Reviewerly/exHarmony huggingface dataset:
The Devil is in the Details: Evaluating Limitations of Transformer-based Methods for Granular Tasks

Contextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity in recent years.Extensive work shows how accurately such models can represent abstract, semantic information present in text.In this expository work, we explore a tangent direction and analyze such models' performance on tasks that require a more granular level of representation.We focus on the problem of textual similarity from two perspectives: matching documents on a granular level (requiring embeddings to capture fine-grained attributes in the text), and an abstract level (requiring embeddings to capture overall textual semantics).We empirically demonstrate, across two datasets from different domains, that despite high performance in abstract document matching as expected, contextual embeddings are consistently (and at times, vastly) outperformed by simple baselines like TF-IDF for more granular tasks.We then propose a simple but effective method to incorporate TF-IDF into models that use contextual embeddings, achieving relative improvements of up to 36% on granular tasks.
ForceReader: a BERT-based Interactive Machine Reading Comprehension Model with Attention Separation

The release of BERT revolutionized the development of NLP.Various BERT-based reading comprehension models have been proposed, thus updating the performance ranking of reading comprehension tasks.However, the above BERT-based models inherently employ BERT's combined input method, representing the input question and paragraph as a single packed sequence, without further modification for reading comprehension.This paper makes an in-depth analysis of this input method, proposes a problem of this approach.We call it attention deconcentration.Accordingly, this paper proposes ForceReader, a BERT-based interactive machine reading comprehension model.First, ForceReader proposes a novel solution called the Attention Separation Representation to respond to attention deconcentration.Moreover, starting from the logical nature of reading comprehension tasks, ForceReader adopts Multi-mode Reading, and Interactive Reasoning strategy.For the calculation of attention, ForceReader employs Conditional Background Attention to solve the lack of the overall context semantic after the separation of attention.As an integral model, ForceReader shows a significant improvement in reading comprehension tasks compared to BERT.Moreover, this paper makes detailed visual analyses of the attention and propose strategies accordingly.
A Meta-Analysis of Ontological Guidance and Users' Understanding of Conceptual Models

Information systems are intended to be faithful accounts of real-world applications. As an integral part of the development process, analysts create conceptual models in order to understand the application and communicate requirements. Failure to do so has been a prominent reason for IT projects' failure. Hence, improving the quality of models could have a major impact on the information systems' success. To guide the modeling process, researchers use ontology to create more expressive representations of reality. However, improving expressiveness can make the models complicated and cause cognitive hurdles for users. Therefore, the question is whether ontological guidance is worth the trade-off between expressiveness and complexity. This paper describes a meta-analysis of empirical research examining the impact of ontological guidance on users' understandability. The results show that ontological guidance can improve users' understanding of conceptual models, especially those requiring deeper understanding, thus providing support for ontological guidance in conceptual modeling.
Sistem Informasi Pemasaran dengan Penerapan CRM (Customer Relationship Management) Berbasis Website menggunakan Metode Waterfall dan Agile

Bella Frame ART shop is a business shop that is engaged in selling paintings, calligraphy, and frames. Until now, the promotion method still uses brochures, posters, and pamphlets, so shop owners need a lot of money to convey information to a broad target market. Storing the results of feedback, or messages from customers is also needed, in order to assist the store in making decisions in product marketing and customer service in the future. With the application of CRM in the website-based marketing information system at the Bella Frame ART Shop, data storage can be stored neatly and in detail, and delivery of product information to customers can be done more easily and quickly and is expected to help the store make decisions in marketing products and customer service for the future. In this study, the authors used application development methods, namely the waterfall and agile methods. Web-based information systems with the proposed Customer Relationship Management (CRM) method are systems that meet the needs and have the advantage of being integrated with systems related to marketing information systems. As well as using the waterfall method can help writers in the first stage of application development because testing is carried out after the application has been run while the agile method is used at a later stage so that it can cover deficiencies in the first stage of application development, namely requests for changing needs so that applications can continue to be developed despite requests needs change and application testing can continue at any time so that errors or bugs can be found more easily.Keywords:Information Systems, Customer Relationship Management, Website, Waterfall, Agile.
RAILS: A Robust Adversarial Immune-Inspired Learning System

