Reviewerly/exHarmony huggingface dataset:
eChain: Leveraging Toward Blockchain Technology for Smart Energy Utilization

Blockchain accomplishes an extensive spectrum of applications such as transaction of cryptocurrency, providing financial services, building smart cities, securing Internet of things and Big data and so on. It has astounding benefits such as anonymity, consistency, decentralization and persistency. Smart energy utilization can be defined as using latest electronic technology to focus mainly on the electricity sector to enable identification of more attainable and affordable solutions to be transformed into future renewable and sustainable energy solutions. This paper mainly emphasizes and proposes eChain architecture safeguarded with blockchain technology to attain smart energy autilization by ensuring judicious generation, uniform distribution and shielded monitoring along with guaranteed security and privacy of the havoc data stored and analyzed for the same
Towards a question answering assistant for software development using a transformer-based language model

Question answering platforms, such as Stack Overflow, have impacted substantially how developers search for solutions for their programming problems. The crowd knowledge content available from such platforms has also been used to leverage software development tools. The recent advances on Natural Language Processing, specifically on more powerful language models, have demonstrated ability to enhance text understanding and generation. In this context, we aim at investigating the factors that can influence on the application of such models for understanding source code related data and produce more interactive and intelligent assistants for software development. In this preliminary study, we particularly investigate if a how-to question filter and the level of context in the question may impact the results of a question answering transformer-based model. We suggest that fine-tuning models with corpus based on how-to questions can impact positively in the model and more contextualized questions also induce more objective answers.
Evolutionary reinforcement learning for sparse rewards

Temporal logic (TL) is an expressive way of specifying complex goals in reinforcement learning (RL), which facilitates the design of reward functions. However, the combination of these two techniques is prone to generate sparse rewards, which might hinder the learning process. Evolutionary algorithms (EAs) hold promise in tackling this problem by encouraging the diversification of policies through exploration in the parameter space. In this paper, we present GEATL, the first hybrid on-policy evolutionary-based algorithm that combines the advantages of gradient learning in deep RL with the exploration ability of evolutionary algorithms, in order to solve the sparse reward problem pertaining to TL specifications. We test our approach in a delayed reward scenario. Differently from previous baselines combining RL and TL, we show that GEATL is able to tackle complex TL specifications even in sparse-reward settings.
Empathetic Chatbot Enhancement and Development: A Literature Review

Chatbots are dialog engines for interactive user experience which help by providing stakeholders such as consumers, device owners, maintenance workers, and so on with real-time tools (answers to any questions, instructions to use the equipment, help for decisions, etc.). Nowadays, chatbot usage is not only for closed domain needs but has also become common across companies. Some businesses use chatbots for their customer support to provide details for the client and also to allow online transactions. It is crucial that businesses should not look at chatbots simply as a digital medium for advertisement. They should be focusing on the part of the Chatbot communication service. To improve the interaction of chatbot communication service, a blended skill chatbot was proven to have a great performance which also having an inference, personalization, empathy, and knowledge. In this paper, we conduct a literature review that giving an insight into the recent development and statistical inference for empathetic chatbots and having a result of 13% of a hybrid model, 27% of a retrieval model, and 60% of the generative model to be analyzed its trends.
Quantum Communication for Post-Pandemic Cybersecurity

Current approaches to cybersecurity will become increasingly inadequate as the use of networks grows and hacking becomes more skilled. One response to this problem lies in quantum technologies. In particular, the extreme sensitivity of quantum communication makes interference readily detectable and can provide secure encryption-key distribution. However, this is likely to benefit primarily high-value networks that use encryption, leaving insecure the growing use of mass networks for distributed work. The options to approaching this conundrum are (1) to accept where quantum technology leads, (2) to accelerate the technology in general without regard to how it is used, or (3) to push the technology to include mass use. We recommend a public-private strategy for the United States and its allies to effect both high-end and mass use. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> The authors would especially like to acknowledge Professor Nathalie de Leon of the Princeton Quantum Initiative, Princeton University, whose input on quantum science has been invaluable. Errors in this paper are, of course, ours.
Abstraction-based Multi-object Acoustic Anomaly Detection for Low-complexity Big Data Analysis

In the deployments of cyber-physical systems, specifically predictive maintenance and Internet of Things applications, a staggering amount of data can be harvested, transmitted, and recorded. Although the collection of large data sets can be used for many solutions, its utilization is made difficult by the increased overhead on the transmission and limited processing capabilities of the underlying physical system. For such highly correlated and extensive data, this situation is usually described as data-rich, information-poor. We propose for the first time a novel one-stage method, called Information-Abstraction-Net (IA-Net), for the detection of abnormal events in multi-object anomaly detection scenarios by utilizing highly abstracted sensory information instead of the entire sampled data set to elevate the transmission and analysis needs of the system. We find that the computation complexity of IA-Net is reduced by half compared to competing solutions and the detection accuracy is increased by about 5-47%, as well.
Sparsity-Induced Graph Convolutional Network for Semisupervised Learning

