Reviewerly/exHarmony huggingface dataset:
NLP at SemEval-2019 Task 6: Detecting Offensive language using Neural Networks

In this paper we built several deep learning architectures to participate in shared task Of-fensEval: Identifying and categorizing Offensive language in Social media by semEval-2019(Zampieri et al., 2019b).The dataset was annotated with three level annotation schemes and task was to detect between offensive and not offensive, categorization and target identification in offensive contents.Deep learning models with POS information as feature were also leveraged for classification.The three best models that performed best on individual sub tasks are stacking of CNN-Bi-LSTM with Attention, BiLSTM with POS information added with word features and Bi-LSTM for third task.Our models achieved a Macro F1 score of 0.7594, 0.5378 and 0.4588 in Task(A,B,C) respectively with rank of 33 rd , 54 th and 52 nd out of 103, 75 and 65 submissions.
Machine Learning Blunts the Needle of Advanced SQL Injections

SQL injection is one of the most popular and serious information security threats. By exploiting database vulnerabilities, attackers may get access to sensitive data or enable compromised computers to conduct further network attacks. Our research is focused on applying machine learning approaches for identication of injection characteristics in the HTTP query string. We compare results from Rule-based Intrusion Detection System, Support Vector Machines, Multilayer Perceptron, Neural Network with Dropout layers, and Deep Sequential Models (Long Short-Term Memory, and Gated Recurrent Units) using multiple string analysis, bag-of-word techniques, and word embedding for query string vectorization. Results proved benets of applying machine learning approach for detection malicious pattern in HTTP query string.
HATERecognizer at SemEval-2019 Task 5: Using Features and Neural Networks to Face Hate Recognition

This paper presents a detailed description of our participation in task 5 on SemEval-2019 1 .This task consists of classifying English and Spanish tweets that contain hate towards women or immigrants.We carried out several experiments; for a finer-grained study of the task, we analyzed different features and designing architectures of neural networks.Additionally, to face the lack of hate content in tweets, we include data augmentation as a technique to increase hate content in our datasets.
Content-Based Image Retrieval Based on Texture and Color Combinations Using Tamura Texture Features and Gabor Texture Methods

As the development of data storage technology from various sources of information then increasingly cause problems in the search and processing, of course with the existence of this problem will be feared can cause big losses.Various existing search techniques have not been able to provide clear results between query testing and training.To overcome this problem required a Content-Based Image Retrieval (CBIR) approach which is a technique for content-based image search.In this study using texture and color information from image training to present the results both in query testing and database training using Tamura texture method features Gabor texture features.Before displaying the query results first the image testing in extracts using Tamura texture features and Gabor texture features to get the feature values used for image testing and then matching it with the value of features available in the training database.The application used in this research is an application from LIRE and database image that used is database from image.orig.the results obtained in this research is, the application of texture Tamura method features and Gabor texture features based on the features and colors can provide significant results between image testing and image training.
Impact of Using Technology in Auditing on Reducing the Fees of Auditors Offices and Companies in Jordan

The current study aimed to identify the impact of using technology in auditing on reducing the fees of the auditors offices and companies operating in Jordan. To achieve this objective, the study adopted the descriptive analytical approach due to its suitability to the nature of the study. A questionnaire was adopted as the study tool where 280 questionnaires were distributed to the study sample, 216 were retrieved and 184 were analyzed after excluding 32 questionnaires since they were invalid for the statistical analysis. The researcher used some appropriate statistical methods such as T test, mean and standard deviation. The study had many results, most important of which are that using technology has an impact on reducing the audit fees for the auditor offices and companies due to its accuracy in data output and tabulation, and that the auditor practices the E-auditing efficiently since he realizes its various benefits for the audit offices and companies operating in Jordan, and they applied it to a high degree. The study recommended the need for the audit offices and companies to continue operating in Jordan through e-auditing and they should work on developing their methods of application.
SUMMARIZING INDONESIAN NEWS ARTICLES USING GRAPH CONVOLUTIONAL NETWORK

Multi-document summarization transforms a set of related documents into one concise summary. Existing Indonesian news articles summarizations do not take relationships between sentences into account and heavily depends on Indonesian language tools and resources. In this paper, we employ Graph Convolutional Network (GCN) which accepts word embedding sequence and sentence relationship graph as input for Indonesian news articles summarization. Our system is comprised of four main components, which are preprocess, graph construction, sentence scoring, and sentence selection components. Sentence scoring component is a neural network that uses Recurrent Neural Network (RNN) and GCN to produce the scores of all sentences. We use three different representation types for the sentence relationship graph. Sentence selection component then generates summary with two different techniques, which are by greedily choosing sentences with the highest scores and by using Maximum Marginal Relevance (MMR) technique. The evaluation shows that GCN summarizer with Personalized Discourse Graph (PDG) graph representation system achieves the best results with average ROUGE-2 recall score of 0.370 for 100-word summary and 0.378 for 200-word summary. Sentence selection using greedy technique gives better results for generating 100-word summary, while MMR performs better for generating 200-word summary.
Generating adversarial examples for DNN using pooling layers

Deep Neural Network is an application of Big Data, and the robustness of Big Data is one of the most important issues. This paper proposes a new approach named PCD for computing adversarial examples for Deep Neural Network (DNN) and increase the robustness of Big Data. In safety-critical applicatio ns, adversarial examples are big threats to the reliability of DNNs. PCD generates adversarial examples by generating different coverage of pooling functions using gradient ascent. Among the 2707 input images, PCD generates 672 adversarial examples with L∞ distances less than 0.3. Comparing to PGD (state-of-art tool for generating adversarial examples with distances less than 0.3), PCD finds 1.5 times more adversarial examples than PGD (449) does.
DOMLIN at SemEval-2019 Task 8: Automated Fact Checking exploiting Ratings in Community Question Answering Forums

