Reviewerly/exHarmony huggingface dataset:
A New Improved D-S Evidence Theory Based on BJS Divergence in Multi-source Information Fusion

Multi-source information fusion is widely used in target recognition. Affected by interference factors, target recognition has great uncertainty. Relevant information cannot be accurately obtained only through a single evidence, which leads to unreliable results of target recognition. Dempster-Shafer (D-S) evidence theory is widely applied in multi-source information fusion due to the advantage of flexibility and effectiveness in combining plenty of uncertain information without prior probabilities. But if there are some high conflict elements in the evidence groups, it will produce some counterintuitive fusion effect, which is worse than before. To solve the above-mentioned problem, a new improved D-S evidence theory based on Belief Jensen-Shannon (BJS) divergence was proposed. We first utilize BJS divergence to locate the evidence groups of which discrepancy exceeds a certain threshold, and the threshold can be set according to the requirements of evidence groups. Subsequently, let the evidence group of which the BJS divergence exceeds the threshold change into the mean value of all the evidence events, this approach can evenly distribute the degree of conflict. Finally, the fusion results can be calculated by the combination rules of D-S evidence theory. Some experiments indicate that this method can get more reasonable results.
Transformer-Based Extractive Social Media Question Answering on TweetQA

The paper tackles the problem of question answering on social media data through an extractive approach. The task of question answering consists in obtaining an answer from the context given the context and a question. Our approach uses transformer models, which were fine-tuned on SQuAD. Usually, SQuAD is used for extractive question answering for comparing the results with human judgments in social mediaTweetQA dataset. Our experiments on multiple transformer models indicate the importance of application of pre-processing in the question answering on social media data and elucidates that extractive question answering fine-tuning even on other type of data can significantly improve the results reducing the gap with human evaluation. We use ROUGE, METEOR, and BLEU metrics.
i-Mix: A Strategy for Regularizing Contrastive Representation Learning

Contrastive representation learning has shown to be an effective way of learning representations from unlabeled data. However, much progress has been made in vision domains relying on data augmentations carefully designed using domain knowledge. In this work, we propose i-Mix, a simple yet effective regularization strategy for improving contrastive representation learning in both vision and non-vision domains. We cast contrastive learning as training a non-parametric classifier by assigning a unique virtual class to each data in a batch. Then, data instances are mixed in both the input and virtual label spaces, providing more augmented data during training. In experiments, we demonstrate that i-Mix consistently improves the quality of self-supervised representations across domains, resulting in significant performance gains on downstream tasks. Furthermore, we confirm its regularization effect via extensive ablation studies across model and dataset sizes. The code will be released.
A Momentum-Assisted Single-Timescale Stochastic Approximation Algorithm for Bilevel Optimization.

This paper proposes a new algorithm -- the Momentum-assisted Single-timescale Stochastic Approximation (MSTSA) -- for tackling unconstrained bilevel optimization problems. We focus on bilevel problems where the lower level subproblem is strongly-convex. Unlike prior works which rely on two timescale or double loop techniques that track the optimal solution to the lower level subproblem, we design a stochastic momentum assisted gradient estimator for the upper level subproblem's updates. The latter allows us to gradually control the error in stochastic gradient updates due to inaccurate solution to the lower level subproblem. We show that if the upper objective function is smooth but possibly non-convex (resp. strongly-convex), MSTSA requires $\mathcal{O}(\epsilon^{-2})$ (resp. $\mathcal{O}(\epsilon^{-1})$) iterations (each using constant samples) to find an $\epsilon$-stationary (resp. $\epsilon$-optimal) solution. This achieves the best-known guarantees for stochastic bilevel problems. We validate our theoretical results by showing the efficiency of the MSTSA algorithm on hyperparameter optimization and data hyper-cleaning problems.
A quantum algorithm for the direct estimation of the steady state of open quantum systems

Simulating the dynamics and the non-equilibrium steady state of an open quantum system are hard computational tasks on conventional computers. For the simulation of the time evolution, several efficient quantum algorithms have recently been developed. However, computing the non-equilibrium steady state as the long-time limit of the system dynamics is often not a viable solution, because of exceedingly long transient features or strong quantum correlations in the dynamics. Here, we develop an efficient quantum algorithm for the direct estimation of averaged expectation values of observables on the non-equilibrium steady state, thus bypassing the time integration of the master equation. The algorithm encodes the vectorized representation of the density matrix on a quantum register, and makes use of quantum phase estimation to approximate the eigenvector associated to the zero eigenvalue of the generator of the system dynamics. We show that the output state of the algorithm allows to estimate expectation values of observables on the steady state. Away from critical points, where the Liouvillian gap scales as a power law of the system size, the quantum algorithm performs with exponential advantage compared to exact diagonalization.
Learning with AMIGo: Adversarially Motivated Intrinsic Goals