Adversarial attacks against deep neural networks (DNNs) are continuously evolving, requiring increasingly powerful defense strategies. We develop a novel adversarial defense framework inspired by the adaptive immune system: the Robust Adversarial Immune-inspired Learning System (RAILS). Initializing a population of exemplars that is balanced across classes, RAILS starts from a uniform label distribution that encourages diversity and uses an evolutionary optimization process to adaptively adjust the predictive label distribution in a manner that emulates the way the natural immune system recognizes novel pathogens. RAILS’ evolutionary optimization process explicitly captures the tradeoff between robustness (diversity) and accuracy (specificity) of the network, and represents a new immune-inspired perspective on adversarial learning. The benefits of RAILS are empirically demonstrated under eight types of adversarial attacks on a DNN adversarial image classifier for several benchmark datasets, including: MNIST; SVHN; CIFAR-10; and CIFAR-10. We find that PGD is the most damaging attack strategy and that for this attack RAILS is significantly more robust than other methods, achieving improvements in adversarial robustness by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\geq 5.62\%, 12.5\%$ </tex-math></inline-formula> , 10.32%, and 8.39%, on these respective datasets, without appreciable loss of classification accuracy. Codes for the results in this paper are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wangren09/RAILS</uri> .
Geophysical Survey of the Hillfort Staré Zámky near Brno-Líšeň, Czech Republic

The aim of this paper is to present the results of geophysical surveys at the Staré zámky site near Brno-Líšeň, which were carried out in 2019. Electrical resistivity tomography, georadar survey and large-scale magnetic prospection were all carried out there. The primary task of the first two methods was to investigate the inner structure of the still-existing ramparts and to identify their individual structural elements. The magnetic survey focused on the identification of areas where potential archaeological features can be found – together with the identification of previously-unknown fortifications. The surveys were successful: we have found numerous settlement features from different phases of settlement, an early medieval cemetery and fortifications of various types, sizes and state of preservation. The results of previous archaeological research of the site played an important role for interpretation of the geophysical data. Together these results provide important insights into the study of the complex fortified settlement of Staré zámky. Despite our results, some questions which cannot be answered by geophysical research alone remain unanswered.
Field hospitals to face COVID-19: Requirements and lessons learned in the design and construction of the Lagoa Barra Hospital - Brazil

The construction of field hospitals has been a strategy adopted worldwide to face the consequences of the pandemic impacts caused by COVID-19 on healthcare systems. Specific characteristics of this pandemic, such as different ways and speeds of the disease transmission, and the implications at the population health and the productive system, has made the project, management, and construction of field hospitals for the patients with COVID-19 present specific features. In this work, for each phase of the FHLB implantation, we present the main concepts, premises, restrictions, and challenges, focusing mainly on the needs programs of the project that guided the configuration of the environments, the definition of the circulation flows, the typology of the beds, the main management tools used during the project's planning and control process, and in the lessons learned.
PRHLT-UPV at SemEval-2020 Task 8: Study of Multimodal Techniques for Memes Analysis

This paper describes the system submitted by the PRHLT-UPV team for the task 8 of SemEval-2020: Memotion Analysis.We propose a multimodal model that combines pretrained models of the BERT and VGG architectures.The BERT model is used to process the textual information and VGG the images.The multimodal model is used to classify memes according to the presence of offensive, sarcastic, humorous and motivating content.Also, a sentiment analysis of memes is carried out with the proposed model.In the experiments, the model is compared with other approaches to analyze the relevance of the multimodal model.The results show encouraging performances on the final leaderboard of the competition, reaching good positions in the ranking of systems.
Mediating Quizzes as Assessment Tool through WhatsApp Auto-response in ELT Online Class

Although extensive studies have been conducted on the use of WhatsApp in language teaching and learning, little empirical research has been conducted to explore the use of WhatsApp modified with artificial intelligence called auto-responder as a media for assessment. To fill this void, this research aims to explore the use of WhatsApp Auto-response developed by the teacher to mediate quizzes as the assessment tool in an online English class. This research was carried out in a Vocational High School in Banyumas Regency, Indonesia, with an English teacher and his 36 students. The researchers used documentation, structured interviews, and open-ended questionnaires to collect the data. The researchers found that modifying WhatsApp with auto-responder made the online assessment more objective, accountable, transparent, easy-to-access, and easy-to-operate for both teacher and students. WhatsApp Auto-response could encourage students’ autonomous learning because they could directly know their scores and the discussion of each question. Thus, students had more interest and motivation to get a higher score. However, unstable internet connection and slow response from the application should be resolved to make it better for learning. The findings imply that supporting teaching with the technological advancements of artificial intelligence in WhatsApp can provide students with realistic self-guided learning. With WhatsApp's popularity as a learning tool nowadays, its features can be embedded with chatbots that can give many advantages as a teacher assistant, especially for facilitating assessments.
New Quantum Codes Constructed by Quantum Caps in PG(3,9) and PG(4,9)

