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"abstract": "In this work, we show how providing a constrained project framework for a second year digital design course improves the number of working student projects from 55% to 86%. Instead of an open-ended project as in previous years, we introduce an optional project framework, called \"Redhawk Duels\". Redhawk Duels is a game framework in which students design control algorithms and interfaces for a virtual ship. Once a competition begins, two opposing groups and their respective ships attempt to incapacitate the opposing ship by finding the opponent, shooting them, and budgeting their energy accordingly. Fifteen of the twenty-one groups in the 2010 class participated in Redhawk Duels for their final project, and 86% of these projects were working and demonstrated with sufficient complexity. The remaining six groups chose to implement open-ended projects and had a 66% success rate. This rate is similar to the 55% success rate of the 2009 class which were all open-ended projects. We surveyed the students involved to see how they felt the project helped them and how much they enjoyed the activity. The results show that the students strongly agree that participating in the framework motivated them and will help them in future engineering design projects.",
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"title": "2014 IEEE Frontiers in Education Conference (FIE)",
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"abstract": "Serious games are beneficial for education in various computer science areas. Numerous works have reported the experiences of using games (not only playing but also development) in teaching and learning. Considering it could be difficult for teachers/students to prepare/develop a game from scratch during one semester, assistant educational materials would be crucial in the corresponding courses. Unfortunately, the literature shows that not many materials from educational game projects are shared. To help different educators identify suitable courseware and help students implement game development, it is worth further investigating and accumulating the educational resources from individual game projects. Following such an idea, this paper proposes a game development project of an object-oriented Sokoban solver, and exposes relevant educational materials. The documented system design can be viewed as a ready-to-use resource for education in object-oriented analysis and design (OOAD), while the Sokoban solver itself may be used as an assignment platform for teaching artificial intelligence (AI). Further documentation, platform, and APIs will be realized and shared in the future to facilitate others' educational activities. Overall, this work is supposed to inspire and encourage other researchers and educators to post available materials of more game projects for the purpose of sharing and reuse.",
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"abstract": "Estimating the distances of the shortest path between given pairs of nodes in a graph is a basic operation in a wide variety of applications including social network analysis, web retrieval, etc. Such applications require a response on the order of a few milliseconds, but exact algorithms to compute the distance of the shortest path exactly do not work on real-world large-scale networks, because of their infeasible time complexities. The landmark-based methods approximate distances by using a few nodes as landmarks, and can accurately estimate shortest-path distances with feasible time complexities. However, they fail at estimating small distances, as it is difficult for a few selected landmarks to cover the shortest paths of many close node pairs. To tackle this problem, we present a novel method EigenSP, that estimates the shortest-path distance by using an adjacency matrix approximated by a few eigenvalues and eigenvectors. The average relative error rate of EigenSP is lower than that of the landmark-based methods on large graphs with many short distances. Empirical results suggest that EigenSP estimates small distances better than the landmark-based methods.",
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"content": "Estimating the distances of the shortest path between given pairs of nodes in a graph is a basic operation in a wide variety of applications including social network analysis, web retrieval, etc. Such applications require a response on the order of a few milliseconds, but exact algorithms to compute the distance of the shortest path exactly do not work on real-world large-scale networks, because of their infeasible time complexities. The landmark-based methods approximate distances by using a few nodes as landmarks, and can accurately estimate shortest-path distances with feasible time complexities. However, they fail at estimating small distances, as it is difficult for a few selected landmarks to cover the shortest paths of many close node pairs. To tackle this problem, we present a novel method EigenSP, that estimates the shortest-path distance by using an adjacency matrix approximated by a few eigenvalues and eigenvectors. The average relative error rate of EigenSP is lower than that of the landmark-based methods on large graphs with many short distances. Empirical results suggest that EigenSP estimates small distances better than the landmark-based methods.",
