Reviewerly/exHarmony huggingface dataset:
Data Transfer Through Blockchain

Blockchain is a sort of database that has become popular due to its decentralized nature. It consists of a collection of blocks in which if one block's hash value changes, all other blocks’ hash values are updated immediately; this allows for a safe and secure data transfer in which a hostile user cannot gain access to the blockchain. The ledger becomes immutable once the transaction is updated to the blockchain. Decentralizing the blockchain eliminates the need for a third party in the transaction. Every node in a peer-to-peer network will have a copy of the blockchain and will follow a consensus procedure, ensuring that the blockchain is distributed and resistant to attacks. When data must be passed to another person, security is paramount. As a result, we may use blockchain to solve this problem and guarantee trust and reliability. This article discusses the concept of creating a Web-based application that uses blockchain technology to transfer data.
Variational Bootstrap for Classification

Carrying out Bayesian inference over parameters of statistical models is intractable when the likelihood and the prior are non-conjugate. Variational bootstrap provides a way to obtain samples from the posterior distribution over model parameters, where each sample is the solution of a task where the labels are perturbed. For Bayesian linear regression with a Gaussian likelihood, variational bootstrap yields samples from the exact posterior, whereas for nonlinear models with a Gaussian likelihood some guarantees of approaching the true posterior can be established. In this work, we extend variational bootstrap to the Bernoulli likelihood to tackle classification tasks. We use a transformation of the labels which allows us to turn the classification task into a regression one, and then we apply variational bootstrap to obtain samples from an approximate posterior distribution over the parameters of the model. Variational bootstrap allows us to employ advanced gradient optimization techniques which provide fast convergence. We provide experimental evidence that the proposed approach allows us to achieve classification accuracy and uncertainty estimation comparable with MCMC methods at a fraction of the cost.
pDPoSt+sPBFT: A High Performance Blockchain-Assisted Parallel Reinforcement Learning in Industrial Edge-Cloud Collaborative Network

With the increasing demand for resource scheduling efficiency in Industrial Internet of Things (IIoT), parallel reinforcement learning (PRL) based distributed edge-cloud collaborative resource scheduling scheme has attracted enormous attention. However, the computing and communication capacities, the security degree of massive distributed edge computing servers are different. It is difficult to make a large number of edge servers carry out security and efficiency PRL based edge-cloud collaboration resource scheduling scheme. Thus, in this paper, a large-scale distributed edge-cloud collaborative resource scheduling method based on picture delegated proof of state and suspicious practical byzantine fault tolerance (pDPoSt+sPBFT) consensus algorithm is proposed. To be specific, we first propose a collaborative edge-cloud industrial network architecture to support massive industrial intelligence tasks, then a distributed PRL based resource allocation scheme is utilized. Secondly, in order to improve the efficiency and security of distributed PRL training, we propose a server filtering strategy based on pDPoSt algorithm. Finally, a sPBFT algorithm is proposed to further realize security parameter aggregation of distributed PRL. Experimental results show that the proposed method has good efficiency and security performance compared with the traditional distributed edge-cloud collaborative resource scheduling algorithm. The proposed approach has great potential in complex IIoT scenarios.
A new educational theory and digital technologies in teaching at a university

The article focuses on the need to create a new educational theory taking into account new reality of teaching languages particularly. Both traditional and innovative educational theories are analyzed, as well as their effectiveness are discussed. Based on the research of the axiomatic method the authors studied the postulate that we need new ways of teaching and currently, learning takes place in various ways: both in the traditional way and digital education i.e. through communities of practitioners, social networks, as well as through the performance of professional tasks. Traditional training and electronic work activities are no longer separate. In many situations, one is not separable from the other. We need the theory which would explain communication between an individual and organized training, a learner and the content of education. Many processes that were previously explained by traditional theories of learning (especially in the processing of cognitive information) can now be supported by technology. In turn, the educational technologies should rest on a the-oretical basis. In our case, this is a new educational theory of digital education.
Defense of Signal Modulation Classification Attack Based on GAN

Although deep learning has been broadly used in all kinds of domains and has also achieved very good performance, it is obvious that deep learning is very vulnerable to be attacked shown by extensive researches (e.g. even a very small perturbation is added to the raw signal data, the result of the modulation classification of the signal by the deep neural network will be wrong), which will cause serious consequences. Thus, it is necessary to find some ways to defense this attack. As a new deep learning model in recent years, Generative Adversarial Network (GAN) has been applied to the field of radio communication, which can encrypt the signal data, so as to ensure the reliable transmission of the signal. In this paper, a defense method against signal modulation classification attack based on GAN is developed, which is used to utilize generator to reconstruct the signal sample to improve the classification accuracy of the model, while the defense ability of the model against adversarial samples is improved. It has been shown that the method achieved good results in experiments.
Attacking Frequency Information with Enhanced Adversarial Networks to Generate Adversarial Samples

