Reviewerly/exHarmony huggingface dataset:
Deep-learning based group-photo Attendance System using One Shot Learning

Smartphone based face attendance system is the need in today's digital world and age for universities and schools as smartphones with very good picture quality provided by its camera makes such systems really affordable and practical as far as implementation is concerned. Automatic attendance systems make this daily practice of marking attendance easy and highly efficient thus helping reduce time wasted during lectures for such administrative work. Face recognition-based attendance systems are one such biometric based attendance systems which are more secure and can evade multiple fake or proxy attendance practices easily. This paper proposes a novel face recognition-based attendance system which can work with group photo of a class providing us a list of present students. It uses one shot learning based face recognition technique for our system which can work for new users by providing only a single image of them thus making the system very robust and efficient. The proposed work presents a fully functional android app and backend system architecture which can easily be utilized by any university or school without requiring any expensive infrastructure setup. The proposed model achieves an accuracy of about 97% on LFW dataset and the proposed attendance system achieves an accuracy of 85% on a public student class photo dataset.
Neural joint attention code search over structure embeddings for software Q&amp;A sites

Code search is frequently needed in software Q&A sites for software development. Over the years, various code search engines and techniques have been explored to support user query. Early approaches often utilize text retrieval models to match textual code fragments for natural query, but fail to build sufficient semantic correlations. Some recent advanced neural methods focus on restructuring bi-modal networks to measure the semantic similarity. However, they ignore potential structure information of source codes and the joint attention information from natural queries. In addition, they mostly focus on specific code structures, rather than general code fragments in software Q&A sites. In this paper, we propose NJACS, a novel two-way attention-based neural network for retrieving code fragments in software Q&A sites, which aligns and focuses the more structure informative parts of source codes to natural query. Instead of directly learning bi-modal unified vector representations, NJACS first embeds the queries and codes using a bidirectional LSTM with pre-trained structure embeddings separately, then learns an aligned joint attention matrix for query-code mappings, and finally derives the pooling-based projection vectors in different directions to guide the attention-based representations. On different benchmark search codebase collected from StackOverflow, NJACS outperforms state-of-art baselines with 7.5% to 6% higher [email protected] and MRR, respectively. Moreover, our designed structure embeddings can be leveraged for other deep-learning-based software tasks.
Coupled Knowledge Transfer for Visual Data Recognition

Transfer learning aims to learn an effective classifier for unlabeled target data by borrowing knowledge from well-labeled source data. However, most existing work has emphasized on learning domain invariant features to reduce the distribution discrepancy, which may suffer from the negative transfer problem caused by structure inconsistencies or distribution outliers. To address this challenge, in this paper, we propose a novel transfer learning approach, which seamlessly integrates domain invariant feature learning, discriminative structure preservation and sample reweighting into a unified learning model. Specifically, we attempt to learn domain invariant features by jointly adapting the marginal and conditional distributions. To transfer discriminative knowledge inferred from data, we enforce the structure consistency between the original feature space and the latent feature space. Furthermore, to enhance the robustness of our model, an efficient and more generalized sample reweighting strategy is developed to assign target predictions with different levels of confidence. The key advantage over previous methods is that our model can adaptively select pivot samples in target domain and retain the properties of discriminative structures underlying data domains, which enables coupled knowledge transfer during the learning process. Experimental results on several benchmark datasets have verified the superiority of the proposed method over other state-of-the-art algorithms.
Software process selection system based on multicriteria decision making

Abstract Whatever be the nature of underlying software, the impact of the software development process that was used to create it would remain vital. The objective of this paper is to provide a process selection framework for the software development firm's engineers and managers, looking to identify the proper way to build software to run on a mobile, web, or desktop. The motivation behind this work comes from the fact that the applications that fall in one of the above three categories can be significantly different in terms of scale, UI, and memory requirements, time to market, and other characteristics. Software development that needs to be completed in a challenging timeframe has to resort to principles and values as declared in Agile Manifesto. The availability of various agile methodologies has made the project managers often stuck when selecting the most suitable one. The proposed agile process identification system addresses this dilemma of engineering fraternity using a fuzzy variant of a popular multiple‐criteria decision‐making (MCDM) technique called the analytic hierarchy process (AHP). The proposed system is validated through a primary dataset generated as a result of the development of 20 software projects. The results are encouraging enough with a probability of true identification close to 88%.
Presence and Platform: Effects of Embodiment Comparing a 2D Computer and 3D VR Game