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"abstract": "Existing visual navigation approaches leverage classification neural networks to extract global features from visual data for navigation. However, these networks are not originally designed for navigation tasks. Thus, the neural architectures might not be suitable to capture scene con-tents. Fortunately, neural architecture search (NAS) brings a hope to solve this problem. In this paper, we propose an Auto-Navigator to customize a specialized network for visual navigation. However, as navigation tasks mainly rely on reinforcement learning (RL) rewards in training, such weak supervision is insufficiently indicative for NAS to optimize visual perception network. Thus, we introduce imitation learning (IL) with optimal paths to optimize navigation policies while selecting an optimal architecture. As Auto-Navigator can obtain a direct supervision in every step, such guidance greatly facilitates architecture search. In particular, we initialize our Auto-Navigator with a learnable distribution over the search space of visual perception architecture, and then optimize the distribution with IL supervision. Afterwards, we employ an RL reward function to fine-tune our Auto-Navigator to improve the generalization ability of our model. Extensive experiments demonstrate that our Auto-Navigator outperforms baseline methods on Gibson and Matterport3D without significantly increasing network parameters.",
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"abstract": "Neural architecture search can discover neural networks with good performance, and One-Shot approaches are prevalent. One-Shot approaches typically require a supernet with weight sharing and predictors that predict the performance of architecture. However, the previous methods take much time to generate performance predictors thus are inefficient. To this end, we propose FOX-NAS that consists of fast and explainable predictors based on simulated annealing and multivariate regression. Our method is quantization-friendly and can be efficiently deployed to the edge. The experiments on different hardware show that FOX-NAS models outperform some other popular neural network architectures. For example, FOX-NAS matches MobileNetV2 and EfficientNet-Lite0 accuracy with 240% and 40% less latency on the edge CPU. Search code and pre-trained models are released at https://github.com/great8nctu/FOX-NAS.<sup>1</sup>",
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"content": "The statistical heterogeneity of the non-independent and identically distributed (non-IID) data in local clients significantly limits the performance of federated learning. Previous attempts like FedProx, SCAFFOLD, MOON, FedNova and FedDyn resort to an optimization perspective, which requires an auxiliary term or re-weights local updates to calibrate the learning bias or the objective inconsistency. However, in addition to previous explorations for improvement in federated averaging, our analysis shows that another critical bottleneck is the poorer optima of client models in more heterogeneous conditions. We thus introduce a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices. We provide theoretical analysis of the possible benefit from FedSkip and conduct extensive experiments on a range of datasets to demonstrate that FedSkip achieves much higher accuracy, better aggregation efficiency and competing communication efficiencys. Source code is available at: https://github.com/MediaBrain-SJTU/FedSkip.",
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"affiliation": "Shanghai Jiao Tong University Shanghai AI Laboratory,CMIC,Shanghai,China",
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"abstract": "Federated learning is a popular privacy-preserving machine learning paradigm that can train a robust centralized model while guaranteeing the privacy of client data. However, traditional federated learning cannot cope with the problem of slow convergence and low accuracy due to model heterogeneity and statistical heterogeneity. To solve the problem, we propose a federated multi-distillation with auxiliary classification layers in IoT (FedACL). In FedACL, we add auxiliary classification layers after certain hidden layers in the local heterogeneous model, thus implementing a multi-distillation framework consisting of self-distillation in the local update and co-distillation in the communication. Besides, in order to improve the stability of model training, we propose a double-weighted method based on the test accuracy of the clients to measure the contribution of each client. A large number of experiments are conducted for different numbers of client and communication volumes under the Non-IID settings of CIFAR100 and FEMNIST datasets, and all of them prove that FedACL has fast convergence speed, high accuracy and good stability.",
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"content": "Federated learning is a popular privacy-preserving machine learning paradigm that can train a robust centralized model while guaranteeing the privacy of client data. However, traditional federated learning cannot cope with the problem of slow convergence and low accuracy due to model heterogeneity and statistical heterogeneity. To solve the problem, we propose a federated multi-distillation with auxiliary classification layers in IoT (FedACL). In FedACL, we add auxiliary classification layers after certain hidden layers in the local heterogeneous model, thus implementing a multi-distillation framework consisting of self-distillation in the local update and co-distillation in the communication. Besides, in order to improve the stability of model training, we propose a double-weighted method based on the test accuracy of the clients to measure the contribution of each client. A large number of experiments are conducted for different numbers of client and communication volumes under the Non-IID settings of CIFAR100 and FEMNIST datasets, and all of them prove that FedACL has fast convergence speed, high accuracy and good stability.",
