Reviewerly/exHarmony huggingface dataset:
A Secure and Decentralized Auditing Scheme for Cloud Ensuring Data Integrity and Fairness in Auditing

With the advent of cloud storage services many users tend to store their data in the cloud to save storage cost. However, this has lead to many security concerns, and one of the most important ones is ensuring data integrity. Public verification schemes are able to employ a third party auditor to perform data auditing on behalf of the user. But most public verification schemes are vulnerable to procrastinating auditors who may not perform auditing on time. These schemes do not have fair arbitration also, i.e. they lack a way to punish the malicious Cloud Service Provider (CSP) and compensate user whose data has been corrupted. On the other hand, CSP might be storing redundant data that could increase the storage cost for the CSP and computational cost of data auditing for the user. In this paper, we propose a Blockchain-based public auditing and deduplication scheme with a fair arbitration system against procrastinating auditors. The key idea requires auditors to record each verification using smart contract and store the result into a Blockchain as a transaction. Our scheme can detect and punish the procrastinating auditors and compensate users in the case of any data loss. Additionally, our scheme can detect and delete duplicate data that improve storage utilization and reduce the computational cost of data verification. Experimental evaluation demonstrates that our scheme is provably secure and does not incur overhead compared to the existing public auditing techniques while offering an additional feature of verifying the auditor’s performance.
Rahhal: A Tourist Arabic Chatbot

A chatbot is an artificial intelligence-based program that is able to interact with humans in a messaging style. It answers queries and provides useful information that could otherwise be offered with costly human labor. Chatbots are important to enhance user and customer experience in multiple domains like education, medicine, entertainment, and commerce. Although the research and commercialization of English chatbots is evolving relatively rapidly, Arabic chatbots are comparably scarce. Moreover, the tourism sector in Saudi Arabia, the second largest in the Middle East, is growing rapidly, especially after 2019. Hence, the contribution of this paper as it presents an Arabic chatbot that aids leisure tourists in Saudi Arabia with two implementations, the first is implemented using Java and the second was developed using IBM Watson. Both chatbots are deployed in Telegram. The testing of the chatbots proved its high usability and utility in aiding tourists.
Graph-based Algorithm For Self-trading Accounts Detection in Bulk Commodity Trade

Bulk commodity trade is vital for daily life and financial security because it influences prices for commodities that vary from raw materials to daily necessities. In order to seize profits, some users use groups of accounts for self-trading which disturbs market order. Some methods and models can deal with fraud detection. However, those methods largely depend on information about accounts. When it comes to self-trading accounts detection, information is mainly stored on links between accounts. Therefore, a new method is needed. We proposed a method that applies Louvain algorithm and PageRank algorithm to self-trading accounts detection. To prove the effectiveness of our method, we generate a new dataset according to realistic records in bulk commodity trade and written decisions of administrative penalty. Extensive experiments and results show that our method achieves a high recall rate and a precision rate in self-trading accounts detection in bulk commodity trade.
A Fundamental Analysis of Standardization for Blockchain and Distributed Ledger Technologies in ISO

Blockchain and Distributed Ledger Technology (DLT) are becoming important driving forces for a new round of scientific and technological development. The construction of blockchain information infrastructure has risen to a national strategic height. The industrial applications of Blockchain and DLT are developing and landing rapidly. At present, it has included many fields such as finance, government affairs and justice. At the same time, blockchain and DLT will have a far-reaching impact on international scientific, technological and economic development. For the healthy and orderly development of the blockchain and DLT industry, blockchain and DLT standards are being drafted, released and implemented one after another. This paper analyzes the current situation of international blockchain and DLT standardization and arranges the current situation of blockchain standardization working group in ISO. This paper introduces the organizational structure of ISO and the specific work of the working group, arranges all the blockchain related standards currently under development by ISO, and makes relevant analysis on the standard stage, standard content and standard classification; At the same time, it defines the direction of in-depth participation in ISO international standards and helps the development of international standardization of blockchain and DLT.
A Lightweight and Secure Authentication Protocol for 5G mMTC

Massive Machine Type Communication (mMTC) is one of the three application scenarios of 5G. In mMTC, massive resource constrained equipment is deployed, which has limited computing and storage capacity. Hence, it is impossible for it to execute complex authentication protocols. This paper proposes a lightweight and secure authentication protocol for 5G mMTC. It can achieve several security properties, such as mutual authentication, session key agreement, identity anonymity and resistance to multiple protocol attacks. What’s more, our proposed protocol performs better in computational cost, signaling cost, transmission cost and storage cost than some other protocols, which is more suitable for 5G mMTC.
Prediction of Hard Drive Failures for Data Center Based on LightGBM

