Reviewerly/exHarmony huggingface dataset:
Fine-Tuning Textrank for Legal Document Summarization: A Bayesian Optimization Based Approach

Automatic text summarization techniques have a very high applicability in the legal domain, due to the complex and lengthy nature of legal documents. Most of the classical text summarization algorithms, which are also used in the legal domain, have certain hyperparameters, which if optimized properly, can further improve these algorithms. The choices of these hyperparameters have a big effect on the performance of such algorithms, yet this step of hyperparameter tuning is often overlooked while applying these algorithms in practice. In this work, a Bayesian Optimization based approach is proposed to optimize one of the classical summarization algorithms, Textrank, over this space of choices, by optimizing a ROUGE score mixture based objective function. The process of fine tuning and further evaluation is performed with the help of a publicly available dataset. From the experimental evaluation, it has been observed that the hyperparameter tuned Textrank is able to outperform baseline one-hot vector based Textrank and word2vec based Textrank models, with respect to ROUGE-1, ROUGE-2 and ROUGE-L metrics. The experimental analysis suggests that if proper hyperparameter tuning is performed, even a simple algorithm like Textrank can also perform significantly in the legal document summarization task.
Spatial-aware stacked regression network for real-time 3D hand pose estimation

Making full use of the spatial information of the depth data is crucial for 3D hand pose estimation from a single depth image. In this paper, we propose a Spatial-aware Stacked Regression Network (SSRN) for fast, robust and accurate 3D hand pose estimation from a single depth image. By adopting a differentiable pose re-parameterization process, our method efficiently encodes the pose-dependent 3D spatial structure of the depth data as spatial-aware representations. Taking such spatial-aware representations as inputs, the stacked regression network utilizes multi-joint spatial context and the 3D spatial relationship between the estimated pose and the depth data to predict a refined hand pose. To further improve the estimation accuracy, we adopt a spatial attention mechanism to reduce the influence of irrelevant features for pose regression. In order to improve the speed of the network, we propose a cross-stage self-distillation mechanism to distill knowledge within the network itself. Experiments on four datasets show that our proposed method achieves state-of-the-art accuracy with high running speed around 330 FPS on a single GPU and 35 FPS on a single CPU.
One Bad Turn Deserves Another: How Terrorism Sustains the Addiction to Capital Flight in Africa

This inquiry assesses if terrorism sustains the capital flight trap and whether the relationship is affected by varying the levels of governance and globalisation. The empirical evidence is based on interactive generalised method of moments with data from 37 African countries for the period 1996–2010. The following are established: (1) Evidence of a capital flight trap is apparent because past values of capital flight have a positive effect on future values of capital flight. (2) Terrorism sustains the positive effect of the capital flight trap on capital flight. (3) For the most part (especially with regard to political governance), terrorism sustains the addiction to capital flight in above-median governance sub-samples. Policy implications are discussed.
Antecedents of support for social media content moderation and platform regulation: the role of presumed effects on self and others

This study examines support for regulation of and by platforms and provides insights into public perceptions of platform governance. While much of the public discourse surrounding platforms evolves at a policy level between think tanks, journalists, academics and political actors, little attention is paid to how people think about regulation of and by platforms. Through a representative survey study of US internet users (N = 1,022), we explore antecedents of support for social media content moderation by platforms, as well as for regulation of social media platforms by the government. We connect these findings to presumed effects on self (PME1) and others (PME3), concepts that lie at the core of third-person effect (TPE) and influence of presumed influence (IPI) scholarship. We identify third-person perceptions for social media content: Perceived negative effects are stronger for others than for oneself. A first-person perception operates on the platform level: The beneficial effects of social media platforms are perceived to be stronger for the self than for society. At the behavioral level, we identify age, education, opposition to censorship, and perceived negative effects of social media content on others (PME3) as significant predictors of support for content moderation. Concerning support for regulation of platforms by the government, we find significant effects of opposition to censorship, perceived intentional censorship, frequency of social media use, and trust in platforms. We argue that stakeholders involved in platform governance must take more seriously the attitudes of their constituents.
BiLO-CPDP

