Reviewerly/exHarmony huggingface dataset:
A Measuring System for Teacher-student Interaction in Classroom

The measurement on teacher-student interactions is important in the area of classroom observation, which is always conducted by the observers manually. Recently, some measuring systems were proposed to detect and record the human behaviors in classroom. However, these work focused on the detection of some specified students' actions rather than the meaningful learning behavior. In this paper, we take the teacher-student interaction as our research objective, and attempt to detect the simplest interactions i.e., students' response to teacher's oral instruction. We develop a measuring system for this purpose, in which the Kinect is adopted to detect the student's body gesture, and teacher's instruction is recognized by online voice services. To deal with the time scale problem in behavior recognition, an improved DTW schema is proposed to adapt to the temporal variance of the same behavior. Experiments demonstrate that this technical prototype can effective recognize the student's response behavior and provide a quantified measurement on this simple teacher-student interaction.
Low-cost and Real-time Hardware Implementation of Stereo Vision System on FPGA

In this work, one of local stereo vision algorithms named SAD approach, which is used in image depth estimation, has been surveyed, and an efficient and real-time new hardware implementation has been proposed. The proposed method has been verified and tested using C implementation. The acceptable simulation results along with the detailed explanation of numerous pre-processing steps are also presented. Our innovations could be divided into two sections: architecture and algorithm. In architecture section, by using a specific architecture, memory access has been lowered and therefore, speed has been increased. In algorithm section, a part of local algorithm, known as refinement, has been substituted with a simpler and more efficient algorithm. Cyclone IV has been utilized as our hardware platform. In this article, this point would demonstrate how to use an exact but less complicated controller, which results in less area, and how to use pipeline architecture and remove repetitive and redundant memory accesses, which conduct our stereo vision system to meet the real-time constraints. Suggested hardware implementation could reach to 53fps processing speed with 100MHz clock. No processing IP core has been used. In comparison with related work, our proposed method is more efficient in logic elements usage, and accordingly in power consumption.
3D UAV Path Planning Using Global-Best Brain Storm Optimization Algorithm and Artificial Potential Field

In this paper, the online obstacle avoidance path planning problem for UAV is studied. The global-best brain storming algorithm (GBSO) and artificial potential field (APF) method are applied to UAV path planning. The global-best brain storm optimization algorithm is applied for avoiding with fixed obstacles, which knowing at path planning stage. The pre-planning path can be obtained by the GBSO. When the sensors carried by the UAV identify the accidental obstacle during flight, the local path re-planning mechanism is triggered. In the re-plan stage, the artificial potential field method is applied for finding path with collision avoidance. The cubic B-spline curve is adopted for UAV 3D path planning. The maximum curvature and climb (or dive) angle constraints of path are taken into consideration in pre-planning stage. The simulation results demonstrate that the effectiveness of the proposed method for online UAV 3D path planning problem.
Evolutionary Feature Selection Based on Semi-Local Search

Feature selection by extracting the most informative features from a dataset improves the accuracy of a classifier, reduces its complexity and helps to speed up the classification tasks. In this paper, a new hybrid feature selection method based on a combination of genetic algorithm (GA) and particle swarm optimization (PSO) is introduced. In this structure, instead of point to point search used in most of the search methods, the subspaces are sequentially determined by an enhanced genetic algorithm, where each subspace is efficiently searched by a PSO method. In the proposed GA, each chromosome is equal to a subspace of the search space. Crossover and mutation operators over the defined chromosomes generate new subspaces. PSO algorithm searches in the zone and returns the fitness value of the corresponding chromosome. The idea of defining subspaces is very efficient in utilizing the exploration ability of GA. In addition, PSO exploitation reduces the time complexity of the pure genetic search. Reported results on 10 UCI benchmark datasets confirm how this method has significant improvement in classification performance.
Design of a SCORM Courseware Player Based on Web AR and Web VR

In this paper, the authors developed a multimedia SCORM courseware player that supports Web AR and Web VR. It was designed based on the Sco units and integrated Web AR and Web VR. This courseware player made up for the flexibility of AR and VR applications in SCORM, and can also expand the possibility of application in teaching practice.
Leveraging Reinforcement Learning Techniques for Effective Policy Adoption and Validation