Today, industrial-scale organizations increasingly rely on data centers to store and process data. Therefore, it is of great value to protect data security and reduce the cost of data center if operators can accurately predict the failure of the hard disks. In this paper, we use data collected by SMART (Self-Monitoring, Analysis and Reporting Technology), which contains 63 attributes as preliminary features to propose a model for hard disk fault prediction using LightGBM (Light Gradient Boosting Machine) detection algorithm. After analyzing the features, we further process them and conduct comparative training. The experimental results show that a reliable prediction model is finally constructed, which has high accuracy in hard disk error prediction.
Computable PAC Learning of Continuous Features

We introduce definitions of computable PAC learning for binary classification over computable metric spaces. We provide sufficient conditions on a hypothesis class to ensure than an empirical risk minimizer (ERM) is computable, and bound the strong Weihrauch degree of an ERM under more general conditions. We also give a presentation of a hypothesis class that does not admit any proper computable PAC learner with computable sample function, despite the underlying class being PAC learnable.
Development of an intention estimation model based on gaze and face information for electric wheelchair operation support

Abstract In recent years, various user interfaces have been developed to meet the diverse needs of physically disabled persons. In this paper, we developed a method to identify gazing and facial movements based on gaze time and eye/face information and developed an electric wheelchair that can be operated with the user's natural gazing and facial movements. This intention estimation model is composed of 1DCNN and LSTM layers. First, 1DCNN is used to extract features from gaze and face information, and then the extracted features are input to LSTM to estimate the user's intentions regarding movement. The evaluation experiments suggest that the combination of gaze and face information improves the estimation accuracy and contributes to the classification. Furthermore, it was confirmed that adding a convolutional filter layer to the LSTM layer improved the accuracy.
Coupled Attention Framework of Convolutional Neural Network Based on Computer Intelligence

Using an attention mechanism based on the convolutional neural networks (CNNs) improves the performance of computer vision tasks by enhancing the representation of the features. The existing attention methods enhance the expression of the features by modeling the internal information of the features. However, due to the limited information flow of the previous features, these methods are difficult to calibrate features more completely. In this paper, we propose a Coupled Attention Framework (CAF) that is a simple attention framework for improving the performance of the existing attention methods. In the CAF, a coupling branch is added to an existing attention method to generate the input attention maps and enhance the input features of the convolution. The input attention is then spread to the output features through coupling between the input attention maps and convolution, the output features. The final result is the experimental results on various visual tasks. The results show that applying CAF to most of the existing attention methods can improve the performance with fewer parameters.
QNUS: Reducing Terminal Resources in Quantum Secure Direct Communication Network Using Switches

The quantum network is an indispensable step toward multiuser and wide-area information interchange in the course of the development of quantum technology. When designing and deploying a quantum network, availability, robustness, flexibility, and expenditure need to be considered in a balanced way. In this article, we propose a network connected through optical switches, QNUS, that requires only terminals in the number of nodes in a quantum network, which is a great saving of resources.
Resource approximation for the λμ-calculus

The λμ-calculus plays a central role in the theory of programming languages as it extends the Curry-Howard correspondence to classical logic. A major drawback is that it does not satisfy Böhm’s Theorem and it lacks the corresponding notion of approximation. On the contrary, we show that Ehrhard and Regnier’s Taylor expansion can be easily adapted, thus providing a resource conscious approximation theory. This produces a sensible λμ-theory with which we prove some advanced properties of the λμ-calculus, such as Stability and Perpendicular Lines Property, from which the impossibility of parallel computations follows.
Choiceless Polynomial Time with Witnessed Symmetric Choice

We extend Choiceless Polynomial Time (CPT), the currently only remaining promising candidate in the quest for a logic capturing Ptime, so that this extended logic has the following property: for every class of structures for which isomorphism is definable, the logic automatically captures Ptime.
Zero-One Laws and Almost Sure Valuations of First-Order Logic in Semiring Semantics

Semiring semantics evaluates logical statements by values in some commutative semiring (K, +, ·, 0, 1). Random semiring interpretations, induced by a probability distribution on K, generalise random structures, and we investigate here the question of how classical results on first-order logic on random structures, most importantly the 0-1 laws of Glebskii et al. and Fagin, generalise to semiring semantics. For positive semirings, the classical 0-1 law implies that every first-order sentence is, asymptotically, either almost surely evaluated to 0 by random semiring interpretations, or almost surely takes only values different from 0. However, by means of a more sophisticated analysis, based on appropriate extension properties and on algebraic representations of first-order formulae, we can prove much stronger results.
A Phenomenological Study on the Experience of High School Senior Students during the COVID-19 Period

