Reviewerly/exHarmony huggingface dataset:
Teaching Arabic Machine Translation to EFL Student Translators: A Case Study of Omani Translation Undergraduates

The present paper describes a machine translation (MT) course taught to undergraduate students in the Department of English Language and Literature at Dhofar University in Oman. The course is one of the major requirements for BA in Translation. Fifteen EFL translation students who were in their third year of study were enrolled in the course. The author presents both the theoretical and practical parts of the course. In the theoretical part, the topics covered in the course are outlined. As for the practical part, it focuses on the translation students&amp;rsquo; post-editing of online MT output. This is beneficial to the students as free online MT systems can potentially be used as a means for improving student translators&amp;rsquo; training and EFL learning. This is achieved through subjecting MT output to analysis or post-editing by the students so that they can focus on the differences between the source and target languages. With this goal in mind, assignments were given to the students to post-edit the Arabic and English MT output of three free online MT systems (Systran, Babylon and Google Translate), discuss the linguistic problems that they spot for each system and choose the one that has the fewest number of errors. The results show that the students, with varying degrees of success, have managed to identify some linguistic errors with the MT output for each MT system and thus produced a better human translation. The paper concludes that there is a need to incorporate MT courses in translation departments in the Arab world, as integrating technology into translation curricula will have great effect on student translators&amp;rsquo; training for their future career as professional translators.
Beyond a Blockchain Paradox: How Intermediaries Can Leverage a Disintermediation Technology

New digital technologies are changing the way organizations create and capture value.In particular, blockchain is bringing up opportunities for organizations in terms of transparency and security, and at the same time threatening the position of intermediaries such as banks and notaries.Therefore, intermediaries need to design new business models to generate value from blockchain.Little academic research has been conducted to identify the business models that intermediaries could exploit to leverage a disintermediation technology such as blockchain.Employing a qualitative research based on focus group and interviews, this study highlights how a specific intermediary, the Italian notaries, tried to design appropriate business models to derive value from blockchain ecosystems.Specifically, drawing on the key concepts of value configuration, value creation and business model dimensions, this paper identifies three different business models that Italian notaries can implement to create and capture value from permissionless blockchain ecosystems.
Video Object Segmentation by Latent Outcome Regression

This paper presents a novel algorithm for unsupervised video object segmentation (UVOS) in unconstrained scenarios. Although a large variety of methods have been proposed in the literature, segmenting generic objects is still challenging because different methods often perform well in different situations, and no single method can outperform the others in all cases. To address this, we propose to solve the problem of UVOS in a crowd-sourcing setting. We claim that one can achieve superior results by aggregating the predictions of multiple imperfect methods in a reasonable way. Specifically, we propose a latent regression algorithm for ensemble-based segmentation by jointly labelling pixels in a sequence and learning an adaptive weight for each single method in an ensemble. The pixel labellings offer the outcome (pseudo groundtruth) for regression and thus promote the procedure of weight learning, while the learnt weights could provide better shape priors for labelling, resulting in more accurate segmentation. Besides, Laplacian regularization is introduced into the regression to facilitate a stable learning of the weights. The most distinct feature of our algorithm is that it adaptively learns the contributions of different single methods for each test sequence, thus is capable of capturing the advantages of those methods while avoiding their weaknesses. In the experiments, our algorithm is built on 14 non-deep learning segmentation methods which are based on handcrafted features and require no training data. Experimental results on popular benchmarks show that our algorithm achieves compelling performance, even in comparison with deep learning-based methods. Furthermore, benefiting from the adaptive weight learning mechanism, our algorithm can achieve good flexibility and usability by choosing the most complementary single methods without losing too much performance.
Mobile Cloud Learning based on User Acceptance using DeLone and McLean Model for Higher Education

Mobile learning has been used in the learning process in several tertiary institutions in Indonesia.However, several universities have not been able to implement mobile learning due to the limitations of computer and network infrastructure.Cloud computing is a solution for agencies that experience limitations in computer infrastructure in the form of internet-based services for their customers.This paper discusses the implementation of mobile cloud learning which is a combination of mobile learning technology and cloud computing using the renewal DeLone and McLean Model which is a successful model to measure how important the implementation of a mobile cloud learning system is.The results showed that from the F test results obtained Fcount of 13,222, then Fcount> Ftable (13,222> 3.01), then Ho was rejected and H1 was accepted.So it can be concluded Information Quality, System Quality, and Service Quality together affect the Intensity of Use.
17 New Existences linear [n,3,d]19 Codes by Geometric Structure Method in PG(2,19)

