Reviewerly/exHarmony huggingface dataset:
The formation of students’ self-organisation and self-assessment skills in a competitive foreign learning environment: Case study

Introduction . Nowadays, creating a new type of economy requires graduates with a set of specific personal competencies that allow them to manage their own professional growth and objectively assess strengths and weaknesses. Aim. The aim of the research is to analyse the experience of forming self-organisation and self-development competencies in non-linguist students due to new approaches to independent work in a competitive learning environment and the procedures of self-assessment, mutual evaluation and peer assessment of the achieved learning results. Methodology and research methods . The model of students' independent work in a foreign language learning and authentic assessment is described. Various data collection strategies were used in a longitudinal three-year study involving 76 students of the experimental group and 75 students of the control group, as well as 4 expert teachers. To prove the statistical significance of the obtained data, Student's t-test and Fisher's test were applied. Results . Comparative analysis of data confirmed different dynamics of formation of self-organisation and self-evaluation competencies in the experimental and control groups. The number of students in the experimental group, experiencing difficulties in organising their independent work in a foreign language, decreased by 36%; the share of students, able to critically evaluate the results of their own activities, increased by 32%. In the control group, similar indicators improved by 4% for each of the criteria, which is statistically insignificant. Scientific novelty. An alternative model of organising students' independent work in a competitive learning environment was developed. It proved to be effective for training students' self-organising skills, critical evaluation skills and personal responsibility to success in life. Practical significance . The implementation of the proposed model in higher education will contribute to the formation of the necessary personal competencies necessary for future professional activities in a rapidly changing world. It is proposed to continue testing new non-standard approaches to the development of these competencies.
Determinants of governance institutional quality in sub-saharan africa

The paper hypothesizes that the quality of institutions tends to evolve slowly and in a piecemeal nature over time. In this process policy is seen to be a mechanism through which this evolutionary process takes place. Policy influences intermediary variables, which in turn create opportunities for institutional innovation. There are two hypotheses derived from this analysis and tested empirically in the paper. It is hypothesized that improvements in institutional quality lead to increases in economic development (using two different measures). It is also hypothesized that changes in intermediary variables resulting from policy changes lead to improvements in institutional quality. These two hypotheses are tested using a panel data set made up of twenty-five countries in Africa. The results indicate that improvements in the quality of governance institutions do lead to improvements in economic development and that policies aimed at influencing intermediary variables lead to improvements in governance institutional quality.
Conversational ontology operator: patient-centric vaccine dialogue management engine for spoken conversational agents

Abstract Background Previously, we introduced our Patient Health Information Dialogue Ontology (PHIDO) that manages the dialogue and contextual information of the session between an agent and a health consumer. In this study, we take the next step and introduce the Conversational Ontology Operator (COO), the software engine harnessing PHIDO. We also developed a question-answering subsystem called Frankenstein Ontology Question-Answering for User-centric Systems (FOQUS) to support the dialogue interaction. Methods We tested both the dialogue engine and the question-answering system using application-based competency questions and questions furnished from our previous Wizard of OZ simulation trials. Results Our results revealed that the dialogue engine is able to perform the core tasks of communicating health information and conversational flow. Inter-rater agreement and accuracy scores among four reviewers indicated perceived, acceptable responses to the questions asked by participants from the simulation studies, yet the composition of the responses was deemed mediocre by our evaluators. Conclusions Overall, we present some preliminary evidence of a functioning ontology-based system to manage dialogue and consumer questions. Future plans for this work will involve deploying this system in a speech-enabled agent to assess its usage with potential health consumer users.
Formal Analysis of Smart Contracts: Applying the KeY System

Smart contracts are programs running on decentralized, distributed ledger platforms. Rigorous formal analysis of these programs is highly desirable because they manage valuable assets and therefore are a prime target for security attacks. In this paper, we show that the computation model of smart contracts allows the application of formal methods designed for analysing single-threaded imperative programs. We discuss different classes of correctness properties and the formal methods that may be applied. Furthermore, we show how deductive program verification in particular can be used to prove correctness of smart contracts, and we discuss two approaches where we have applied the program verification tool KeY.
Continuous Implicit Authentication through Touch Traces Modelling