This study is a qualitative study using a phenomenological approach to understand what high school seniors experience through one-on-one in-depth interviews and the meaning structure of the experience to provide a comprehensive and holistic understanding for students who need to choose and decide a new career in the era of infectious diseases. Participants in this study were first graders in high school when COVID-19 swept the world in 2019 and students who took the entrance exam in 2022. In selecting study participants, purposeful sampling and snowball sampling were used to find participants who could provide the timelyness of the sample, various experiences, and deep insight. Participants in the study were 8 people who provided voluntary consent in advance, and data collection was conducted from January to March 2022. The results of individual in-depth interviews were analyzed by applying Giorgi's phenomenological research method, As a result of the analysis, the experiences of high school seniors were derived into five categories and 15 subcategories, including "confusing", "depression", "difficulty learning on their own", "change of entrance exams" and "adaptation". Through this study, the experience of high school seniors attending high school during the COVID-19 period stated that unstable and confused high school students in our society are more required to actively cope with and support learning, career, and emotional support. Therefore, it is necessary to have a comprehensive understanding of the experiences of high school seniors during the COVID-19 period, and to accurately identify their needs and provide necessary interventions so that they can start as successful career paths and healthy members of society. In addition, it suggests a deep exploration of the role of educational governance in the era of infectious diseases.
Game-Theory-Inspired Novel Mechanism for Assessing Healthcare Quality

Healthcare is the most pivotal domain of every nation. With the sudden upraise of the COVID-19 pandemic, there has been a major concern for the healthcare industry to provide quality medical services to the common people. Vulnerable healthcare conditions have proven to be fatal for the patients. Conspicuously, it has become indispensable to assess the quality of healthcare services provided by the hospitals. The current article focuses on analyzing healthcare service quality delivered by the hospitals and healthcare centers. Specifically, the presented framework utilizes Internet of Things (IoT) technology to acquire real-time ambient data inside smart hospitals. The quantification of the healthcare service is performed using the Probability of Health Grade (PoHG) to classify data segments using the probabilistic Bayesian belief model. Furthermore, the temporal data abstraction is performed for the numerical analysis of healthcare service quality in terms of the health quality index (HQI). Finally, a 2-player game theory-inspired decision modeling is performed to analyze healthcare quality in a time-sensitive manner. The proposed framework is assessed using a simulated environment where 225325 data segments are analyzed. Results are compared with state-of-the-art techniques in which enhanced performance measures are registered in terms of classification efficacy (93.74%), decision-making efficiency [coefficient of determination (95%), accuracy (97.53%), mean square error (2.01%), and root mean square error (1.95%)], temporal delay (96.62 s), and reliability (91.58%).
Feature Selection for Location Metonymy Using Augmented Bag-of-Words

Location metonymy resolution is a study that deals with locations being used in a non-literal way that create problems in several natural language processing tasks such as Named entity recognition and Geographical parsing. Many studies were conducted attempting to accurately classify whether the location is used literally or metonymically, however, most of the approaches that performed well had to employ a considerable amount of resources along with complex machine learning models; those that reduced the resources experienced a decline in performance due to data sparseness. This study proposes a novel feature selection approach that uses bag-of-words and augments it with GloVe embeddings to obtain features that can be recognized based on the context of the sentence. We then implement a minimalist deep learning model making the entire classification task as light as possible. The study found that relying solely on the given datasets to identify features without depending on other external resources can achieve remarkable results despite the small size of the datasets. The results obtained from evaluating our method compared to the state-of-the-art methods show that eliminating noise based on the context notwithstanding the usage of low-cost resources has outperformed all of the previous methods with an accuracy of 99.2% on the WIMCOR dataset.
An NLP-Inspired Data Augmentation Method for Adverse Event Prediction Using an Imbalanced Healthcare Dataset

