Reviewerly/exHarmony huggingface dataset:
XChange: A Universal Mechanism for Asset Exchange between Permissioned Blockchains

Abstract Permissioned blockchains are increasingly being used as a solution to record transactions between companies. Several use cases that leverage permissioned blockchains focus on the representation and management of real-world assets. Since the number of incompatible blockchains is quickly growing, there is an increasing need for a universal mechanism to exchange, or trade, digital assets between these isolated platforms. There currently is no universal mechanism for inter-blockchain asset exchange without a requirement for trusted authorities that coordinate the trade. We address this shortcoming and present XChange, a universal mechanism for asset exchange between permissioned blockchains. To achieve universality and to avoid trusted authorities that coordinate a trade, XChange does not provide atomic guarantees but leverages risk mitigation strategies to reduce value at stake. Our mechanism records the specifications and progression of each trade within records on a distributed log. XChange reduces the economic gains of adversaries by bounding the total amount of fraud they can commit at any time. After having committed fraud, an adversary is forced to finish its ongoing trades before it can engage in new trades. We first present a four-phased protocol that coordinates an asset exchange between two traders. We then outline how trade records can be stored on TrustChain, which is a lightweight distributed ledger specifically built for the tamper-proof storage of data elements. We implement XChange and conduct experiments. Our experiments demonstrate that XChange is capable of reducing the economic gains of adversaries by more than 99.9% when replaying a real-world trading dataset. A deployment on low-resource devices reveals that the latency added to a trade by XChange is only 493 milliseconds. Finally, our scalability evaluation shows that XChange achieves over 1’000 trades per second and that its throughput, in terms of trades per second, scales linearly with the system load.
Compiler Module of Abstract Machine Code for Formal Semantics Course

In this paper, we present a software module developed for the course Semantics of Programming Languages. This software will be part of the intended comprehensive software package to simplify and make the teaching of formal principles in theoretical computer science more attractive. During the pandemic, the need to support illustrative and illustrative online teaching increased. Our software is designed to illustrate and visualize the translation of a higher-level language into code for the Abstract Machine, the definition of which is based on the structural operational semantics of programs.
Interpretation of Sentiment Analysis with Human-in-the-Loop

Human-in-the-Loop has been receiving special attention from the data science and machine learning community. It is essential to realize the advantages of human feedback and the pressing need for manual annotation to improve machine learning performance. Recent advancements in natural language processing (NLP) and machine learning have created unique challenges and opportunities for digital humanities research. In particular, there are ample opportunities for NLP and machine learning researchers to analyze data from literary texts and use these complex source texts to broaden our understanding of human sentiment using the human-in-the-loop approach. This paper presents our understanding of how human annotators differ from machine annotators in sentiment analysis tasks and how these differences can contribute to designing systems for the "human in the loop" sentiment analysis in complex, unstructured texts. We further explore the challenges and benefits of the human-machine collaboration for sentiment analysis using a case study in Greek tragedy and address some open questions about collaborative annotation for sentiments in literary texts. We focus primarily on (i) an analysis of the challenges in sentiment analysis tasks for humans and machines, and (ii) whether consistent annotation results are generated from multiple human annotators and multiple machine annotators. For human annotators, we have used a survey-based approach with about 60 college students. We have selected six popular sentiment analysis tools for machine annotators, including VADER, CoreNLP's sentiment annotator, TextBlob, LIME, Glove+LSTM, and RoBERTa. We have conducted a qualitative and quantitative evaluation with the human-in-the-loop approach and confirmed our observations on sentiment tasks using the Greek tragedy case study.
On Variational Inference for User Modeling in Attribute-Driven Collaborative Filtering

Recommender Systems have become an integral part of online e-Commerce platforms, driving customer engagement and revenue. Most popular recommender systems attempt to learn from users' past engagement data to understand behavioral traits of users and use that to predict future behavior. In this work, we present an approach to use causal inference to learn user-attribute affinities through temporal contexts. We formulate this objective as a Probabilistic Machine Learning problem and apply a variational inference based method to estimate the model parameters. We demonstrate the performance of the proposed method on the next attribute prediction task on two real world datasets and show that it outperforms standard baseline methods.
Visualization techniques to give insight into the operation of the Go policy network

Deep learning for the game of Go achieved considerable success with the victory of AlphaGo against Ke Jie in May 2017. Thus far, there is no clear understanding of why deep learning performed so well in the game of Go. In this paper, we introduce visualization techniques used in image recognition that provide insights into the function of intermediate layers and the operation of the Go policy network. When used as a diagnostic tool, these visualizations enable us to understand what occurs during the training process of policy networks. Further, we attempt to identify important areas through Grad-CAM and combine it with the Go board to provide explanations for next move decisions.
A New Approach of Data Clustering Using Quantum Inspired Particle Swarm Optimization Based Fuzzy c-means