82,3,74]19, [72,3,65]19, [54,3,48]19, [37,3,32]19, [26,3,22]19, [13,3,10]19 codes by geometric structure method in PG(2,19) .
A fault attack on the Niederreiter cryptosystem using binary irreducible Goppa codes

A fault injection framework for the decryption algorithm of the Niederreiter public-key cryptosystem using binary irreducible Goppa codes and classical decoding techniques is described. In particular, we obtain low-degree polynomial equations in parts of the secret key. For the resulting system of polynomial equations, we present an efficient solving strategy and show how to extend certain solutions to alternative secret keys. We also provide estimates for the expected number of required fault injections, apply the framework to state-of-the-art security levels, and propose countermeasures against this type of fault attack.
New Storytelling for Archaeological Museums Based on Augmented Reality Glasses

Museums are places where cultural heritage is preserved and, therefore, we can consider them an essential resource to understand our identity, past and future.In the last two decades, they have increased the use of information and communication technologies in a remarkable way with the intention of reaching new audiences and spreading knowledge.With the recent advent of augmented reality devices of the 'view-through' type, perceiving and interacting with virtual contents in the form of holograms anchored to the real physical space is now possible.One of the most interesting challenges is to leave the screen aside and interact with digital data in an intuitive way, through voice commands and gestures.This offers a new scenery for experimenting with storytelling creation, a current trend in archaeological museums.In our effort to take advantage of the new capabilities of augmented reality glasses, we have developed different applications with the Microsoft HoloLens How to cite this
CONTEXT ONTOLOGY IN MOBILE APPLICATIONS

Mobile applications are expected to receive context input Mobile applications are expected to receive context in put such as location, speech, and network from different context providers. Since context can be considered as knowledge, a formal method is needed to capture this knowledge. There isless work on ontology model that could be reused to model a new context ontology for Android mobile application. Therefore, this study proposed an ontology specifically for Android mobile application, COCCC, to formalize context knowledge present within it. METHONTOLOGY method was used to create COCCC ontology as it offers intermediate representation in the form of concepts. The concepts from the context ontology were extracted from various resources, sorted and categorized based on types and functions for standardization purposes. Survey was given to five domain experts for evaluation of COCCC ontology in terms of its usability. Data from these experts were analyzed and the results have confirmed that the proposed context ontology is usable to Android mobile application developers.
Formal Verification of Security Pattern Composition: Application to SCADA

Information security was initially required in specific applications, however, nowadays, most companies and even individuals are interested in securing their information assets. The new requirement can be costly, especially with the high demand on security solutions and security experts. Security patterns are reusable security solutions that prove to be efficient and can help developers achieve some security goals without the need for expertise in the security domain. Some security pattern combinations can be beneficial while others are inconsistent. Model checking can be used to verify the production of combining multiple security patterns with an architecture. Supervisory control and data acquisition (SCADA) systems control many of our critical industrial infrastructures. Due to their limitations, and their augmented connectivity, SCADA systems have many unresolved security issues. In this paper, we demonstrate how we can automatically generate a secure SCADA model based on an insecure one and how to verify the generated model.
The Review of Non-Technical Assumptions in Digital Identity Architectures

The literature on digital identity management systems (IdM) is abundant and solutions vary by technology components and non-technical requirements.In the long run, however, there is a need for exchanging identities across domains or even borders, which requires interoperable solutions and flexible architectures.This article aims to give an overview of the current research on digital identity management.We conduct a systematic literature review of digital identity solution architectures and extract their inherent non-technical assumptions.The findings show that solution designs can be based on organizational, business and trust assumptions as well as human-user assumptions.Namely, establishing the trust relationships and collaborations among participating organizations; human-users capability for maintaining private cryptographic material or the assumptions that win-win business models could be easily identified.By reviewing the key findings of solutions proposed and looking at the differences and commonalities of their technical, organizational and social requirements, we discuss their potential real-life inhibitors and identify opportunities for future research in IdM.
Influential Factor Analysis and Projection of Industrial CO&lt;sub&gt;2&lt;/sub&gt; Emissions in China Based on Extreme Learning Machine Improved by Genetic Algorithm