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"abstract": "As one of the important features, color provides plenty useful information to represent images. Thus, color harmony, which is defined as “two or more colors are sensed together as a single, pleasing, collective impression” [1], can also be served as a fundamental feature and plays a key role to determine the aesthetics quality of images. To reveal the inherent color harmony attribute within patch and the harmonious relations between image patches which construct the pleasing colorful images, we designed a conditional random field (CRF) based color harmony model in this paper to accomplish the image aesthetic assessment tasks. Unlike the previous learning based color harmony models, we used deep neural networks to obtain the coherence properties between original image patch pairs, and embedded these relations along with each patch's own color harmony characteristic into a CRF to measure the harmony scores of the entire image. The experimental results on a public dataset show that the proposed deep conditional color harmony model is superior to the existing color harmony models in respect of the image aesthetic assessment.",
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"content": "As one of the important features, color provides plenty useful information to represent images. Thus, color harmony, which is defined as “two or more colors are sensed together as a single, pleasing, collective impression” [1], can also be served as a fundamental feature and plays a key role to determine the aesthetics quality of images. To reveal the inherent color harmony attribute within patch and the harmonious relations between image patches which construct the pleasing colorful images, we designed a conditional random field (CRF) based color harmony model in this paper to accomplish the image aesthetic assessment tasks. Unlike the previous learning based color harmony models, we used deep neural networks to obtain the coherence properties between original image patch pairs, and embedded these relations along with each patch's own color harmony characteristic into a CRF to measure the harmony scores of the entire image. The experimental results on a public dataset show that the proposed deep conditional color harmony model is superior to the existing color harmony models in respect of the image aesthetic assessment.",
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"title": "2018 11th International Conference on Intelligent Computation Technology and Automation (ICICTA)",
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"title": "Analysis of Multidimensional Representation of Visual Aesthetic Elements in Graphic Design",
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"abstract": "In order to improve the multidimensional expressive ability of visual aesthetic elements in graphic design, the application of computer graphics and image processing technology in art is analyzed. Based on color difference compensation and 3D vision reconstruction, a multidimensional representation technique of aesthetic elements in graphic design visual images is proposed. The multidimensional content of visual representation in graphic design and the multidimensional expression of visual aesthetic elements in graphic design. The multidimensional content of graphic design visual representation is described, and the multidimensional expression of graphic design visual aesthetic elements is analyzed. Wavelet denoising is carried out on the collected graphic design visual image, and the color difference collocation and aesthetic element feature extraction of the plane design visual image are carried out by using plane projection and color adaptive matching technology. The multi-dimensional reconstruction of visual aesthetic elements is carried out by using shadow segmentation and luminance collocation to improve the multidimensional performance of aesthetic elements in graphic design. The simulation results show that the multi-dimensional representation of aesthetic elements of graphic design visual images by using this technique has a good artistic display ability, and the aesthetic and visual aesthetic expression performance of graphic design is improved. It has good application value of fine arts.",
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"content": "In order to improve the multidimensional expressive ability of visual aesthetic elements in graphic design, the application of computer graphics and image processing technology in art is analyzed. Based on color difference compensation and 3D vision reconstruction, a multidimensional representation technique of aesthetic elements in graphic design visual images is proposed. The multidimensional content of visual representation in graphic design and the multidimensional expression of visual aesthetic elements in graphic design. The multidimensional content of graphic design visual representation is described, and the multidimensional expression of graphic design visual aesthetic elements is analyzed. Wavelet denoising is carried out on the collected graphic design visual image, and the color difference collocation and aesthetic element feature extraction of the plane design visual image are carried out by using plane projection and color adaptive matching technology. The multi-dimensional reconstruction of visual aesthetic elements is carried out by using shadow segmentation and luminance collocation to improve the multidimensional performance of aesthetic elements in graphic design. The simulation results show that the multi-dimensional representation of aesthetic elements of graphic design visual images by using this technique has a good artistic display ability, and the aesthetic and visual aesthetic expression performance of graphic design is improved. It has good application value of fine arts.",
