Reviewerly/exHarmony huggingface dataset:
An automated framework for the extraction of semantic legal metadata from legal texts

Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal corpus with semantic metadata is prohibitively expensive. Our work is motivated by two observations: (1) the existing requirements engineering (RE) literature does not provide a harmonized view on the semantic metadata types that are useful for legal requirements analysis; (2) automated support for the extraction of semantic legal metadata is scarce, and it does not exploit the full potential of artificial intelligence technologies, notably natural language processing (NLP) and machine learning (ML). Our objective is to take steps toward overcoming these limitations. To do so, we review and reconcile the semantic legal metadata types proposed in the RE literature. Subsequently, we devise an automated extraction approach for the identified metadata types using NLP and ML. We evaluate our approach through two case studies over the Luxembourgish legislation. Our results indicate a high accuracy in the generation of metadata annotations. In particular, in the two case studies, we were able to obtain precision scores of 97,2% and 82,4%, and recall scores of 94,9% and 92,4%.
A Graph Neural Network For Assessing The Affective Coherence Of Twitter Graphs

Graph neural networks (GNNs) is an emerging class of iterative connectionist models taking full advantage of the interaction patterns in an underlying domain. Depending on their configuration GNNs aggregate local state information to obtain robust estimates of global properties. Since graphs inherently represent high dimensional data, GNNs can effectively perform dimensionality reduction for certain aggregator selections. One such task is assigning sentiment polarity labels to the vertices of a large social network based on local ground truth state vectors containing structural, functional, and affective attributes. Emotions have been long identified as key factors in the overall social network resiliency and determining such labels robustly would be a major indicator of it. As a concrete example, the proposed methodology has been applied to two benchmark graphs obtained from political Twitter with topic sampling regarding the Greek 1821 Independence Revolution and the US 2020 Presidential Elections. Based on the results recommendations for researchers and practitioners are offered.
Skill-based Career Path Modeling and Recommendation

The development of new technologies at an unprecedented rate is rapidly changing the landscape of the labor market. Therefore, for workers who want to build a successful career, acquiring new skills required by new jobs through lifelong learning is crucial. In this paper, we propose a novel and interpretable monotonic nonlinear state-space model to analyze online user professional profiles and provide actionable feedback and recommendations to users on how they can reach their career goals. Specifically, we use a series of binary-valued and non-decreasing latent states to represent the expanding skill set of each user throughout their career and propose an efficient inference method under our model. Using a series of experiments on two large real-world datasets, we show that our model (sometimes significantly) outperforms existing methods on the tasks of company, job title, and skill prediction. More importantly, our model is interpretable and can be used for other important tasks including skill gap identification and career path planning. Using a series of case studies, we show that our model can provide i) actionable feedback to users and guide them through their upskilling and reskilling processes and ii) recommendations of feasible paths for users to reach their career goals.
A joint cumulative distribution function and gradient fusion based method for dehazing of long shot hazy images

Hazy or foggy weather conditions significantly degrade the visual quality of an image in an outdoor environment. It also changes the color and reduces the contrast of an image. This paper introduces a novel single image dehazing technique to restore a hazy image without considering the physical model of haze formation. In order to find haze-free image, the proposed method does not require the transmission map and its costly refinement process. Since haze effect is dependent on the depth, it severely degrades the visibility of the objects located at a far distance. The objects close to the camera are unaffected. In this paper, we propose a fusion-based haze removal method based on the joint cumulative distribution function (JCDF) that treats faraway haze and nearby haze separately. The output images after the JCDF module, fused in the gradient domain to produce a haze-free image. The proposed method not only significantly enhances visibility but also preserves texture details. The proposed method is experimented and evaluated on a large set of challenging hazy images (large scene depth, night time, dense fog, etc.). Both qualitative and quantitative measures show that the performance of the proposed method is better than the state-of-the-art dehazing techniques.
Evaluation of ISO 52010-1: 2017 and proposal for an alternative calculation procedure