This randomized control study focused on how presence - the feeling of being there - is affected by the two factors of 1) embodiment (low versus high) and 2) learning platform (2D PC versus 3D Virtual Reality (VR)). Embodiment was deemed low if participants watched playbacks of the natural selection game in action, and it was deemed high if players used a physical gesture, either with a mouse or the VR hand control to manipulate the content. Two dependent variables of presence and engagement were assessed. The 147 participants were randomly assigned. Overall, those in the high embodiment conditions report higher presence, and overall those in the VR platform report higher presence. However, pairwise analyses showed that the high embodied PC group reported more presence than the low embodied VR group. This suggests that `platform is not destiny'. If an educational game or application is not one that gives the learner control and agency, then both presence and engagement will be diminished.
An Edge Computing Platform of Guide-dog Robot for Visually Impaired

Information processing of guide dog robot requires expensive computing resources to meet real-time performance. We propose an edge computing framework based on Intel Up-Squared board and neural compute stick. Image processing and real-time control are performed on the framework. In addition, the voice recognition and commanding are also implemented, which are processed on Amazon cloud service. Vision, speech, and control services are integrated by ASU VIPLE (Visual IoT/Robotics Programming Language Environment). A prototype is developed, which implements the guide dog's obstacle avoidance, traffic sign recognition and following, and voice interaction with human.
Guard-NoC: A Protection Against Side-Channel Attacks for MPSoCs

Multi-Processor System-on-Chips (MPSoCs) are popular computational platforms for a wide variety of applications due to their energy efficiency and flexibility. Like many other platforms they are vulnerable to Side Channel Attacks (SCAs). In particular, Logical SCAs (LSCAs) are very powerful as sensitive information can be retrieved by simply observing system properties that depend on the victim's software execution on the MPSoC. Unfortunately, many of the current protection mechanisms are either platform dependent or are effective only against a reduced set of attacks. In this work, we present Guard-NoC, a secure Network-on-Chip (NoC) architecture able to protect MPSoCs against a wide variety of LSCAs. The secure NoC employs three application-independent strategies to hide and isolate sensitive information: i) blinding the execution time of operations; ii) masking the execution time of operations; and iii) dual communication strategy (i.e., use packet and circuit switching simultaneously). Our results show that our secure NoC is resilient against practical LSCAs and leaks almost no information while having a minimal area and power overhead.
VSEPR Theory, An Interactive and Immersive Virtual Reality

This work explores the role of immersive learning in regards to predicting molecular structures and orbital hybridization through an Oculus Rift HMD. Specifically, this research utilizes an interactive virtual environment to help teach the concept of the Valence Shell Electron Pair Repulsion Theory. We developed an interactive and immersive simulation in which participants constructed virtual molecules. Following the simulation, participants were asked to identify shapes of similar molecules to measure their understanding of the theory as compared to a control group. Finally, participants answered the Game Experience Questionnaire to assess the level of Immersion. The results show that our immersive virtual reality (IVR) experience improves comprehension of three dimensional structures in chemistry.
An Opportunistic Strategy for Motion Planning in the Presence of Soft Task Constraints

Consider the problem of planning collision-free motions for a robot that is assigned a soft task constraint, i.e., a desired path in task space with an associated error tolerance. To this end, we propose an opportunistic planning strategy in which two subplanners take turns in generating motions. The hard planner guarantees exact realization of the desired task path until an obstruction is detected in configuration space; at this point, it invokes the soft planner, which is in charge of exploiting the available task tolerance to bypass the obstruction and returning control to the hard planner as soon as possible. As a result, the robot will perform the desired task for as long as possible, and deviate from it only when strictly needed to avoid a collision. We present several planning experiments performed in V-REP for the PR2 mobile manipulator in order to show the effectiveness of the proposed planner.
A Cotton Disease Diagnosis Method Using a Combined Algorithm of Case-Based Reasoning and Fuzzy Logic

Abstract In this study, a cotton disease diagnosis method that uses a combined algorithm of case-based reasoning (CBR) and fuzzy logic was designed and implemented. It focuses on the prevention, diagnosis and control of diseases affecting cotton production in China. Conventional methods of disease diagnosis are primarily based on CBR with reference to user-provided symptoms; however, in most cases, user-provided symptoms do not fully meet the requirements of CBR. To address this problem, fuzzy logic is incorporated into CBR to allow for more flexible and accurate models. With the help of CBR and fuzzy reasoning, three diagnostic results can be obtained by the cotton disease diagnosis system (CDDS) constructed in this study: success, success but not exact and failure. To verify the reliability of the CDDS and its ability to diagnose cotton diseases, its diagnostic accuracy and stability were analyzed and compared with the results obtained by the traditional expert scoring method. The analysis results reveal that the CDDS can achieve a high diagnostic success rate (above 90%) and better diagnostic stability than the traditional expert scoring method when at least four disease symptoms are input. The CDDS provides an independent and objective source of information to assist farmers in the diagnosis and prevention of cotton diseases.
Speaker Recognition using fusion of features with Feedforward Artificial Neural Network and Support Vector Machine

