Reviewerly/exHarmony huggingface dataset:
Hand Gesture Controlled Robot

The work proposes constructive design of hand gesture control robot. This system acts as a channel between the human and the robot through physical change such as tilting of hand. Instead of using joystick or physical controller with buttons, hand gestures are used. This device can be very useful for surveillance, military operations and industrial grade robotic arms physically challenged individuals.
Using Adversarial Examples to Bypass Deep Learning Based URL Detection System

Due to the outstanding performance on the feature extraction and classification, Deep Learning (DL) models have been developed in many existing network systems. Nowadays, the DL can be used for cyber security systems such as building the detection system for the malicious Uniform Resource Locator (URL) links. For the practical usage, the DL-based models are proved to have better accuracy and efficiency on detecting malicious URL links in the current networking systems. However, some DL models are vulnerable to the subtle change of inputs such as the Adversarial Example (AE) which also exists in the URL detection scenario: the malicious URL links can bypass the DL-based detection with a crafty change to threat the security of the network systems. In this paper, we present an AE generation method against DL-Based web Uniform Resource Locator (URL) detection system by generating AEs. We could generate AEs with minimum changes (one byte in the URL) in the inputs to bypass the DL-based URL classification model with a high success rate.
Reducing Task Load with an Embodied Intelligent Virtual Assistant for Improved Performance in Collaborative Decision Making

Collaboration in a group has the potential to achieve more effective solutions for challenging problems, but collaboration per se is not an easy task, rather a stressful burden if the collaboration partners do not communicate well with each other. While Intelligent Virtual Assistants (IVAs), such as Amazon Alexa, are becoming part of our daily lives, there are increasing occurrences in which we collaborate with such IVAs for our daily tasks. Although IVAs can provide important support to users, the limited verbal interface in the current state of IVAs lacks the ability to provide effective non-verbal social cues, which is critical for improving collaborative performance and reducing task load.In this paper, we investigate the effects of IVA embodiment on collaborative decision making. In a within-subjects study, participants performed a desert survival task in three conditions: (1) performing the task alone, (2) working with a disembodied voice assistant, and (3) working with an embodied assistant. Our results show that both assistant conditions led to higher performance over when performing the task alone, but interestingly the reported task load with the embodied assistant was significantly lower than with the disembodied voice assistant. We discuss the findings with implications for effective and efficient collaborations with IVAs while also emphasizing the increased social presence and richness of the embodied assistant.
Artificial Neural Network and SVM based Voice Disorder Classification

In cognitive infocommunication inspired speech processing one of the most important, but challenging areas of research is the analysis and classification of voice disorders. This paper investigates and compares various input vectors and classification models to find the use of input vectors for separating healthy from disordered voices. We compare acoustic parameters as input vectors with phone-level posterior probabilities computed by the DNN soft-max layer of the speech recognition system. An attempt is made to separate voice disorders from healthy voice in adults using different classification methods. The classification is implemented using Support Vector Machine (SVM) and Sequential Deep Neural Network (DNN). Our results show that using acoustic parameters instead of phone-specific posteriors as input features increases the accuracy of the classification, furthermore the DNN approach outperformed the SVM classifier in case of leave-one-out cross validation (LOOCV) and in case of 70-30% data split method as well.
Chinese Learning Anxiety on Foreign Students

&lt;p style="text-align:justify"&gt;The purpose of this study is to investigate the level of anxiety in learning Chinese, discover the dominant factors of anxiety in learning Chinese language and analyze differences in Chinese language anxiety based on gender, year of study and HSK in foreign students in Wuhan, China. The research method used was quantitative using a survey approach. There are 250 foreign students as the samples, consisting of 108 male and 142 female students. The data collection technique used is questionnaire called CLAS (Chinese Learning Anxiety Survey) adapted from FLCAS (Foreign Language Classroom Anxiety Survey). The data were analyzed using SPSS with descriptive statistical techniques and Multivariate Analysis. The results showed that Chinese language anxiety among foreign students is on a moderate level, with communication anxiety as the most dominant aspect. The male and female students have the same anxiety level in learning Chinese. The foreign students who had longer duration of learning Chinese and had higher HSK tend to have lower level of anxiety in learning Chinese. This showed that years of study and HSK were two factors that determine the high or low level of anxiety in learning Chinese for foreign students.&lt;/p&gt;
The Effects of Multi-sensory Aerial Firefighting Training in Virtual Reality on Situational Awareness, Workload, and Presence