In recent years, convolutional neural networks (CNNs) are widely used in various computer vision tasks with advanced performance. However, adversarial samples which add small-magnitude perturbation to images or videos are seriously threatening the application of CNNs. Some existing attack methods pay attention to the time domain information of the inputs, while the information in frequency domain is usually ignored. Others attack frequency domain by massive queries or significantly perceivable perturbation. In this paper, we propose a new method to attack the frequency information. The frequency information is combined with the Generative Adversarial Network (GAN) to design a novel algorithm called Frequency Attack Framework (FAF), which can attack the high-frequency information and the low-frequency information. Double discriminators are constructed on the GAN architecture to make attack more efficient in different frequency bands. The proposed algorithm generates optimal perturbation, resulting in adversarial samples with high attack transferability and quality. Several well-trained CNNs are fooled by FAF, and all of them have high error rates. Even when CNNs add defenses, our algorithm has a good performance.
Knowledge Retrieval using Question Answering approach on Epidemiology data

The tried and tested way for effective Knowledge Retrieval is by posting questions and retrieving data from the huge information repository. In the recent past the prevalence of pandemics and the spread of COVID-19, has led people to rigorously question the various forms of epidemiology data available on different sources. In general, the amount of information gathered is proportionate to the questioning patterns by the knowledge seeker. Question answering (QA) system is useful during unexpected situations, especially during a pandemic. In this paper, we have proposed a Knowledge Retrieval Question Answering system (KRQA) for answering the queries of users related to COVID-19. The KRQA system is divided into two modules. The first module consists of preprocessing (tokenization, stemming, bag of words) of the question to produce a word vector. The second module involves building, training, and testing the data repository. Feedforward neural network is used to extract the most relevant answer from a repository of all possible answers. The volume and quality of information about the pandemic scenario around the world are increased at a tremendous rate. Hence our work focuses on effective knowledge retrieval using question and answering approach. Our experimental results are found to give better results based on Percentage closeness, precision, and recall parameters. KRQA has the novelty of retrieving more relevant answers with good quality.
Fast-Track Product Evaluation From Text Reviews in M-Commerce

The popularity of mobile commerce has offered many new challenges for investigating public sentiments. With an uncountable number of stores and products available on the marketplace, customers heavily relied on the comments or reviews posted by others to support their buying decisions. For the online retailer's side, these text reviews are valuable resources to understand the latest customer expectations and devise a better product plan for launching suitable products to customers. Sentiment analysis is then developed for the evaluation of a significant amount of text data by searching the sentiment words. Nevertheless, different writers may have different perceptions on the sentiment words, and hence, this inconsistency would be amplified. In this connection, a novel approach to obtain public sentiment by combining the topic modeling, fuzzy set, and multi-criteria decision-making approaches is proposed. The uncertainty of different perceptions on the sentiment words is remedied by fuzzy-set.
A Formal Algebraic Approach for the Quantitative Modeling of Connectors in Architectures

In this paper, we propose an algebraic formalization of connectors in the quantitative setting. We firstly present a weighted Algebra of Interactions over a set of ports and a commutative and idempotent semiring, which is proved sufficient for modeling well-known coordination schemes in the weighted setup. In turn, we study a weighted Algebra of Connectors over a set of ports and a commutative and idempotent semiring, which extends the former algebra with types that describe Rendezvous and Broadcast synchronization. We show the expressiveness of the algebra by modeling several weighted connectors, and we provide conditions for proving a concept of congruence relation between weighted connectors.
Sentiment Analysis using Modified GRU

Recently, everyone is becoming part of this digital world and spending time on social media like Twitter etc., posting their content. So a large number of heterogeneous Tweets have been coming each day. Due to heterogeneous structure, alone semantic Analysis can not classify and understand the sentences. To resolve this issue, the Deep Learning model plays an important role in understanding the meaning of content. In this paper, we proposed a modified Grated Recurrent Unit (GRU) for classification and understanding of the tweet. It consists of two gates update and a reset gate. The update gate knows how much memory to retain, and the reset gate knows how much to forget. We trained the proposed GRU model using the Sentiment140 dateset. Then experiments were conducted to find out the accuracy of the proposed model. Finally, we compared the accuracy of the proposed model with Long Short Terms Memory (LSTM) and Bi-directional Long short terms memory (BiLSTM). The experimental result shows that modified GRU outperformed the LSTM and BiLSTM models.
DEPTH ESTIMATION AI INFERENCING COMPARISON OF JETSON XAVIER NX AND CORAL DEV BOARD