Speaker recognition is a task of identifying/verifying individual identity with the help of input voice sample. Speaker recognition is further classified into speaker identification (SI) and speaker verification (SV). Unlike other approaches using ELSDSR speech database, which only focuses on speaker identification performance, proposed work also focuses on speaker verification performance results, along with SI performance. Main goal of this research, is to find out best model for speaker identification and speaker verification system for clean speech database. Comparative study is done between performances by various combinations of features, for speaker identification and speaker verification system with Feedforward Artificial Neural Network (FFANN) and Support Vector Machine(SVM) as an classification technique using ELSDSR voice database. Features named as Linear predictive coding (LPC), Mel frequency cepstral coefficient (MFCC) and Perceptual linear prediction (PLP) are used. All features are tested separately and in fusion among each other's with FFANN and SVM classifier on MATLAB software. Also Proposed model results are compared with some famous techniques using same ELSDSR database for speaker identification system. By comparing experimental results of proposed model and others model, it is observed that fusion of different features gives better results and speaker identification accuracy increases to 3%-5% when compared with single feature's result. In addition, proposed method are giving best result of100% accuracy for speaker identification system and 0 equal error rate(EER) value for speaker verification system, when fusion of MFCC, LPC and PLP features are used with ANN and SVM classifier. Therefore, it can be said that fusion of MFCC, LPC and PLP features greatly enhances the speaker recognition system's performance.
Adaptive Bulk Search: Solving Quadratic Unconstrained Binary Optimization Problems on Multiple GPUs

The quadratic unconstrained binary optimization (QUBO) is recently gathering attention in conjunction with quantum annealing (QA), since it is equivalent to finding the ground state of an Ising model. Due to the limitation of current QA systems, classical computers may outperform them. Researchers have thus been proposed to solve QUBO on FPGAs, GPUs, and special purpose processors. In this paper, we propose an adaptive bulk search (ABS), a framework for solving QUBO that can perform many searches in parallel on multiple GPUs. It supports fully-connected Ising models with up to 32k spins and 16-bit weights. In our ABS, a CPU host performs genetic algorithm (GA) while GPUs asynchronously perform local searches. A bottleneck for solving QUBO exists in the evaluation of the energy function, which requires computational cost for each solution. We show this can be reduced to in our ABS. The experimental results show that, with four NVIDIA GeForce RTX 2080 Ti GPUs, our framework can search up to 1.24 × 1012 solutions per second. We also show that our system quickly solves maximum cut and traveling salesman problems.
Reducing Latency in Multi-Tenant Data Centers via Cautious Congestion Watch

Modern data centers host a plethora of interactive data-intensive applications. These are known to often generate large numbers of short-lived parallel flows that must complete their transfer quickly to meet the stringent performance requirements of interactive applications. Network resources (e.g., switch buffer space) in the data center are scarce and are easily congested, which unfortunately results in long latency. As a first solution, following a traditional design rationale, Data Center TCP (DCTCP) was proposed to speed up the completion time of short-lived flow by maintaining a low buffer occupancy in the switch. In general, DCTCP performs well in homogeneous environments, however, its performance degrades quickly in heterogeneous environments or when it uses a large initial congestion window. To resolve this problem, we propose a Hypervisor-based congestion watching mechanism (HWatch), which measures the network load in the data center via ECN and uses the resulting statistics to determine the appropriate initial congestion window size to avoid congestion. HWatch neither needs modification to the TCP stack in the VMs nor requires any specialized network hardware features to meet its targets. In our evaluation, we demonstrate the benefits of HWatch in improving the performance of TCP flows through large-scale ns2 simulation and via testbed experiments in a small data center.
Wireless Devices Identification with Light-Weight Convolutional Neural Network Operating on Quadrant IQ Transition Image

This paper presents a wireless device identification method that uses a convolutional neural network (CNN) operating on a quadrant IQ transition image. The proposed method can identify IoT wireless devices by exploiting their RF fingerprints, which is a technology to identify wireless devices using variation in analog signals. We proposed a quadrant IQ image technique to reduce the size of CNN while maintaining the accuracy. The CNN utilizes the IQ transition image, which is cut out into four-part. The over-the-air measurement was performed with six Zigbee wireless devices to confirm the validity of the proposed identification method. The measurement results demonstrate that the proposed method can achieve 99% accuracy with the light-weight CNN model with 36,500 trainable parameters. Furthermore, the proposed threshold algorithm can realize the detection of unknown devices that are not trained with 80% accuracy for further secured wireless communication.
An augmented reality application for robotic cell customization

