considerable amount of running time is employed, it is difficult to obtain the global optimal solution. This paper presents a robot patrol path planning method based on combined deep reinforcement learning (DRL). This method combines reinforcement learning and neural networks to perform path optimization. The first step is performing DRL pre-training through asynchronous advantage actor-critic (A3C). Training data is adopted to optimize the recurrent neural networks (RNN) that parameterizes the stochastic policy. The second step is effective planning (EP) without pre-training. Using the expected reward objective, it iteratively optimizes the RNN parameters on test instances. The experimental results show that combined deep reinforcement learning method can jump out of the local optimal solution and outperform other state-of-the-art methods.",
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"content": "The optimal path planning for robot patrol is a combinatorial optimization problem that aims to solve the smallest Hamiltonian circle of a complete graph. Such problems are typical NP-hard problems, and the computational complexity of the existing precise algorithm increases exponentially with the expansion of the problem scale. Even if a considerable amount of running time is employed, it is difficult to obtain the global optimal solution. This paper presents a robot patrol path planning method based on combined deep reinforcement learning (DRL). This method combines reinforcement learning and neural networks to perform path optimization. The first step is performing DRL pre-training through asynchronous advantage actor-critic (A3C). Training data is adopted to optimize the recurrent neural networks (RNN) that parameterizes the stochastic policy. The second step is effective planning (EP) without pre-training. Using the expected reward objective, it iteratively optimizes the RNN parameters on test instances. The experimental results show that combined deep reinforcement learning method can jump out of the local optimal solution and outperform other state-of-the-art methods.",
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"abstract": "Time series anomaly detection has become more critical with the rapid development of network technology, especially in cloud monitoring. We focus on applying deep reinforcement learning (DRL) in this question. It is not feasible to simply use the traditional value-based DRL method because DRL cannot accurately capture important time information in time series. Most of the existing methods resort to the RNN mechanism, which in turn brings about the problem of sequence learning. In this paper, we conduct progressive research work on applying value-based DRL in time series anomaly detection. Firstly, because of the poor performance of traditional DQN, we propose an improved DQN-D method, whose performance is improved by 62% compared with DQN. Second, for RNN-based DRL, we propose a method based on improved experience replay pool (DRQN) to make up for the shortcomings of existing work and achieve excellent performance. Finally, we propose a Transformer-based DRL anomaly detection method to verify the effectiveness of the Transformer structure. Experimental results show that our DQN-D can obtain performance close to RNN-based DRL, DRQN and DTQN perform well on the dataset, and all methods are proven effective.",
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"abstract": "The use of Optical See-Through Head-Mounted Displays (OST-HMDs) for presenting Augmented Reality experiences has become more common, due to the increasing availability of lower cost head-worn device options. Despite this growth, commercially available OST hardware remains devoid of the integrated eye-tracking cameras necessary for automatically calibrating user-specific view parameters, leaving manual calibration methods as the most consistently viable option across display types. The Single Point Active Alignment Method (SPAAM) is currently the most-cited manual calibration technique, due to the relaxation of user constraints with respect to allowable motion during the calibration process. This work presents the first formal study directly investigating the effects that alignment point distribution imposes on SPAAM calibration accuracy and precision. A user experiment, employing a single expert user, is presented, in which SPAAM calibrations are performed under each of five conditions. Four of the conditions cross alignment distance (arm length, room scale) with user pose (sitting, standing). The fifth condition is a control condition, in which the user is replaced with a rigidly mounted camera; the control condition removes the effect of noise from uncontrollable postural sway. The final experimental results show no significant impact on calibration due to user pose (sitting, standing). The control condition also did not differ from the user produced calibration results, suggesting that posture sway was not a significant factor. However, both the user and control conditions show significant improvement using arm's length alignment points over room scale alignments, with an order of magnitude difference in eye location estimate error between conditions.",
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"content": "The use of Optical See-Through Head-Mounted Displays (OST-HMDs) for presenting Augmented Reality experiences has become more common, due to the increasing availability of lower cost head-worn device options. Despite this growth, commercially available OST hardware remains devoid of the integrated eye-tracking cameras necessary for automatically calibrating user-specific view parameters, leaving manual calibration methods as the most consistently viable option across display types. The Single Point Active Alignment Method (SPAAM) is currently the most-cited manual calibration technique, due to the relaxation of user constraints with respect to allowable motion during the calibration process. This work presents the first formal study directly investigating the effects that alignment point distribution imposes on SPAAM calibration accuracy and precision. A user experiment, employing a single expert user, is presented, in which SPAAM calibrations are performed under each of five conditions. Four of the conditions cross alignment distance (arm length, room scale) with user pose (sitting, standing). The fifth condition is a control condition, in which the user is replaced with a rigidly mounted camera; the control condition removes the effect of noise from uncontrollable postural sway. The final experimental results show no significant impact on calibration due to user pose (sitting, standing). The control condition also did not differ from the user produced calibration results, suggesting that posture sway was not a significant factor. However, both the user and control conditions show significant improvement using arm's length alignment points over room scale alignments, with an order of magnitude difference in eye location estimate error between conditions.",