Rewards and punishments in different forms are pervasive and present in a wide variety of decision-making scenarios. By observing the outcome of a sufficient number of repeated trials, one would gradually learn the value and usefulness of a particular policy or strategy. However, in a given environment, the outcomes resulting from different trials are subject to chance influence and variations. In learning about the usefulness of a given policy, significant costs are involved in systematically undertaking the sequential trials; therefore, in most learning episodes, one would wish to keep the cost within bounds by adopting learning stopping rules. In this paper, we examine the deployment of different stopping strategies in given learning environments which vary from highly stringent for mission critical operations to highly tolerant for non-mission critical operations, and emphasis is placed on the former with particular application to aviation safety. In policy evaluation, two sequential phases of learning are identified, and we describe the outcomes variations using a probabilistic model, with closed-form expressions obtained for the key measures of performance. Decision rules that map the trial observations to policy choices are also formulated. In addition, simulation experiments are performed, which corroborate the validity of the theoretical results.
Research on recognition and application of hand gesture based on skin color and SVM

Aiming at the problem that hand gesture recognition difficult issues in the real scene, a method of hand gesture recognition that combines skin color with SVM was proposed. The skin color area was separated by Otsu adaptive threshold algorithm in the YCbCr color space, and hand gesture was segmente d by hand gesture area criterion. Hu moment features and finger number were extracted on the hand gesture contour as the feature vector. Six common static gestures were classified and recognized by SVM classifier. Experimental result showed that this method had good stability and real-time performance, average recognition rate could reach 94%. On the hand gesture recognition application, the hand gesture recognition results were converted into instructions, which achieved the real-time controlled simulation of the NAO robot in Webots simulation environment, and verified the feasibility of hand gesture recognition algorithm.
A Multi-level Aggregated Network for Image Restoration

Recently, significant progress has been witnessed in image restoration benefited from the development of deep convolutional neural networks (CNN). However, we note that many state-of-the-art image restoration networks can be unfolded as a one-level architecture, which is constructed by stacking multiple convolution layers or blocks. As the depth of network grows, the information flow is weakened. And, existing skip connection has restricted ability to pass the previous state to the latter layers in the network. Based on above observations, we explore a multi-level aggregated network (MLAN) to fully exploit features of deeper layers. The proposed MLAN can extract more features by merging layers at different levels progressively, and can better aggregate the features by the augmented connection manner. Experimental results demonstrate a satisfactory performance of the proposed model on different image restoration tasks.
Deep Learning Competition Framework on Othello for Education

Deep learning has become a hot topic in recent years. There are many teaching frameworks that ease the education process for deep learning. However, most current teaching examples either require a lot of training time or do not have interaction with users. Usually, the testing accuracy is the only evaluation criterion. However, it does not mean too much for a novice. We provide a framework that can teach the deep learning concepts with limited time or computational resource constraints. It offers students quick feedback, i.e., the trained model can be easily tested on this framework. Students can increase training data, design a deep learning model, or modify input planes to learn more advanced topics. The framework provides a graphical user interface, which connects to the trained model directly. It is a well-designed deep learning competition framework. This framework has been applied in the “artificial intelligence” undergraduate course for three semesters. The student feedback was quite positive. Furthermore, based on this framework, deep learning Othello competitions were held in TAAI and TCGA international computer game tournaments in 2017. We hope that this framework can encourage more people to be engaged in the research of deep learning and board games.
Blockchain-Based Distributed Optimization for Energy Management Systems

A blockchain has attracted in many research fields. In this paper, a blockchain-based distributed optimization method for energy management systems (EMSs) is studied. In distributed optimization, the original problem is decomposed, and the local problems are solved by multiple agents. Hence, distributed optimization is appropriate for large-scale multi-agent systems. In this paper, ADMM (Alternating Direction Method of Multipliers) is utilized as one of the powerful methods in distributed optimization. In ADMM, an aggregator is required for collecting the computation result in each agent. Using blockchains, the function of an aggregator can be contained in a distributed ledger, and an aggregator may not be required. As a result, tampering from attackers can be prevented. In this paper, after ADMM and blockchains are summarized, an EMS studied in this paper is introduced. By numerical experiments, the effectiveness and limitation of blockchain-based distributed optimization are clarified from the viewpoints of tamper tolerance and computation time.
Accelerator Design for Vector Quantized Convolutional Neural Network

