Reviewerly/exHarmony huggingface dataset:
Archiving and Document Management at Taibah University: A Case Study

Archiving and digital document management in educational institutions are not amply studied despite the active use of technologies for these purposes. In this research, a qualitative descriptive case study has been conducted to examine how digital archiving and document management are organised at Taibah University to address the needs of researchers, professors, administrators and students as well as to guarantee secure storage of and access to data. The selection of the case study design has allowed for in-depth analyses of the strategy for managing and archiving data in the selected institution. The interpretation of data with reference to the previously formulated propositions have indicated that Taibah University follows a certain strategy for managing electronic documents, but they do not use a unique data management system. Furthermore, archiving is applied only to a limited number of documents and security of stored data is guaranteed according to the University&amp;rsquo;s policies.
REKER: Relation Extraction with Knowledge of Entity and Relation

Relation Extraction (RE) is an important task to mine knowledge from massive text corpus. Existing relation extraction methods usually purely rely on the textual information of sentences to predict the relations between entities. The useful knowledge of entity and relation is not fully exploited. In fact, off-the-shelf knowledge bases can provide rich information of entities and relations, such as the concepts of entities and the semantic descriptions of relations, which have the potential to enhance the performance of relation extraction. In this paper, we propose a neural relation extraction approach with the knowledge of entity and relation (REKER) which can incorporate the useful knowledge of entity and relation into relation extraction. Specifically, we propose to learn the concept embeddings of entities and use them to enhance the representation of sentences. In addition, instead of treating relation labels as meaningless one-hot vectors, we propose to learn the semantic embeddings of relations from the textual descriptions of relations and apply them to regularize the learning of relation classification model in our neural relation extraction approach. Extensive experiments are conducted and the results validate that our approach can effectively improve the performance of relation extraction and outperform many competitive baseline methods.
Intelligent Neural Network Schemes for Multi-Class Classification

Multi-class classification is a very important technique in engineering applications, e.g., mechanical systems, mechanics and design innovations, applied materials in nanotechnologies, etc. A large amount of research is done for single-label classification where objects are associated with a single category. However, in many application domains, an object can belong to two or more categories, and multi-label classification is needed. Traditionally, statistical methods were used; recently, machine learning techniques, in particular neural networks, have been proposed to solve the multi-class classification problem. In this paper, we develop radial basis function (RBF)-based neural network schemes for single-label and multi-label classification, respectively. The number of hidden nodes and the parameters involved with the basis functions are determined automatically by applying an iterative self-constructing clustering algorithm to the given training dataset, and biases and weights are derived optimally by least squares. Dimensionality reduction techniques are adopted and integrated to help reduce the overfitting problem associated with the RBF networks. Experimental results from benchmark datasets are presented to show the effectiveness of the proposed schemes.
Digital Forensics System Using PLC for Inter-Floor Noise Measurement: Detailing PLC-Based Android Solution Replacing CCTV-based Solution

The neighborly dispute arising from the inter-floor noises has been increasing for the past two decades in Korean apartments or multi-unit houses sometimes leading to a serious consequence. Although there have been some attempts to resolve such a dispute, one of the underlying problems has to be solved first. That is, in addition to identifying the cause for the noise, it is necessary to prove who has actually suffered from the noise itself. Now that many of such a dispute is being settled at the civil court, producing objective evidence has become important. Therefore, digital forensics which is being widely used at the crime scenes by the police or other anti-crime organizations to collect evidences scientifically has begun to receive attention for the purpose of measuring noise levels. This technique has evolved in recent years following the rapid development in IT and ICT technologies but its problem is that such a technique has to be performed by the experts due to its complicated system so that those who need to measure the noise level usually outsource this work spending quite a sum. Thus, this study introduces a digital forensic system design which allows the user to effectively measure the inter-floor or neighborly noises directly and conveniently without much cost. The relevant system elements have been implemented with Java Android.
APLIKASI EVALUASI KERUSAKAN JEMBATAN BETON BERBASIS SISTEM PAKAR DENGAN TEKNOLOGI ANDROID

&lt;p class="Abstract"&gt;&lt;em&gt;Bridges are a part of transportation infrastructure that has a vital role in connecting separated places separated by natural geographical conditions. Periodical evaluation/investigation the existing condition of the bridge is very necessary to be done because the load passing over the bridge today tends to increase due to community activities. Expert system is one of the artificial intelligence technologies that aims to help the user in solving problems that require expert expertise in the field in accordance with the problem at hand. By entering data into the system, the user will get a solution related to the problem at hand. The study discusses the use of Android-based smartphone technology to implement expert systems in applications aimed at evaluating the extent of damage to concrete bridges. This research was conducted by making an expert system model which is implemented into an application using android technology (Android Studio). The resulting Android application is used to capture visual damage data of the concrete bridge. This application offers convenience and flexibility in its application because its mobility&lt;/em&gt;&lt;em&gt;.&lt;/em&gt;&lt;/p&gt;
Superintelligence Safety: A Requirements Engineering Perspective