In this paper, we present a computer-supported method of searching for quantum caps. By means of this method and relevant knowledge of combinatorics, many quantum caps in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PG(3,9)$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$PG(4,9)$ </tex-math></inline-formula> are constructively proven to exist. Then, according to the theorem that each quantum cap corresponds to a quantum error-correcting code with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$d=4$ </tex-math></inline-formula> , we obtain 278 quantum error-correcting codes. Most of these results break the GV bound, and a number of them are optimal quantum codes or have improved parameters.
Bracketing Encodings for 2-Planar Dependency Parsing

We present a bracketing-based encoding that can be used to represent any 2-planar dependency tree over a sentence of length n as a sequence of n labels, hence providing almost total coverage of crossing arcs in sequence labeling parsing.First, we show that existing bracketing encodings for parsing as labeling can only handle a very mild extension of projective trees.Second, we overcome this limitation by taking into account the well-known property of 2-planarity, which is present in the vast majority of dependency syntactic structures in treebanks, i.e., the arcs of a dependency tree can be split into two planes such that arcs in a given plane do not cross.We take advantage of this property to design a method that balances the brackets and that encodes the arcs belonging to each of those planes, allowing for almost unrestricted non-projectivity (∼ 99.9% coverage) in sequence labeling parsing.The experiments show that our linearizations improve over the accuracy of the original bracketing encoding in highly non-projective treebanks (on average by 0.4 LAS), while achieving a similar speed.Also, they are especially suitable when PoS tags are not used as input parameters to the models.
Masked Reasoner at SemEval-2020 Task 4: Fine-Tuning RoBERTa for Commonsense Reasoning

This paper describes the masked reasoner system that participated in SemEval-2020 Task 4: Commonsense Validation and Explanation.The system participated in the subtask B. We propose a novel method to fine-tune RoBERTa by masking the most important word in the statement.We believe that the confidence of the system in recovering that word is positively correlated to the score the masked language model assigns to the current statement-explanation pair.We evaluate the importance of each word using InferSent and do the masked fine-tuning on RoBERTa.Then we use the fine-tuned model to predict the most plausible explanation.Our system is fast in training and achieved 73.5% accuracy.
KEY FACTORS AFFECTING UNEMPLOYMENT IN THE ARAB WORLD

This paper aims to study the factors that determine unemployment in the Arab world. The study utilizes the panel regression method for the time series period from 2000 to 2016. The study tested the impact of many variables on unemployment such as macroeconomics variables, educational variables, labour market variables and besides studying the impact of economic freedom and the financial crisis of 2008. The results show that economic freedom has negative and significant relationships with total unemployment, the male and female unemployment as well. The impact of 2008 financial crisis on total unemployment appeared to have no significant impact on total unemployment.
Survey Opinion using Sentiment Analysis

Sentiment analysis or opinion mining is a computational study of the opinions, judgments, attitudes, and emotions of a person towards an entity, individual, issue, event, topic, and attributes. This task is very challenging technically but very useful in practice. For example, a business always wants to seek opinion about its products and services from the public or the consumers. Additionally, potential consumers want to learn what users think they have when using a service or purchasing a product. To get public opinion on food habits, ad strategies, political trends, social issues and business policy, this is a very critical factor. This paper will explain a survey of key sentiment-extraction approaches.
Attention Word Embedding

Word embedding models learn semantically rich vector representations of words and are widely used to initialize natural processing language (NLP) models.The popular continuous bag-ofwords (CBOW) model of word2vec learns a vector embedding by masking a given word in a sentence and then using the other words as a context to predict it.A limitation of CBOW is that it equally weights the context words when making a prediction, which is inefficient, since some words have higher predictive value than others.We tackle this inefficiency by introducing the Attention Word Embedding (AWE) model, which integrates the attention mechanism into the CBOW model.We also propose AWE-S, which incorporates subword information.We demonstrate that AWE and AWE-S outperform the state-of-the-art word embedding models both on a variety of word similarity datasets and when used for initialization of NLP models.
Hierarchical Group-key Management for NoC-Based MPSoCs Protection

