Reviewerly/exHarmony huggingface dataset:
The concept of a technology neutral payment instrument in criminal law

The development of electronic payment instruments and their online availability constitute important parts of the development of the EU payments market.Individual states adopt different approaches towards interpretation (legal aspects) and types of electronic payment instruments.To encourage sustainable payment services, minimize possible threats, and create favourable conditions for the development of new payment instruments, legislators adopt general legal acts stipulating the legal status of electronic payment instruments.However, the actual interpretation of payment instruments is often narrowed in legal practice, and does not cover payment instruments newly introduced to the market.A new insight into electronic payment instruments corresponding to the latest trends in the market is important in criminal law as well, because crimes related to the use of a payment instrument are common and difficult to investigate.In view of recent changes in payment services (the new Payment Services Directive in 2019), the norms of criminal law stipulating liability for the illegal disposal of electronic payment instruments must reflect circumstances predetermined by today's technological developments.In terms of criminal law, a technologically neutral conception of the payment instrument is necessary, to include a wider range of payment instruments that differ considerably from conventional personalized payment cards.The aim of this article is, therefore, to demonstrate that the current regulation of the Criminal Code of Lithuania lags behind the development of payment instruments, and in order to avoid excessive criminalization it is proposed to narrow the application of Article 214 of the Lithuanian Criminal Code.
Risk Disclosure Patterns among Jordanian Companies: An Exploratory Study during Covid-19 Pandemic

This current exploratory study comes at a critical time to determine the risk disclosure pattern of Jordanian companies during Covid-19 pandemic in response to the request of JSC for Jordanian listed companies to prepare and send disclosure reports include the effect of Covid-19 pandemic on their activities in terms of material events, operational activities and the decisions of board of directors during the period of disclosure suspension extending from March 18, 2020 to May 5, 2020. Based on all the non-financial companies that listed in the first market, the results of the study indicated that the entire study sample (100%) did send the disclosure reports to JSC. In terms of the quality of disclosed risks, extraction process resulted in finding 20 risk items distributed over 5 categories. The results show that the average disclosure level is 65.6%, with the operational category ranked first and followed by investor relation category, financial category, strategic category and finally the market category. Results show that those sectors that were suspended completely during Covid-19 pandemic provided risk disclosures in all categories and vice versa. In addition to the several implications, the study offers many avenues for future study based on the risk disclosure model of the current study.
Multi-sense Embeddings Using Synonym Sets and Hypernym Information from Wordnet

Word embedding approaches increased the efficiency of natural language processing (NLP) tasks.Traditional word embeddings though robust for many NLP activities, do not handle polysemy of words.The tasks of semantic similarity between concepts need to understand relations like hypernymy and synonym sets to produce efficient word embeddings.The outcomes of any expert system are affected by the text representation.Systems that understand senses, context, and definitions of concepts while deriving vector representations handle the drawbacks of single vector representations.This paper presents a novel idea for handling polysemy by generating Multi-Sense Embeddings using synonym sets and hypernyms information of words.This paper derives embeddings of a word by understanding the information of a word at different levels, starting from sense to context and definitions.Proposed sense embeddings of words obtained prominent results when tested on word similarity tasks.The proposed approach is tested on nine benchmark datasets, which outperformed several state-of-the-art systems.
Verifying Strategic Abilities of Neural-symbolic Multi-agent Systems

We investigate the problem of verifying the strategic properties of multi-agent systems equipped with machine learning-based perception units. We introduce a novel model of agents comprising both a perception system implemented via feed-forward neural networks and an action selection mechanism implemented via traditional control logic. We define the verification problem for these systems against a bounded fragment of alternating-time temporal logic. We translate the verification problem on bounded traces into the feasibility problem of mixed integer linear programs and show the soundness and completeness of the translation. We show that the lower bound of the verification problem is PSPACE and the upper bound is coNEXPTIME. We present a tool implementing the compilation and evaluate the experimental results obtained on a complex scenario of multiple aircraft operating a recently proposed prototype for air-traffic collision avoidance.
The Power of the Hybrid Model for Mean Estimation

