Reviewerly/exHarmony huggingface dataset:
Part-of-Speech Tagging with Rule-Based Data Preprocessing and Transformer

Part-of-Speech (POS) tagging is one of the most important tasks in the field of natural language processing (NLP). POS tagging for a word depends not only on the word itself but also on its position, its surrounding words, and their POS tags. POS tagging can be an upstream task for other NLP tasks, further improving their performance. Therefore, it is important to improve the accuracy of POS tagging. In POS tagging, bidirectional Long Short-Term Memory (Bi-LSTM) is commonly used and achieves good performance. However, Bi-LSTM is not as powerful as Transformer in leveraging contextual information, since Bi-LSTM simply concatenates the contextual information from left-to-right and right-to-left. In this study, we propose a novel approach for POS tagging to improve the accuracy. For each token, all possible POS tags are obtained without considering context, and then rules are applied to prune out these possible POS tags, which we call rule-based data preprocessing. In this way, the number of possible POS tags of most tokens can be reduced to one, and they are considered to be correctly tagged. Finally, POS tags of the remaining tokens are masked, and a model based on Transformer is used to only predict the masked POS tags, which enables it to leverage bidirectional contexts. Our experimental result shows that our approach leads to better performance than other methods using Bi-LSTM.
Human-AI Collaboration for UX Evaluation: Effects of Explanation and Synchronization

Analyzing usability test videos is arduous. Although recent research showed the promise of AI in assisting with such tasks, it remains largely unknown how AI should be designed to facilitate effective collaboration between user experience (UX) evaluators and AI. Inspired by the concepts of agency and work context in human and AI collaboration literature, we studied two corresponding design factors for AI-assisted UX evaluation: explanations and synchronization. Explanations allow AI to further inform humans how it identifies UX problems from a usability test session; synchronization refers to the two ways humans and AI collaborate: synchronously and asynchronously. We iteratively designed a tool-AI Assistant-with four versions of UIs corresponding to the two levels of explanations (with/without) and synchronization (sync/async). By adopting a hybrid wizard-of-oz approach to simulating an AI with reasonable performance, we conducted a mixed-method study with 24 UX evaluators identifying UX problems from usability test videos using AI Assistant. Our quantitative and qualitative results show that AI with explanations, regardless of being presented synchronously or asynchronously, provided better support for UX evaluators' analysis and was perceived more positively; when without explanations, synchronous AI better improved UX evaluators' performance and engagement compared to the asynchronous AI. Lastly, we present the design implications for AI-assisted UX evaluation and facilitating more effective human-AI collaboration.
Identification of with Face Mask and without Face Mask using Face Recognition Model

In this paper, we present a method for automatically detecting mask elements from faces and synthesizing the affected region with fine details while preserving the original structure of the face. Wearing face masks appears to be a promising approach for reducing COVID-19 spread. Effective recognition technologies are crucial in this situation to keep people’s faces hidden in limited places. As a result, in order to train deep learning models to distinguish between those wearing masks and those who aren’t, a huge dataset of masked faces is required. A technique has been devised for researching COVID-19 related behaviours and contamination processes. A potential method for minimising COVID-19 transmission through health education has been identified. With 96.70% accuracy, the suggested approach recognised faces with and without masks. They will give you a decent detection result with or without a mask.
World-GAN: a Generative Model for Minecraft Worlds

This work introduces World-GAN, the first method to perform data-driven Procedural Content Generation via Machine Learning in Minecraft from a single example. Based on a 3D Generative Adversarial Network (GAN) architecture, we are able to create arbitrarily sized world snippets from a given sample. We evaluate our approach on creations from the community as well as structures generated with the Minecraft World Generator. Our method is motivated by the dense representations used in Natural Language Processing (NLP) introduced with word2vec [1]. The proposed block2vec representations make World-GAN independent from the number of different blocks, which can vary a lot in Minecraft, and enable the generation of larger levels. Finally, we demonstrate that changing this new representation space allows us to change the generated style of an already trained generator. World-GAN enables its users to generate Minecraft worlds based on parts of their creations.
Linguistic Mathematical Relationships Saved or Lost in Translating Texts: Extension of the Statistical Theory of Translation and Its Application to the New Testament