Situational Awareness (SA) and managing workload are key factors in aerial firefighting, also known as Air Attack. Training for Air Attack is logistically challenging, risky, expensive, and difficult to practise in naturalistic settings. Simulation training in Virtual Reality (VR) has good potential to mitigate these challenges. This paper explored the application of VR Air Attack training with two multisensory immersive display types, an Oculus Rift CV1 VR Head Mounted Display (HMD) and a 270° Cylindrical Projection Display (CPD). Firefighters training to become Air Support Supervisors were recruited to participate in the study. SA, workload, and sense of presence in the virtual environment were measured. Qualitative data on the firefighters’ user experience and their subjective evaluation of the systems were also collected. This was to gain insights on the suitability of the two systems for Air Attack training, and whether the new systems were an improvement on earlier designs. The results show that the participants experienced a high level of SA, workload and presence in both conditions, with a direct comparison tentatively suggesting that the HMD system invoked slightly higher SA, greater overall presence and lower workload. The collected qualitative data suggest that the participating firefighters appreciated both systems for their realism, extensibility, cost-effectiveness, and accessibility. In particular participants noted that the virtual experience facilitated their understanding of the Air Attack role, and they recommended the use of virtual reality simulation training to supplement existing Air Attack training.
Subverting Surveillance or Accessing the Dark Web? Interest in the Tor Anonymity Network in U.S. States, 2006–2015

The U.S. government engaged in unprecedented forms of mass surveillance in the twenty-first century. Users of the The Onion Router (Tor), an anonymity-granting technology, mask themselves from state surveillance and can gain access to illicit content on the Dark Web. Drawing on theory regarding “exposure” to surveillance, this study examines how two issue-attention cycles (related to the Edward Snowden state surveillance revelations and the Dark Web respectively) are associated with public interest in the Tor browser in the United States. Using data at the state-year level from 2006 to 2015, this study estimates fixed effects models, controlling for sociodemographics, the presence of journalism, tech, and political jobs, as well as multiple measures of state political ideology. The results indicate that state-years with greater popularity of Google searches related to the Snowden story had significantly higher popularity of searches for Tor. By contrast, there was no association between Dark Web search popularity and Tor search popularity. These findings are consistent with the notion that the Snowden incident increased Americans’ sense of exposure, leading to interest in anonymity-granting technology.
Augmented reality worksheets in field trip learning

Worksheets are often used during field trips and utilize a learning cycle with three stages (exploration, concept introduction and concept application) to engage learners in the learning activities of observation and exploration. However, traditional paper worksheets do not provide multimedia and interactive presentations with physical objects, which made learners losing interaction with the physical context during field trips and failing to implement all the stages completely. This study designed an augmented reality worksheet with a three-stage learning cycle and applied it to learning about plants, specifically assisting learners in observing and classifying plants. A pretest-posttest quasi-experimental design was used to show the effect of learning when learners used augmented reality worksheets. Lag sequence analysis was used to identify learning behavioral patterns. The findings indicate that the learning effect of using augmented reality worksheets is much better than that of paper worksheets and improves the learners’ interaction with plants.
Online job scheduling for distributed machine learning in optical circuit switch networks

Networking has become a well-known performance bottleneck for distributed machine learning (DML). Although lots of works have focused on accelerating the communication process of DML, they ignore the impact of the physical network on the DML performance. Concurrently, optical circuit switches (OCSes) are increasingly applied in data centers and clusters, which can fundamentally improve DML performance. It is worth noting that the non-negligible OCS reconfiguration delay makes OCS scheduling algorithms have a great impact on the upper application performance. However, existing OCS scheduling solutions are not suitable for DML jobs due to the iterative nature of DML jobs and their interleaving characteristics of communication and computation stages. Therefore, in this paper, we study the online multi-job scheduling for DML in OCS networks. Firstly, we propose heaviest-load-first (HLF), a heuristic algorithm for intra-job scheduling, which is based on the fact that the completion time of flows on the heaviest load port has a significant impact on the job completion time. Furthermore, we present Shortest Weighted Remaining Time First (SWRTF) algorithm for inter-job scheduling. In SWRTF, an available DML job is scheduled when the served job moves from communication stage to the computation stage, which significantly improves the circuit utilization. Based on large-scale simulations, we demonstrate HLF can significantly reduce the iteration communication time by up to 64.97% compared to the state-of-the-art circuit scheduler Sunflow. Besides, SWRTF can save up to 42.9%, 54.2%, 27.2% of Weighted-Job-Completion-Time (WJCT) compared to Shortest-Job-First, Baraat and Weighted-First inter-job scheduling algorithms, respectively.
Consideration of existence of phase information of object shape in zeroth-order diffraction beam using electromagnetic simulation with aperture in front of objective