In China, CO 2 emissions from industrial sectors on a larger scale than other end-use sectors.In order to reduce CO 2 emissions, it is necessary to study the influencing factors and projection of industrial CO 2 emissions.Based on accounting for CO 2 emissions from the industrial sectors, this paper carries out bivariate correlation analysis and linear regression analysis on 15 preselected influencing factors and industrial CO 2 emissions, removing two factors that have failed the significance test.In order to obtain some potential commonalities among the influencing factors, the remaining 13 influencing factors are divided into four categories, and then factor analysis is performed on each category in order to obtain five latent factors.An extreme learning machine algorithm that uses genetic algorithms to optimize the input weights and bias thresholds -the genetic algorithm extreme learning machine (GA-ELM) algorithmto predict industrial CO 2 emissions, the empirical results show that the GA-ELM algorithm using five factors as inputs has a higher prediction accuracy and performance for industrial CO 2 emissions than the extreme learning machine, back propagation neural network, and back propagation neural network optimized by the genetic algorithm.It also shows that the five influencing factors have a significant impact on industrial CO 2 emissions.Finally, based on the analysis of five influencing factors, some policy recommendations are proposed for the CO 2 emissions reduction path in the industrial sectors.
Truth-Teller–Liar Puzzles with Self-Reference

In this paper, we use commonsense reasoning and graph representation to study logical puzzles with three types of people. Strong Truth-Tellers say only true atomic statements, Strong Liars say only false atomic statements, and Strong Crazy people say only self-contradicting statements. Self-contradicting statements are connected to the Liar paradox, i.e., no Truth-Teller or a Liar could say “I am a Liar”. A puzzle is clear if it only contains its given statements to solve it, and a puzzle is good if it has exactly one solution. It is known that there is no clear and good Strong Truth-Teller–Strong Liar (also called SS) puzzle. However, as we prove here, there are good and clear Strong Truth-Teller, Strong Liar and Strong Crazy puzzles (SSS-puzzles). The newly investigated type ‘Crazy’ drastically changes the scenario. Some properties of the new types of puzzles are analyzed, and some statistics are also given.
Prediction of Solar Radiation Based on Spatial and Temporal Embeddings for Solar Generation Forecast

A novel method for real-time solar generation forecast using weather data, while exploiting both spatial and temporal structural dependencies is proposed.The network observed over time is projected to a lower-dimensional representation where a variety of weather measurements are used to train a structured regression model while weather forecast is used at the inference stage.Experiments were conducted at 288 locations in the San Antonio, TX area on obtained from the National Solar Radiation Database.The model predicts solar irradiance with a good accuracy (R 2 0.91 for the summer, 0.85 for the winter, and 0.89 for the global model).The best accuracy was obtained by the Random Forest Regressor.Multiple experiments were conducted to characterize influence of missing data and different time horizons providing evidence that the new algorithm is robust for data missing not only completely at random but also when the mechanism is spatial, and temporal.
Topic Modeling and Transfer Learning for Automated Surveillance of Injury Reports in Consumer Product Reviews

Many modern firms and interest groups are tasked with the challenge of monitoring the status and performance of a bevy of distinct products.As online user-generated content has increased in volume, new unstructured data sources are available for mining unique insights.Reports of injuries arising as a result of product usage are particularly concerning.In this paper, we utilize complimentary approaches to address this problem.We analyze two novel datasets; first, a government-maintained dataset of hazard and injury reports and second, a large dataset of cross-industry consumer product reviews manually coded for the presence of hazard and injury reports.We apply an unsupervised topic modeling approach to characterize the hazard and injury reports detected.Then, we implement a supervised transfer learning technique, using information obtained from the governmentmaintained dataset to detect hazard and injury reports in online reviews.Our results offer improved surveillance for monitoring hazards across multiple industries.
Analyze Before You Sensitize: Preparation of a Targeted ISA Training

This paper describes a procedure to enable the planning of targeted measures to increase the Information Security Awareness (ISA) of employees of an institution.The procedure is practically applied at a German university.With the help of a comprehensive analysis, which is based on findings of social psychology, necessary topics for ISA measures are identified.In addition, reasons are sought for why employees do not conduct information security.The procedure consists of a qualitative phase with interviews and a quantitative phase with a questionnaire.It turned out that the procedure provided many clues to the design of ISA measures.These include organizational and technical measures that can help employees to ensure information-safe behavior.In addition, it was found that there were deviations between the qualitative and quantitative phases and therefore, both phases are necessary.The paper critically discusses the procedure and also addresses the strengths and weaknesses of the analysis.
Identifying Citation Sentiment and its Influence while Indexing Scientific Papers