AI inferencing, especially the real time processing of neural networks may require excessive calculation power in terms of speed and memory, thus opening a big area for research and design of new devices intended for AI acceleration. Examples of such devices are Nvidia’s Jetson series, Google’s Coral. Also, there are FPGA solutions such as Xilinx’s AI applicable FPGA. Depending on application requirements it could be difficult to choose between these devices, as in most cases, speed and accuracy are the most important factors, while there are also applications which require low power and low cost. Thus, investigation and comparison of these inferencing devices in terms of speed, memory, power and cost for the chosen field of subject may be useful for choosing the right device for the given task. We have done such an analysis for the depth estimation task using Jetson Xavier NX and Coral Dev Board as inferencing devices.
Plausible Proxy Mining With Credibility for Unsupervised Person Re-Identification

One effective way to address unsupervised person re-identification is to use a clustering-based contrastive learning approach. Existing state-of-the-art methods adopt clustering algorithms ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , DBSCAN) and camera ID information to divide all person images into several camera-aware proxies. Then, for each person image, the extracted feature representation is pulled closer to the centroids of its pseudo-positive proxies (the proxies that share the same pseudo-identity label with this image) and pushed away from the centroids of other pseudo-negative proxies (the proxies that share the different pseudo-identity label with this image). However, the quality of the proxy centroid is significantly affected by the proxy impurity issue and thus deteriorates the learned feature representations. On the premise that we cannot introduce superior supervision signals by thoroughly solving the proxy impurity issue, for a person image, identifying its plausible proxies: the pseudo-negative proxies which potentially include its wrongly-clustered instances (the instances with the same ground-truth identity with this image), and further fixing the resulted incorrect supervision signals become an urgent and challenging problem. This paper proposes a simple yet effective approach to address this problem. With a given image, our method can effectively locate its plausible proxies. Then we introduce credibility to measure how much we should treat the centroid of each mined plausible proxy as a positive supervision signal rather than entirely negative. Extensive experiments on three widely-used person re-ID datasets validate the effectiveness of our proposed approach. Codes will be available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Dingyuan-Zheng/PPCL</uri> .
Sentiment Text Analysis for Providing Individualized Feed of Recommendations Using Reinforcement Learning

We present an implementation of a new method of producing personalized text recommendations to users based on their sentiment. This method aims to identify patterns in user behavior based on a reward that results from each actual application in which the algorithm is used. The system consists of a semantic analysis algorithm that combines clustering and word processing, a level of neural networks designed to predict user reward for each text, and a level of algorithms that attempt to explore the problem area to adapt to changes in user behavior. This research work combines many known methods as well as variations thereof into a single system which in addition has the technical specifications to be used in typical applications like newsfeeds.
Evaluation of User Experience on the Augmented Reality Glovic Cafe and Bakery Jember Application to Maximize User Experience

Technological developments increasingly support the design and implementation of more interactive learning media in the field of education, one of which is the use of augmented reality (AR) technology. The conditions and situation of the Covid-19 pandemic are also increasingly making education digital and online. The more various kinds of applications are developed, the more they must evaluate the user experience of the appearance and function of the application. Measurement of user experience in applications can use the User Experience Questionnaire (UEQ) tool, with 26 questionnaire points. The UEQ Quality results show that the Attractiveness scale is 2.04, the Pragmatic Quality scale is 1.68, and the Hedonic Quality scale is 1.17 which shows positive evaluation results, which means that it can be concluded that users tend to like the application in terms of user experience. Based on this data, users have used similar applications, and the updates are still lacking. The scales that show well are attractiveness, perspicuity, efficiency, and stimulation. From the results of the respondent’s data processing, it was concluded that the convenience of using the application was good enough from the user’s perception. The data also showed that the user enjoyed the application.
A Hybrid Data Model for the Assessment of Border Control Technologies

The increasing demand for traveler clearance at international border crossing points (BCPs) has motivated research for finding more efficient solutions. Automated border control (ABC) is emerging as a solution to enhance the convenience of travelers, the throughput of BCPs, and national security. In recent years, focus has been placed on the use and acceptance of these technologies, in order to understand the barriers to use and the acceptance problems people experience. Despite the growing interest in novel Smart Border Control (SBC) technologies and in the assessment of their usability and acceptance, there are currently no standardized ways to capture the data needed to assess their acceptance. This paper provides a review concerning recent approaches in data and ontology modelling in addition to relevant to the border control domain data models and ontologies. We then explain in detail the application of a hybrid approach, both knowledge-based and data-driven, in order to derive the core entities, attributes and relationships among them. Moreover, we propose a data model which aims to capture the data needed for the border control technologies assessment. Finally, the data model is evaluated by domain experts and the results are presented and discussed.
Investigating Black-Box Function Recognition Using Hardware Performance Counters