It has been reported that the shape of a measured object can be determined by detecting the change in phase distribution generated by a lateral shift of the object using speckle interferometry. In speckle interferometry technique, it was also reported that the shape of the object beyond the diffraction limit can be measured. In this study, the principle of this method is discussed through experiments and electromagnetic analysis. Accordingly, the results of the experiments and electromagnetic analysis of phase change only under zeroth-order diffraction light through the lateral shift of the object are investigated by shutting out the higher harmonic diffraction lights using an aperture in front of the lens. The results indicate that even if the image of the object cannot be focused upon, three-dimensional shape measurement can be performed by analysing only the zeroth-order diffraction light from the object.
Adaptive Distributed Partitioning in Apache Flink

Dynamically adapting the workload of each worker in Flink is a challenging issue. In this work, we deal with a special case, where the data are conceptually split in contiguous overlapping regions. This scenario is encountered in several streaming applications, such as those employing nearest neighbor queries. We propose (i) an architecture for allowing such adaptations in Flink and (ii) specific data repartitioning techniques. We apply our proposal to a specific use case, namely continuous distancebased outlier detection. Our experimental evaluation provides insights into the efficiency and effectiveness of the approach.
Time Series QoS Forecasting for Web Services Using Multi-Predictor-Based Genetic Programming

Quality of service (QoS) time series forecasting for web services (WSs) has been studied for over a decade. In recent years, this problem has been investigated in its multistep-ahead version (namely, the problem where the prediction horizon is greater than one) for the long-term rental and use of cloud-based WSs. To solve this multistep-ahead QoS time series forecasting problem, previous research has adopted single-predictor-based strategies and conventional time series methods, such as autoregressive integrated moving average (ARIMA) models and exponential smoothing. In this article, however, we propose applying genetic programming (GP) to search for and evolve a set of multiple predictors, in which each predictor is dedicated to forecasting a specific future time point. Our GP-based approach proposes and tests two types of multiple predictors that differ from the consumed predictor inputs that drive each predictor to produce its QoS forecasting results. In the first type, the input of each predictor is a sequence of fixed previous QoS observations; in the second type, each predictor dynamically consumes the most recent QoS values, which could consist of the forecasting results of its previous predictors). Furthermore, we propose two techniques for our multipredictor-based GP approach, namely, elite individual composition (EIC) and hybrid evolution, and apply them to enhance the forecasting accuracy of our approach. Finally, based on a real-world QoS time series dataset, the proposed approach is validated and compared with several conventional methods to demonstrate its superiority in terms of accuracy; in addition, the effectiveness and efficiency of the proposed multiple predictors and the two techniques are verified in the experiment.
Dual-Path Part-Level Method for Visible–Infrared Person Re-identification

Visible–infrared cross-modality person re-identification is a realistic problem of person re-identification. Under poor illumination scenario, general methods of visible–visible person re-identification can not solve the problem well. If we directly compare the visible images of pedestrians captured under dark lighting with the visible images of pedestrians captured under normal light, this extreme color deviation will greatly reduce the recognition ability of the learned representations. In this paper, we propose a dual-path framework for visible–infrared cross-modality person re-identification based human part level features. Feature learning module contains modality-specific dual-path layers and modality-shared human part-level layers, which achieve discriminative global and local representations. In order to better optimize the proposed network, we design a global loss function and a local loss function for the global features and local features, respectively. The two loss functions are integrated together to train the network. We verify the effectiveness of our method on the challenging benchmarks: SYSU-MM01 and RegDB. Experimental results show that, compared with other cross-modality methods, our method has better effect in improving visible–infrared cross-modality person re-identification tasks.
Further improvement of the HILL method applied in image encryption

In this article, a new encryption system that takes as an input an arbitrary size color image (n, m), and generates an encrypted image of the same size at the output, will be proposed. On each three-pixel block, an improvement of the classical HILL method provided by a size matrix (3, 3) invertible in the Z/256Z ring, accompanied by a chaotic translation vector. Then a confusion with the chaotic map is established with the output vector. Finally, a chain with the clear block is performed to increase the avalanche effect. Simulations performed on a large number of images of different sizes and formats, show that our approach can withstand all known attacks.
Grain depot image dehazing via quadtree decomposition and convolutional neural networks