Cross-Project Defect Prediction (CPDP), which borrows data from similar projects by combining a transfer learner with a classifier, have emerged as a promising way to predict software defects when the available data about the target project is insufficient. However, developing such a model is challenge because it is difficult to determine the right combination of transfer learner and classifier along with their optimal hyper-parameter settings. In this paper, we propose a tool, dubbed BiLO-CPDP, which is the first of its kind to formulate the automated CPDP model discovery from the perspective of bi-level programming. In particular, the bi-level programming proceeds the optimization with two nested levels in a hierarchical manner. Specifically, the upper-level optimization routine is designed to search for the right combination of transfer learner and classifier while the nested lower-level optimization routine aims to optimize the corresponding hyper-parameter settings. To evaluate BiLO-CPDP, we conduct experiments on 20 projects to compare it with a total of 21 existing CPDP techniques, along with its single-level optimization variant and Auto-Sklearn, a state-of-the-art automated machine learning tool. Empirical results show that BiLO-CPDP champions better prediction performance than all other 21 existing CPDP techniques on 70% of the projects, while being overwhelmingly superior to Auto-Sklearn and its single-level optimization variant on all cases. Furthermore, the unique bi-level formalization in BiLO-CPDP also permits to allocate more budget to the upper-level, which significantly boosts the performance.
Cryptocurrencies and fundamental rights

Cryptocurrencies,1 like bitcoin, raise new legal questions due to their innovative technological concepts. While academic research covers nearly all areas of the technological concepts of those currencies, legal studies focus only on a few topics. The papers that have been published so far discuss mainly economic law, tax law, and financial regulations. At the same time, governments are starting to explicitly regulate cryptocurrencies in terms of anti-money-laundering (AML) and to clarify or strengthen the legal basis for prosecuting crimes in the context of cryptocurrencies. Furthermore, criminal investigation in the context of cryptocurrencies is intensifying with the rising number of cryptocurrency-related crimes. Moreover, governments should also start to consider crime prevention in the context of cryptocurrencies. AML regulation, crime prevention, and prosecution have to take heed of the fundamental rights of the citizens affected. To date, legal research has not discussed the relationship between AML regulation (regarding cryptocurrencies), crime prevention (in conjunction with cryptocurrencies), the prosecution of crimes involving cryptocurrencies and fundamental rights. Many future regulatory concepts will collide with the fundamental right to property of the owners of cryptocurrency units and the freedom to pursue a trade or profession of owners and operators of exchange platforms, mining pools, etc. In cryptocurrencies organized as peer-to-peer systems, the freedom of association also has to be mentioned. With particular regard to prosecution, law enforcement agencies restrict the freedom of telecommunication, data privacy (including the right to informational self-determination), freedom of expression, and the freedom of information. Whenever some of these fundamental rights are impinged upon, regulation concepts and investigation or prosecution approaches must be provided for by law and must fulfill the criterion of necessity. Further interdisciplinary research is needed to develop efficient and legit prevention as well as criminal investigation concepts.
Teachers in the lifelong learning process: examples of digital literacy

The text aims at measuring digital literacy among teachers of the third stage of education in Poland (lower secondary schools). The study was commissioned by the Ministry of National Education and was conducted in 2018 in Poland, and involved a group of 484 teachers. The goal of the research was to present the teachers' knowledge and skills related to digital threats in the areas of the assessment of the reliability of information, sexting, cyberbullying, intellectual property rights, the protection of online images, and protection against malware. Digital literacy (DL) was measured using a knowledge and competence test and a diagnostic survey. The ability to enter the LLL process thanks to the ongoing advancement of ICT increases the online safety of all of the school-related stakeholders. A detailed analysis of the results also revealed that the teachers obtained good results in the test in terms of their knowledge about sexting and image protection but scored poorly regarding copyright and the assessment of the reliability of online information; male teachers know more about the technical aspects of digital safety than female teachers; trainees need particular support in the form of informal and non-formal education.
Graph regularized nonnegative matrix factorization with label discrimination for data clustering

Non-negative Matrix Factorization (NMF) is an effective method in multivariate data analysis, such as feature learning, computer vision and pattern recognition. For practical clustering tasks, NMF ignores both the local geometry of data and the discriminative information of different classes. In this paper, we propose a new NMF method under graph and label constraints, named Graph Regularized Nonnegative Matrix Factorization with Label Discrimination (GNMFLD), which attempts to find a compact representation of the data so that further learning tasks can be facilitated. The proposed GNMFLD jointly incorporates a graph regularizer and the prior label information as additional constraints, such that it can effectively enhance the discrimination and the exclusivity of clustering, and improve the clustering performance. Empirical experiments demonstrate the effectiveness of our new algorithm through a set of evaluations based on real-world applications.
On the Orchestration of Federated Learning through Vehicular Knowledge Networking

