Reviewerly/exHarmony huggingface dataset:
MultiLegalSBD

Sentence Boundary Detection (SBD) is one of the foundational building blocks of Natural Language Processing (NLP), with incorrectly split sentences heavily influencing the output quality of downstream tasks. It is a challenging task for algorithms, especially in the legal domain, considering the complex and different sentence structures used. In this work, we curated a diverse multilingual legal dataset consisting of over 130'000 annotated sentences in 6 languages. Our experimental results indicate that the performance of existing SBD models is subpar on multilingual legal data. We trained and tested monolingual and multilingual models based on CRF, BiLSTM-CRF, and transformers, demonstrating state-of-the-art performance. We also show that our multilingual models outperform all baselines in the zero-shot setting on a Portuguese test set. To encourage further research and development by the community, we have made our dataset, models, and code publicly available.
Hyperparameter analysis of wide-kernel CNN architectures in industrial fault detection: an exploratory study

Abstract Industrial fault detection has become more data-driven due to advancements in automated data analysis using deep learning. Such methods make it possible to extract useful features, e. g., from time series data retrieved from sensors, which is typically of complex nature. This allows for effective fault detection and prognostics that boost the efficiency and productivity of industrial equipment. This work explores the influence of a variety of architectural hyperparameters on the performance of one-dimensional convolutional neural networks (CNN). Using a multi-method approach, this paper focuses specifically on wide-kernel CNN models for industrial fault detection, that have proven to perform well for tasks such as classifying vibration signals retrieved from sensors. By varying hyperparameters such as the kernel size, stride and number of filters, an extensive hyperparameter space search was conducted; to identify optimal settings, we collected a total of 12,960 different combinations on three datasets into a model hyperparameter dataset, with their respective performance on the underlying fault detection task. Afterwards, this dataset was explored with follow-up analysis including statistical, feature, pattern and hyperparameter impact analysis. We find that although performance varies substantially depending on hyperparameter choices, there is no single simple strategy to optimise performance across the three datasets. However, an optimal setting in terms of performance can be found in the number of filters used in the later layers of the architecture for all datasets. Furthermore, hyperparameter importance differs across and within the datasets, and we found nonlinear relationships between hyperparameter settings and performance. Our analysis highlights key considerations when applying a wide-kernel CNN architecture to new data within the field of industrial fault detection. This supports practitioners who wish to apply and train state-of-the-art convolutional learning methods to apply to similar fault detection settings, e. g., vibration data arising from new combinations of sensors and/or machinery in the context of bearing faults.
Study of image sensors for enhanced face recognition at a distance in the Smart City context

Smart monitoring and surveillance systems have become one of the fundamental areas in the context of security applications in Smart Cities. In particular, video surveillance for Human Activity Recognition (HAR) applied to the recognition of potential offenders and to the detection and prevention of violent acts is a challenging task that is still undergoing. This paper presents a method based on deep learning for face recognition at a distance for security applications. Due to the absence of available datasets on face recognition at a distance, a methodology to generate a reliable dataset that relates the distance of the individuals from the camera, the focal length of the image sensors and the size in pixels of the target face is introduced. To generate the extended dataset, the Georgia Tech Face and Quality Dataset for Distance Faces databases were chosen. Our method is then tested and applied to a set of commercial image sensors for surveillance cameras using this dataset. The system achieves an average accuracy above 99% for several sensors and allows to calculate the maximum distance for a sensor to get the required accuracy in the recognition, which could be crucial in security applications in smart cities.
A Formal Framework for Combining Legal Reasoning Methods

This paper proposes a novel argumentation-based approach to combine legal-reasoning methods that each solve a subproblem of an overall legal problem. The methods can be of any nature (for instance, logical, case-based or probabilistic), as long as their input-output behaviour can be described at the metalevel with deductive or defeasible rules. The model is formulated in the ASPIC+ framework, to profit from its metatheory and explanation methods, and to allow for disagreement about how to solve a subproblem. The model is not meant to be directly implementable but to serve as a semantics for architectures and implementations.
A claim-centric perspective on abstract argumentation semantics: Claim-defeat, principles, and expressiveness