Nowadays, the continuously increasing use of smart-phones as the primary way of dealing with day-to-day tasks raises several concerns mainly focusing on privacy and security. In this context and given the known limitations and deficiencies of traditional authentication mechanisms, a lot of research efforts are targeted towards continuous implicit authentication on the basis of behavioral biometrics. In this work, we propose a methodology towards continuous implicit authentication that refrains from the limitations imposed by small-scale and/or controlled environment experiments by employing a real-world application used widely by a large number of individuals. Upon constructing our models using Support Vector Machines, we introduce a confidence-based methodology, in order to strengthen the effectiveness and the efficiency of our approach. The evaluation of our methodology on a set of diverse scenarios indicates that our approach achieves good results both in terms of efficiency and usability.
Optimal tests for continuous-variable quantum teleportation and photodetectors

Quantum teleportation is a primitive in several important applications, including quantum communication, quantum computation, error correction, and quantum networks. In this work, we propose an optimal test for the performance of continuous-variable (CV) quantum teleportation in terms of the energy-constrained channel fidelity between ideal CV teleportation and its experimental implementation. Work prior to ours considered suboptimal tests of the performance of CV teleportation, focusing instead on its performance for particular states, such as ensembles of coherent states, squeezed states, cat states, etc. Here we prove that the optimal state for testing CV teleportation is an entangled superposition of twin Fock states. We establish this result by reducing the problem of estimating the energy-constrained channel fidelity between ideal CV teleportation and its experimental approximation to a quadratic program and solving it. As an additional result, we obtain an analytical solution to the energy-constrained diamond distance between a photodetector and its experimental approximation. These results are relevant for experiments that make use of CV teleportation and photodetectors.
Association Rules and Machine Learning for Enhancing Undeclared Work Detection

Undeclared work is, by definition, a multi-faceted phenomenon that needs to be detected. In welfare states, undeclared work results in loss of public revenue and thus resources critical for welfare mechanisms' funding, lack of worker protection and, last but not least, unfair competition for legitimate businesses. Yet, little to no studies have proposed the use of sophisticated machine learning methods in tackling this severe socioeconomic problem. In this study, we demonstrate the application of an advanced data analysis method, the association rule mining, which has significant advantages over rule-based systems, in classifying employers likely to engage in undeclared work. Indeed, the results of this pilot project proved divulging, even to the most experienced labour inspectors, offering insights in patterns of employers' illegal behaviour, that were previously unidentified.
A Report on the First Workshop on Document Intelligence (DI) at NeurIPS 2019

The first workshop on Document Intelligence (DI-2019) was held on December 14, 2019 at NeurIPS 2019 conference in Vancouver, Canada. The report summarizes the workshop, with a summary of the talks, papers and posters presented, and discusses common themes, issues and open questions that came up in the workshop.
Immersive Virtual Reality and Gamification Evaluation on Treadmill Exercise by Using Electrophysiological Monitoring Device

research-article Immersive Virtual Reality and Gamification Evaluation on Treadmill Exercise by Using Electrophysiological Monitoring Device Share on Authors: Elbert Libriandy University of Indonesia, Indonesia University of Indonesia, IndonesiaView Profile , Maya Arlini Puspasari University of Indonesia University of IndonesiaView Profile Authors Info & Claims ICIBE 2020: 2020 The 6th International Conference on Industrial and Business EngineerinSeptember 2020 Pages 191–195https://doi.org/10.1145/3429551.3429553Published:27 September 2020 0citation58DownloadsMetricsTotal Citations0Total Downloads58Last 12 Months58Last 6 weeks8 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
PRCMLA: Product Review Classification Using Machine Learning Algorithms

In our modern era, where the Internet is ubiquitous, everyone relies on various online resources for shopping and the increase in the use of social media platforms like Facebook, Twitter, etc. The user review spread rapidly among millions of users within a brief period. Consumer reviews on online products play a vital role in the selection of a product. The customer reviews are the measurement of customer satisfaction. This review data in terms of text can be analyzed to identify customers' sentiment and demands. In this paper, we wish to perform four different classification techniques for various reviews available online with the help of artificial intelligence, natural language processing (NLP), and machine learning concepts. Moreover, a Web crawling methodology has also been proposed. Using this Web crawling algorithm, we can collect data from any website. We investigate and compare these techniques with the parameter of accuracy using different training data numbers and testing. Then we find the best classifier method based on accuracy.
Analysis of Cryptography Encryption for Network Security