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"abstract": "3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline and inevitably require a heuristic matching step for the detection association. In this paper, we present SimTrack to simplify the hand-crafted tracking paradigm by proposing an end-to-end trainable model for joint detection and tracking from raw point clouds. Our key design is to predict the first-appear location of each object in a given snippet to get the tracking identity and then update the location based on motion estimation. In the inference, the heuristic matching step can be completely waived by a simple read-off operation. SimTrack integrates the tracked object association, newborn object detection, and dead track killing in a single unified model. We conduct extensive evaluations on two large-scale datasets: nuScenes and Waymo Open Dataset. Experimental results reveal that our simple approach compares favorably with the state-of-the-art methods while ruling out the heuristic matching rules.",
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"affiliation": "Johns Hopkins University",
"fullName": "Alan Yuille",
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"abstract": "In this work, we tackle the problem of category-level online pose tracking of objects from point cloud sequences. For the first time, we propose a unified framework that can handle 9DoF pose tracking for novel rigid object instances as well as per-part pose tracking for articulated objects from known categories. Here the 9DoF pose, comprising 6D pose and 3D size, is equivalent to a 3D amodal bounding box representation with free 6D pose. Given the depth point cloud at the current frame and the estimated pose from the last frame, our novel end-to-end pipeline learns to accurately update the pose. Our pipeline is composed of three modules: 1) a pose canonicalization module that normalizes the pose of the input depth point cloud; 2) RotationNet, a module that directly regresses small interframe delta rotations; and 3) CoordinateNet, a module that predicts the normalized coordinates and segmentation, enabling analytical computation of the 3D size and translation. Leveraging the small pose regime in the pose-canonicalized point clouds, our method integrates the best of both worlds by combining dense coordinate prediction and direct rotation regression, thus yielding an end-to-end differentiable pipeline optimized for 9DoF pose accuracy (without using non-differentiable RANSAC). Our extensive experiments demonstrate that our method achieves new state-of-the-art performance on category-level rigid object pose (NOCSREAL275 [29]) and articulated object pose benchmarks (SAPIEN [34], BMVC [18]) at the fastest FPS ∼ 12.",
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"abstract": "Nowadays, there are increasing demand from electric vehicles that need to be charged during peak hours in a city. The number of traditional fixed charging stations is limited, which may not be sufficient to satisfy the charging demand of electric vehicle users. Mobile charging vehicles can be scheduled at fixed positions to serve as a fixed charging station, providing charging services for electric vehicle users, for relieving the pressure of mass charging demand. In this paper, the charging scheduling problem of mobile charging vehicle is modeled as a Markov Decision Process(MDP) problem, and a reinforcement learning method based on policy evaluation is adopted to solve this problem. The decision of the specific position to which the mobile charging vehicle is scheduled is determined by this method. In addition, the decision of how to dispatch electric vehicle charging orders is determined by a fixed algorithm. We propose two different order dispatching algorithms. According to the simulation, our proposed reinforcement learning method has superior results in terms of the efficiency of order processing and user satisfaction compared with the traditional greedy method. The two different electric vehicle charging order dispatching algorithms have their own advantages and disadvantages in terms of processing speed and effectiveness.",
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"content": "Nowadays, there are increasing demand from electric vehicles that need to be charged during peak hours in a city. The number of traditional fixed charging stations is limited, which may not be sufficient to satisfy the charging demand of electric vehicle users. Mobile charging vehicles can be scheduled at fixed positions to serve as a fixed charging station, providing charging services for electric vehicle users, for relieving the pressure of mass charging demand. In this paper, the charging scheduling problem of mobile charging vehicle is modeled as a Markov Decision Process(MDP) problem, and a reinforcement learning method based on policy evaluation is adopted to solve this problem. The decision of the specific position to which the mobile charging vehicle is scheduled is determined by this method. In addition, the decision of how to dispatch electric vehicle charging orders is determined by a fixed algorithm. We propose two different order dispatching algorithms. According to the simulation, our proposed reinforcement learning method has superior results in terms of the efficiency of order processing and user satisfaction compared with the traditional greedy method. The two different electric vehicle charging order dispatching algorithms have their own advantages and disadvantages in terms of processing speed and effectiveness.",