In view of the fact that the existing defog methods often ignore the key atmospheric light estimation, a method based on quadtree decomposition is proposed, which avoids the influence of bright white area on atmospheric light estimation and accurately estimates atmospheric light in the sky region. In order to avoid the limitation of manual feature extraction, three convolution scales are used to check the original fog image for convolution operation, and the propagation map to be refined is obtained after a series of feature learning of the network, and then the image fusion method is used to refine it. Finally, the estimated parameters are brought into the atmospheric scattering model to deduce a clear image. The quantitative and qualitative experimental results of synthetic and real-world grain depot fog and dust images show that the algorithm has a good effect on image texture details and sky region processing, and has high robustness and universality.
Phase extraction from two randomly phase shifted interferograms by combining advanced principal component analysis and Lissajous ellipse fitting

In order to achieve the high accuracy and high speed in phase shifting interferometry, a phase extraction approach by combining advanced principal component analysis and Lissajous ellipse fitting (APCA-LEF) is proposed. It can obtain accurate phase distribution with only two no pre-filtering phase shifted interferograms, and it costs less time simultaneously. It removes the restriction that PCA needs more than three interferograms with well distributed phase shifts to subtract relatively accurate mean. Moreover, adjacent pixels taken part in the APCA process increases the accuracy by suppressing the effect of noise, also, it is suitable for different levels of noises. Last but not least, if the high accuracy is required, the phase shift would be best to far away from 0 rad and π rad. The simulations and experiments verify the correctness and feasibility of APCA-LEF.
Which Process Metrics Are Significantly Important to Change of Defects in Evolving Projects: An Empirical Study

Process metrics can reflect the software development process and the code changes which are the main causes of defects. So, recently, the researches have put more emphasis on process metrics in the field of software defect prediction. For evolving projects, it is more meaningful to study whether the software module introduces or eliminates defects or not, not whether the software module is defective or defect-free. However, no such work is available in the literature focusing on the change of defect state. Discovering the factors that influence the change of defect state in the process of software development can help us to understand the causes of software defects and improve the quality of subsequent software versions. Therefore, this paper presents an extensive empirical study on which process metrics are significantly important to change of defects in evolving projects. Five process metrics of 37 versions in 12 software projects are collected. We not only analyze the class correlation values and the classification performance values among five process metrics, but also perform statistical analysis to verify whether the experimental results are of practical value. The experimental results indicate that Number of Distinct Committers plays a significantly important role in the change of defect state, especially for elimination of defects, and Number of Revisions is the second, whereas Degree of Code Modification is the last. In addition, Average Number of Modified Lines is superior to Number of Modified Lines. Based on the experimental results, some suggestions for software development and software defect prediction are also discussed.
Using Screen Capture Video to Understand Learning in Virtual Reality

Research on immersive learning in natural setting such as schools, especially with/in virtual reality (VR), is only just emerging. While current inquiry concentrates on measuring content and procedural knowledge acquisition and to a lesser extent attitudinal change associated with exposure to VR applications, scant attention has been paid to what learners actually do in immersive virtual environments. This includes on- and off-task behavior, higher order thinking, and regulation of learning related to self and others. This methodological paper describes the use of screen capture video as a means of recording and analysing these types of learning behaviors in virtual environments with junior high school students. The paper outlines barriers, limitations and benefits to using screen capture video and the types of inductive and deductive analysis conducted as part of the research. It concludes that screen capture video is a promising medium for understanding verbal and non-verbal learning behavior as it actually unfolds in immersive VR.
An Energy Trace Compression Method for Differential Power Analysis Attack

Differential power analysis attacks are the most commonly used means to break cryptographic devices within the side-channel attack technology. Since there is a lot of noise in the energy trace of cryptographic devices, a large number of energy traces are needed to carry out the attack, resulting in a high computational cost. To solve this problem, this study starts with an analysis of the characteristics of power waveform formation from the inherent properties of the complementary metal oxide semiconductor circuit. Then, based on the Hamming distance classification method and the results of power waveform analysis, the useful information interval in the energy trace is located, that is, the interval with a strong correlation with the key. Thus, we achieve energy trace compression. Finally, a system on chip with a 128-bit AES algorithm is used to conduct various attack experiments in the effective interval. The results show that the calculation is cut off by 96%, which greatly reduces the computational cost for differential power analysis attacks.