Abstract We explore the power of the hybrid model of differential privacy (DP), in which some users desire the guarantees of the local model of DP and others are content with receiving the trusted-curator model guarantees. In particular, we study the utility of hybrid model estimators that compute the mean of arbitrary realvalued distributions with bounded support. When the curator knows the distribution’s variance, we design a hybrid estimator that, for realistic datasets and parameter settings, achieves a constant factor improvement over natural baselines.We then analytically characterize how the estimator’s utility is parameterized by the problem setting and parameter choices. When the distribution’s variance is unknown, we design a heuristic hybrid estimator and analyze how it compares to the baselines. We find that it often performs better than the baselines, and sometimes almost as well as the known-variance estimator. We then answer the question of how our estimator’s utility is affected when users’ data are not drawn from the same distribution, but rather from distributions dependent on their trust model preference. Concretely, we examine the implications of the two groups’ distributions diverging and show that in some cases, our estimators maintain fairly high utility. We then demonstrate how our hybrid estimator can be incorporated as a sub-component in more complex, higher-dimensional applications. Finally, we propose a new privacy amplification notion for the hybrid model that emerges due to interaction between the groups, and derive corresponding amplification results for our hybrid estimators.
Addressing Neural Network Robustness with Mixup and Targeted Labeling Adversarial Training

Despite their performance, Artificial Neural Networks are not reliable enough for most of industrial applications. They are sensitive to noises, rotations, blurs and adversarial examples. There is a need to build defenses that protect against a wide range of perturbations, covering the most traditional common corruptions and adversarial examples. We propose a new data augmentation strategy called M-TLAT and designed to address robustness in a broad sense. Our approach combines the Mixup augmentation and a new adversarial training algorithm called Targeted Labeling Adversarial Training (TLAT). The idea of TLAT is to interpolate the target labels of adversarial examples with the ground-truth labels. We show that M-TLAT can increase the robustness of image classifiers towards nineteen common corruptions and five adversarial attacks, without reducing the accuracy on clean samples.
Conjectures on spectral properties of ALIF algorithm

A new decomposition method for nonstationary signals, named Adaptive Local Iterative Filtering (ALIF), has been recently proposed in the literature. Given its similarity with the Empirical Mode Decomposition (EMD) and its more rigorous mathematical structure, which makes feasible to study its convergence compared to EMD, ALIF has really good potentiality to become a reference method in the analysis of signals containing strong nonstationary components, like chirps, multipaths and whistles, in many applications, like Physics, Engineering, Medicine and Finance, to name a few. In [11], the authors analyzed the spectral properties of the matrices produced by the ALIF method, in order to study its stability. Various results are achieved in that work through the use of Generalized Locally Toeplitz (GLT) sequences theory, a powerful tool originally designed to extract information on the asymptotic behavior of the spectra for PDE discretization matrices. In this manuscript we focus on answering some of the open questions contained in [11], and in doing so, we also develop new theory and results for the GLT sequences.
AI outguns a human fighter pilot

Artificial intelligence defeats a human pilot in simulated air combat
Digital Financial Inclusion and Economic Growth: Evidence from Sub-Saharan Africa (2011-2017)

Digital financial inclusion for the poor is becoming a reality.While traditional microfinance and banks remain important, the potential of using new technology-based platforms to serve the poor is huge.In particular, digital finance network coverage and the use of a variety of indirect channels (e.g., agents) reduce the costs compared to more customary full service branches owned by banks.Cash is the main barrier to financial inclusion.In as much as poor people rely on cash or barter, it remains too costly for financial institutions to serve them.Once the poor have access to cost-effective digital means of payments, they exit this trap and could in principle be profitably supplied by a range of financial institutions.Providers can offer not only mobile money, but also savings, credit, insurance, and other digital financial products to the poor at low cost.Digital Financial inclusion is usually defined as the percentage of individuals and firms that have access to or use financial services (World Bank, 2014).Burkett and Sheehan (2009) define financial exclusion as:A situation where a person, group or organization lacks or is deprived of access to affordable, appropriate and fair financial products and services, with the result that their capability to participate fully in social and economic activities is reduced, financial adversity is increased, and poverty (measured by income, debt and assets) is exacerbated.Digital Financial inclusion, therefore, is beyond access to finance, usage and quality are also important.Many people may have access to digitalfinancial services at affordable prices, but decide not to use certain financial services for reasons such as religion or culture.Statistics show disparities due to factors such as income, age and gender (Demirgüc-Kunt et al, 2015).Others may lack access due to exorbitant costs of the services, unavailability of services because of regulatory barriers, or a variety of other market and cultural factors.Fuhrmann, (2017) postulated that the business of banks involves taking deposits and using the same deposits to make loans.It could be complicated, but this is the basic model used in banking.In the past banks in Asia have been accused of not reaching out in areas where the transaction or deposit size is very low.In places with low deposits, the volumes are usually low, and the costs of serving are high.The banks did not see any sense to open up branches in areas with low volumes and the high cost of operation.The revolution of information technology (IT) has changed every aspect
Proactive Data Center Management Using Predictive Approaches