Under the headline “AI safety”, a wide-reaching issue is being discussed, whether in the future some “superhuman artificial intelligence” / “superintelligence” could pose a threat to humanity. In addition, the late Steven Hawking warned that the rise of robots may be disastrous for mankind. A major concern is that even benevolent superhuman artificial intelligence (AI) may become seriously harmful if its given goals are not exactly aligned with ours, or if we cannot specify precisely its objective function. Metaphorically, this is compared to king Midas in Greek mythology, who expressed the wish that everything he touched should turn to gold, but obviously this wish was not specified precisely enough. In our view, this sounds like requirements problems and the challenge of their precise formulation. (To our best knowledge, this has not been pointed out yet.) As usual in requirements engineering (RE), ambiguity or incompleteness may cause problems. In addition, the overall issue calls for a major RE endeavor, figuring out the wishes and the needs with regard to a superintelligence, which will in our opinion most likely be a very complex software-intensive system based on AI. This may even entail theoretically defining an extended requirements problem.
Estimation of global statistical significance of a new signal within the GooFit framework on GPUs

GPUs represent one of the most sophisticated and versatile parallel computing architectures that have recently entered in the HEP field. GooFit is an open source tool interfacing ROOT/RooFit to the CUDA platform that allows to manipulate probability density functions and perform fitting tasks. The computing capabilities of GPUs with respect to traditional CPU cores have been explored with a high-statistics pseudo-experiment method implemented in GooFit with the purpose of estimating the local statistical significance of an already known signal. The striking performance obtained by using GooFit on GPUs has been discussed in the previous edition (XII) of this conference. This method has been extended to situations when, dealing with an unexpected new signal, a global significance must be estimated. The LEE is taken into account by means of a scanning/clustering technique in order to consider, within the same background only fluctuation and anywhere in the relevant mass spectrum, any fluctuating peaking behaviour with respect to the background model. The presented results clearly indicate that the systematic uncertainty associated to the method is negligible and that the p-value estimation is not affected by the clustering configuration. A comparison with the evaluation of the global significance provided by the method of trial factors is also provided.
Distinctive Lexical Patterns in Russian Patient Information Leaflets: A Corpus-Driven Study

This methodologically-oriented corpus-driven study focuses on distinctive patterns of language use in a specialized text type, namely Russian patient information leaflets. The study’s main goal is to identify keywords and recurrent sequences of words that account for the leaflets’ formulaicity, and - as a secondary goal - to describe their discoursal functions. The keywords were identified using three methods (G2, Hedges’ g and Neozeta) and the overlap between the three metrics was explored. The overlapping keywords were qualitatively analyzed in terms of discoursal functions. As for the distinctive multi-word patterns, we focused on recurrent n-grams with the largest coverage in the corpus: these were identified using the Formulex method (Forsyth, 2015b), which provides complementary data with respect to more conservative n- gram and lexical bundles approaches. The results revealed that the most distinctive keywords were identified using Hedges’ g metric, that the largest overlap occurred between G2 and Neozeta metrics, and that the frequent use and discoursal functions of the identified lexical patterns correspond with situational contexts and communicative purposes of patient information leaflets. It is hoped that this study will provide an opportunity for a methodological reflection and inspire further corpus-driven research on distinctive recurrent lexical patterns (e.g., keywords, n-grams, lexical bundles) or - more generally - on formulaic language in texts originally written in Russian.
Survey of Flight Anomaly Detection Methods: Challenges and Opportunities

Safety enhancement is a major goal of the aviation industry owing to the predicted increase in air travel. There is also the need to prevent fatalities, increase reliability and reduce monetary costs suffered as a result of delays and accidents that still occur. Accidents today are complex as a result of many causal factors acting alone but more often as a combination with other contributing factors. In tackling this trend, proactive measures have been put in place to find hazardous combinations that occur during flights in order to mitigate them before accidents occur. Flight Anomaly Detection (AD) methods are aimed at highlighting abnormal occurrences of a flight, that are different from the norm. As an improvement on the current state-of-the-art method, previous works have proposed different AD techniques for detection of previously unknown flight risks such as component faults, aircraft operational inefficiencies and some abnormal crew behaviour. However, current AD methods individually have limitations that prevent them from detecting certain significant anomalies in flight data. This paper surveys current flight AD approaches, their strengths and limitations as well as brings to light the benefits of a hybrid AD method to extend previous work and find safety-critical events, particularly those related to abnormal crew activity: a class of events known to amount for a substantial number of accidents/incidents today. It also highlights another emerging AD application opportunity, its challenges and how AD is beneficial in addressing them.
An Investigation of Negative Reviews on Tripadvisor about the Seafood Restaurants in Kyrenia

