Reviewerly/exHarmony huggingface dataset:
CIM-Based Smart Pose Detection Sensors

The majority of digital sensors rely on von Neumann architecture microprocessors to process sampled data. When the sampled data require complex computation for 24×7, the processing element will a consume significant amount of energy and computation resources. Several new sensing algorithms use deep neural network algorithms and consume even more computation resources. High resource consumption prevents such systems for 24×7 deployment although they can deliver impressive results. This work adopts a Computing-In-Memory (CIM) device, which integrates a storage and analog processing unit to eliminate data movement, to process sampled data. This work designs and evaluates the CIM-based sensing framework for human pose recognition. The framework consists of uncertainty-aware training, activation function design, and CIM error model collection. The evaluation results show that the framework can improve the detection accuracy of three poses classification on CIM devices using binary weights from 33.3% to 91.5% while that on ideal CIM is 92.1%. Although on digital systems the accuracy is 98.7% with binary weight and 99.5% with floating weight, the energy consumption of executing 1 convolution layer on a CIM device is only 30,000 to 50,000 times less than the digital sensing system. Such a design can significantly reduce power consumption and enables battery-powered always-on sensors.
Contrasting Explanations for Understanding and Regularizing Model Adaptations

Abstract Many of today’s decision making systems deployed in the real world are not static—they are changing and adapting over time, a phenomenon known as model adaptation takes place. Because of their wide reaching influence and potentially serious consequences, the need for transparency and interpretability of AI-based decision making systems is widely accepted and thus have been worked on extensively—e.g. a very prominent class of explanations are contrasting explanations which try to mimic human explanations. However, usually, explanation methods assume a static system that has to be explained. Explaining non-static systems is still an open research question, which poses the challenge how to explain model differences, adaptations and changes. In this contribution, we propose and (empirically) evaluate a general framework for explaining model adaptations and differences by contrasting explanations. We also propose a method for automatically finding regions in data space that are affected by a given model adaptation—i.e. regions where the internal reasoning of the other (e.g. adapted) model changed—and thus should be explained. Finally, we also propose a regularization for model adaptations to ensure that the internal reasoning of the adapted model does not change in an unwanted way.
FPGA-based high-throughput Montgomery modular multipliers for RSA cryptosystems

This paper presents an FPGA-based Montgomery modular multiplier for implementing high-throughput RSA cryptosystems. First, we propose a variable segmentation Montgomery modular multiplication (VSMMM) algorithm which enables the radix of the multiplier and the multiplicand adapt to any given datawidth. Then, to make trade-offs among latency, area and throughput, we design a dual-path fully concurrent MMM architecture based on VSMMM algorithm. As a case study, a RSA processor has been implemented using the proposed method. Experimental results show that the proposed MMM multiplier and RSA processor achieve much higher throughput than existing works.
Blockchain Technology in the Chemical Industry

This article presents a review of the application of blockchain and blockchain-based smart contracts in the chemical and related industries. We introduce the basic concepts of blockchain and smart contracts and explain how some of their features are enabled. We review several typical or novel applications of blockchain and smart contract technologies and their enabling concepts and underlying technologies. We classify the selected literature into five categories and discuss their motivations and technical designs. We recognize that the trend of decentralization creates a need to use blockchain and smart contracts to implement trust and distributed control mechanisms. We also speculate on future applications of blockchain and smart contracts. We believe that, in the future, blockchains with different consensus mechanisms will be studied and applied to achieve more efficient and practical decentralized systems. Also, blockchain-based smart contracts will be more widely applied to enhance autonomous distributed controls in decentralized systems.
Aggregated Pyramid Vision Transformer: Split-transform-merge Strategy for Image Recognition without Convolutions

This paper proposes a new pure attention model, Aggregated Pyramid Vision Transformer (APVT), for computer vision applications. Based on the Vision Transformer (ViT) architecture, APVT adopts the classic pyramid architecture of CNN and employs the group encoder technique to replace the traditional encoder for feature enhancement. APVT uses the split-transform-merge strategy to refine the group encoder operation. The model performs image classification on CIFAR-10 dataset and object detection on COCO 2017 dataset for verification. Experimental results show that APVT has excellent performance compared to other Transformer network architectures.
Convergence Analysis of Nonconvex Distributed Stochastic Zeroth-order Coordinate Method