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"abstract": "In many real-world applications such as the inspection of powerlines, the automated detection of anomalies can minimise damage and reduce costs that result from the presence of unknown anomalies. Technologies such as LiDAR scans obtained from Unmanned Aerial Vehicles (UAV) are becoming prominent due to the data depth they provide. In the context of powerline transmission, investigators must search for anomalous elements such as line defects or obstructions. Such occurrences are not always apparent and detecting them requires extensive analysis of data within vast areas of wilderness. Automating this process can reduce time and labor costs. We propose a methodology to define what constitutes an anomaly within mapped real-world scenes, and a technique to address different types of anomalies. The notion of unknowns and knowns composed of unknown to both human and machine, known to human and unknown to machine, unknown to human and known to machine, and known to both human and machine is considered to develop a novel framework that detects anomalous patterns. For the purpose of evaluation, we introduce synthetic anomalous data points through our data augmentation methods. Our framework achieved 63.78% accuracy in detecting the points known to the machine and unknown to the machine from the Sensat Urban validation scene. Within the scene, 78.22% of the incorrectly classified data were detected as unknown to the machine. Furthermore, our framework achieved 84.34% accuracy in detecting the synthetic data and 35.5% accuracy in detecting those data as anomalies.",
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"title": "A Comparative Study of Extraction Cylinder Features in Industrial Point Clouds",
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"abstract": "With the technological advancement in the field of Computer Aided Design such as the rapid development of scanning technologies, the reconstruction of complete and incomplete cylinders given noisy point clouds with form defects becomes an important issue. In fact, cylindrical surfaces are found in domestic to industrial contexts. In this paper, a comparative study of cylinder fitting algorithms manufactured in the LIPPS laboratory is proposed. The aim of the proposed approach is to determine the diameter of cylindrical feature for minimizing roundness error from experimental data-points. The roundness error is evaluated using two internationally defined methods: Minimum Circumscribed Cylinder (MCC) and Maximum Inscribed Cylinder (MIC). All algorithms give similar results in the case where the scanned cylinder is complete and without form defects, but in the case of missing data some algorithms give unacceptable results. The two reference cylinders have been independently analyzed, respecting six criteria (calculation complexity, damping parameter, initial guess, time, circularity error and complexity cylinder). The results of algorithms are also compared to help manufacturers and inspectors facilitate and improve the application of these methods and to select the appropriate algorithm for size and form evaluation.",
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"title": "A preliminary study of a hybrid user interface for augmented reality applications",
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"abstract": "Augmented Reality (AR) applications are nowadays largely diffused in many fields of use, especially for entertainment, and the market of AR applications for mobile devices grows faster and faster. Moreover, new and innovative hardware for human-computer interaction has been deployed, such as the Leap Motion Controller. This paper presents some preliminary results in the design and development of a hybrid interface for hand-free augmented reality applications. The paper introduces a framework to interact with AR applications through a speech and gesture recognition-based interface. A Leap Motion Controller is mounted on top of AR glasses and a speech recognition module completes the system. Results have shown that, using the speech or the gesture recognition modules singularly, the robustness of the user interface is strongly dependent on environmental conditions. On the other hand, a combined usage of both modules can provide a more robust input.",
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"content": "Augmented Reality (AR) applications are nowadays largely diffused in many fields of use, especially for entertainment, and the market of AR applications for mobile devices grows faster and faster. Moreover, new and innovative hardware for human-computer interaction has been deployed, such as the Leap Motion Controller. This paper presents some preliminary results in the design and development of a hybrid interface for hand-free augmented reality applications. The paper introduces a framework to interact with AR applications through a speech and gesture recognition-based interface. A Leap Motion Controller is mounted on top of AR glasses and a speech recognition module completes the system. Results have shown that, using the speech or the gesture recognition modules singularly, the robustness of the user interface is strongly dependent on environmental conditions. On the other hand, a combined usage of both modules can provide a more robust input.",
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"title": "Gamification of Hand Rehabilitation Process Using Virtual Reality Tools: Using Leap Motion for Hand Rehabilitation",
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"abstract": "Nowadays virtual reality (VR) technology give us the considerable opportunities to develop new methods to supplement traditional physiotherapy with sustain beneficial quantity and quality of rehabilitation. VR tools, like Leap motion have received great attention in the recent few years because of their immeasurable applications, whish include gaming, robotics, education, medicine etc. In this paper we present a game for hand rehabilitation using the Leap Motion controller. The main idea of gamification of hand rehabilitation is to help develop the muscle tonus and increase precision in gestures using the opportunities that VR offer by making the rehabilitation process more effective and motivating for patients.",
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"content": "Nowadays virtual reality (VR) technology give us the considerable opportunities to develop new methods to supplement traditional physiotherapy with sustain beneficial quantity and quality of rehabilitation. VR tools, like Leap motion have received great attention in the recent few years because of their immeasurable applications, whish include gaming, robotics, education, medicine etc. In this paper we present a game for hand rehabilitation using the Leap Motion controller. The main idea of gamification of hand rehabilitation is to help develop the muscle tonus and increase precision in gestures using the opportunities that VR offer by making the rehabilitation process more effective and motivating for patients.",
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