This paper proposes a data augmentation method for imbalanced healthcare datasets.This method was inspired by a data augmentation method in natural language processing (NLP) that generates synthetic sentences for training by replacing some words with similar words.The proposed method generates synthetic patient records by replacing patient backgrounds with similar backgrounds.In this paper, the cosine similarity of the distributed representations was used as the similarity metric between patient backgrounds.The distributed representations of the patient backgrounds were generated by the skip-gram model.To confirm the performance improvement with the proposed data augmentation method, the prediction performance of adverse events (AEs) caused by drug administration was experimentally evaluated on a real-world medical dataset with 1,510,137 records.The combination of the proposed data augmentation method and a conventional undersampling method resulted in an 80.0 % improvement in accuracy and a 40.0 % improvement in the precision and F1-score.The multifaceted evaluation demonstrated that the proposed method is effective, especially for predicting AEs with positive ratios ranging from 1.0 % to 2.1 %, which are difficult to predict with conventional machine learning methods but should be predictable in the medical field.
Compilation, Analysis and Application of a Comprehensive Bangla Corpus KUMono

Research in Natural Language Processing (NLP) and computational linguistics highly depends on a good quality representative corpus of any specific language. Bangla is one of the most spoken languages in the world but Bangla NLP research is in its early stage of development due to the lack of quality public corpus. This article describes the detailed compilation methodology of a comprehensive monolingual Bangla corpus, KUMono. The newly developed corpus consists of more than 350 million word tokens and more than one million unique tokens from 18 major text categories of online Bangla websites. We have conducted several word-level and character-level linguistic phenomenon analyses based on empirical studies of the developed corpus. The corpus follows Zipf&#x2019;s curve and hapax legomena rule. The quality of the corpus is also assessed by analyzing and comparing the inherent sparseness of the corpus with existing Bangla corpora, by analyzing the distribution of function words of the corpus and vocabulary growth rate. We have developed a Bangla article categorization application based on the KUMono corpus and received compelling results by comparing to the state-of-the-art models.
An Enhancement and Detection Method for a Glue Dispensing Image Based on the CycleGAN Model

During the active alignment focusing process of car camera assembly, lenses and holders need to be gummed by creamy white and translucent UV glue. The quality of glue dispensing can directly influence the performance of car cameras. Because of the translucency of UV glue, the glue dispensing image may present a low contrast situation, which increases the difficulty of vision detection. This paper proposes a method based on CycleGAN to enhance the glue dispensing image and effectively overcome the problems of blurred and low contrast edges. First, the glue part of the image is segmented into twenty regions. Second, the VGG16 model is used to divide the abovementioned twenty regions into high-contrast images and low-contrast images. Next, the CycleGAN model is trained to enhance the low-contrast images, and then convert them to high-contrast images. Finally, glue contours are extracted by using thresholding segmentation and edge detection to ensure that the quality of glue dispensing can be detected. The success rates of the VGG16 model and the CycleGAN model are 96% and 58%, respectively. The results show that the proposed method can effectively enhance the low contrast part of the glue region and improve the detection accuracy. Specifically, it can increase the gray value difference between the glue and the background from 20 to 55, while the background is substantially retained. The detailed information of the edges of the images is enriched. The accuracy of glue edge extraction can be increased to 99%, which is an approximately 75% improvement compared to the methods without enhancement.
Gitranking: A Ranking of Github Topics for Software Classification Using Active Sampling

Context: GitHub is the world's most prominent host of source code, with more than 327M repositories. However, most of these repositories are not labelled or inadequately, making it harder for users to find relevant projects. Various proposals for software application domain classification over the past years have been proposed. However, these several of those approaches suffer from multiple issues, called \textit{antipatterns} of software classification, that reduce their usability. Objective: In this paper, we propose a new taxonomy in the GitHub ecosystem, called GitRanking, starting from a well-structured dataset, composed of curated repositories annotated with topics. The main objective is to create a baseline methodology for software classification that is expandable, hierarchical, grounded in a knowledge base, and free of antipatterns.Method: We collected 121K topics from GitHub and used GitRanking to create a taxonomy of 301 ranked application domains. GitRanking 1) uses active sampling to ensure a minimal number of annotations to create the ranking; and 2) links each topic to Wikidata, reducing ambiguities and improving the reusability of the taxonomy. Furthermore, we adopt the conceived taxonomy in a classification task by considering a state-of-the-art classifier.Results: Our results show that GitRanking can effectively rank terms in a hierarchy according to how general or specific their meaning is. Furthermore, we show that GitRanking is a dynamically extensible method: it can currently accept further terms to be ranked, and with a minimum number of annotations (~ 15). Concerning the classification task, we show that the model achieves an F1-score of 34%, with a precision of 54%. Conclusion: This paper is the first collective attempt at building a ground-up taxonomy of software domains. Our vision is that our taxonomy, and its extensibility, can be used to better and more precisely label software projects.