In this article, a Quantum inspired Particle swarm optimization (QtPSO) based Fuzzy c-means algorithm is proposed to cluster multidimensional data. Sometimes, fuzzy c-means used to get stuck at local minima due to the improper selection of cluster centers initially. To sort out the drawback, the intended QtPSO algorithm is applied to generate the cluster centers for a dataset. The effectivity of quantum computing is melted with the well known PSO algorithm. For designing this proposed algorithm, the feature of qubit is applied in association with particle swarm optimization. The proposed algorithm has been compared rigorously with the conventional fuzzy c-means algorithm and modified quantum-inspired particle swarm optimization algorithm (MQPSO) on four well known dataset. The superiority of the proposed algorithm is demonstrated on the basis of two standard cluster evaluation criteria, min value, max value, mean Value, median Value, standard deviation and best convergence times, mean convergence times and one statistical significance test, called Kruskal-Wallis H-test for different levels of clustering.
Japanese Discourse Relation Analysis: Task Definition, Connective Detection, and Corpus Annotation

談話関係解析は自然言語処理の基盤的な解析の一つであるが，日本語におけるコーパスベースの談話関係解析の研究はほとんどない．本研究では日本語の談話関係解析を実用化するため，日本語の談話関係タグ付きコーパスを構築する．日本語の談話関係タグ付きコーパスでは，談話単位，談話標識，談話関係タグの3項目をアノテーションする．その際，高速にコーパスを構築するため，以下の4つの手法を採用する．(1) Web ページの冒頭3文を収集したコーパスにアノテーションする．(2) 談話関係タグセットは2階層7種類とする．(3) 談話単位と談話標識は自動認識する．(4) 熟練のアノテータによる小規模・高品質なものとクラウドソーシングを用いた大規模なものの 2 種類のアノテーションを実施する．構築したコーパスを分析した結果，クラウドソーシングを用いたものは改善の余地があることが分かった．構築した談話関係タグ付きコーパスを用いて，談話関係解析器を訓練する．実験の結果，タグ付きコーパスが機械学習ベースの解析モデルの学習に有効であることが分かった．また，明示的な談話関係に限れば，本研究で整備した談話標識の自動認識が高精度な解析器として利用可能であることを示した．本研究で構築した日本語談話関係タグ付きコーパスは公開し，談話単位と談話標識の自動認識器は日本語構文・格解析器KNPに実装されている．
Detection of potential gold mineralization areas using MF-fuzzy approach on multispectral data

The northeast area of Ardabil, a city located in northwestern Iran, is one of the potential gold mineralization areas. In this study, ASTER data were used to identify the alteration events in this region. For this purpose, a novel approach was used in which the fuzzy logic was implemented to extract the co-occurrence map of the endmembers. This method revealed alterations more accurately than SID. Stream sediment samples were employed to validate the obtained results. Since these samples are alluvial, their catchment basins were determined and overlaid with the alteration maps. To the best of the authors' knowledge, this validation approach has not been used in previous studies. The extracted alteration zones were in high conformity to the stream sediment samples. Next, X-ray diffraction (XRD) analysis and field spectrometry were used for delineation of the mineralogical phases present in the anomalous areas. Finally, the potential gold mineralization zones were identified.
SDRM-LDP: A Recommendation Model Based on Local Differential Privacy

The development of 5G technology has driven the rise of e-commerce, social networking, and the Internet of Things. Under the high-speed transmission, the data volume increases, and the user demand also changes. Personalized customization has become the mainstream trend of network development. However, as the speed of the Internet increases, a series of problems also arise. The increase in data volume results in a reduction of bandwidth, a growth of the central processor’s pressure, and a higher risk of data leakage. A search system and a recommendation platform are the tools to improve people’s search efficiency. However, providing personalized recommendations to different users according to their needs is still an urgent problem. Simultaneously, the big data volume means that attackers can also get more information. They can use background knowledge and various reasoning methods to deduce the user’s private information using nonprivate items. In this paper, the solutions to safe and reliable recommendation services are the main problem explored. Based on this idea, this paper proposed short-term dynamic recommendation model based on local differential privacy (SDRM-LDP). This model uses a small amount of user information to construct short-term user preference behaviors and provides recommendations for users based on the similarity between items. We consider that an attacker uses nonprivate items to derive privacy items. Therefore, we randomly replace the original data in the same category. At the same time, the local differential privacy (LDP) is added to the privacy item query to make the private data available and protect the privacy information. In this paper, two real-world datasets, ML-100K and ML-10M, are used for experiments. Experimental results show that the results of SDRM-LDP are superior to other models.
Deep Reinforcement Learning Based Online Area Covering Autonomous Robot