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"normalizedAbstract": "In order to improve the multidimensional expressive ability of visual aesthetic elements in graphic design, the application of computer graphics and image processing technology in art is analyzed. Based on color difference compensation and 3D vision reconstruction, a multidimensional representation technique of aesthetic elements in graphic design visual images is proposed. The multidimensional content of visual representation in graphic design and the multidimensional expression of visual aesthetic elements in graphic design. The multidimensional content of graphic design visual representation is described, and the multidimensional expression of graphic design visual aesthetic elements is analyzed. Wavelet denoising is carried out on the collected graphic design visual image, and the color difference collocation and aesthetic element feature extraction of the plane design visual image are carried out by using plane projection and color adaptive matching technology. The multi-dimensional reconstruction of visual aesthetic elements is carried out by using shadow segmentation and luminance collocation to improve the multidimensional performance of aesthetic elements in graphic design. The simulation results show that the multi-dimensional representation of aesthetic elements of graphic design visual images by using this technique has a good artistic display ability, and the aesthetic and visual aesthetic expression performance of graphic design is improved. It has good application value of fine arts.",
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"title": "The Design and Application of College Aesthetic Education Micro-course Based on Intelligent Mobile Terminal",
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"abstract": "In the context of mobile technology and computers, major changes have taken place in the form of organization, dissemination and the final presentation of information. Media forms with “micro” as the key word are rapidly born and developed, showing significant differences and dynamics. The purpose of this paper is to study the design and application of college aesthetic education micro-lectures based on intelligent mobile terminals. This paper studies the key technologies for the development of the micro-course mobile learning platform, including the Android platform and MySQL. This paper proposes a general model of micro-course teaching design for public art education in colleges and universities, summarizes the design of micro-course modules, and applies it to the course of “Chinese Folklore”. A questionnaire survey was conducted on the application effect of micro-lectures in college aesthetic education. Through the implementation of the curriculum, the form of public art teaching in colleges and universities has been expanded, students' learning resources have been enriched, and the teaching of public art education has been improved to a certain extent.",
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"content": "In the context of mobile technology and computers, major changes have taken place in the form of organization, dissemination and the final presentation of information. Media forms with “micro” as the key word are rapidly born and developed, showing significant differences and dynamics. The purpose of this paper is to study the design and application of college aesthetic education micro-lectures based on intelligent mobile terminals. This paper studies the key technologies for the development of the micro-course mobile learning platform, including the Android platform and MySQL. This paper proposes a general model of micro-course teaching design for public art education in colleges and universities, summarizes the design of micro-course modules, and applies it to the course of “Chinese Folklore”. A questionnaire survey was conducted on the application effect of micro-lectures in college aesthetic education. Through the implementation of the curriculum, the form of public art teaching in colleges and universities has been expanded, students' learning resources have been enriched, and the teaching of public art education has been improved to a certain extent.",
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"abstract": "After the public relations crisis, the endless roar of network public opinion brings great pressure of public opinion and economic benefit to the operation of social media. This paper discusses the public opinion of social media, which will highlight the concept of “user center” of social media, attach importance to the user value of social media, re-examine the “expected vision” of social media, pay attention to the “aesthetic experience” of social media, and improve the acceptance effect. Pay attention to social media aesthetic field and their own experience fusion.",
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"affiliation": "Guangdong Polytechnic of Science and Trade,Guangzhou,Guangdong,China",
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"content": "Autism spectrum disorder (ASD) is a developmental condition that involves persistent challenges in social interaction, speech, nonverbal communication, and repetitive behaviors in individuals with ASD. Eye-tracking technology has recently been used to differentiate between individuals with and without the presence of ASD, using a variety of variables, parameters, and conditions. In this research work, a method was designed, implemented, and analyzed to recognize visual preferences in children diagnosed with ASD using Eye-Tracking for generation of Heatmaps as an analysis tool. As visual stimulus for the participants, images were shown using social content distributed in three experiments with emphasis on faces, gestures, and interaction between people respectively, to establish similarities and differences. The population included 20 patients diagnosed with ASD between 3 up to 6 years old, from the western region of the state of Michoacán, Mexico, showing a strong bias to visual preference for inanimate images, avoiding faces, gestures and people interaction in the images used in the experiments.",