The purpose of the paper is to extend the general theory of translation to texts written in the same language and show some possible applications. The main result shows that the mutual mathematical relationships of texts in a language have been saved or lost in translating them into another language and consequently texts have been mathematically distorted. To make objective comparisons, we have defined a “likeness index”—based on probability and communication theory of noisy binary digital channels-and have shown that it can reveal similarities and differences of texts. We have applied the extended theory to the New Testament translations and have assessed how much the mutual mathematical relationships present in the original Greek texts have been saved or lost in 36 languages. To avoid the inaccuracy, due to the small sample size from which the input data (regression lines) are calculated, we have adopted a “renormalization” based on Monte Carlo simulations whose results we consider as “experimental”. In general, we have found that in many languages/translations the original linguistic relationships have been lost and texts mathematically distorted. The theory can be applied to texts translated by machines. Because the theory deals with linear regression lines, the concepts of signal-to-noise-ratio and likenss index can be applied any time a scientific/technical problem involves two or more linear regression lines, therefore it is not limited to linguistic variables but it is universal.
A data hiding scheme based on the difference of image interpolation algorithms

Interpolation is a mature technique used in image processing and many data hiding schemes based on image interpolation have been proposed. In this paper, a novel efficient data hiding scheme based on the difference between image interpolation algorithms is proposed. The interpolated values obtained by neighboring points using different interpolation algorithms are generally different, and the difference can be used for encoding secret data. 2k different interpolated values calculated by 2k different interpolation algorithms respectively in the ideal case, and they are numbered in increasing order to correspond to the k bits secret data in each interpolated pixel. At the receiver's side, the secret data can be obtained based on the matching of the sorting results of these interpolation values of the accepted interpolation values. In fact, different algorithms may produce the same interpolation values, it is necessary to modify the appropriate number of interpolation value so that the number of different interpolation values reaches 2k, then the following image hiding procedures can employ the method of ideal case. The experimental results show that the proposed hiding scheme provides high embedding capacity with good visual quality.
TreeCare: Development and Evaluation of a Persuasive Mobile Game for Promoting Physical Activity

Increased physical activity has been shown to reduce morbidity and mortality among adults. Over the years, mobile apps have been developed to encourage people to engage in physical activity, such as walking or running, by employing various persuasive strategies. However, the choice of these strategies is often based on designers' intuition without knowing if the strategies will be effective for target audience and the target behaviour. To address this gap, we conduct a study with 103 adults to assess the perceived effectiveness of 12 widely used strategies in health games design. The strategies are based on the Persuasive Systems Design (PSD) framework. Our results reveal that the strategies are effective for promoting physical activity at varying degrees. These results inform the development of the game, called TreeCare. Next, we conduct a 3-week field study involving 23 target users to evaluate the game in terms of effectiveness and usability. Our results show that TreeCare significantly improved users' physical activity levels. In addition, the game is found to be easy to use, engaging, aesthetically pleasing, and enjoyable. We reflect on our findings and offer practical guidelines to inform the design of effective and usable persuasive applications.
Occupancy‐based utility pattern mining in dynamic environments of intelligent systems

Utility pattern mining is a branch of data mining that extracts valid patterns by considering the quantity and weight of the items. In addition, utility occupancy pattern mining, which considers the quantity, importance, and proportion of the pattern in the transaction, has been proposed. Despite this advantage, there is no utility seizing approach to handle the dynamically generated data flows. As electronics are interconnected and intelligent systems are constructed, data is generated in real-time and accumulated rapidly. Therefore, a method to read data immediately in a dynamic environment and efficiently analyze massive data is required. To overcome the limitations of the existing utility occupancy methods, we propose a novel mining approach, HUOMI, which performs quickly on an increasing database. The suggested algorithm has an optimized data structure and an improved pruning technique, which can respond to the dynamic environment promptly. To indicate the effectiveness of the proposed method, performance evaluations were conducted on real and synthetic data sets. In the experimental results, the suggested algorithm showed a better performance than the other state-of-the-art algorithms.
A new cell group clustering algorithm based on validation &amp; correction mechanism

Clustering used to discover hidden patterns in unlabeled data sets is an important task in data mining. Therefore, clustering validation applied to evaluating the clustering results has been recognized as one of the vital issues for clustering applications. The existing validation index is often used to evaluate the results rather than guide the clustering process dynamically. However, using the index can automatically adjust the algorithm's operation according to the actual data distribution, thereby improving the algorithm's adaptability. Simulating the division and aggregation process of cells in biology, in this paper, we propose a new two-phase (grouping and merging) cell group clustering algorithm by using a continuous validation and correction mechanism. In the grouping phase, a new clustering internal validation index called Split Index (SI) is utilized to evaluate the cohesion of a cell group continuously, and then a validation and correction mechanism is adopted to validate and split the cell groups so that the SI of each cell group can meet the split threshold ε, finally the cell nucleus of each cell group is determined by finding the sum of the minimum distances from the nucleus to other samples. In the merging phase, the cell group merge method is adopted to merge all the reachable cell groups in a density-reachable manner. Ultimately the clustering problem of arbitrarily distributed samples is completed. Experiments on the synthetic and the UCI Machine Learning Repository1 data set show that the validation index can effectively guide the clustering process, and the algorithm can deal with various data sets, including imbalanced data sets and spherical non-spherical clusters.
Towards user-centered and legally relevant smart-contract development: A systematic literature review