Dung's abstract argumentation frameworks (AFs) are a key formalism in AI research nowadays. Claims are an inherent part of each argument; they substantially determine the structure of the abstract representation. Nevertheless, they are often not taken into account on the abstract level, which restricts the modeling capacities of AFs to problems that do not involve claims in the evaluation. In this work, we address this shortcoming and conduct a structural analysis of claim-based argumentation semantics utilizing claim-augmented argumentation frameworks (CAFs) which extend AFs by assigning a claim to each argument. Our main contributions are as follows: We first propose novel variants for preferred, naive, stable, semi-stable, and stage semantics based on claim-defeat and claim-set maximization, complementing existing CAF semantics. Among our findings is that for a certain subclass, namely well-formed CAFs, the different versions of preferred and stable semantics coincide, which is not the case for the other semantics. We then conduct a principle-based analysis of the semantics with respect to general and well-formed CAFs. Finally, we study the expressiveness of the semantics by characterizing their signatures. In summary, this paper provides a thorough analysis of fundamental properties of abstract argumentation semantics (along the lines of existing results for AFs) but from the perspective of the claims the arguments represent. This shift of perspective provides novel results which we deem relevant when abstract argumentation is used in an instantiation-based setting.
Custom attribute image generation based on improved StyleGAN2

The continuous development of generative adversarial networks [1] has made it easier and easier to generate forged face images. Using generative adversarial networks can easily generate a large number of face images. However, the face images generated by generative adversarial networks are not of high quality and the face attributes cannot be controlled. In order to solve these problems, In this paper, we first use a style-based generative adversarial network [2] as a face image generator to generate high-quality face images, and then we create a convolutional neural network [3] that can predict the age and gender attributes of the input face image to control the age and gender of the generated face image. We experimentally demonstrated the reliability of the idea and finally achieved face image generation with customized attributes.
Data analytics for prediction of solar PV power generation and system performance: A real case of Bui Solar Generating Station, Ghana

The grid-connected solar power generated by the Bui Power Authority is sold to Ghana Grid Company Limited (GRIDCo) and other customers through bilateral contracts. However, there have been challenges in meeting the supply commitments due to the stochastic nature of solar energy. Fluctuating weather and climatic conditions make it difficult for operators to predict the output of the solar photovoltaic (PV) plant in advance. This has the tendency of leading to anticipated power loss or excess power not being managed effectively. In this study, the random forest and gradient boosting regressor algorithms were used to produce deterministic and probabilistic predictions of solar power generation by using data collected over an eleven-month span. Some of the findings show that the random forest model that produced probabilistic predictions performed better than the other compared models in terms of its accuracy. It produces reliable predictions with a normalized mean absolute error of 1.18%. This is deemed acceptable for operational purposes in the sphere of renewable energy prediction. As a result, these predictions are efficient enough to be used by utility companies as input to numerous decision-making problems during operations which consequently leads to the grid being managed effectively. It may also help them to choose the appropriate modelling approach for predicting solar PV power. Overall, this study supports the generation of clean and reliable energy through improved solar power prediction which directly contributes to African Union's agenda 2063 goals especially, in creating environmentally sustainable and climate resilient economies and communities. This is directly linked to the 7th and 13th UN sustainable developmental goals in ensuring access to affordable, reliable, sustainable, and modern energy for all and, combating climate change and its impacts.
Enhancing vulnerability scoring for information security in intelligent computers

Rapid computer and internet technology advancements have catalyzed continuous breakthroughs and innovations in related fields, significantly impacting social production and individual lifestyles. A critical challenge addressed here is the inadequacy of existing methods for assessing computer vulnerabilities. The vulnerability scoring model for intelligent vehicles (VSMIV) is proposed to solve the issue in intelligent computers. A key aspect involves optimizing the attack vector and attack complexity of common vulnerabilities and exposures (CVEs) to align with the specific behaviour of intelligent computers. The model's effectiveness is enhanced by integrating four distinct indicators: property security, privacy security, functional security, and life security. The diversity ratings indicate that the VSMIV scoring system has the highest diversity of system distribution at 95%, followed by CVSSIoT at 88%, and CVSS with a slightly lower diversity score of 85%. The proposed methodology holds promise in establishing a new paradigm for strengthening the security of computer systems, thereby stimulating their flexibility in the face of emerging threats.
Legal Holding Extraction from Italian Case Documents using Italian-LEGAL-BERT Text Summarization

Legal holdings are used in Italy as a critical component of the legal system, serving to establish legal precedents, provide guidance for future legal decisions, and ensure consistency and predictability in the interpretation and application of the law. They are written by domain experts who describe in a clear and concise manner the principle of law applied in the judgments.
Whatever Happened to Hypotheticals?