Abstract In order to secure network and data transmission via a wireless network, cryptography and network encryption is being used. Providing data protection is one of the key aspects of wireless network data transmission. There are sensors in the wireless networks; they are linked to the base station. The need for protection of the wireless network sensor is very critical, and encryption and network security are necessary. Network security includes security for the terminal system as well as for the whole network system. Network security is one of the main concerns as the world transitions into the digital world. Security of the network provides security for administrator managed data. Increasing communication technology also requires safe communication which is met through various encryption techniques such as cryptography, digital signatures, watermarking, steganography and other applications. Cryptography is a technique of encryption used to protect the network, as various networks are related and admire attacks and intrusions. In this paper we discuss the cryptography with its aims, forms and algorithms. Intrusion and computer protection technologies are also used in attack forms.
A Benchmark and Baseline for Language-Driven Image Editing

Language-driven image editing can significantly save the laborious image editing work and be friendly to the photography novice. However, most similar work can only deal with a specific image domain or can only do global retouching. To solve this new task, we first present a new language-driven image editing dataset that supports both local and global editing with editing operation and mask annotations. Besides, we also propose a baseline method that fully utilizes the annotation to solve this problem. Our new method treats each editing operation as a submodule and can automatically predict operation parameters. Not only performing well on challenging user data, but such an approach is also highly interpretable. We believe our work, including both the benchmark and the baseline, will advance the image editing area towards a more general and free-form level.
Research on classification algorithms for attention mechanism

In this paper, we solve the image classification task by capturing context dependencies based on spatial and channel attention mechanism. Unlike previous research on feature fusion, we propose an attention module based on spatial and channel dimensions. This module derives attention maps respectively from spatial and channel, then for feature refinement we multiply the attention map into the feature map. Meanwhile, the module can be easily embedded into the network structures due to it is lightweight. The channel attention module selectively enhances some feature channels and suppresses certain feature channels by integrating the relationship between each feature channel. By weighting the features of all locations, spatial attention module aggregates location features. Regardless of distance, similar features are interrelated. Our module is evaluated through experiments on the ImageNet-1K and CIFAR-100 datasets.
An Automatic-Addressing Architecture With Fully Serialized Access in Racetrack Memory for Energy-Efficient CNNs

Racetrack memory, an emerging low-power magnetic memory, promises a competitive replacement for traditional memory in the accelerators. However, random access in racetrack memory is time and energy expenditure for CNN accelerators because of its large amount of invalid-shifts. In this article, we propose an automatic-addressing architecture that builds a novel data layout to guarantee that the next round of memory access can be always satisfied at the in-situ or rigorously adjacent cells of current round, producing a fully serialized access footprint that can drive instant port-alignment without any invalid-shifts in racetrack memory. By this way, original address-based access degrades to the selections repeated among the three candidates, i.e., one <i>in-situ</i> cell and two neighbor cells. Based on this simplification, a lightweight access management can generate the sequence of one-out-three selections according to the deterministic access behaviors defined by CNN hyper-parameters. The evaluation shows that, when deploying the five popular CNN applications to our architecture, the physical shifts of racetrack is curtailed by 74.64 percent over legacy layout, which achieves 54.2 and 42.1 percent energy reduction on read and write, respectively. A case study of YOLOv2 indicates that our architecture performs 6.503 GOp/J that achieves <inline-formula><tex-math notation="LaTeX">$18.5 \times$</tex-math></inline-formula> improvement to server-level GPUs.
Attacking Graph-Based Classification without Changing Existing Connections

In recent years, with the rapid development of machine learning in various domains, more and more studies have shown that machine learning models are vulnerable to adversarial attacks. However, most existing researches on adversarial machine learning study non-graph data, such as images and text. Though some previous works on graph data have shown that adversaries can make graph-based classification methods unreliable by adding perturbations to features or adjacency matrices of existing nodes, these kinds of attacks sometimes have limitations for real-world applications. For example, to launch such attacks in real social networks, the attacker cannot force two good users to change (e.g., remove) the connection between them, which means that the attacker can not launch such attacks. In this paper, we propose a novel attack on collective classification methods by adding fake nodes into existing graphs. Our attack is more realistic and practical than the attack mentioned above. For instance, in a real social network, an attacker only needs to create some fake accounts and connect them to existing users without modifying the connections among existing users. We formulate the new attack as an optimization problem and utilize a gradient-based method to generate edges of newly added fake nodes. Our extensive experiments show that the attack can not only make new fake nodes evade detection, but also make the detector misclassify most of the target nodes. The proposed new attack is very effective and can achieve up to 100% False Negative Rates (FNRs) for both the new node set and the target node set.
Method for Effectiveness Assessment of Electronic Warfare Systems in Cyberspace