A proper evaluation of the building energy consumption implies an accurate assessment of the solar gains and consequently an exact estimation of the direct and diffuse solar radiation on tilted and oriented surfaces. The new standard EN ISO 52016-1: 2017 introduces a new hourly methodology for the calculation of energy performance of buildings and is complemented by EN ISO 52010-1:2017, which specifies the procedure for the conversion of climate data for energy calculations. These standards represent a good compromise between the old EN ISO 13790:2008 and Trnsys, since they allow for a more accurate evaluation of energy consumption than the former, while being less onerous than the latter. In the present work, EN ISO 52010-1:2017 is analysed for the first time and an alternative calculation procedure is proposed. These two algorithms are then compared with two other irradiance splitting methods implemented in Meteonorm and Trnsys code. In order to test performance and limits of the different calculation algorithms, we make a comparison among such methods on five European cities characterized by different climatic characteristics. Results show that the model implemented in EN ISO 52010-1:2017 overestimates the diffuse solar irradiance on horizontal plane for all the considered locations, while the proposed procedure and those implemented in the Trnsys and Meteonorm softwares show similar results. Using Trnsys algorithm as a baseline, the proposed calculation algorithm gives the best approximation among the considered models for what concerns the direct, diffuse and global irradiance on vertical surfaces exposed to south, east and west.
Dynamic network intelligent hybrid recommendation algorithm and its application in online shopping platform

With the rapid development of e-commerce, whether network intelligent recommendation can attract customers has become a measure of customer retention on online shopping platforms. In the literature about network intelligent recommendation, there are few studies that consider the difference preference of customers in different time periods. This paper proposes the dynamic network intelligent hybrid recommendation algorithm distinguishing time periods (DIHR), it is a integrated novel model combined with the DEMATEL and TOPSIS method to solved the problem of network intelligent recommendation considering time periods. The proposed method makes use of the DEMATEL method for evaluating the preference relationship of customers for indexes of merchandises, and adopt the TOPSIS method combined with intuitionistic fuzzy number (IFN) for assessing and ranking the merchandises according to the indexes. We specifically introduce the calculation steps of the proposed method, and then calculate its application in the online shopping platform.
Selective Sharing of Outsourced Encrypted Data in Cloud Environments

Owing to the vast volume of information gathered by computers, data protection and security has become a problem for organizations with the enormous rise in data transmission. Due to many advantages that cloud service providers provide, mainly economic benefits, several data owners outsource their data to cloud repositories. However, data owners do not have full ownership of the data after their data are outsourced. Thus, external data management systems are implemented to manage the data. Several kinds of research refer to the use of encryption techniques to prevent unauthorized access to data. Selective encryption aims at supporting selective and private access to outsourced data. However, the combination of this approach and indexing techniques cause confidentiality violations. In this article, a blockchain-based approach to data access is presented by implementing smart contracts over data access. These executable scripts bind users by stating access policies on the data. Furthermore, we provide a system, where users can offload their computational capabilities due to limitations in their computations. Our systems' computational capabilities outperform that of when users do the computation on their own. The results show a practical approach to data access management using blockchain technology.
First-Arrival Picking for Microseismic Monitoring Based on Deep Learning

In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. Using the advanced idea of deep learning-based end-to-end classification and the prominent feature extraction advantages of a fully convolution neural network, this paper proposes a first-arrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of first-arrival picking. In this paper, we first introduced the methodology of the UNet++-based picking method. And then, the performance of the proposed method is verified by the experiments with finite-difference forward modeling simulated signals and actual microseismic records under different signal-to-noise ratios, and finally, comparative experiments are performed using the U-Net-based first-arrival picking algorithm and the Short-Term Average to Long-Term Average (STA/LTA) algorithm. The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms.
Lightweight Internet of Things Device Authentication, Encryption, and Key Distribution Using End-to-End Neural Cryptosystems

Device authentication, encryption, and key distribution are of vital importance to any Internet of Things (IoT) systems, such as the new smart city infrastructures. This is due to the concern that attackers could easily exploit the lack of strong security in IoT devices to gain unauthorized access to the system or to hijack IoT devices to perform denial-of-service attacks on other networks. With the rise of fog and edge computing in IoT systems, increasing numbers of IoT devices have been equipped with computing capabilities to perform data analysis with deep learning technologies. Deep learning on edge devices can be deployed in numerous applications, such as local cardiac arrhythmia detection on a smart sensing patch, but it is rarely applied to device authentication and wireless communication encryption. In this article, we propose a novel lightweight IoT device authentication, encryption, and key distribution approach using neural cryptosystems and binary latent space. The neural cryptosystems adopt three types of end-to-end encryption schemes: 1) symmetric; 2) public-key; and 3) without keys. A series of experiments was conducted to test the performance and security strength of the proposed neural cryptosystems. The experimental results demonstrate the potential of this novel approach as a promising security and privacy solution for the next-generation of IoT systems.
Accelerating Text Mining Using Domain-Specific Stop Word Lists