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"affiliation": "Universidad Autónoma de Aguascalientes,Department of Medicine,Aguascalientes,Mexico",
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"title": "2017 Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE)",
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"title": "Kinematic Analysis of Tokamak In-Vessel Orbiting Robot",
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"abstract": "Tokamak In-Vessel Orbiting robot is mainly used for carrying working robot, which carries out maintenance operations in vessel. With the forward and inverse kinematic algorithms, the continuous path trajectory planning methods are used according to different posture of the robot. The relational curves of these joints angular displacement, angular velocity and angular acceleration are educed by this method. With these movement curves of the joint and the relative position between the circular orbit and the orbit extending robot, we have gotten the movement curves of the three kinematic units belonged to the orbit extending robot, which is smooth and continuous.",
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"fno": "2628a017",
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"fullName": "Chen Lu",
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"affiliation": "Huainan Normal University",
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"abstract": "Recent developments in quantum computers have been pushing up the number of qubits. However, the state-of-the-art Noisy Intermediate Scale Quantum (NISQ) computers still do not have enough qubits to accommodate the error correction circuit. Noise in quantum gates limits the reliability of quantum circuits. To characterize the noise effects, prior methods such as process tomography, gateset tomography and randomized benchmarking have been proposed. However, the challenge is that these methods do not scale well with the number of qubits. Noise models based on the understanding of underneath physics have also been proposed to study different kinds of noise in quantum computers. The difficulty is that there is no widely accepted noise model that incorporates all different kinds of errors. The realworld errors can be very complicated and it remains an active area of research to produce accurate noise models. In this paper, instead of using noise models to estimate the reliability, which is measured with success rates or inference strength, we treat the NISQ quantum computer as a black box. We use several quantum circuit characteristics such as the number of qubits, circuit depth, the number of CNOT gates, and the connection topology of the quantum computer as inputs to the black box and derive a reliability estimation model using (1) polynomial fitting and (2) a shallow neural network. We propose randomized benchmarks with random numbers of qubits and basic gates to generate a large data set for neural network training. We show that the estimated reliability from our black-box model outperforms the noise models from Qiskit. We also showcase that our black-box model can be used to guide quantum circuit optimization at compile time.",
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"content": "Recent developments in quantum computers have been pushing up the number of qubits. However, the state-of-the-art Noisy Intermediate Scale Quantum (NISQ) computers still do not have enough qubits to accommodate the error correction circuit. Noise in quantum gates limits the reliability of quantum circuits. To characterize the noise effects, prior methods such as process tomography, gateset tomography and randomized benchmarking have been proposed. However, the challenge is that these methods do not scale well with the number of qubits. Noise models based on the understanding of underneath physics have also been proposed to study different kinds of noise in quantum computers. The difficulty is that there is no widely accepted noise model that incorporates all different kinds of errors. The realworld errors can be very complicated and it remains an active area of research to produce accurate noise models. In this paper, instead of using noise models to estimate the reliability, which is measured with success rates or inference strength, we treat the NISQ quantum computer as a black box. We use several quantum circuit characteristics such as the number of qubits, circuit depth, the number of CNOT gates, and the connection topology of the quantum computer as inputs to the black box and derive a reliability estimation model using (1) polynomial fitting and (2) a shallow neural network. We propose randomized benchmarks with random numbers of qubits and basic gates to generate a large data set for neural network training. We show that the estimated reliability from our black-box model outperforms the noise models from Qiskit. We also showcase that our black-box model can be used to guide quantum circuit optimization at compile time.",