A key challenge for reinforcement learning (RL) consists of learning in environments with sparse extrinsic rewards. In contrast to current RL methods, humans are able to learn new skills with little or no reward by using various forms of intrinsic motivation. We propose AMIGo, a novel agent incorporating -- as form of meta-learning -- a goal-generating teacher that proposes Adversarially Motivated Intrinsic Goals to train a goal-conditioned policy in the absence of (or alongside) environment reward. Specifically, through a simple but effective constructively adversarial objective, the teacher learns to propose increasingly challenging -- yet achievable -- goals that allow the student to learn general skills for acting in a new environment, independent of the task to be solved. We show that our method generates a natural curriculum of self-proposed goals which ultimately allows the agent to solve challenging procedurally-generated tasks where other forms of intrinsic motivation and state-of-the-art RL methods fail.
Task-based performance evaluation of deep neural network-based image denoising

Deep neural network (DNN)-based image denoising methods have been proposed for use with medical images. These methods are commonly optimized and evaluated by use of traditional physical measures of image quality (IQ). However, the objective evaluation of IQ for such methods remains largely lacking. In this study, task-based IQ measures are used to evaluate the performance of DNN-based denoising methods. Specifically, we consider signal detection tasks under background-known-statistically conditions. The performance of the ideal observer (IO) and the Hotelling observer (HO) are quantified and detection efficiencies are computed to investigate the impact of the denoising operation on task performance. The experimental results show that, in the cases considered, the application of a denoising network generally results in a loss of task-relevant information. The impact of the depth of the denoising networks on task performance is also assessed. While mean squared error improved as the network depths were increased, signal detection performance degraded. These results highlight the need for the objective evaluation of IQ for DNN-based denoising technologies and may suggest future avenues for improving their effectiveness in medical imaging applications.
Magnetform: a Shape-change Display Toolkit for Material-oriented Designers

This work presents Magnetform, a shape-change display toolkit designed to enable exploration of movement in soft materials. The toolkit allows designers with no technical knowledge to leverage their material expertise to experiment with shape-change. We present the toolkit design, and case studies with two design studios who used the toolkit for 15-days each. Through the presentation of their process, we reflect on two main themes: empowering designers to participate in shape-change exploration; and the developing practice of designing objects which integrate motion. We situate this work as part of the growing efforts in the TEI community to involve designers in the evolution of shape-changing interfaces, and demonstrate how material-oriented designers could contribute to this field in which materiality plays a major role in.
A Novel Signature Watermarking Scheme for Identity Protection

A novel non-blind watermarking technique for identity protection is presented. The proposed watermarking scheme uses the owner's signature as the watermark through which the ownership and validity of the document can be proven and kept intact. The proposed scheme is robust, imperceptible and faster in comparison to the other state of the art methods. Experimental simulations and evaluations of the proposed method show excellent results from both objective and subjective view points.
Understanding Employee Information Security Policy Compliance from Role Theory Perspective

Previous research indicated security-related stress at the workplace accounts for employee non-compliant behavior with information security policy (ISP). Drawing on the role theory, we investigate stress rooted in employee job role expectations arising from role-ambiguity, role-conflict, and role-overload known as role-stressors. These role-stressors cause employees to endeavor to perform their role tasks and, in turn, provide favorable situations for them to neglect ISP requirements. Using a survey of 350 employees, we found a combination of role-stressors contributes to employees' ISP violations. Furthermore, we posit that this relationship is partially mediated by organizational commitment. Also, we examined the effect of one mitigating factor: organizational support. However, a statistically significant mitigation effect of organizational support was not found on the relationship between role stress and intention toward ISP compliant behavior. This paper contributes to the behavioral information security literature by demonstrating the importance of role-stressors on employees' security-related behaviors.
IIE-SegNet: Deep Semantic Segmentation Network With Enhanced Boundary Based on Image Information Entropy