Current electronic warfare (EW) systems, along with the rapid development of information and communication technology, are essential elements in the modern battlefield associated with cyberspace. In this study, an efficient evaluation framework is proposed to assess the effectiveness of various types of EW systems that operate in cyberspace, which is recognized as an indispensable factor affecting modern military operations. The proposed method classifies EW systems into primary and sub-categories according to EWs’ types and identifies items for the measurement of the effectiveness of each EW system by considering the characteristics of cyberspace for evaluating the damage caused by cyberattacks. A scenario with an integrated EW system incorporating two or more different types of EW equipment is appropriately provided to confirm the effectiveness of the proposed framework in cyber electromagnetic warfare. The scenario explicates an example of assessing the effectiveness of EW systems under cyberattacks. Finally, the proposed method is demonstrated sufficiently by assessing the effectiveness of the EW systems using the scenario.
Load Balancing Approach for a MapReduce Job Running on a Heterogeneous Hadoop Cluster

Hadoop MapReduce has become the de-facto standard in today’s Big data world to process the more prominent data sets on a distributed cluster of commodity hardware. Today computing nodes in a commodity cluster do not have the same hardware configuration, which leads to heterogeneity. Heterogeneity has become common in the industry, research institutes, and academics. Our study shows that the current rules for calculating the required number of Reduce tasks (Reducers) for a MapReduce job are fallacious, leading to significant computing resources’ overutilization. It also degrades MapReduce job performance running on a heterogeneous Hadoop cluster. However, there is no definite answer to the question: What is the optimal number of Reduce tasks required for a MapReduce job to get Hadoop’s most accomplished performance in a heterogeneous cluster? We have proposed a new rule that decides the required number of reduce tasks for a MapReduce job running on a heterogeneous Hadoop cluster accurately. The proposed rule balances the load among the heterogeneous nodes in the Reduce phase of MapReduce. It also minimizes computing resources’ overutilization and improves the MapReduce job execution time by an average of 18% and 28% for TeraSort and PageRank applications running on a heterogeneous Hadoop cluster.
A Fully Decentralized Infrastructure for Subscription-based IoT Data Trading

On top of subscription-based pricing models such as Software-as-a-Service (SaaS), monetizing the subscription enables the payment of data streams with data usage instead of a particular price for a fixed data set. This pricing model allows data providers to have a better vision of managing budgets and data consumers to have the flexibility to subscribe and unsubscribe. However, unlike static data collections, the streaming data increases difficulties of dynamic data ownership and identity verification. A trustless data trading infrastructure is required where the entities can trade, validate data ownership and data integrity without trusting any service or participants. In addition, an automated subscription procedure is also demanded for the sake of data monetization. In this paper, we leverage usages of distributed ledger technologies (DLTs) to construct a decentralized data trading platform on top of the IoT brokered infrastructure. This approach can efficiently enhance the degree of transparency and scalability. The storage built upon cryptographic message protocols allows transmitting, accessing and validating data streams over distributed ledgers without authorities, and the digital rights of trading participants deserve a guarantee, which is enabled by design.
An analysis of cybersecurity in Dutch annual reports of listed companies

In this paper we study the disclosure of cybersecurity information in Dutch annual reports, such as cybersecurity measures and cyber incidents, from a financial law and economics perspective. We start our discussion with an analysis of the requirements in financial law to disclose cybersecurity information in annual reports. Hereafter, we discuss the incentives for the board regarding disclosing cybersecurity related information and its effect on stakeholders and shareholders. We draft hypotheses regarding the actual disclosure of cybersecurity information and propose a research design of an exploring empirical study. The results of our study show that although there is no strict legal obligation to do so, 87% of the companies mention cybersecurity or similar words in their annual report in 2018. However, only 4 out of 75 companies disclosed more than six specific cybersecurity measures, while openness would generate the highest surplus for society from a social welfare perspective. Some major Dutch banks and employment agencies did not disclose any specific information with regard to their cybersecurity strategy, while those companies are highly vulnerable for cybersecurity incidents. This hampers the protection of creditors, investors and other stakeholders. Our analysis aims to propel the debate on stimulation of self-regulation or possible obligations in financial law concerning cybersecurity in annual reports.