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"content": "As in classical computing, compilers play an important role in quantum computing. Quantum processors typically support a limited set of primitive operations or quantum gates and have certain hardware-related limitations. A quantum compiler is responsible for adapting a quantum program to these constraint environments and decomposing quantum gates into a sequence of the primitive ones. During the compilation process, it is also critical for the compiler to optimize the quantum circuits in order to reduce the noise in the computation results. Since the noise is introduced by operations and decoherence, reducing the gate count is the key for improving performance. In this paper, we propose a novel quantum compiler optimization, named relaxed peephole optimization (RPO) for quantum computers. RPO leverages the single-qubit state information that can be determined statically by the compiler. We define that a qubit is in a basis state when, at a given point in time, its state is either in the X-, Y-, or Z-basis (|+) / |-〉, |L〉 / R〉 and 10〉 / |1〉). When basis qubits are used as inputs to quantum gates, there exist opportunities for strength reduction, which replaces quantum operations with equivalent but less expensive ones. Compared to the existing peephole optimization for quantum programs, the difference is that our proposed optimization does not require an identical unitary matrix, thereby named `relaxed' peephole optimization. We also extend our approach to optimize the quantum gates when some input qubits are in known pure states. Both optimizations, namely the Quantum Basis-state Optimization (QBO) and the Quantum Pure-state Optimization (QPO), are implemented in the IBM's Qiskit transpiler. Our experimental results show that our proposed optimization pass is fast and effective. The circuits optimized with our compiler optimizations obtain up to 18.0% (11.7% on average) fewer CNOT gates and up to 8.2% (7.1% on average) lower transpilation time than that of the most aggressive optimization level in the Qiskit compiler. When running on real quantum computers, the success rates of 3-qubit quantum phase estimation algorithm improve by 2.30X due to the reduced gate counts.",
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"affiliation": "North Carolina State University,Raleigh,USA",
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"abstract": "Statistical debuggers use data collected during test case execution to automatically identify the location of faults within software. Recent work has applied causal inference to eliminate or reduce control and data flow dependence confounding bias in statement-level statistical debuggers. The result is improved effectiveness. This is encouraging but motivates two novel questions: (1) how can causal inference be applied in predicate-level statistical debuggers and (2) what other biases can be eliminated or reduced. Here we address both questions by providing a model that eliminates or reduces control flow dependence and failure flow confounding bias within predicate-level statistical debuggers. We present empirical results demonstrating that our model significantly improves the effectiveness of a variety of predicate-level statistical debuggers, including those that eliminate or reduce only a single source of confounding bias.",
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"content": "Statistical debuggers use data collected during test case execution to automatically identify the location of faults within software. Recent work has applied causal inference to eliminate or reduce control and data flow dependence confounding bias in statement-level statistical debuggers. The result is improved effectiveness. This is encouraging but motivates two novel questions: (1) how can causal inference be applied in predicate-level statistical debuggers and (2) what other biases can be eliminated or reduced. Here we address both questions by providing a model that eliminates or reduces control flow dependence and failure flow confounding bias within predicate-level statistical debuggers. We present empirical results demonstrating that our model significantly improves the effectiveness of a variety of predicate-level statistical debuggers, including those that eliminate or reduce only a single source of confounding bias.",
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"normalizedAbstract": "Statistical debuggers use data collected during test case execution to automatically identify the location of faults within software. Recent work has applied causal inference to eliminate or reduce control and data flow dependence confounding bias in statement-level statistical debuggers. The result is improved effectiveness. This is encouraging but motivates two novel questions: (1) how can causal inference be applied in predicate-level statistical debuggers and (2) what other biases can be eliminated or reduced. Here we address both questions by providing a model that eliminates or reduces control flow dependence and failure flow confounding bias within predicate-level statistical debuggers. We present empirical results demonstrating that our model significantly improves the effectiveness of a variety of predicate-level statistical debuggers, including those that eliminate or reduce only a single source of confounding bias.",
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"abstract": "As equality issues in the use of face recognition have garnered a lot of attention lately, greater efforts have been made to debiased deep learning models to improve fairness to minorities. However, there is still no clear definition nor sufficient analysis for bias assessment metrics. We propose an information-theoretic, independent bias assessment metric to identify degree of bias against protected demographic attributes from learned representations of pretrained facial recognition systems. Our metric differs from other methods that rely on classification accuracy or examine the differences between ground truth and predicted labels of protected attributes predicted using a shallow network. Also, we argue, theoretically and experimentally, that logits-level loss is not adequate to explain bias since predictors based on neural networks will always find correlations. Further, we present a synthetic dataset that mitigates the issue of insufficient samples in certain cohorts. Lastly, we establish a benchmark metric by presenting advantages in clear discrimination and small variation comparing with other metrics, and evaluate the performance of different debiased models with the proposed metric.",
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