In recent years, deep convolutional neural networks (CNNs) achieve ground-breaking success in many computer vision research fields. Due to the large model size and tremendous computation of CNNs, they cannot be efficiently executed in small devices like mobile phones. Although several hardware accelerator architectures have been developed, most of them can only efficient address one of the two major layers in CNN, convolutional (CONV) and fully connected (FC) layers. In this paper, based on algorithm-architecture-co-exploration, our architecture targets at executing both layers with high efficiency. Vector quantization technique is first selected to compress the parameters, reduce the computation, and unify the behaviors of both CONV and FC layers. To fully exploit the gain of vector quantization, we then propose an accelerator architecture for quantized CNN. Different DRAM access schemes are employed to reduce DRAM access. We also design a high-throughput processing element architecture to accelerate quantized layers. Compare to previous accelerators for CNN, the proposed architecture achieves 1.2-5× less DRAM access and 1.5-5× higher throughput for both CONV and FC layers.
Surgeries That Would Benefit from Augmented Reality and Their Unified User Interface

Augmented Reality (AR) has a high potential to help healthcare professionals in the operating room and researchers have been working on bringing AR to the operating room for many different types of surgeries so far. However, there are thousands of surgery types that might benefit from the AR. Our goal is to identify which surgeries could benefit the most from AR and identify the common functionality desired in an AR app for surgery and then come up with a unified user interface (UI) design for these surgeries. To achieve this goal, we analyzed the historical number of surgeries performed at a teaching and research hospital to find the most frequently performed surgeries. Starting with the most frequently performed surgeries, we collaborated with the experienced surgeons to find out the surgeries that could benefit from AR the most. For each surgery, we identified the functionality and the information that could be presented on an AR app before, during and after surgery. We prepared the list of surgeries that might benefit from AR through a series of thorough interviews with experienced surgeons. We also identified the functionalities desired in AR apps for surgery and prepared a unified UI design for the common functionality. We believe that the unified UI that we designed for the AR App of surgical procedures is the first step towards an AR app that might be beneficial for many types of surgeries. We believe surgeons equipped with AR apps will achieve better results; the success rates of surgical procedures will increase while the number of errors decreases.
Unsupervised Keyphrase Extraction for Web Pages

Keyphrase extraction is an important part of natural language processing (NLP) research, although little research is done in the domain of web pages. The World Wide Web contains billions of pages that are potentially interesting for various NLP tasks, yet it remains largely untouched in scientific research. Current research is often only applied to clean corpora such as abstracts and articles from academic journals or sets of scraped texts from a single domain. However, textual data from web pages differ from normal text documents, as it is structured using HTML elements and often consists of many small fragments. These elements are furthermore used in a highly inconsistent manner and are likely to contain noise. We evaluated the keyphrases extracted by several state-of-the-art extraction methods and found that they did not transfer well to web pages. We therefore propose WebEmbedRank, an adaptation of a recently proposed extraction method that can make use of structural information in web pages in a robust manner. We compared this novel method to other baselines and state-of-the-art methods using a manually annotated dataset and found that WebEmbedRank achieved significant improvements over existing extraction methods on web pages.
Design and implementation of invention learning curriculum-based serious game contents

In this paper, invention learning curriculum-based serious game contents were designed and developed. Developed serious game contents evaluated effectiveness among elementary school students with participants classified according to enthusiasm for learning and communication condition for a period of first semester. Existing game-based learning contents tend to give too much importance to learning or amusement which has made it difficult to develop contents. To solve the above-mentioned problems, this study designed and developed serious game which strengthened organic combination between amusement and learning. Serious game contents were designed and developed ways which understand invention principle of curriculum as game play. Evaluation of effectiveness showed that serious game is effective in learning of participants in this study. It was ascertained that students with greater enthusiasm for learning who volunteered invention education course showed better recognition of invention and game than those who studied under general curriculum.
Spatial–Temporal Attention-Based Human Dynamics Retrospection

Motivated by impressive success of deep recurrent neural networks (RNNs), sequence-to-sequence (seq2seq) architecture has been widely adapted to tackle human motion prediction. However, forecasting in longer time horizons always leads to implausible human poses or converges to mean poses. To address these challenges, we dig into the root causes and lay emphasis on two key principles. First, error can be easily accumulated on seq2seq architecture without modifications and thus RNNs cannot recover from its own mistakes in longer time horizons. Second, all the frames or joints are treated equally, whereas both of them often have different levels of importance in human motion. To mitigate this gap, we propose to retrospect human dynamics with attention. We design a retrospection module built upon seq2seq architecture to recollect previous subsequences and correct mistakes in time which enables a self-correction ability. This assists the original seq2seq architecture to eliminate error accumulation which improves significantly both short-term and long-term performances. Besides, we present two attention techniques to explore correlations among different joints as well as different frames in both spatial and temporal domains, which successfully capture key properties of different actions and enable our model to generate more realistic human poses. Quantitative and qualitative experiments have been both conducted to evaluate the our proposed model. Experimental results clearly demonstrate the superiority of proposed model over other baselines.
Robust Deep Tracking with Two-step Augmentation Discriminative Correlation Filters