The graph representation (GR) in a data space reveals the intrinsic information as well as the natural relationships of data, which is regarded as a powerful means of representation for solving the semisupervised learning problem. To effectively learn on a predefined graph with both labeled data and unlabeled data, the graph convolutional network (GCN) was proposed and has attracted a lot of attention due to its high-performance graph-based feature extraction along with its low computational complexity. Nevertheless, the performance of GCNs is highly sensitive to the quality of the graph, meaning with high probability, the GCNs will achieve poor performances on a badly defined graphs. In numerous real-world semisupervised learning problems, the graph connecting each entity in the data space implicitly exists so that there is no naturally predefined graph in these problems. To overcome the issues, in this article, we apply unified GR techniques and GCNs in a framework that can be implemented in semisupervised learning problems. To achieve this framework, we propose sparsity-induced graph convolutional network (SIGCN) for semisupervised learning. SIGCN introduces the sparsity to formulate significant relationships between instances by constructing a newly proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_0$</tex-math></inline-formula> -based graph (termed as the sparsity-induced graph) before applying graph convolution to capture the high-quality features based on this graph for label propagation. We prove and demonstrate the feasibility of the unified framework as well as effectiveness in capturing features. Extensive experiments and comparisons were performed to show that the proposed SIGCN obtains a state-of-the-art performance in the semisupervised learning problem.
MT-ResNet: A Multi-Task Deep Network for Facial Attractiveness Prediction

Facial attractiveness prediction (FAP) is an intriguing and challenging problem that draws attention of researchers in recent years. Unlike other objective computer vision topics such as face detection, FAP also involves deep facial feature extraction and attractiveness pattern recognition which is relatively subjective. The work of FAP requires both mass collection of people's appreciations of beauty and the learning, replication of people's aesthetic standards by the model. Work regarding FAP in the early stage focuses on representing facial features using machine learning algorithms. In recent years, neutral networks, especially convolutional neural networks show its great performance in related areas. In this paper, a multi-task FAP model, MT-ResNet is proposed which could automatically predict the facial attractiveness score and the gender given a portrait. The results are compared with other existing models, which shows MT-ResNet's efficiency and high-accuracy among similar works.
exYOLO: A small object detector based on YOLOv3 Object Detector

Object detection is a hot and popular research area in computer vision. Although object detection algorithm will be greatly speeding up with Deep Neural Networks, like YOLOv3 Object Detector, its performance in accuracy and precision was not satisfactory in small object detection which has less than 1% area in one image. This paper proposes a small object detector, exYOLO, which enhances the shallow feature information by superimposing the 2-fold down-sampled feature map from the original network model. The author attempted the set of experiments with a view to demonstrating accuracy and precision in small object detection, the results of the experiment indicate that exYOLO is a better performance in accuracy and precision.
Countervalues of the digital ethos perceived by future trainers

The digital ethosphere, as a cultural environment of digital interactions, provides spaces for social and citizen participation where certain values and counter-values are promoted that determine the users’ construction of their personal and cultural identity. The lack of studies that analyze the counter-values immersed in digital interactions and spaces has led to the development of this study, which seeks to analyze the students' perception of the presence of counter-values on the Internet by examining the social sphere (with classmates, friends or family), the type and the way in which they are transmitted, as well as the associated risks (information or communication) and the resources or applications available. A mixed methodological approach study was designed through an ad-hoc questionnaire that was answered by 305 students from education and teacher training faculties. The results indicated that the majority of students identify the presence of counter-values when using the Internet, highlighting manipulation and violence, followed by lack of respect, inequality, and dishonesty, as well as the risks associated with information and communication, emphasizing the implicit presence of counter-values in the social networks logics and discourses. The conclusions provide evidence on the need to include values education in critical media education, reinforcing the preparation of future teachers who can teach how to deconstruct and eradicate counter-values in the digital sphere. La ethosfera digital, como entorno cultural de interacciones digitales, propicia espacios de participación social y ciudadana donde se promueven valores y contravalores que determinan la construcción de la identidad personal de sus usuarios. La carencia de estudios para analizar los contravalores inmersos en las interacciones y espacios digitales ha propiciado el desarrollo de esta investigación, que busca analizar el alcance de la percepción de los estudiantes sobre la presencia de contravalores en Internet examinando en qué esfera social (con compañeros, amigos o familiares), de qué tipo y cómo se transmiten, a qué riesgos pueden asociarse (información o comunicación) y en qué recursos o aplicaciones. Se diseñó un estudio de enfoque metodológico mixto con un cuestionario ad-hoc que fue respondido por 305 estudiantes de facultades de educación y formación docente. Los resultados indican que la mayoría de los estudiantes identifican la presencia de contravalores cuando usan Internet, destacando la manipulación y la violencia, seguido de la falta de respeto, la desigualdad y la deshonestidad, así como los riesgos asociados a la información y la comunicación, evidenciando la presencia implícita de contravalores en los discursos de las redes sociales. Se concluye resaltando la necesidad de incluir la educación en valores en la educación crítica para los medios, reforzando la formación de futuros formadores que pueden enseñar a deconstruir y erradicar los contravalores de la esfera digital.
Real-Time Intelligent Vishing Prediction and Awareness Model (RIVPAM)