Federated Learning (FL) is a recent distributed technique to extract knowledge, i.e. an abstract understanding obtained from a set of information through experience and analysis. Vehicular networks are highly mobile networks in which a large spectrum of data types is distributed. So far, no mechanisms have been defined that distribute FL model updates in vehicular networks based on which nodes are likely to hold the right data for training, and when. In turn, this potentially limits FL model training speed and accuracy. In this paper, we describe protocols to exchange model-based training requirements based on the Vehicular Knowledge Networking framework. Based on this understanding, we define vehicular mobility and data distribution-aware FL orchestration mechanisms. An evaluation of the approach using a federated variant of the MNIST dataset shows training speed and model accuracy improvements compared to traditional FL training approaches.
Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Models and Algorithms

In this article, autonomous ground vehicle (AGV) path planning is considered. The AGV is assumed to be equipped with receivers capable of producing pseudorange measurements to overhead global navigation satellite systems (GNSS) satellites and to cellular base stations in its environment. Parameters of the cellular pseudoranges related to the transmitter clock bias are estimated in an initialization step in an open-sky environment. The AGV fuses these pseudoranges to produce an estimate about its own states. The AGV is also equipped with a three-dimensional building map of the environment. Starting from a known starting point, the AGV desires to reach a known target point by taking the shortest distance, while minimizing the AGV's position estimation error and guaranteeing that the AGV's position estimation uncertainty is below a desired threshold. Toward this objective, a so-called signal reliability map is first generated, which provides information about regions where large errors due to poor GNSS line-of-sight or cellular signal multipath are expected. The vehicle uses the signal reliability map to calculate the position mean-squared error (MSE). An analytical expression for the AGV's state estimates is derived, which is used to find an upper bound on the position bias due to multipath. An optimal path planning generation approach, which is based on Dijkstra's algorithm, is developed to optimize the AGV's path while minimizing the path length and position MSE, subject to keeping the position estimation uncertainty and position estimation bias due to multipath below desired thresholds. The path planning approach yields the optimal path together with a list of feasible paths and reliable GNSS satellites and cellular base stations to use along these paths.
Real-Time Encrypted Traffic Classification via Lightweight Neural Networks

The fast growth of encrypted traffic puts forward burning requirements on the efficiency of traffic classification. Although deep learning models perform well in the classification, they sacrifice the efficiency to obtain high-precision results. To reduce the resource and time consumption, a novel and lightweight model is proposed in this paper. Our design principle is to “maximize the reuse of thin modules A thin module adopts the multi-head attention and the 1D convolutional network. Attributed to the one-step interaction of all packets and the parallelized computation of the multi-head attention mechanism, a key advantage of our model is that the number of parameters and running time are significantly reduced. In addition, the effectiveness and efficiency of 1D convolutional networks are proved in traffic classification. Besides, the proposed model can work well in a real time manner, since only three consecutive packets of a flow are needed. To improve the stability of the model, the designed network is trained with the aid of ResNet, layer normalization and learning rate warm up. The proposed model outperforms the state-of-the-art works based on deep learning on two public datasets. The results show that our model has higher accuracy and running efficiency, while the number of parameters used is 1.8% of the 1D convolutional network and the training time halves.
A true positives theorem for a static race detector

RacerD is a static race detector that has been proven to be effective in engineering practice: it has seen thousands of data races fixed by developers before reaching production, and has supported the migration of Facebook's Android app rendering infrastructure from a single-threaded to a multi-threaded architecture. We prove a True Positives Theorem stating that, under certain assumptions, an idealized theoretical version of the analysis never reports a false positive. We also provide an empirical evaluation of an implementation of this analysis, versus the original RacerD. The theorem was motivated in the first case by the desire to understand the observation from production that RacerD was providing remarkably accurate signal to developers, and then the theorem guided further analyzer design decisions. Technically, our result can be seen as saying that the analysis computes an under-approximation of an over-approximation, which is the reverse of the more usual (over of under) situation in static analysis. Until now, static analyzers that are effective in practice but unsound have often been regarded as ad hoc; in contrast, we suggest that, in the future, theorems of this variety might be generally useful in understanding, justifying and designing effective static analyses for bug catching.
Tree-Chain: A Fast Lightweight Consensus Algorithm for IoT Applications