The following topics are dealt with: mobile robots; learning (artificial intelligence); path planning; multi-robot systems; robot vision; object detection; autonomous aerial vehicles; remotely operated vehicles; position control; cameras.
An evaluation of machine learning methods for domain name classification

For a long time researchers have focused on the binary classification of domain names sent to DNS servers for resolutions to IP addresses. The objective is to identify malicious domains versus legitimate ones to protect networks from attacks. For legitimate domains, an emerging interest is to classify them into content categories to enable DNS servers deployed in an organization to monitor and potentially block the resolution of irrelevant domains. For example, a financial organization wants to flag gaming related domains, and an elementary school wants to block suspicious adult domains. Classifying a domain by just the domain name is a challenging task. Currently, there exist no publicly available datasets that include an extensive mapping of domains to content categories, since this is usually proprietary information. Our focus is three-fold in this work: a) to develop a data collection methodology and create rich labelled datasets that are appropriate for training such predictive models, b) to share the datasets with the research community by making them publicly available, and c) to evaluate and identify appropriate machine learning and deep learning algorithms for this problem domain. We consider two different datasets. The first is created following a SERP (Search Engine Response Page)-mining approach, having a set of content categories as input. The second is an enhancement of the DMOZ dataset that is publicly available, including both domains and category names as input. In addition to the dataset creation input and methodology, these two datasets differ in the content category number and distribution, yielding different results in our analysis. Overall, we observe that the deep learning-based approach carefully considers the key features of the input data and hence outperforms existing traditional machine learning pipelines, achieving 98.37% and 79.29% accuracy on the respective datasets.
Hybrid evolutionary network architecture search (<scp>HyENAS</scp>) for convolution class of deep neural networks with applications

Abstract Convolutional Neural Networks (CNNs) and its variants are increasingly used across wide domain of applications achieving high performance measures. For high performance, application specific CNN architecture is required, hence the need for network architecture search (NAS) becomes essential. This paper proposes a hybrid evolutionary approach for network architecture search (HyENAS), and targets convolution class of neural networks. One of the significant contribution of this technique is to completely evolve the high performance network by simultaneously finding network structures and their corresponding parameters. An elegant string representation has been proposed which efficiently represents the network. The concept of sparse block evolving requisite layer wise features for dense network is deployed. This permits the network to dynamically evolve for a specific application. In comparison to the other state‐of‐art methods, the high performance of the proposed HyENAS approach is demonstrated across various benchmark data sets belonging to the domain of malariology, oncology, neurology, ophthalmology, and genomics. Further, to deploy the proposed model on lower hardware specification devices, another salient feature of the HyENAS technique is to seamlessly sift out the simpler network architecture with comparable accuracy.
A Multi-Modal Approach to Patient Activity Monitoring

This paper presents our patient activity monitoring system (PAMS), which combines a wrist-worn device and a real-time location system (RTLS) with active tags. Four main data streams were used: location coordinates, 3D accelerometer, heart rate, and electrodermal activity (EDA) data. Using non-overlapping, normalized power windows, data are fed into random forest and KNN classifiers. These classifiers divide data into specific ambulation classes including time of inactivity, posture, and wheelchair monitoring data. The classification performance (F1 score) of our machine learning techniques ranges from 87.6% to 99.2% which is quite promising. These metrics provide clinicians with valuable insight into the activity levels of patients and patients' progress towards achieving their ambulation goals.
Requirements Engineering Practices and Challenges in the Context of Big Data Software Development Projects: Early Insights from a Case Study

This paper reports on the results of an exploratory case study on a large-scale Big Data systems development project in the Oil&Gas domain within a non-profit organisation. The aim of this study was to investigate the RE practices and challenges in such projects, currently bereft in the scientific literature. This investigation was focused on: (a) RE practices; (b) sources and distribution of requirements; (c) the role of Big Data characteristics and technologies in RE and systems design; and (d) RE challenges in engineering Big Data Systems. The main results show that (a) there is a lack of specific project tailored RE practices, tools, and frameworks for elicitation, specification and modelling, analysis, and prioritisation of requirements; (b) 40% of the system's requirements are considered Big Data-related from which 75% are identified from internal sources; (c) Big Data characteristics and technologies play an important role in defining quality requirements and system's architecture; (d) five challenges in eliciting, documenting, and analysing Big Data related requirements were identified and discussed. The findings suggest academics and practitioners opportunities to engage in further research in this area.
A CNN-based Quality Model for Image Interpolation

Image interpolation techniques have aroused wide attention, which is dedicated to improving the resolution of image and providing a better visual perception. However, how to evaluate the perceptual quality of interpolated images is still an ongoing problem. In this paper, a no-reference method built on Convolutional Neural Network (CNN) is proposed for interpolated image quality assessment. To enhance the performance, we incorporate attention modules with the proposed network to facilitate feature extraction and quality prediction. Experimental results show that the proposed method outperforms related IQA metrics in perceptual quality evaluation of image interpolation.
Fuzzy identification of systems based on adaptive neurons