Social Engineering based attacks are most convenient and successful form of exploiting humans. While researchers have made substantial contributions to detect and mitigate Phishing, Vishing in real-time has been least explored research area. Interestingly, it is cost-effective, scheming, strategic, and has increased during Pandemic. This paper proposes a Real-Time Vishing Prediction and Awareness Model (RIVPAM) to help potential unwary Vishing targets in being cautious and resilient against real-time Vishing attacks.
Development of an Oceanographic Databank Based on Ontological Interactive Documents

The article proposes an approach to development of an oceanographic databank based on the formation of an ontological representation of information arrays and accessing them with an ontology-defined interface. For processing large amounts of oceanographic data located in disparate archives and databases (including those represented as natural language documents) recursive reduction method is proposed. For providing interactive access to information and services ontological interactive document is suggested. Its core element, called ontological presentation template, ensures the most effective work of the experts with the information and allows them to change system’s structure, composition and functions with minimal time. Control ontologies as a mechanism for defining the configuration of the natural system can be used. For increasing the efficiency of the natural system by optimizing the information transferring process ontological integration descriptors are suggested. Approach to oceanographic data processing that includes recursive reduction method for textual scientific reports and measurement result files processing is presented. Oceanographic databank software system with ontology-based user interface and GIS application viewing mode is presented.
Mixed Numerology Interference Recognition Approach for 5G NR

The coexistence of multiple numerologies can cause interference and degrade system performance in 5G devices. In this letter, a convolutional neural networks (CNN)-based mixed numerology interference (MNI) recognition approach is devised to resolve the interference for user equipment (UE) to receive new radio (NR) downlink signal. The results are learned from the frequency domain magnitude and phase waveform, for the best precision and efficiency. We use as little sample data as possible to reduce memory/buffer costs and to alleviate the computing resources. Simulations are performed to verify the viability on various signal-noise ratio (SNR), channel condition, modulation, etc. It is shown that the accuracy can reach 97% or higher at varying SNR and fading channels. Furthermore, a dynamic filtering/windowing (DFW) scheme is proposed. The link level simulation results show that the scheme can improve the attainable Signal to Interference plus Noise Ratio (SINR) by 3~6dB for the indicated cases.
Adapting Ethical Sensitivity as a Construct to Study Technology Design Teams

The design of new technologies is a cooperative task (between designers on teams, and between designers and users) with ethical import. Studying technology development teams' engagement with the ethical aspects of their work is important, but engagement with ethical issues is an unobservable construct without agreement on what observable factors comprise it. Ethical sensitivity (ES), a construct studied in medicine, accounting, and other professions, offers a framework of observable factors by operationalizing ethical engagement in workplaces into component parts. However, ES has primarily been studied as a property of individuals rather than groups and in professions outside of computing. This paper uses a corpus of 108 ES studies from 1985-2020 to adapt the framework for studies of technology design teams. From the ES corpus, we build an umbrella framework that conceptualizes ES as comprising the moment of noticing an ethical problem (recognition), the process of building understanding of the situation (particularization), and the decision about what to do (judgment). This framework makes theoretical and methodological contributions to the study of how ethics are operationalized on design teams. We find that ethical sensitivity provides useful language for studies of collaboration and communication around ethics; suggests opportunities for, and evaluations of, ethical interventions for design workplaces; and connects team members' backgrounds, educational experiences, work practices, and organizational factors to design decisions. Simultaneously, existing research in HCI and CSCW addresses the limited range of research methods currently employed in the ES literature, adding rich, contextualized data about situated and embodied ethical practice to the theory.
Epistemic extensions of substructural inquisitive logics