Text preprocessing is an essential step in text mining. Removing words that can negatively impact the quality of prediction algorithms or are not informative enough is a crucial storage-saving technique in text indexing and results in improved computational efficiency. Typically, a generic stop word list is applied to a dataset regardless of the domain. However, many common words are different from one domain to another but have no significance within a particular domain. Eliminating domain-specific common words in a corpus reduces the dimensionality of the feature space, and improves the performance of text mining tasks. In this paper, we present a novel mathematical approach for the automatic extraction of domain-specific words called the hyperplane-based approach. This new approach depends on the notion of low dimensional representation of the word in vector space and its distance from hyperplane. The hyperplane-based approach can significantly reduce text dimensionality by eliminating irrelevant features. We compare the hyperplane-based approach with other feature selection methods, namely χ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and mutual information. An experimental study is performed on three different datasets and five classification algorithms, and measure the dimensionality reduction and the increase in the classification performance. Results indicate that the hyperplane-based approach can reduce the dimensionality of the corpus by 90% and outperforms mutual information. The computational time to identify the domain-specific words is significantly lower than mutual information.Reproducibility: code and results can be found at https://github.com/FarahAlshanik/Domain-Specific-Word-List.
A concept for low-emission production using Distributed Ledger Technology

The vision of the Paris Climate Convention is global greenhouse gas neutrality by 2050. Manufacturing industry accounts for approximately 25% of global greenhouse gas. The achievement of emission targets requires joint implementation and the participation of the employee. An incentive concept based on a Distributed Ledger Technology allows the automatically reward of climate protection measures based on tamper-proof stored Key Performance Indicators. The technology also enables the automated payment of a CO2-tax proportional to energy consumption. A technology recommendation for the incentive concept based on an analysis of the technological requirements for a distributed ledger completes the paper.
RETRACTED ARTICLE: Residual attention network for deep face recognition using micro-expression image analysis

Discriminative feature embedding is about vital appreciation within the research area of deep face identification. During this paper, we would suggest a remaining attention based convolutional neural network (ResNet) because differs from facial characteristic implanting, who objectives in conformity with locate outdoors the long-range dependencies regarding rear images through reducing the knowledge redundancy amongst channels and as specialize of the important informative factors on spatial feature maps (SFM). More specifically, the proposed interest module consists about self channel attention (SCA) block then self spatial attention (SSA) barrier who adaptively aggregates the characteristic maps within each duct and spatial domains after find outdoors the inter-channel affinity form yet consequently the interspatial kindred matrix, and then matrix multiplications are conducted because a refined yet robust back feature. With the sight module we proposed, we wish make grade convolutional neural networks (CNNs), like ResNet-50 have greater discriminative government for deep face recognition. The experiments on labelled faces of SMIC-HS, SMIC-NIR, SMIC-VIS, CASME II and CASME I are show that our developed ResNet shape constantly outperforms naive CNNs and achieved the state-of-the-art performance. The proposed work on these micro-expression datasets yields better results with a state-of-the-art accuracy of quite 98% in each dataset. For verification purposes, the videos collected real-time manually from different gender people were tested and an accuracy score of 99.88% and an F1_score of 99.88% was achieved.
Biometric Technologies for eLearning: State-of-the-Art, Issues and Challenges

In the recent past, the use of learning management systems has increased as these enable remote education via electronic media, i.e. eLearning. The use of such platforms was further elevated by needs for eLearning due to the COVID-19 pandemic. In order to achieve secure and reliable identity management in eLearning systems, biometric technologies are increasingly deployed for remote student authentication. Numerous researchers proposed concepts and methods on how to effectively integrate biometric recognition into eLearning. This work provides a conceptual categorisation and survey regarding the state-of-the-art of biometric technologies for eLearning along with a detailed discussion on open issues and challenges in this emerging field of research.
Analytic approach to dynamics of the resonant and off-resonant Jaynes-Cummings systems with cavity losses