Smart contracts (SC) run on blockchain technology (BCT) to implement agreements between several parties. As BCT grows, organizations aim to automate their processes and engage in business collaborations using SCs. The translation of contract semantics into SC language semantics is difficult due to ambiguous contractual interpretation by the several parties and the developers. Also, an SC language itself misses the language constructs needed for semantically expressing collaboration terms. This leads to SC coding errors that result in contractual conflicts over transactions during the performance of SCs and thus, novel SC solutions incur high development and maintenance costs. Various model-based and no/low code development approaches address this issue by enabling higher abstractions in SC development. Still, the question remains unanswered how contractual parties, i.e., end-users with non-IT skills, manage to develop legally relevant SCs with ease. This study aims to (1) identify and categorize the state of the art of SC automation models, in terms of their technical features, and their legal significance, and to (2) identify new research opportunities. The review has been conducted as a systematic literature review (SLR) that follows the guidelines proposed by Kitchenham for performing SLRs in software-engineering. As a result of the implementation of the review protocol, 1367 papers are collected, and 33 of them are selected for extraction and analysis. The contributions of this article are threefold: (1) 10 different SC automation models/frameworks are identified and classified according to their technical and implementation features; (2) 11 different legal contract parameters are identified and categorized into 4 legal criteria classes; (3) a comparative analysis of SC-automation models in the context of their legal significance is conducted that identifies the degrees to which the SC-automation models are considered legally relevant. As a conclusion, we produce a comprehensive and replicable overview of the state of the art of SC automation models and a systematic measure of their legal significance to benefit practitioners in the field.
Automatic Arabic Grammatical Error Correction based on Expectation-Maximization routing and target-bidirectional agreement

Automatic Grammar Error Correction (GEC) detects and corrects various types of syntax, spelling, and grammatical errors. Different approaches such as rule-based, Statistical Machine Translation (SMT), and Neural Machine Translation (NMT) have been proposed. Among these approaches, NMT based on seq2seq multi-head attention (Transformer) performs the best. The key shortcoming of GEC seq2seq models with multiple encoder-decoder layers is that only the top layer is exploited in the subsequent processes. In addition, due to the exposure bias problem during inference, some of the previous target words are deleted and replaced by other words generated by the model itself, which leads to unsatisfactory output. This paper proposed GEC model based on seq2seq Transformer for low-resource languages such as Arabic to address these issues. Initially, we proposed a noising method for constructing synthetic parallel data to overcome the bottleneck arising from the lack of corpus. Furthermore, motivated by the success of capsule networks in computer vision, we used the Expectation-Maximization routing algorithm to dynamically aggregate information across layers in Arabic GEC. Moreover, to conquer the exposure bias problem, we introduced a bidirectional regularization term using Kullback-Leibler divergence in the training objective to improve the agreement between Right-to-left and Left-to-right models. Experiments performed on two benchmarks QALB-2014 and QALB-2015 showed that our proposed model achieved the best F1 score compared to the existing Arabic GEC systems.
Real-time obstacle detection by stereo vision and ultrasonic data fusion

Advanced driving assistance systems include some features such as changing lanes, avoiding obstacles and moving in cruise mode. Creating the perception of the environment is one of the most critical steps in developing an advanced driving assistance system. One of the main tasks in the perceptual layer is the timely detection of obstacles. In this study, a mixed method underpinned by the ultrasonic sensors and stereo vision was proposed to reduce the computational complexity in achieving acceptable frequency in the perception of the environment. The modified cross-check is employed for quality improvement while maintaining real-time. Moreover , we proposed an implementation form for the designed algorithm on GPU, which allows all data required for the cross-check process to be reached without changing the reference image. The proposed method accelerates the obstacle-detection 316 times faster than the typical stereo vision.
Exploratory Data Analysis and Classification of a New Arabic Online Extremism Dataset