With the vigorous development of deep learning and the widespread use of mobile robots, automatic driving has gradually become a research hotspot. Environment perception is the most important part of automatic driving technology, and the purpose of environment perception is to distinguish the environmental content. Therefore, accurate and efficient image semantic segmentation method is becoming more and more important. In this paper, we introduce a deep semantic segmentation solution: IIE-SegNet: Deep semantic segmentation network with enhanced boundary based on image information entropy. At present, deep learning based on semantic segmentation solutions has some problems, such as low segmentation accuracy for small-scale objects and unclear boundary of segmented objects. Our method preserves the boundary of the segmentation object, and has higher segmentation accuracy for small-scale objects. In our method, the features of the underlying pooling layer are added to the ASPP structure of the encoding module, and the image information entropy of the previous pooling layers is introduced into the decoding module. We also introduce focal loss to solve the problem of imbalance between positive and negative samples. Finally, the test results on the extended Pascal VOC 2012 test set, abbreviated to Exp-Pascal VOC 2012 test set show that the proposed method has better performance on Exp-Pascal VOC 2012 test set compared with the advanced methods at the present stage, the segmentation accuracy of small-scale targets is higher, and the boundary is clearer.
Blockchain: Speed, Efficiency, Decreased Costs, and Technical Challenges

This chapter explores blockchain's five key elements ‒ distribution, encryption, immutability, tokenization, and decentralization. It also examines blockchain's potential to revolutionize today's businesses by looking at the efficiency and transparency blockchain brings to business processes. In addition, the chapter discusses the added benefits of traceability and potentially decreasing long-term costs before reviewing some of the known technical challenges blockchain faces today especially pertaining to commercial and enterprise adoption. Limited block size and low-transaction rates potentially inhibit blockchain's ability to scale to meet the needs of current and future enterprises. The lack of blockchain standards, along with creating multiple blockchains, complicates interoperability between the various blockchain platforms. The chapter concludes by offering potential solutions to blockchain's current technical challenges and how enterprises might adapt blockchain in the future.
Optically-reconfigurable phase change material nanoantenna-embedded metamaterial waveguide

Heterogeneous integration of phase change materials (PCM) into photonic integrated circuits is of current interest for all-optical signal processing and photonic in-memory computing. The basic building block consists of waveguides or resonators embedded with state-switchable PCM cells evanescently coupled to the optical mode. Despite recent advances, further improvements are desired in performance metrics like switching speeds, switching energies, device footprint, and fan-out. We propose an architecture using resonant metamaterial waveguides loaded with Ge 2 Sb 2 Te 5 (GST) nanoantenna, and present a numerical study of its performance. Our proposed design is predicted to have a write energy of 16 pJ, an erase energy of 190 pJ (which is three to four times lower than previous reports), and, an order of magnitude improvement in the write-process figure-of-merit. Additional advantages include lowered ON state insertion loss and GST volume reduction.
Art, Design and Communication Theory in Creating the Communicative Social Robot ‘Haru’

Haru is a social, affective robot designed to support a wide range of research into human–robot communication. This article analyses the design process for Haru beta, identifying how both visual and performing arts were an essential part of that process, contributing to ideas of Haru’s communication as a science and as an art. Initially, the article examines how a modified form of Design Thinking shaped the work of the interdisciplinary development team—including animators, performers and sketch artists working alongside roboticists—to frame Haru’s interaction style in line with sociopsychological and cybernetic–semiotic communication theory. From these perspectives on communication, the focus is on creating a robot that is persuasive and able to transmit precise information clearly. The article moves on to highlight two alternative perspectives on communication, based on phenomenological and sociocultural theories, from which such a robot can be further developed as a more flexible and dynamic communicative agent. The various theoretical perspectives introduced are brought together by considering communication across three elements: encounter, story and dance. Finally, the article explores the potential of Haru as a research platform for human–robot communication across various scenarios designed to investigate how to support long-term interactions between humans and robots in different contexts. In particular, it gives an overview of plans for humanities-based, qualitative research with Haru.
9.7 A 184 µ W Real-Time Hand-Gesture Recognition System with Hybrid Tiny Classifiers for Smart Wearable Devices

Recently, vision-based hand gesture recognition (HGR) has emerged as a natural and flexible human-computer interaction (HCI) approach. Users can control smart devices by applying hand gestures to imagers. However, prior efforts suffer from various limitations, such as excessive power consumption, low accuracy, and poor flexibility. The 3D HGR processors -[2] suffer from extremely large power overhead due to the employment of complex image processing, for example using Convolutional Neural Networks (CNNs). The grayscale sensor-based SoC [3] consumes less power. However, its accuracy is compromised, especially when the contrast between a hand gesture and the background is low. The infrared sensor-based SoC [4] can recognize 8 dynamic gestures with high accuracy (96%). Nevertheless, the over-simplified algorithm requires hand motion with a fixed gesture type, which limits the number of recognized dynamic gestures. Therefore, an ultra-low-power, flexible, and high accuracy HGR system is required.
Solar Power Output Prediction Using Multilayered Feedforward Neural Network: A Case Study of Jaipur