This paper investigates the stochastic distributed nonconvex optimization problem of minimizing a global cost function formed by the summation of n local cost functions. We solve such a problem by involving zeroth-order (ZO) information exchange. In this paper, we propose a ZO distributed primal–dual coordinate method (ZODIAC) to solve the stochastic optimization problem. Agents approximate their own local stochastic ZO oracle along with coordinates under an adaptive smoothing parameter. We show that the proposed algorithm achieves the convergence rate of ${\mathcal{O}}(\sqrt p /\sqrt T )$ for general nonconvex cost functions. We demonstrate the efficiency of proposed algorithms through a numerical example in comparison with the existing state-of-the-art centralized and distributed ZO algorithms.
The Regulation of Cryptocurrencies in the United States of America

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A Differentially Private Framework for Deep Learning With Convexified Loss Functions

Differential privacy (DP) has been applied in deep learning for preserving privacy of the underlying training sets. Existing DP practice falls into three categories - objective perturbation, gradient perturbation and output perturbation. They suffer from three main problems. First, conditions on objective functions limit objective perturbation in general deep learning tasks. Second, gradient perturbation does not achieve a satisfactory privacy-utility trade-off due to over-injected noise in each epoch. Third, high utility of the output perturbation method is not guaranteed because of the loose upper bound on the global sensitivity of the trained model parameters as the noise scale parameter. To address these problems, we analyse a tighter upper bound on the global sensitivity of the model parameters. Under a black-box setting, based on this global sensitivity, to control the overall noise injection, we propose a novel output perturbation framework by injecting DP noise into a randomly sampled neuron (via the exponential mechanism) at the output layer of a baseline non-private neural network trained with a convexified loss function. We empirically compare the privacy-utility trade-off, measured by accuracy loss to baseline non-private models and the privacy leakage against black-box membership inference (MI) attacks, between our framework and the open-source differentially private stochastic gradient descent (DP-SGD) approaches on six commonly used real-world datasets. The experimental evaluations show that, when the baseline models have observable privacy leakage under MI attacks, our framework achieves a better privacy-utility trade-off than existing DP-SGD implementations, given an overall privacy budget $\epsilon \leq 1$ for a large number of queries.
Hierarchical Cloud-Based Consortium Blockchains for Healthcare Data Storage

Blockchain technology can be used as a healthcare solution that allows hospitals to store and share electronic health records (EHRs) in a secure and reliable manner. However, the number of hospitals that can participate in a blockchain network is limited by the inclusion of big data such as multimedia files, which presents an obvious scalability problem. In this paper, we introduce a hierarchical cloud-based consortium blockchain framework for storing big data including multimedia files within cloud-based local hospital blockchain networks and sharing them with hospitals outside the networks through high-level blockchain networks, called city blockchain networks and state blockchain network. We present procedures for concurrently searching EHRs, creating access control policies for authorized access, and retrieving EHRs through hierarchical blockchain networks. The experimental results show that our approach is feasible and efficient for accessing and sharing EHRs using hierarchical cloud-based consortium blockchains throughout a country.
An Efficient Security Solution for Industrial Internet of Things Applications

The Industrial Internet of Things (IIoT) has been growing for presentations in industry in recent years.Security for the IIoT has unavoidably become a problem in terms of creating safe applications.Due to continual needs for new functionality, such as foresight, the number of linked devices in the industrial environment increases.Certification of fewer signatories gives strong authentication solutions and prevents trustworthy third parties from being publicly certified among available encryption instruments.Hence this blockchain-based endpoint protection platform (BCEPP) has been proposed to validate the network policies and reduce overall latency in isolation or hold endpoints.A resolver supports the encoded model as an input; network functions can be optimized as an output in an infrastructure network.The configuration of the virtual network functions (VNFs) involved fulfills network characteristics.The output ensures that the final service is supplied at the least cost, including processing time and network latency.According to the findings of this comparison, our design is better suited to simplified trust management in IIoT devices.Thus, the experimental results show the adaptability and resilience of our suggested confidence model against behavioral changes in hostile settings in IIoT networks.The experimental results show that our proposed method, BCEPP, has the following, when compared to other methods: high computational cost of 95.3%, low latency ratio of 28.5%, increased data transmitting rate up to 94.1%, enhanced security rate of 98.6%, packet reception ratio of 96.1%, user satisfaction index of 94.5%, and probability ratio of 33.8%.
Total probability and Bayes formulae for joint multidimensional-matrix Gaussian distributions