In this research, negative reviews written on tripadvisor.comabout the seafood restaurants in Kyrenia were examined.The average of the ratings of the restaurants is 4.14 on a 5 point scale.This average indicates high satisfaction.Customers wrote 938 reviews about the restaurants.Of the customers who wrote these reviews, 62% rated their experience as excellent, 24% as very good 7% as average, 3% as poor, and 4% as terrible.These ratings also indicate high satisfaction.Of all the reviews, 137 are negative.Of the customers who wrote these negative reviews, 3% rated their experience as excellent, 6% as very good, 45% as average, 18% as poor, and 29% as terrible.Of these negative reviews, 30% are about the dishes, 23% about the staff, 19% about the prices, 16% about the service, 6% about the service environment, and 6% about the menus.
Fraud Management among Small and Medium Enterprises in Lagos, Nigeria

Fraud is a debilitating factor that affects business growth and a major problem for many organizations especially SMEs since they have smaller resources to both prevent and recover from a fraud.SMEs have fewer anti-fraud controls hence they are more vulnerable to fraud.In a bid to reduce fraud and manage the risks proactively, SMEs needs to identify factors leading to fraudulent behavior.The paper seeks to evaluate fraud management among SMEs.Data was collected from two hundred (200) SME owners using questionnaire, however only 197 questionnaires were viable.The questionnaire was formulated using a five-point Likert scale.It was observed that the major causes of fraud are the weakling of societal values and financial pressure while changes in lifestyles indicate that fraud is being perpetuated.The study concluded that for effective fraud management, SMEs should implement fraud prevention measures such as strengthening societal values (integrity) building strong internal control measures to block the loopholes within the system, training programmes for SMEs operators and stringent disciplinary actions against erring officers.
A New Kind of High Capacity and Security Reversible Data Hiding Scheme

A novel high capacity and security reversible data hiding scheme is proposed in this article, in which the secret data is represented by different orthogonal spreading sequences and repeatedly embedded into the cover image without disturbing each other in the light of Code Division Multiple Access (CDMA) technique, and thus the embedding capacity is enlarged. As most elements of orthogonal spreading sequences are mutually canceled in the process of repeated embedding, it keeps the distortion of the embedded image at a low level even with high embedding capacity. Moreover, only the receiver who has the spreading sequence and the embedding gain factor the same as the sender can extract the secret data and achieve the original image exactly, thus the proposed scheme achieves high embedding security than other schemes. The results of the experiment demonstrates that the CDMA based reversible data hiding scheme could achieve higher image quality at moderate-to-high embedding capacity compared with other state-of-the-art schemes.
Utilization of Computer Aided Design Software as a Visual Simulation

Software is known as one of the components found on a computer. As a software, computer-aided design can be used as a tool to realize ideas. In the process, to be able to realize the idea, it is necessary to have a visual simulation as an aid to be able to see conditions that are able to represent the results of the ideas made. By using qualitative descriptive methods. this research will be useful to be able to provide information to users of computer-aided design software as a basis for using software to produce visual simulations Computer-aided design software in its development can help users to be able to see the condition of a design in a form that represents the original shape. and also can minimize the use of real prototypes to deliver design results. Keywords: Devices, Software, computer-aided design, Simulation, Visual.
eTextiles: reviewing a practice through its tool/kits.

Electronic textiles, or eTextiles, and connected research and practice communities grew within and across diverse disciplines over the past 20 years. Initially evolving from academic investigations, eTextiles now play a growing role in both industry and education alike. While we are increasingly confronted with resulting eTextile artefacts, we lack a thorough understanding of the underlying making practices, and in particular what role toolkits play in framing, promoting and supporting creation practices. It is timely then to undertake a review of currently available eTextile tools and kits, discussing the technical, cultural and social expectations inscribed in these settings and how their design and technology impacts the emerging field of eTextiles. Here we compile the first overview of both academic research and popular available toolkits, as a basis for an analysis of potential strategies for future directions: how to diversify and professionalise the field and its community of practice.
An Analysis of Students’ Reading Comprehension in Narrative Text at Second Grade At SMAN 1 Talamau