An analytic approach to investigate the zero-temperature time evolution of the Jaynes-Cummings system with cavity losses is developed. With realistic coupling between the cavity and the environment assumed, a simple master equation is derived, leading to the explicit analytic solution for the resonant case. This solution is suitable for analyses not only on single-excitation states but also on many-excitation states, which enables us to investigate the photon coherent state and to observe sharp collapses and revivals under dissipation. For the off-resonant case, on the other hand, the present study presents an analytic, systematic method instead. We examine the small and large detuning limits and discuss the condition where the widely used phenomenological treatment is justified. Explicit evaluations of the time evolutions for various initial states with finite detuning are also presented.
An Analytical Framework for Measuring Inequality in the Public Opinion on Policing—Assessing the Impacts of COVID-19 Pandemic Using Twitter Data

As the COVID-19 pandemic sweeps across the globe, police forces are charged with new roles as they engage and enforce new policies and laws governing societal behaviours.However, how the police exercise these powers is an important factor in shaping public opinion and confidence concerning their activities across space and time.This research developed an analytical framework for measuring the inequality in the public opinion towards policing efforts during the pandemic using Twitter data.We demonstrate the utility of our framework using 3-months of tweets across 42 police force areas (Pfas) of England and Wales (UK).The results reveal that public opinions on policing is overwhelmingly negative across space and time, and that these opinions have been most exacerbated by the COVID-19 pandemic in three specific Pfas, namely Staffordshire, Thames Valley, and North Wales.We provided the link to the open-source script by which this research could be replicated and adapted to other study areas.This research has the potential to help law enforcement understand the dynamics of public confidence and trust in policing and facilitate action towards improved police services.
Fault Tree Analysis (FTA)

The fault tree analysis (FTA) is invaluable as this is the only deductive approach (i.e. top down or effect to cause approach) used to deal with safety and dependability modelling. This chapter describes its position within the probabilistic approaches in general and the Boolean family approaches in particular. It aims to model how a system is faulty (i.e. in down state) from the logic combination of the faults (i.e. down states) of its components: it is the dual approach of RBDs and both approaches are equivalent from a mathematical point of view. An FT is a directed acyclic graph drafted by using specific symbols (primary events) and logic gates (e.g. OR, AND, k out of n, NOT). The various primary events and logic gates are analysed and simple examples are provided to explain how to effectively build such models. The concept of minimal cut sets introduced in the RBD chapter is still valid with FTs and this is the basis for qualitative analysis from FT models. The success trees which describe how a system operates from the good operating states of its components are also briefly described.
Embarrassingly Simple Binarization for Deep Single Imagery Super-Resolution Networks

Deep convolutional neural networks (DCCNs) have shown pleasing performance in single image super-resolution (SISR). To deploy them onto real devices with limited storage and computational resources, a promising solution is to binarize the network, i.e., quantize each float-point weight and activation into 1 bit. However, existing works on binarizing DCNNs still suffer from severe performance degradation in SISR. To mitigate this problem, we argue that the performance degradation mainly comes from no appropriate constraint on the network weights, which causes it difficult to sensitively reverse the binarization results of these weights using the backpropagated gradient during training and thus limits the flexibility of network in respect of fitting extensive training samples. Inspired by this, we present an embarrassingly simple but effective binarization scheme for SISR, which can obviously relieve the performance degeneration resulted from network binarization and is applicable to different DCNN architectures. Specifically, we force each weight to follow a compact uniform prior, with which the weight will be given a very small absolute value close to zero and its binarization result can be straightforwardly reversed even by a small backpropagated gradient. By doing this, the flexibility and the generalization performance of the binarized network can be improved. Moreover, such a prior performs much better when introducing real identity shortcuts into the network. In addition, to avoid falling into bad local minima during training, we employ a pixel-wise curriculum learning strategy to learn the constrained weights in an easy-to-hard manner. Experiments on four SISR benchmark datasets demonstrate the effectiveness of the proposed binarization method in terms of binarizing different SISR network architectures, e.g., it even achieves performance comparable to the baseline with 5 quantization bits.
Optimal Policies for Quantum Markov Decision Processes

Abstract Markov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.