Abstract In this paper, we study the epistemic extensions of distributive substructural inquisitive logics. Substructural inquisitive logics are logics of questions based on substructural logics of declarative sentences. They generalize basic inquisitive logic which is based on the classical logic of declaratives. We show that if the underlying substructural logic is distributive, the generalization can be extended to embrace also the epistemic modalities ‘knowing whether’ and ‘wondering whether’ that are applicable to questions. We construct a semantic framework for a language of propositional substructural logics enriched with a question-forming operator (inquisitive disjunction) and epistemic modalities. We show that within this framework, one can define a canonical model with suitable properties for any (syntactically defined) epistemic inquisitive logic. This leads to a general approach to completeness proofs for such logics. A deductive system for the weakest epistemic inquisitive logic is described and completeness proved for this special case using the general method.
No-Reference Image Quality Assessment with Multi-Scale Orderless Pooling of Deep Features

The goal of no-reference image quality assessment (NR-IQA) is to evaluate their perceptual quality of digital images without using the distortion-free, pristine counterparts. NR-IQA is an important part of multimedia signal processing since digital images can undergo a wide variety of distortions during storage, compression, and transmission. In this paper, we propose a novel architecture that extracts deep features from the input image at multiple scales to improve the effectiveness of feature extraction for NR-IQA using convolutional neural networks. Specifically, the proposed method extracts deep activations for local patches at multiple scales and maps them onto perceptual quality scores with the help of trained Gaussian process regressors. Extensive experiments demonstrate that the introduced algorithm performs favorably against the state-of-the-art methods on three large benchmark datasets with authentic distortions (LIVE In the Wild, KonIQ-10k, and SPAQ).
Relation-Based Associative Joint Location for Human Pose Estimation in Videos

Video-based human pose estimation (VHPE) is a vital yet challenging task. While deep learning methods have made significant progress for the VHPE, most approaches to this task implicitly model the long-range interaction between joints by enlarging the receptive field of the convolution. Unlike prior methods, we design a lightweight and plug-and-play joint relation extractor (JRE) to model the associative relationship between joints explicitly and automatically. The JRE takes the pseudo heatmaps of joints as input and calculates the similarity between pseudo heatmaps. In this way, the JRE flexibly learns the relationship between any two joints, allowing it to learn the rich spatial configuration of human poses. Moreover, the JRE can infer invisible joints according to the relationship between joints, which is beneficial for the model to locate occluded joints. Then, combined with temporal semantic continuity modeling, we propose a Relation-based Pose Semantics Transfer Network (RPSTN) for video-based human pose estimation. Specifically, to capture the temporal dynamics of poses, the pose semantic information of the current frame is transferred to the next with a joint relation guided pose semantics propagator (JRPSP). The proposed model can transfer the pose semantic features from the non-occluded frame to the occluded frame, making our method robust to the occlusion. Furthermore, the proposed JRE module is also suitable for image-based human pose estimation. The proposed RPSTN achieves state-of-the-art results on the video-based Penn Action dataset, Sub-JHMDB dataset, and PoseTrack2018 dataset. Moreover, the proposed JRE improves the performance of backbones on the image-based COCO2017 dataset. Code is available at https://github.com/YHDang/pose-estimation.
A Security Scoring Framework to Quantify Security in Cyber-Physical Systems

The need to achieve a suitable level of security in Cyber-Physical Systems (CPS) presents a major challenge for engineers. The unpredictable communication of highly constrained, but safety-relevant systems in a heterogeneous environment, significantly impacts the number and severity of vulnerabilities. Consequently, if security-related weaknesses can successfully be exploited by attackers, the functionality of critical infrastructure could be denied or malfunction. This might consequently threaten life or leak sensitive information. A toolkit to quantitatively express security is essential for security engineers in order to define security-enhancing measurements. For this purpose, security scoring frameworks, like the established Common Vulnerability Scoring System can be used. However, existing security scoring frameworks may not be able to handle the proposed challenges and characteristics of CPS. Therefore, in this work, we aim to elaborate a security scoring system that is tailored to the needs of CPS. In detail, we analyze security on a System-of-Systems level, while considering multiple attacks, as well as potential side effects to other security-related objects. The positive effects of integrated mitigation concepts should also be abbreviated by our proposed security score. Additionally, we generate the security score for interacting AUTOSAR platforms in a highly-connected Vehicle-to-everything (V2x) environment. We refer to this highly relevant use case scenario to underline the benefits of our proposed scoring framework and to prove its effectiveness in CPS.