This research was conducted based on the problems found during the teaching and learning process in class IX IPS 1 of SMAN 1 Talamau, specifically about students' understanding of narrative text. The problem found is that students cannot determine the main ideas and topics in narrative text, understand the meaning of words, understand implied information, and understand the general structure and features of language in narrative texts.This research is included in quantitative descriptive research. The population in this study were students of class IX SMAN 1 Talamau, amounting to 159 people, divided into 4 classes. While this sample is class X8 of SMAN 8 Padang. The instrument used was an objective test. Data from students' objective tests are analyzed based on indicators and then interpreted and become research findings.Based on the results of the study showed that students of class IX IPS 1 SMAN 1 Talamau: 1. The ability to write students in determining the main ideas and topics in the narrative text showed at the intermediate level of 14 people out of 23 students (60,875%). 2. The ability to understand the meaning of words in the narrative text shows at the intermediate level that is 15 people out of 23 students (65.22%). 3. The ability to understand the information implicit in the narrative text shows that at the intermediate level, there are 14 out of 23 students (60.87%). 4. The ability to understand the general structure and language features of narrative texts shows that at the intermediate level, there are 16 students out of 23 students (69.57).From the results of the above research, it can be concluded that students still experience many difficulties in understanding a text, especially in narrative texts in accordance with applicable rules. Furthermore, based on the results of this study teachers are expected to be able to provide better teaching techniques and students can do more exercises to improve students' reading comprehension abilities.
Learning Domain Invariant Word Representations for Parsing Domain Adaptation

We show that strong domain adaptation results for dependency parsing can be achieved using a conceptually simple method that learns domain-invariant word representations. Lacking labeled resources, dependency parsing for low-resource domains has been a challenging task. Existing work considers adapting a model trained on a resource-rich domain to low-resource domains. A mainstream solution is to find a set of shared features across domains. For neural network models, word embeddings are a fundamental set of initial features. However, little work has been done investigating this simple aspect. We propose to learn domain-invariant word representations by fine-tuning pretrained word representations adversarially. Our parser achieves error reductions of 5.6% UAS, 7.9% LAS on PTB respectively, and 4.2% UAS, 3.2% LAS on Genia respectively, showing the effectiveness of domain invariant word representations for alleviating lexical bias between source and target data.
Automatic Kidney and Tumor Segmentation with Hybrid Hierarchical Networks

Automatic segmentation of kidney and its tumors is an essential but challenging step for extracting quantitative imaging biomarkers for accurate tumor detection, diagnosis, prognosis and treatment assessment.Kidney Tumor Segmentation Challenge (KiTS) provides a common platform for comparing different automatic algorithms on abdominal CT images in tasks of 1) kidney segmentation and 2) kidney tumor segmentation .We participate this challenge by developing a fully automatic framework based on deep neural networks.By observing that clinicians usually contour organs and tumors in the axial view while evaluating the contours in 3D space, we adopt a 3-step hierarchical structure with hybrid 2D and 3D models.In the first step, a simple 2D U-Net model is trained to obtain a quick but coarse segmentation of the kidney region on the entire 3D CT volume; then another 2D U-Net using residual blocks with channel-wise attention is applied to each kidney region for kidney and tumor segmentation.At last, the segmented tumor is refined by a 3D model for final tumor segmentation.Our framework was trained using the 210 challenge training cases provided by KiTS.By 5-fold evaluation, our method achieved an average Dice Similarity Coefficient (DSC) of 0.970 on kidneys and 0.756 on kidney tumors, respectively.
Psychological And Pedagogical Education Of Parents In The Educational Institution

The process of reformation from the industrial to the post-industrial information society in Russia causes the growing challenges to the system of human socialization. To create parental education system becomes one of the actual and demanded problems. This system would help a person to be capable to build up his own strategy in the question of bringing up children through clear understanding the goals of education, ways of their implementation, and consequences of all the decisions which have been made. One of the important trend in the process of parental education is the educational activities with younger generation. The key side of the state family policy in the Russian Federation is the assertion of the family and family lifestyle value, the revival and preservation of spiritual and moral traditions in family relations, family education and the formation of responsible parenthood of citizens. The concept of modernization in Russian education emphasizes the exceptional role of the family in solving the problems of upbringing. At the same time, the successful solution of many emerging problems is possible only under the coordinated interaction of the family with institutions of additional education, schools and society. Therefore, the fruitfulness and effectiveness of the educational process directly depends on the consistency of goals and pedagogical efforts both family and teachers. The presented article reflects the theoretical aspects of the problem and analysis of the implementation of the tasks considering psychological and pedagogical education of parents on the example of educational organizations in the Moscow region.