In the early days of AI and Law, the use of hypotheticals - imaginary cases constructed to test or explore a particular point - was seen as an important part of legal reasoning. Hypotheticals, have however, attracted increasingly less interest, and have hardly been seen for a decade or more. In this short paper we discuss why hypotheticals disappeared, and the need to reintroduce them if a comprehensive account of reasoning with legal cases is to be produced. The paper includes a discussion of how hypotheticals can be deployed within a recent framework of argumentation schemes describing reasoning with legal cases.
Performance benchmark of deep learning human pose estimation for UAVs

Abstract Human pose estimation (HPE) is a computer vision application that estimates human body joints from images. It gives machines the capability to better understand the interaction between humans and the environment. For this accomplishment, many HPE methods have been deployed in robots, vehicles, and unmanned aerial vehicles (UAVs). This effort raised the challenge of balance between algorithm performance and efficiency, especially in UAVs, where computational resources are limited for saving battery power. Despite the considerable progress in the HPE problem, there are very few methods that are proposed to face this challenge. To highlight the severity of this fact, the proposed paper presents a brief review and an HPE benchmark from the aspect of algorithms performance and efficiency under UAV operation. More specifically, the contribution of HPE methods in the last 22 years is covered, along with the variety of methods that exist. The benchmark consists of 36 pose estimation models in 3 known datasets with metrics that fulfill the paper aspect. From the results, MobileNet-based models achieved competitive performance and the lowest computational cost, in comparison with ResNet-based models. Finally, benchmark results are projected in edge devices hardware specifications to analyze the appropriateness of these algorithms for UAV deployment.
RFSD-REI: A Real and Fake Samples-Driven Radar Emitter Identification Method for Imbalanced Data

To address the issue of radar emitter identification (REI) with imbalanced data, we propose a real and fake samples-driven REI method for imbalanced data. First, we use the synthetic minority oversampling technique (SMOTE) to generate fake samples and balance the imbalanced data. Next, we use the edited nearest neighbors (ENN) to purify the balanced dataset composed of real samples and generated fake samples. We then train a convolutional neural network (CNN) using the purified, balanced dataset containing both real and fake samples. Subsequently, we create a one-step optimization dataset using only the real samples from the original imbalanced data to further enhance the performance of the trained CNN. Finally, we use the enhanced CNN to identify different radar emitters. Our experimental results demonstrate the effectiveness, robustness, and generalization of the proposed method. This work provides a new solution to the problem of REI with imbalanced data and has the potential to improve the measurement and perception capabilities of electronic warfare forces.
Research on Robot Accuracy Compensation Method Based on Modified Grey Wolf Algorithm

This paper proposes a method using the modified grey wolf algorithm for optimizing robot motion accuracy to address problems of insufficient robot trajectory accuracy and low efficiency of traditional optimization algorithms. First, the Denavit-Hartenberg method is used to establish a robotics kinematic error model. Considering the parameters for optimization in the model as variables in the system, the problem of improving the accuracy of the robot is transformed into a problem of optimization for a nonlinear system. An objective function is designed according to the robot's trajectory it will be solved by the MGWO (modified grey wolf) algorithm to obtain the optimal parameters of the robot in order to improve the positioning accuracy of the robot. The experimental results show that this method is effective and can effectively reduce the robot motion error and improve positioning accuracy after algorithm optimization.
A Private and Efficient Triple-Entry Accounting Protocol on Bitcoin

The ‘Big Four’ accountancy firms dominate the auditing market, auditing almost all the Financial Times Stock Exchange (FTSE) 100 companies. This leads to people having to accept auditing results even if they may be poor quality and/or for inadequate purposes. In addition, accountants may provide different auditing results with the same financial data. These issues are hard for regulators such as the Financial Reporting Council to identify because of insufficient resources or inconsistent compliance. In this paper, we proposed a triple-entry accounting protocol to allow users to report Bitcoin transactions to a third-party auditor to comply with regulations such as the travel rule. It allows the auditor to easily detect anomalies and identify the non-compliant parties, whilst the blockchain itself provides a transparent and immutable record of these anomalies. Despite building on a public ledger, our solution preserves privacy and offers an interoperability layer for information exchange. Merkle proofs were used to record non-compliant transactions whilst allowing compliant transactions to be pruned from an auditor’s active database.
Here's to the future: Conversational agents in higher education- a scoping review

With AI's advancing technology, pedagogical changes are occurring, and chatbots are becoming more intertwined in our daily lives. While these can be used in a variety of disciplines, they play a particularly significant role in the digital transformation of education. We present a scoping review of chatbots in higher education (HE), investigating where chatbots are being applied. The main benefits and challenges of chatbots were explored. The quality of the selected papers was assessed using the International Organization for Standards (ISO) 9241–11 guidelines. A total of 2692 citations were found while searching recognized digital databases. After reading the abstract and full text, 64 publications were considered. Backward and forward reference checking yielded four further studies. Altogether, 66 studies were included. This review provides a comprehensive overview of research on using chatbots in HE, including advantages, and challenges. Research demonstrates the versatility and the promising aspects of this type of support system for university education.
The reconciliation mechanism by cooperative intention index for managing non-cooperative behaviors in social network group decision making