This paper is devoted to the development of a mathematical tool for obtaining the Bayesian estimations of the parameters of multidimensional regression objects in their finite-dimensional multidimensional-matrix description. Such a need arises, particularly, in the problem of dual control of regression objects when multidimensional-matrix mathematical formalism is used for the description of the controlled object. In this paper, the concept of a one-dimensional random cell is introduced as a set of multidimensional random matrices (in accordance with the “cell array” data type in the Matlab programming system), and the definition of the joint multidimensional-matrix Gaussian distribution is given (the definition of the Gaussian one-dimensional random cell). This required the introduction of the concepts of one-dimensional cell of the mathematical expectation and two-dimensional cell of the variance-covariance of the one-dimensional random cell. The integral connected with the joint Gaussian probability density function of the multidimensional matrices is calculated. The two formulae of the total probability and the Bayes formula for joint multidimensional-matrix Gaussian distributions are given. Using these results, the Bayesian estimations of the unknown coefficients of the multidimensional-matrix polynomial regression function are obtained. The algorithm of the calculation of the Bayesian estimations is realized in the form of the computer program. The results represented in the paper have theoretical and algorithmic generality.
Reviews in Online Data Stream and Active Learning for Cyber Intrusion Detection - A Systematic Literature Review

Intrusion Detection Systems (IDS) monitor com-puter networks for attack. Network data streams are potentially infinite and require real-time processing in order to provide timely detection of changing attacks. To address the nature of the network data stream it is important to consider the use of online data stream learning methods for IDS. Online data stream learning is an extension of Machine Learning (ML) where special consideration is given to finding anomalies in the data stream via supervised and unsupervised methods, adapting to concept drift, processing real-time events, and management of labelling cost by using Active Learning (AL). This paper asks the question of which online data stream and AL methods for IDS have been recently reviewed? A Systematic Literature Review (SLR) was performed and found that there is currently no reviews available that focus primarily on IDS data stream learning. Reviews were organised into categories and key considerations presented.
Motivation and Retention Strategies of Undergraduates' Online Course Learning Motivation in the Information Age

With the rapid development of the education industry and the popularization of multi-element teaching models, online courses have become a stepping stone for college students to broaden their horizons and constantly enrich their knowledge. The official website course selection includes courses in the liberal arts and sciences, which can effectively avoid shortcomings in the disciplines of the liberal arts students in employment. In fact, the learning scope of online courses is not limited to the learning of the courses by students in the school, students can also supplement the knowledge they need to understand outside the class. Especially for students who need to take the annual postgraduate entrance examination and students who take the English professional level examination, online learning is very important. However, as the progress of learning progresses, learners' motivation for learning gradually weakens, and there have been cases where homework cannot be completed during online learning or online learning cannot continue. To this end, this study mainly provides relevant opinions on the motivation of college students in online courses through questionnaires and interviews. This contributes to the promotion of online courses and enhances the enthusiasm of college students to learn.
Adoption of Blockchain for Data Privacy in 6G-Envisioned Augmented Reality: Opportunities and Challenges

The widespread adoption of real-interactivity, fuelled with developments in wireless communication infrastructures, such as sixth generation (6G), made augmented reality (AR) a possibility in a range of vertical applications. AR integrates the digital visuals into a virtual space that emulates the real-world feedback for the user. It supports a range of haptic and interactive communication and thus has attracted the interest of the research community to provide useful AR frameworks. However, as the user-private data is communicated through open channels, it raises privacy and security concerns. In parallel, decentralized computing has shifted resource provisioning to near device interactions that support the real-time connectivity of AR. To preserve privacy and induce trust among AR stakeholders, blockchain (BC)-based AR use cases are presented by researchers. However, the existing surveys have not focused on reference architectures and implementation challenges. Motivated by the research gap, the paper presents a systematic survey of the integration of BC to induce data privacy in AR ecosystems. A comparative analysis is presented, and the potential application of BC-based AR is discussed. A case study on VoRtex enterprise is presented, and a supporting architectural framework is discussed. The survey intends to present the readers deep insights about BC adoption in AR that would motivate industry practitioners, academicians and researchers to propose scalable BC-integrated solutions in AR/VR-based ecosystems.
Tikkoun Sofrim: Making Ancient Manuscripts Digitally Accessible: The Case of Midrash Tanhuma