Recently, deep trackers have proven success in visual tracking due to their powerful feature representation. Among them, discriminative correlation filter (DCF) paradigm is widely used. However, these trackers are still difficult to learn an adaptive appearance model of the object due to the limited data available. To address that, this paper proposes a two-step augmentation discriminative correlation filters (TADCF) approach to improve robustness. Firstly, we propose an online frame augmentation scheme to obtain rich and robust deep features which can effectively alleviate background distractors, leading to better generalization and adaptation of the learned model. Secondly, an object augmentation mechanism is implemented by exploiting rotation continuity restriction, which simultaneously models target appearance changes from rotation and scale variations. Extensive experiments on four benchmarks illustrate that the proposed approach performs favorably against state-of-the-art trackers.
Towards Assessment of Behavioral Patterns in a Virtual Reality Environment

Virtual Reality (VR) is a powerful modern medium for immersive data visualization and interaction. However, only a few research efforts targeted the issue of complementing VR applications with features derived from real-time human behavior analysis in virtual environments. This paper addresses an interactive application for analysis of user behavior in a VR environment. In this work, real-time data communication is employed to collect data about the VR user's location and actions in the virtual environment. To ensure the authenticity of interactions in the virtual environment, the VR application aims at achieving complete immersion. Our findings pertaining to behavioral patterns in immersive environment suggest that there is a potential in applying knowledge of user behavior models to improve the interactivity between the user and the virtual environment. Analysis of VR users' behavioral models also complements studies typically performed by traditional survey techniques.
An efficient algorithm for type-safe structural diffing

Effectively computing the difference between two version of a source file has become an indispensable part of software development. The de facto standard tool used by most version control systems is the UNIX diff utility, that compares two files on a line-by-line basis without any regard for the structure of the data stored in these files. This paper presents an alternative datatype generic algorithm for computing the difference between two values of any algebraic datatype. This algorithm maximizes sharing between the source and target trees, while still running in linear time. Finally, this paper demonstrates that by instantiating this algorithm to the Lua abstract syntax tree and mining the commit history of repositories found on GitHub, the resulting patches can often be merged automatically, even when existing technology has failed.
Conceptual Model of an Intelligent System for Detecting Fraud During Mobile Applications Installation

The conceptual model of an intelligent system for fraud detection during mobile applications installation is proposed in this paper. It is desirable that the proposed model to develop the systems' class. Conceptual model includes: subsystem of identifying user data characteristics, subsystem of overcoming heterogeneity of input data, subsystem of classification model training, subsystem of classification, subsystem of fraudsters database formation, subsystem of knowledge base (for detecting fraud) formation, subsystem of data mining and user patterns formation, subsystem of general fraudster fingerprint prediction. The proposed conceptual model of an intelligent system allows processing heterogeneous input data, which gives the opportunity to form a generalized fraudsters fingerprint in the process.
Generic and flexible defaults for verified, law-abiding type-class instances

Dependently typed languages allow programmers to state and prove type class laws by simply encoding the laws as class methods. But writing implementations of these methods frequently give way to large amounts of routine, boilerplate code, and depending on the law involved, the size of these proofs can grow superlinearly with the size of the datatypes involved.
Multi-Intent Text Classification Using Dual Channel Convolutional Neural Network

During the conversations among human and machine, how to comprehend user's intention according to the context is a crucial issue, which is very difficult. The commonly used Convolutional Neural Network (CNN) does not work well in this situation: due to the short length and the local features of dialogue sentences, CNN cannot well capture the global features and the semantic information. In this paper, we propose an intent classification dual-channel Convolutional Neural Networks (ICDC NN): we first extract semantic features by using Word2vec and Embedding layer to train the word vector; then, two different channels are used for convolution, one for character level word vector, the other for word-level word vector; thirdly, the character level word vectors (fine-grained) are combined with word-level word vectors to mine more in-depth semantic information of natural language question; finally, with convolution kernels of different sizes, more in-depth abstract features inside the sentences are learned. We then apply the ICDCNN to English text but with a different network structure. Experimental results show that the algorithm achieves high accuracy on both English and Chinese datasets, which shows a significant improvement compared to other methods.