Social network group consensus is a hybrid cooperative and non-cooperative problem, with the management of non-cooperative behavior as a key topic. In response, this study investigates the reconciliation mechanism to manage non-cooperative behaviors based on a cooperative intention index (CII). First, a CII index driven by preference similarity and trust relationships is constructed, which predicts the compromise domain of decision makers (DMs). Subsequently, this study recognizes three types of non-cooperative behaviors from the group and individual proposal perspectives in turn: (i)Type-1 cooperation with the group; (ii)Type-2 non-cooperation with the group but cooperation with the individual; and (iii)Type-3 non-cooperation with both the group and the individual. Then, reconciliation mechanisms applicable to different types of mediation are proposed. Then, group-orientated, individual-orientated and compulsory reconciliation mechanisms are proposed for different types. Finally, a demonstration example and discussions are provided to illustrate the efficacy of the proposed model.
A Multilayered Preprocessing Approach for Recognition and Classification of Malicious Social Network Messages

The primary methods of communication in the modern world are social networks, which are rife with harmful messages that can injure both psychologically and financially. Most websites do not offer services that automatically delete or send malicious communications back to the sender for correction, or notify the sender of inaccuracies in the content of the messages. The deployment of such systems could make use of techniques for identifying and categorizing harmful messages. This paper suggests a novel multilayered preprocessing approach for the recognition and classification of malicious social network messages to limit negative impact, resulting in fewer toxic messages, scams, and aggressive comments in social media messages and commenting areas. As a result, less technical knowledge would be required to investigate the effects of harmful messages. The dataset was created using the regional Lithuanian language with four classes: aggressive, insulting, toxic, and malicious. Three machine learning algorithms were examined, five use cases of a multilayered preprocessing approach were suggested, and experiments were conducted to identify and classify harmful messages in the Lithuanian language.
Primary pre-service teachers’ beliefs about developing future-ready learners: a qualitative inquiry in Hong Kong

This study explores the readiness of pre-service primary school teachers in Hong Kong to develop future-ready learners. Societal advances require the education sector to continuously update curricula to prepare young people for the future. Schools must equip students with the flexibility to use technologies that have not yet been invented and solve unanticipated problems. In this qualitative study, we analysed the perceptions of 15 pre-service primary school English language teachers at a university in Hong Kong. The study focused on the knowledge and learning environment necessary for developing future-ready learners. Via semi-structured interviews, the findings revealed that the participants believed it is essential to integrate technology into classrooms to address future digital literacy needs. They also noted that students must develop critical thinking skills, problem-solving skills, and soft skills to succeed in the future. The findings also suggest that teachers need to be innovative and adaptable in their pedagogical approaches as the existing primary school curriculum is not cultivating learners’ future-readiness.
South African Postgraduate STEM Students’ Use of Mobile Digital Technologies to Facilitate Participation and Digital Equity during the COVID-19 Pandemic

The transition to online mobile teaching and learning during the COVID-19 pandemic required more than access to mobile digital technologies, particularly in South Africa, a country trying to address the injustices and inequalities of the pre-democracy period. The argument advanced in this research is that the transition to online mobile teaching and learning could be used as leverage to promote active participation, quality inclusive learning, develop relevant and responsive content and achieve mobile digital equity. This interpretative case study was conducted at a South African teacher-training institution. The primary aim of this study is to explore the following question: How did postgraduate STEM students use mobile digital technologies to facilitate teaching and learning under COVID-19 conditions? Data was obtained from 20 purposively selected postgraduate STEM students enrolled for their Bachelor of Education honours degree. They participated in online discussion forums and maintained reflective journals. Due diligence was taken to ensure all the required ethical protocols were observed. Mishra and Koehler’s Technological Pedagogical and Content Knowledge (TPACK) model framed this study. The constructs of the model were used during the analysis of data. The findings highlight that mobile digital technologies were used for translanguaging, supporting students in developing disciplinary science understanding through exposure to practical work and linking cross-cutting concepts in STEM subjects. The findings of this study are significant as they indicate how to operationalize STEM teaching in developing countries; how to address issues of access and social justice; and how to leverage mobile technologies to improve practice in STEM subjects, post-COVID.