Making ancient handwritten manuscripts accessible to the general public is challenging, for several reasons. Foremost, they are handwritten. Each and every one is unique, so there is a need for manual transcription for providing enough examples for training a machine-learning-based algorithm to automatically transcribe the handwritten text. Moreover, the quality of the text is diverse—over time the ink faded, pages were damaged, and so forth. Furthermore, the boundaries of the textual regions on a page and the lines of text are not standard. Sometimes there are corrections above the lines, the lines are curved, there are comments and annotations on the margins, and more. A possible solution for these challenges is having a “person in the loop.” However, manual correction brings with it another challenge—how to address disagreement between annotations (as usually several corrections are considered before a decision is taken about the correct transcription). Tikkoun-Sofrim is a system that integrates automatic handwritten text recognition with manual, crowdsourced error correction, introducing an automatic decision process about when to stop asking for additional transcription and selecting the best transcription, declaring it as the recommended agreed reading. The system was applied to several manuscripts of “Midrash Tanhuma,” a medieval Hebrew rabbinic homiletic text, achieving a high level of success.
Research of Big Data Information Mining and Analysis : Technology Based on Hadoop Technology

Nowadays, with the continuous development of modern Internet and e-commerce technology, the network data is growing geometrically and the era of big data has come. The massive data information in the network contains a lot of industry resources, and how to properly mine the data information required has become a new issue for enterprises. In this paper, a new data mining and analysis technology is built based on the traditional Apriori mining algorithm and applying the latest Hadoop technology. This technology mainly focuses on the following two aspects. The first is the improvement of the traditional algorithm, which is mainly applied to Hadoop technology to upgrade the traditional data algorithm; the second part is the parallel analysis processing of mining data. The two major parts constitute the new big data mining analysis technology, which provides some references for the current logical data mining and network data applications.
Recognition of inter-class variation of human actions in sports video

Human action recognition encompasses a scope for an automatic analysis of current events from video and has varied applications in multi-various fields. Recognizing and understanding of human actions from videos still remains a difficult downside as a result of the massive variations in human look, posture and body size inside identical category. This paper focuses on a specific issue related to inter-class variation in Human Action Recognition. To discriminate the human actions among the category, a novel approach which is based on wavelet packet transformation for feature extraction. As we are concentrating on classifying similar actions non-linearity among the features are analyzed and discriminated by proposed by Deterministic Normalized –Linear Discriminant Analysis (DN-LDA). However the major part of the recognition system relays on classification part and the dynamic feeds are classified by Hidden Markov Model at the final stage based on rule set. With a trained dataset and rules framed with the end user, our intelligent HAR system is capable of achieving the accuracy rate of 97.4% which is higher than the other state of art approaches. Experiments results have shown that the proposed approach is discriminative for similar human action recognition and well adapted to the inter-class variation.
On the Computational Efficiency of Geometric Multidimensional Scaling

Real-life applications often deal with multidimensional data. In the general case, multidimensional data means a table of numbers whose rows correspond to different objects and columns correspond to features characterizing the objects. Usually, the number of objects is large, and the dimensionality (number of features) is greater than it is possible to represent the objects as points in 2D. The goal is to reduce the dimensionality of data to such one that objects, characterized by a large number of features or by proximities between pairs of the objects, be represented as points in lower-dimensional space or even on a plane. Multidimensional scaling (MDS) is an often-used method to reduce the dimensionality of multidimensional data nonlinearly and to present the data visually. MDS minimizes some stress function. We have proposed in [8] and [9] to consider the stress function and multidimensional scaling, in general, from the geometric point of view, and the so-called Geometric MDS has been developed. Geometric MDS allows finding the proper direction and step size forwards the minimum of the stress function analytically. In this paper, we disclose several new properties of Geometric multidimensional scaling and compare the simplest realization (GMDS1) of Geometric MDS experimentally with the well-known SMACOF version of MDS.
On Architectures and Training for Raw Waveform Feature Extraction in ASR

With the success of neural network based modeling in auto-matic speech recognition (ASR), many studies investigated acoustic modeling and learning of feature extractors directly based on the raw waveform. Recently, one line of research has focused on unsupervised pre-training of feature extractors on audio-only data to improve downstream ASR performance. In this work, we investigate the usefulness of one of these front-end frameworks, namely wav2vec, in a setting without additional untranscribed data for hybrid ASR systems. We compare this framework both to the manually defined stan-dard Gammatone feature set, as well as to features extracted as part of the acoustic model of an ASR system trained su-pervised. We study the benefits of using the pre-trained feature extractor and explore how to additionally exploit an ex-isting acoustic model trained with different features. Finally, we systematically examine combinations of the described features in order to further advance the performance.