Blockchain has received tremendous attention in non-monetary applications including the Internet of Things (IoT) due to its salient features including decentralization, security, auditability, and anonymity.Most conventional blockchains rely on computationally expensive validator selection and consensus algorithms, have limited throughput, and high transaction delays.In this paper, we propose tree-chain a scalable fast blockchain instantiation that introduces two levels of randomization among the validators: i) transaction level where the validator of each transaction is selected randomly based on the most significant characters of the hash function output (known as consensus code), and ii) blockchain level where validator is randomly allocated to a particular consensus code based on the hash of their public key.Tree-chain introduces parallel chain branches where each validator commits the corresponding transactions in a unique ledger.
Probability-optimal leader comprehensive learning particle swarm optimization with Bayesian iteration

In this paper, a novel comprehensive learning particle swarm optimization algorithm, which is based on the Bayesian iteration method and named as Bayesian comprehensive learning particle swarm optimization (BCLPSO), is proposed. In the original PSO, the flying direction of each particle is based on its own historical best position and global optimum. This updating mechanism, however, easily falls into the local optimum, and the potential optimum solution may be ignored in the iteration and update process. Therefore, the BCLPSO is designed to facilitate discovering potential solution and avoid the problem of premature convergence. In the BCLPSO algorithm, the exemplar of the swarm is not the global best position but the particle location with the largest posterior probability based on the Bayesian formula. The posterior probability is developed by historical prior information. This means that the posterior probability can inherit the historical information of particles that may be exploited. In this way, the swarm diversity can be preserved to prevent premature convergence. The BCLPSO is experimentally validated on the CEC2017 benchmark functions and compared with other state-of-the-art particle swarm optimization algorithms. The results show that BCLPSO outperforms other comparative PSO variants on the CEC 2017 test suite. Furthermore, the algorithm is applied to the quality control process of an automated welding production line for the automobile body and is found to exhibit superior performance.
Twitter Sentiment Analysis of Covid-19 Using Term Weighting TF-IDF And Logistic Regresion

attack world population, and has brought much impact in all aspects of life.Stay at home and doing less in terms of social interactions.This can have a negative effect on mental health, so in this study we use sentiment analysis to know about mental health through public opinion on Twitter.Dataset which used in this study in this study is covid-19 tweets collected at 30 April 2020.Essentially, this dataset consists of 355384 tweets reviews.Covid-19 tweets will classify with the Logistic Regression method.Based on this research, the accuracy of the covid-19 tweeets sentiment classification is 94.71%.
Young people's information practices in library makerspaces

Abstract While there have been a growing number of studies on makerspaces in different disciplines, little is known about how young people interact with information in makerspaces. This study aimed to unpack how young people (middle and high schoolers) sought, used, and shared information in voluntary free‐choice library makerspace activities. Qualitative methods, including individual interviews, observations, photovoice, and focus groups, were used to elicit 21 participants' experiences at two library makerspaces. The findings showed that young people engaged in dynamic practices of information seeking, use, and sharing, and revealed how the historical, sociocultural, material, and technological contexts embedded in makerspace activities shaped these information practices. Information practices of tinkering, sensing, and imagining in makerspaces were highlighted. Various criteria that young people used in evaluating human sources and online information were identified as well. The study also demonstrated the communicative and collaborative aspects of young people's information practices through information sharing. The findings extended Savolainen's everyday information practices model and addressed the gap in the current literature on young people's information behavior and information practices. Understanding how young people interact with information in makerspaces can help makerspace facilitators and information professionals better support youth services and facilitate makerspace activities.
Jointly modeling and simultaneously discovering topics and clusters in text corpora using word vectors

An innovative model-based approach to coupling text clustering and topic modeling is introduced, in which the two tasks take advantage of each other. Specifically, the integration is enabled by a new generative model of text corpora. This explains topics, clusters and document content via a Bayesian generative process. In this process, documents include word vectors, to capture the (syntactic and semantic) regularities among words. Topics are multivariate Gaussian distributions on word vectors. Clusters are assigned corresponding topic distributions as their semantics. Content generation is ruled by text clusters and topics, which act as interacting latent factors. Documents are at first placed into respective clusters, then the semantics of these clusters is then repeatedly sampled to draw document topics, which are in turn sampled for word-vector generation. Under the proposed model, collapsed Gibbs sampling is derived mathematically and implemented algorithmically with parameter estimation for the simultaneous inference of text clusters and topics. A comparative assessment on real-world benchmark corpora demonstrates the effectiveness of this approach in clustering texts and uncovering their semantics. Intrinsic and extrinsic criteria are adopted to investigate its topic modeling performance, whose results are shown through a case study. Time efficiency and scalability are also studied.