Integrating the Internet of Things in Industry 4.0 demands the combination of existing practises with new technologies. Augmented Reality (AR) is a cutting-edge technology of the new manufacturing era. The fourth industrial revolution challenges and AR technology advances, promise to improve productiveness, working quality, user experience and allow better use of resources. AR combined with mass customization could fulfil rising market demands and customer functional requirements. This work presents the development of an AR application to integrate customers in the designing process with product customization. The application is to be validated and used in the industry of Robotic Cell Manufacturing.
DACNN: Dynamic Weighted Attention with Multi-channel Convolutional Neural Network for Emotion Recognition

In recent years, due to the vigorous development of social network media, a large amount of social data can be obtained through social media. This trend has made it easier for researchers in natural language processing to obtain textual research materials, and has further stimulated the development of natural language processing. Emotion recognition is an important task in the field of natural language processing, and emotion recognition helps improve interaction in social. In this study, the emotion recognition of the content of twitter users' posts and comments is based on the sentence level. We use large datasets to collect users' posts and comments on Twitter for comparative analysis, and we attempt to understand the emotions expressed by users. Most of the recent studies focus on the analysis of vocabulary and syntactic features in sentences to learn the emotion of the sentence. However, the property of multi-feature may be conducive for promoting the performance of the model and may also be a disturbance of the training of the model. Our study is based on the latest of attention mechanism and XLNet to improve the recognition of emotions. We propose a novel emotion recognition model called Dynamic Weighted Attention with Multichannel Convolutional Neural Network (DACNN), which combines the multi-channel convolutional neural network and the attention mechanism of automatic weight adjustment to effectively improve the outcome of emotion recognition. In addition, we use the latest word embedding technology XLNet to obtain high-quality feature vectors of sentences. In the experiments, we compare the DACNN with the baselines on various datasets, and the results show that DACNN is better than the state-of-the-art methods in accuracy, precision, recall, and fl-score. The experimental results also prove that our attention mechanism and dynamic weighting can well promote the accuracy of emotion recognition.
Entanglement Enhancement from a Two-Port Feedback Optical Parametric Amplifier

How to achieve the maximal entanglement, for given finite entanglement resources? In this paper, we propose a two-port feedback optical parametric amplifier (TFPA) consisting of a nondegenerate optical parametric amplifier (NOPA) and two linear beam splitters. The former is an entanglement resource, and the latter serve as feedback controllers that feed the two entangled beams generated from the NOPA back into its input ports. By manipulating the reflectivity of the beam splitters and the phase delays introduced by the feedback paths, an entanglement enhancement can be achieved by the TFPA. Furthermore, we demonstrate that such entanglement enhancement can happen at all phase points for the ideal case, and the maximal entanglement can be infinitely large. For the lossy case, our TFPA also has its unique advantages in the robustness of the losses of the system.
Heuristic algorithms based on deep reinforcement learning for quadratic unconstrained binary optimization

Abstract The unconstrained binary quadratic programming (UBQP) problem is a difficult combinatorial optimization problem that has been intensively studied in the past decades. Due to its NP-hardness, many heuristic algorithms have been developed for the solution of the UBQP. These algorithms are usually problem-tailored, which lack generality and scalability. To address these issues, a heuristic algorithm based on deep reinforcement learning (DRLH) is proposed in this paper. It features in inputting specific features and using a neural network model called NN to guild the selection of variable at each solution construction step. Also, to improve the algorithm speed and efficiency, two algorithm variants named simplified DRLH (DRLS) and DRLS with hill climbing (DRLS-HC) are developed as well. These three algorithms are examined through extensive experiments in comparison with famous heuristic algorithms from the literature. Experimental results show that the DRLH, DRLS, and DRLS-HC outperform their competitors in terms of both solution quality and computational efficiency. Precisely, the DRLH achieves the best-quality results, while DRLS offers a high-quality solution in a very short time. By adding a hill-climbing procedure to DRLS, the resulting DRLS-HC algorithm is able to obtain almost the same quality result as DRLH with however 5 times less computing time on average. We conducted additional experiments on large-scale instances and various data distributions to verify the generality and scalability of the proposed algorithms, and the results on benchmark instances indicate the ability of the algorithms to be applied to practical problems.