Future is envisaged to have renewable energy resources replacing conventional sources of energy like fossil fuels. In this direction solar energy is emerging out to be a vital source of green energy. Although solar energy is a promising aspect in proving clean and cheap electrical energy but one demerit is that it is intermittent nature and therefore unpredictable. This intermittent nature pose challenge maintaining the balance between generation and demand of electrical energy thus adversely effecting the system control. Also, the electrical energy companies involved in selling by participating in electricity pool market need highly accurate solar energy prediction for maximizing their profit. These issues demand a tool for accurate prediction of solar energy generation. This paper proposes a multi-layered feedforward neural network based solar energy prediction tool. For this a case study is done considering weather data of Malviya National Institute of Technology Jaipur. The data is used to train the neural network to predict the solar radiation. The trained neural network is tested with unseen data and shown to have reasonably good accuracy in predicting solar radiation. A comparative study of neural network performance is done with multiple-linear regression (MLR) technique in terms of prediction accuracy. It is shown that neural network (NN) based prediction outperforms MLR technique. It is expected that proposed methodology would find useful application with electricity companies involved in selling renewable energy generation.
Source Code Obfuscation for Smart Contracts

State-of-the-art work of evaluating smart contract static analysis tools faces a major problem: most test cases (i.e., labeled buggy contracts) are too simple and lack complexity, which makes the evaluation unable to show the real performance of the analysis tools when complex contracts are analyzed. To fill the gap, we propose a novel source code obfuscation approach for Ethereum smart contracts. We use the buggy contracts in the public dataset <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> https://github.com/smartbugs/smartbugs/tree/master/dataset to evaluate our approach. The evaluation result shows that our approach can effectively increase the complexity of a contract. Besides, we use obfuscated contracts to evaluate the static analysis tools. The evaluation result shows that the performance of most smart contract static analysis tools decreases in different degree when the original contracts are obfuscated.
A new design of NTRU encryption with high security and performance level

A multi-dimensional QTRU public-key cryptosystem improves NTRU by replacing the original ring in NTRU by quaternion algebra. In this paper, we introduce QMNTR an improvement of QTRU by using a new mathematical structure of two public keys and five private keys. This modification has enhanced the public key system to be more robust and secure.
Pattern Recognition based Tasks and Achievements on Handwritten Bengali Character Recognition

Bengali, is not only a major language in two states West Bengal, and Tripura of India, but it is the main language of an Entire country Bangladesh. Having such large application areas of Bengali language, and with the evolution of automation, machine learning, and trending technological development, it is also high time to make a complete product which can easily and efficiently recognize Bengali handwritten characters. Research in this area is attracting attention day by day, and there are the obvious reasons. Application area of such Bengali handwritten character recognition system is undoubtedly very high, but unfortunately, there is not such success in research, which can produce a complete operational system that can be commercialized or practically implemented in day to day life. The current work is a study of the different tasks to be done along with achievements throughout the last decade for different tasks in the field of handwritten Bengali character recognition.
Deepfake Forensics via an Adversarial Game

With the progress in AI-based facial forgery (i.e., deepfake), people are increasingly concerned about its abuse. Albeit effort has been made for training classification (also known as deepfake detection) models to recognize such forgeries, existing models suffer from poor generalization to unseen forgery technologies and high sensitivity to changes in image/video quality. In this paper, we advocate adversarial training for improving the generalization ability to both unseen facial forgeries and unseen image/video qualities. We believe training with samples that are adversarially crafted to attack the classification models improves the generalization ability considerably. Considering that AI-based face manipulation often leads to high-frequency artifacts that can be easily spotted by models yet difficult to generalize, we further propose a new adversarial training method that attempts to blur out these specific artifacts, by introducing pixel-wise Gaussian blurring models. With adversarial training, the classification models are forced to learn more discriminative and generalizable features, and the effectiveness of our method can be verified by plenty of empirical evidence. Our code will be made publicly available.
Penerapan Teknologi Augmented Reality Katalog Perumahan Sebagai Media Pemasaran Pada PT. San Esha Arthamas

PT. San Esha Arthamas is a company engaged in the sale of housing. In marketing its products other than social media, the company uses a housing catalog in the form of a book. However, with this method, people feel confused and less interested in knowing and looking at the products offered. To assist in marketing, the product to be attractive, the housing catalog augmented reality (AR) technology was developed as a marketing medium. AR is a technology that can combine real and virtual objects in 3D in realtime. This research produces an application that can display the types of houses and can perform marker scans by pointing the smartphone camera at the catologist book from various angles including: top, left and right side and back. In addition, users can see interior, exterior, can zoom in, zoom out and rotate. Based on the questionnaire, it shows that the application has an impact in helping marketing with the results that 93% of respondents said they agree. Overall, based on beta testing, it shows an average value of 88%, when converted the application is included in the Good category.