Data Center (DC) management aims at promptly serving user requests while minimizing the energy consumed. This is achieved by turning off unnecessary servers to save energy and adapting the number of servers that are $on$ to the time-varying and heterogeneous user requests. A great change in the number of servers $on$ leads to a considerable management effort, also called control effort in the literature, which should be reduced as much as possible. Since feedback control can improve the performance of computing systems and networks, we propose to use it to achieve this dynamic capacity provisioning of the DC. In order to design this feedback control, first, we developed a dynamic model of the DC. The purpose of this paper is to design a feedback control strategy based on the DC model, able to optimize i) the Quality of Service, ii) the energy consumed and iii) the management effort. A simple Reactive open-loop Control which provides an amount of energy equal to the amount requested in the previous time interval is considered as a benchmark for comparison. Second, two feedback controls based on the balance equations of the DC are studied, namely i) Reactive Feedback Control providing an amount of energy equal to that provided by the reactive open-loop control but adding the accumulated demand that has not yet been served, and ii) Model Predictive Control optimizing a constrained cost that weights the management effort and the prediction error. Reactive Control, Reactive Feedback Control and Model Predictive Control are compared in terms of energy consumed, energy error and management effort. Quantitative results of the comparative performance evaluation are given, based on a data set collected from a real DC.
Assessment of the innovative potential of Moscow schools: limitations and development resources

О. В. Рогач, Т
Implementation of the REAP Strategy in the Aqidah Akhlak Course to Improve Student Literacy Ability

Aqidah Akhlak is a course that must be attended by PAI students because they will become teachers who will teach this course. The Aqidah Akhlak teacher requires the ability of "information literacy" in the themes that are taught. In this study, the writer will implement the REAP strategy in The Aqidah Akhlak subject to improve the information literacy ability of college students. This research is a Classroom Action Research (CAR) and the subject of research is a PAI UMSIDA student. This research was conducted in several stages, namely planning, action, observation, and reflection. Data collection is done by observation, field notes, interviews, tests, and documentation. After conducting classroom action research on the Aqidah Akhlak course, the results obtained in the Pre-Assessment averaged 50.5, then increased in the Post-Assessment an average of 86.7. Improvement in each cycle is followed by an increase in students' ability to browse the literature appropriately and use it in argumentation, increased activity in discussions, and increased task completion. Based on these results it can be concluded that the implementation of REAP strategy can improve students' information literacy abilities.
Multipopulation Genetic Algorithm Based on GPU for Solving TSP Problem

A GPU-based Multigroup Genetic Algorithm was proposed, which parallelized the traditional genetic algorithm with a coarse-grained architecture island model. The original population is divided into several subpopulations to simulate different living environments, thus increasing species richness. For each subpopulation, different mutation rates were adopted, and the crossover results were optimized by combining the crossover method based on distance. The adaptive mutation strategy based on the number of generations was adopted to prevent the algorithm from falling into the local optimal solution. An elite strategy was adopted for outstanding individuals to retain their superior genes. The algorithm was implemented with CUDA/C, combined with the powerful parallel computing capabilities of GPUs, which greatly improved the computing efficiency. It provided a new solution to the TSP problem.
A Novel Perspective of the Kalman Filter from the Rényi Entropy

Rényi entropy as a generalization of the Shannon entropy allows for different averaging of probabilities of a control parameter α. This paper gives a new perspective of the Kalman filter from the Rényi entropy. Firstly, the Rényi entropy is employed to measure the uncertainty of the multivariate Gaussian probability density function. Then, we calculate the temporal derivative of the Rényi entropy of the Kalman filter's mean square error matrix, which will be minimized to obtain the Kalman filter's gain. Moreover, the continuous Kalman filter approaches a steady state when the temporal derivative of the Rényi entropy is equal to zero, which means that the Rényi entropy will keep stable. As the temporal derivative of the Rényi entropy is independent of parameter α and is the same as the temporal derivative of the Shannon entropy, the result is the same as for Shannon entropy. Finally, an example of an experiment of falling body tracking by radar using an unscented Kalman filter (UKF) in noisy conditions and a loosely coupled navigation experiment are performed to demonstrate the effectiveness of the conclusion.
Information Needs and Seeking Behaviour of Nurses at Rivers State University Teaching Hospital (RSUTH), Port-Harcourt