Group keys can be used in order to communicate secretly sensitive data among IP cores. However, the flexibility and dynamic nature of MPSoCs force reshaping the security zones at runtime. Members of a zone must be able to efficiently compute the new group key while former members must be prevented for data disclosure. Efficiently creating security zones for achieving sensitive traffic isolation in MPSoC environments is a challenging problem. In this work we present the implementation of hierarchical group-key management for NoC-based systems in order to efficiently perform the rekeying process. We implement three hierarchical protocols and we show that by decentralizing the security management of the rekeying process, it is possible to achieve an improvement of the performance when compared to the previous flat approaches.
Adaptive Multipoint Calibration Non-uniformity Correction Algorithm

The multipoint calibration method provides high calibration accuracy.However, a reasonable number of calibration points are required in order to achieve the theoretical correction effect.This work proposes an algorithm for selecting multipoint legal punctuation.The algorithm uses the residual as the criterion for selecting calibration points and adaptively determines the calibration points on the focal plane response curve.The non-uniformity of the response rate of the original image collected by the detector and corrected by the proposed algorithm is 0.31%, which is about 31% less than that of the traditional uniform segmented multipoint method.In addition, the correction accuracy of the multipoint method is significantly improved.
5G Technology: A New Step to IoT Platform

The article is dedicated to the study of 5G technology, the name of which we have heard a lot recently. This technology is one of the most talked about technologies of recent times. 5G has entered our lives as the latest and fastest version of mobile networks. Here's a look at how 5G technology came into our lives, how it has captured the attention of users, and finally, what they think about the technology. In addition, what areas are affected by this technology, in particular, its impact on the IoT field has been studied and key theoretical information has been provided.
Cross-lingual Transfer Learning for Grammatical Error Correction

In this study, we explore cross-lingual transfer learning in grammatical error correction (GEC) tasks.Many languages lack the resources required to train GEC models.Cross-lingual transfer learning from high-resource languages (the source models) is effective for training models of low-resource languages (the target models) for various tasks.However, in GEC tasks, the possibility of transferring grammatical knowledge (e.g., grammatical functions) across languages is not evident.Therefore, we investigate cross-lingual transfer learning methods for GEC.Our results demonstrate that transfer learning from other languages can improve the accuracy of GEC.We also demonstrate that proximity to source languages has a significant impact on the accuracy of correcting certain types of errors.
Towards automatically generating Questions under Discussion to link information and discourse structure

Questions under Discussion (QUD; Roberts, 2012) are emerging as a conceptually fruitful approach to spelling out the connection between the information structure of a sentence and the nature of the discourse in which the sentence can function.To make this approach useful for analyzing authentic data, Riester, Brunetti & De Kuthy (2018) presented a discourse annotation framework based on explicit pragmatic principles for determining a QUD for every assertion in a text.De Kuthy et al. (2018) demonstrate that this supports more reliable discourse structure annotation, and Ziai and Meurers (2018) show that based on explicit questions, automatic focus annotation becomes feasible.But both approaches are based on manually specified questions.In this paper, we present an automatic question generation approach to partially automate QUD annotation by generating all potentially relevant questions for a given sentence.While transformation rules can concisely capture the typical question formation process, a rule-based approach is not sufficiently robust for authentic data.We therefore employ the transformation rules to generate a large set of sentence-question-answer triples and train a neural question generation model on them to obtain both systematic question type coverage and robustness.
Models and scenarios of implementation of threats for internet resources

To facilitate the detection of various vulnerabilities, there are many different tools (scanners) that can help analyze the security of web applications and facilitate the development of their protection. But these tools for the most part can only identify problems, and they are not capable of fixing them. Therefore, the knowledge of the security developer is a key factor in building a secure Web resource. To resolve application security problems, developers must know all the ways and vectors of various attacks in order to be able to develop various protection mechanisms. This review discusses two of the most dangerous vulnerabilities in the field of Web technologies: SQL injections and XSS attacks (cross-site scripting – XSS), as well as specific cases and examples of their application, as well as various approaches to identifying vulnerabilities in applications and threat prevention. Cross-site scripting as well as SQL-injection attacks are related to validating input data. The mechanisms of these attacks are very similar, but in the XSS attacks the user is the victim, and in the SQL injection attacks, the database server of the Web application. In XSS attacks, malicious content is delivered to users by means of a client-side programming language such as JavaScript, while using SQL injection, the SQL database query language is used. At the same time, XSS attacks, unlike SQL injections, harm only the client side leaving the application server operational. Developers should develop security for both server components and the client part of the web application.