Sentiment analysis has proven to be a popular research area for analyzing social media texts, newspaper articles, and product reviews.However, sentiment analysis of citation instances is a relatively unexplored area of research.For scientific papers, it is often assumed that the sentiment associated with citation instances is inherently positive.This assumption is due to the hedged nature of sentiment in citations, which is difficult to identify and classify.As a result, most of the existing indexes focus only on the frequency of citation.In this paper, we highlight the importance of considering sentiment of citation while preparing ranking indexes for scientific literature.We perform automatic sentiment classification of citation instances on the ACL Anthology collection of papers.Next, we use the sentiment score in addition to the frequency of citation to build a ranking index for this collection of scientific papers.By using various baselines, we highlight the impact of our index on the ACL Anthology collection of papers.Our research contributes toward building more sentiment sensitive ranking index which better underlines the influence and usefulness of research papers.
Measuring Cyber Security Awareness within Groups of Medical Professionals in Poland

The goal of this study is to measure the cyber security awareness of medical professionals in Poland, i.e. to verify whether healthcare specialists have knowledge and understanding of basic cyber security threats.This survey was based on the cyber security recommendations from the European Union Agency for Network and Information Security and the U.S. Department of Health and Human Services.The survey consisted of 23 single and multiple-choice questions divided into four parts.The results categorized the respondents and measured the level of cyber security awareness.Among the 620 persons invited to participate in the survey, 300 (48.39%) responded and answered all of the questions.The results show a an unsatisfactory level of knowledge regarding information security in Poland.The main conclusion drawn from the survey is that the quality of cyber security training among medical professionals should be improved and frequency of the trainings should be increased.
Reference-frame-independent measurement-device-independent quantum key distribution under reference frame fluctuation

Reference-frame-independent measurement-device-independent quantum key distribution is adopted to avoid aligning the reference frames in realistic setup, which can guarantee the system security against the slow drift of reference frame. However, the relative motion of reference frame including deviation and fluctuation can influence the performance of reference-frame-independent measurement-device-independent quantum key distribution in practical experimental demonstration. In this paper, taking finite effect into consideration, the performance of reference-frame-independent measurement-device-independent quantum key distribution with biased bases under reference frame deviation and fluctuation is presented to evaluate the effect of the relative motion of reference frame on our scheme, which makes the analysis conform to reality. Our simulation results imply that the key rates fluctuate periodically with the reference frame rotating, while declining with the reference frame fluctuation increasing.
Open Challenges for Crowd Density Estimation

Nowadays, many emergency systems and surveillance systems are related to the management of the crowd.The supervision of a crowded area presents a great challenge especially when the size of the crowd is unknown.This issue presents a point of start to the field of the estimation of the crowd based on density or counts.The density of a crowded area is one of the important topics dealt with in many kinds of applications like surveillance, security, biology, traffic.In this paper, we try not only to present a deep review of the different approaches/techniques used in the previous works to estimate the size of the crowd but also to describe the different datasets used.A comparison of some related works based on the weakness and the strength features of each approach is highlighted to show the important research key related to the field of the estimation of the crowded area.
Deep Unsupervised Progressive Learning for Distant Domain Adaptation

The superiority of deeply learned representation has been reported in very recent literature of re-identification (Re-ID) task. In this paper, we study a novel transfer learning problem termed Distant Domain Transfer Learning (DDTL) for Re-ID task. Different from existing transfer learning problems which assume that there is a close relation between source domain and target domain, in the DDTL problem, target domain can be totally different from source domain. For example, the source domain classifies pedestrian images but the target domain distinguishes vehicle images. In this work, our goal is to execute an unseen and unrelated task based on a labeled dataset training previously without any samples from intermediate domains. Particularly, we consider the more pragmatic issue of learning a deep feature with no labels, and propose a Deep Unsupervised Progressive Learning (DUPL) method to transfer pretrained deep representations to unseen domains. Specifically, our work performs clustering and fine-tuning of the CNN to improve the performance of original model trained on the irrelevant labeled dataset. Empirical studies on distant domain adaptation task (pedestrian -> vehicle) demonstrate the effectiveness of the proposed method, and the improvement in terms of the mAP accuracy is up to 15% over non-transfer methods.