The study investigated information needs and seeking behaviour of nurses at Rivers State University Teaching Hospital (RSUTH), Port Harcourt. It adopted a descriptive research survey. The population of the study was 352 nurses at RSUTH from which a sample 100 was drawn. Self-structured questionnaire was used. The questionnaire included a four point likert scale (4-1). Out of 100 copies of the questionnaire administered, 94 were found valid for analysis. Data was analysed using simple percentages and mean score. The findings of the study showed that nurses needed information on drugs therapy, diagnostic, patients’ management and personal development. The factors that motivated their information seeking include working environment, patients’ care, professionalism and personal development. To satisfy their curiosity, they consulted different sources of information including colleagues, internet and textbook. It also showed that nurses had a negative perception about library and information services in meeting their information needs. Inadequate information resources in the library, inadequate information retrieval skills and closed access were some of the challenges they faced when seeking for their information needs. However, they suggested training of nurses and provision of standard library for the nurses. It was recommended among other things that the library should be well equipped to meet the information needs of the nurses also the nurses should acquire information retrieval skills to enable them access information resources on the internet and databases.&#x0D; Keywords: Information needs, Information seeking behaviour, Nurses, Rivers, Health information
FORCING CREATIVITY IN AGILE INNOVATION PROCESSES THROUGH ASD-INNOVATION COACHING

The challenge to handle uncertainties as well as a rapidly changing and challenging environment in product development is increasingly faced by implementing agile approaches and creativity methods into the companies' organization.To foster the goal of agility in product development processes the approach of Innovation Coaching in ASD -Agile Systems Design helps organizations to leverage their innovation potential.This empirical research defines the concept of ASD-Innovation Coaching, which enables product development teams to introduce and deepening their competencies into an agile and structured innovation process with the aim of implementing a degree of agility that is appropriate to the complexity of the task to be accomplished.The necessary activities of an ASD-Innovation Coach are described and located contentwise in order to enable problem-solving teams in agile development projects of ASD.Coaching the team with a deep understanding of the technical system and agile approaches fosters systematic agility and generates increased creativity into development projects.
A New Hybrid Encryption Approach for Secure Communication: GenComPass

When looking at the daily life flow and working sectors, it is seen that almost all work and transactions are carried out electronically.It performs many data streams in the electronic transactions performed.The importance of information security is exactly at this point.To ensure the security of the data, the journey of the data between the sender and the receiver is encrypted.In this study, a hybrid application that creates encrypted text using genetic algorithm and particle swarm algorithm has been developed.In the first step of the study, two separate keys were generated to encode the message using the genetic algorithm and particle swarm algorithm.Shannon Entropy method was used as a fitness function in both algorithms.The message was encrypted with the genetic algorithm method by choosing the key that obtained the best result from the compliance function.The encrypted message was decoded by applying a reverse genetic algorithm to the recipient.The encryptions made using the generated key were measured and the results of the AES algorithm were compared.In the proposed model, successful performances were obtained as the maximum switching space and encryption time for encryption.As a result, the proposed application offers an alternative method of data encryption and decryption that can be used for message transmission.
Analysis of Information-Based Nonparametric Variable Selection Criteria

We consider a nonparametric Generative Tree Model and discuss a problem of selecting active predictors for the response in such scenario. We investigated two popular information-based selection criteria: Conditional Infomax Feature Extraction (CIFE) and Joint Mutual information (JMI), which are both derived as approximations of Conditional Mutual Information (CMI) criterion. We show that both criteria CIFE and JMI may exhibit different behavior from CMI, resulting in different orders in which predictors are chosen in variable selection process. Explicit formulae for CMI and its two approximations in the generative tree model are obtained. As a byproduct, we establish expressions for an entropy of a multivariate gaussian mixture and its mutual information with mixing distribution.
Fuzzy metric space and applications in image processing

In this paper, the notions of fuzzy T-metric and fuzzy S-metric have been introduced, and then, examples of known fuzzy metrics are provided, as well as theorems that enable algorithms for putting up new metrics.Recently, there has been renewed interest in some of their properties, which are further shown, these being polygonal inequality, and two new classes of functions have been shown to be regarded as fuzzy metric.By applying these fuzzy metrics, an algorithm has been used in order to remove image noise.The goal was to improve the sharpness and the quality of the image, which is expressed and measured by means of the image quality index UIQI.It has been shown that the general image, which is filtered by this algorithm, has greater sharpness than the image filtered by the median filter, which is probably the most commonly used vector filter.