The identification of nonlinear systems is a complex task. This article presents a method comparison between the new Fuzzy Adaptive Neurons (FAN), Radial Basis Function Network (RBF), and Adaptive Network-Based Fuzzy Inference System (ANFIS). The nonlinear systems presented are solved with stable and optimal learning. The simulation of the results for two models presented, are carried out in Matlab®, the optimization of the system identification for the first and second systems were obtained with great success.
A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video

The paper studied the problems of soccer detection and tracking in soccer tracking, in soccer detection; as the size of the soccer is too small to extract distinguishable feature, it is difficult to detect the soccer automatically. To solve this problem, a soccer detection algorithm was based on class weighted spatial Fuzzy C-means (ws-FCM) was proposed. Firstly, the target function of the spatial Fuzzy C-means was improved. Subsequently, a bi-threshold strategy was proposed to detect the soccer automatically. In the aspect of soccer tracking, existing methods fail to detect the soccer when it was occluded by several players successively. To solve this problem, the motion state of soccer of broadcast soccer video was analyzed, which is inspired by the contextual cueing effect of human visual search. According to the motion state of the soccer, parameters updating function of dynamic Kalman filter (DKF) were improved. Thus, a soccer tracking algorithm based on multiple search regions dynamic Kalman filter (MDKF) was proposed, which enhances the robustness of soccer tracking by extending the search area. The experiments show that the proposed algorithm can automatically detect soccer in images with high detection accuracy and can track the soccer more robustly, with better occlusion handle ability.
Effective and Potential Implications of Blockchain Technology for Auditing

The development of blockchain technology and some of its peculiarities such as smart contracts and asset tokenization have important implications in the auditing environment. This paper aims to evaluate the current stage of blockchain technology application on auditing area, analyzing scientific publications since the emergence of this distributed ledger technology, identifying the main implications what is already reality and the potential effects of its improvements in the audit professionals' activities performance, based on the proposals and suggestions on the main research indexed by Scopus and Web of Science databases. Through a proof of concept, this study advances with the contribution of demonstrating the creation and tracking a token on a public blockchain, evidencing the representation of assets in digital form, known as tokenization. The token example was created on the Ethereum testing platform (Ropsten Test Network). It is possible the traceability for different wallets and proof of ownership, for the purposes of possible disclosure to any interested with internet connection. The new possibilities, like real time auditing, associated with the conference of assets physical existence and their comparison of what is on the blockchain with the accounting records and the real world by the auditors represents a new reality and new challenges in terms of skills and knowledge.
Optimal selection for good polynomials of degree up to five

An $$(r,\ell )$$ -good polynomial is a polynomial of degree $$r+1$$ that is constant on $$\ell $$ subsets of $$\mathbb F_q$$ , each of size $$r+1$$ . For any positive integer $$r\le 4$$ we provide an $$(r,\ell )$$ -good polynomial such that $$\ell =C_rq+O(\sqrt{q})$$ , with $$C_r$$ maximal. This directly provides an explicit estimate (up to an error term of $$O(\sqrt{q})$$ , with explict constant) for the maximal length and dimension of a Tamo–Barg LRC. Moreover, we explain how to construct good polynomials achieving these bounds. Finally, we provide computational examples to show how close our estimates are to the actual values of $$\ell $$ , and we explain how to obtain the best possible good polynomials in degree 5. Our results complete the study by Chen et al. (Des Codes Cryptogr 89(7):1639–1660, 2021), providing $$(r,\ell )$$ -good polynomials of degree up to 5, with $$\ell $$ maximal (up to an error term of $$\sqrt{q}$$ ), and our methods are independent.
Analysis of Machine Learning based Feature Selection Method for Sentiment Analysis

An outfit method to include decrease techniques relevant to the production and conduct of research based on the findings would have an important purpose in this article. An outfit strategy means that it is possible to combine at least two strategies. The element reducing approach used is Principal Component Analysis (PCA) for extraction and Pearson Chi-squared factual research findings. The concept investigation is a field where the concepts expressed are understood and organized into positive, negative, and impartial polarities. The highlight is the pivotal machine learning process. This article investigated the implementation, by Naïve Bayes, k-Nearest Neighbor, Support Vector Machine, Logistic Regression, and Random Forest with various Unigram, Bigram, Chi-Square, and Gini Index FSMs, of five machine learning arrangement calculations. However, there has been very little attempt to highlight methods of estimating the Turkish audits. Further component choice strategy, and Question Expansion Ranking, is provided, which will depend on the extended-term weighting strategies used in the field of information recovery to determine mainly important conditions for growth in study.