This paper presents new methods and results for recognising black-box program functions using hardware performance counters (HPC), where an investigator can invoke and measure function calls. Important use cases include analysing compiled libraries, e.g. static and dynamic link libraries, and trusted execution environment (TEE) applications. We develop a generic approach to classify a comprehensive set of hardware events, e.g. branch mis-predictions and instruction retirements, to recognise standard benchmarking and cryptographic library functions. This includes various signing, verification and hash functions, and ciphers in numerous modes of operation. Three architectures are evaluated using off-the-shelf Intel/X86-64, ARM, and RISC-V CPUs. Next, we show that several known CVE-numbered OpenSSL vulnerabilities can be detected using HPC differences between patched and unpatched library versions. Further, we demonstrate that standardised cryptographic functions within ARM TrustZone TEE applications can be recognised using non-secure world HPC measurements, applying to platforms that insecurely perturb the performance monitoring unit (PMU) during TEE execution. High accuracy was achieved in all cases (86.22–99.83%) depending on the application, architectural, and compilation assumptions. Lastly, we discuss mitigations, outstanding challenges, and directions for future research.
Inverse Kinematics Assistance for the Creation of Redirected Walking Paths

Virtual reality interactions that require a specific relationship between the virtual and physical coordinate systems, such as passive haptic interactions, are not possible with locomotion techniques using redirected walking. To address this limitation, recent research has introduced environmental alignment, which is the notion of using redirected walking techniques to align the virtual and physical coordinate systems such that these interactions are possible. However, the previous research has only implemented environmental alignment in a reactive manner, and the authors posited that better results could be achieved if a predictive algorithm was instead used. In this work, we introduce a novel way to model the environmental alignment problem as a version of the inverse kinematics problem which can be incorporated into several existing predictive algorithms, as well as a simple proof-of-concept implementation. An exploratory human subject study (N=17) was conducted to evaluate this implementation’s usability as a tool for authoring planned path redirected walking scenarios that incorporate physical interactivity. To our knowledge, this is the first study to evaluate redirected walking experience design tools and provides a possible framework for future studies. Our qualitative analysis of the results generated both guidance for integrating automatic solvers and broad recommendations for designing redirected path authoring tools.
Deep Learning for Waveform Level Receiver Design With Natural Redundancy

High-speed information transmission system demands more powerful communication receivers. In this paper, we integrate deep learning algorithms into constructing waveform level receiver, which is capable of harnessing natural redundancy (NR) in raw message sources. Owing to end-to-end learning paradigm of neural network (NN), the proposed NN receiver can accomplish joint symbol timing synchronization and demodulation based on oversampled waveform sequences directly. The prevalent bidirectional LSTM (BiLSTM) and Transformer structures, suitable for excavating sequential correlation of complex sources and waveform sequences, are considered as the backbone of NN receivers. Under the cases of clean label and noisy label, we conceive two novel training algorithms and interpret their principles in theory. The clean label bits are accessible from original source encoding schemes, while noisy label bits are available from the decision results of canonical receivers with independently noised waveform sequences. When noisy labels possess sufficiently small error rates, optimized NN receiver is competitive with that adopting the accurate labels. As for Markov sources and natural language text sources, simulation results show that the proposed NN receivers’ performances are superior to the classical receivers without a priori knowledge, especially when high spectral efficiency modulation schemes are encountered and random noise is significant.
Design of Active-RC Filters Minimizing Sensitivities with Ant Colony Optimization

This work employs Ant Colony Optimization (ACO) to solve active-RC bi-quadratic filter design considering passive sensitivities minimization. We propose a new discrete version (D-ACOR) based on the operating principles of ACO with real domain. We also solve the problem exactly to compare the results of the algorithms against the best possible solutions. For showing the D-ACOR performance, we take as case studies two biquadratic filters: a low-pass and a bandpass, both with an infinite-gain multiple-feedback topology. The results are excel-lent because the algorithm achieves solutions with minimal sen-sitivities that meet the specifications. Furthermore, the performance comparison with other heuristics shows that D-ACOR has lower sensitivity values and presents better computational efficiency even if it requires a more significant number of independent runs to find successful solutions.