The dissemination of extremist ideas and causes online has intensified over the last decade. Extremist organizations use social media to gain publicity and new recruits, often with little interference from network providers. New techniques are being developed to identify extremist content, ensuring it can be promptly removed and its authors blocked from network access. However, most techniques are only compatible with the English language, despite the fact that extremist propaganda is frequently shared in other languages, including Arabic. Since the most effective methods for automated linguistic analysis use deep learning and require large, high-quality datasets, creating specialised data samples containing examples of extremist communication is an essential step toward a practical solution. In this paper, we present a dataset compiled for this purpose and discuss the classification methods that can be used for extremism detection. The manually annotated Arabic Twitter dataset consists of 89,816 tweets published between 2011 and 2021. Using guidelines, three expert annotators labelled the tweets as extremist or non-extremist. Exploratory data analysis was performed to understand the dataset’s features. Classification algorithms were used with the dataset, including logistic regression, support vector machine, multinominal naïve Bayes, random forest, and BERT. Among the traditional machine learning models, support vector machine with term frequency-inverse document frequency features achieved the highest accuracy (0.9729). However, BERT outperformed the traditional models with an accuracy of 0.9749. This dataset is expected to enhance the accuracy of Arabic online extremism classification in future research, and so we have made it publicly available.
Resource Sharing and Trading of Blockchain Radio Access Networks: Architecture and Prototype Design

Recently, blockchain radio access network (B-RAN) arises as an innovative paradigm for the sixth-generation (6G) wireless communications to build cooperative trust, aggregate wireless resources, and schedule inter- and intra-network tasks among independent network entities. It establishes an open platform based on blockchain to provide diverse wireless services and applications, such as radio access, Internet of Things (IoT), and mobile-edge computing, via trusted interactions with enhanced security and efficiency. As a distinctive feature, B-RAN enables secure and efficient resource sharing and trading by aggregating, pooling, and coordinating resources from multiple resource hosts and owners across subnetworks. Therefore, an implementable architecture along with various functional modules shall be delicately designed. This work aims to establish a unified architecture with enhanced efficiency, security, compatibility, and flexibility for resource sharing and trading in B-RAN. Specifically, we develop a six-layer architecture that incorporates a number of novel features, such as enhanced blockchain structures, secure interaction methods, efficient service mechanisms, and scalable transaction patterns. We design a number of pluggable functional modules in each layer to support diverse functions, services, and applications of resource sharing and trading. Finally, we implement a practical prototype based on the layered architecture for resource-limited devices. Multiple experiments are presented to verify the performance of the proposed architecture from different aspects.
USMD: UnSupervised Misbehaviour Detection for Multi-Sensor Data

Cyber-Physical Systems (CPSs) enable Information Technology to be integrated with Operation Technology to efficiently monitor and manage the physical processes of various critical infrastructures. Recent incidents in cyber ecosystems have shown that CPSs are becoming increasingly vulnerable to complex attacks. These incidents often lead to sensing and actuation misbehaviour by illegal manipulations of data, which can severely impact the underlying physical processes of critical infrastructures. Current research acknowledges that IT-based security measures cannot entirely protect CPSs from such threats. Moreover, they are not designed to monitor the measurement level activities of physical processes, and they fail to mitigate blended cyberattacks, especially multi-stage and zero-day ones. This article addresses these limitations by proposing a framework, named UnSupervised Misbehaviour Detection (USMD), comprising a deep neural network that learns about a system's expected behaviour from data-driven representations. USMD can identify in real-time the attacks on CPSs by using the long-short term memory and Attention method for multi-sensor data. The USMD's performance is evaluated on various known data sets (i.e., ToN_IoT, SWaT, WADI and Gas pipeline datasets). The experimental results indicate that the superior performance of USMD compared with six state-of-the-art methods, which we implemented and extensively tested. USMD achieves F-scores of 0.9699 and 0.9702 on SWaT and WADI datasets, respectively.
Semi-automated geological mapping and target generation from geochemical and magnetic data in Halkidiki region, Greece

This study focuses on using multivariate analyses to generate semi-automated geological maps and exploration targets associated with porphyry Au-Cu mineralization within the Kassandra mining district, Greece. We use principal component analysis (PCA) and self-organizing maps (SOM) to reveal variations in geochemical and magnetic signatures within the input datasets. We visualize the results as pseudo-geological maps reflecting the associated geological processes and their end products. In specific, we utilize the potential of these two methods through an integrated interpretation and comparison of the results. We test the validity of the unsupervised PCA- and SOM-derived lithological and prospectivity models by comparing them with existing geological observations and interpretations. The results of this investigation show that both PCA and SOM are able to reproduce the key features of existing geological observations within the study area, but more importantly, also provide useful information that can be used to recognize prospective geological units and exploration targets from previously unknown locations.