In the following, we describe our system developed for the Semeval2019 Task 8. We finetuned a BERT checkpoint on the qatar living forum dump and used this checkpoint to train a number of models.Our hand-in for subtask A consists of a fine-tuned classifier from this BERT checkpoint.For subtask B, we first have a classifier deciding whether a comment is factual or non-factual.If it is factual, we retrieve intra-forum evidence and using this evidence, have a classifier deciding the comment's veracity.We trained this classifier on ratings which we crawled from qatarliving.com.
Assisting Forensic Identification through Unsupervised Information Extraction of Free Text Autopsy Reports: The Disappearances Cases during the Brazilian Military Dictatorship

Anthropological, archaeological, and forensic studies situate enforced disappearance as a strategy associated with the Brazilian military dictatorship (1964–1985), leaving hundreds of persons without identity or cause of death identified. Their forensic reports are the only existing clue for people identification and detection of possible crimes associated with them. The exchange of information among institutions about the identities of disappeared people was not a common practice. Thus, their analysis requires unsupervised techniques, mainly due to the fact that their contextual annotation is extremely time-consuming, difficult to obtain, and with high dependence on the annotator. The use of these techniques allows researchers to assist in the identification and analysis in four areas: Common causes of death, relevant body locations, personal belongings terminology, and correlations between actors such as doctors and police officers involved in the disappearances. This paper analyzes almost 3000 textual reports of missing persons in São Paulo city during the Brazilian dictatorship through unsupervised algorithms of information extraction in Portuguese, identifying named entities and relevant terminology associated with these four criteria. The analysis allowed us to observe terminological patterns relevant for people identification (e.g., presence of rings or similar personal belongings) and automate the study of correlations between actors. The proposed system acts as a first classificatory and indexing middleware of the reports and represents a feasible system that can assist researchers working in pattern search among autopsy reports.
An Ontological Approach for Dental Extraction Decision Making and Knowledge Dissemination – A Pilot Study for Dental Extraction Forceps

Tooth extraction is one of the most usual surgical procedure in the field of dental.Not having proper knowledge of tooth and extraction instruments may cause too much complexity in extraction procedure or even some damages to patients' jaws.Mainly when using extraction forceps, the proper forceps should be used according to the teeth and the situation.So, it is very much important to have a sound knowledge of the instruments to be used, especially on extraction forceps.So, the knowledge of extraction forceps should be disseminated properly.After identifying this need, as a first stage, we gathered the information regarding the dental extraction forceps from the experts in the field.Then we started developing ontology as a second stage.Finally, the Knowledge Management (KM) Portal, which helps to share the knowledge of dental extraction, was developed.Since the quality and the accuracy of the ontology is the key in this research, it was evaluated and validated by using inbuilt FaCT++ 1.6.5 reasoner, online validator OOPS! and ontology experts as an iterative approach.It was also evaluated by using ontology non-experts in the final stage as an application-based (field test) evaluation.A questionnaire survey was conducted to the users to evaluate the KM portal (i.e., ontology).The results show that 85% of them are agreed and strongly agreed on the usefulness of the system.We confidently believe that our novel approach on dental extraction forceps KM portal can support all the dental related personnel to improve the knowledge and helpful in learning practices.Our next step is to model the ontology for the whole extraction process.
Improved Meet-in-the-Middle Preimage Attacks against AES Hashing Modes

Hashing modes are ways to convert a block cipher into a hash function, and those with AES as the underlying block cipher are referred to as AES hashing modes. Sasaki in 2011, introduced the first preimage attack against AES hashing modes with the AES block cipher reduced to 7 rounds, by the method of meet-in-the-middle. In his attack, the key-schedules are not taken into account. Hence, the same attack applies to all three versions of AES. In this paper, by introducing neutral bits from the key, extra degree of freedom is gained, which is utilized in two ways, i.e., to reduce the time complexity and to extend the attack to more rounds. As an immediate result, the complexities of 7-round pseudo-preimage attacks are reduced from 2120 to 2104, 296, and 296 for AES-128, AES-192, and AES-256, respectively. By carefully choosing the neutral bits from the key to cancel those from the state, the attack is extended to 8 rounds for AES-192 and AES-256 with complexities 2112 and 296. Similar results are obtained for Kiasu-BC, a tweakable block cipher based on AES-128, and interestingly the additional input tweak helps reduce the complexity and extend the attack to one more round. To the best of our knowledge, these are the first preimage attacks against 8-round AES hashing modes.
Modal embeddings and calling paradigms

We study the computational interpretation of the two standard modal embeddings, usually named after Girard and Godel, of intuitionistic logic into IS4. As source system we take either the call-by-name (cbn) or the call-by-value (cbv) lambda-calculus with simple types. The target system can be taken to be the, arguably, simplest fragment of IS4, here recast as a very simple lambda-calculus equipped with an indeterminate lax monoidal comonad. A slight refinement of the target and of the embeddings shows that: the target is a calculus indifferent to the calling paradigms cbn/cbv, obeying a new paradigm that we baptize call-by-box (cbb), and enjoying standardization; and that Girard's (resp. Godel's) embbedding is a translation of cbn (resp. cbv) lambda-calculus into this calculus, using a compilation technique we call protecting-by-a-box, enjoying the preservation and reflection properties known for cps translations - but in a stronger form that allows the extraction of standardization for cbn or cbv as consequence of standardization for cbb. The modal target and embeddings achieve thus an unification of call-by-name and call-by-value as call-by-box.
On the Leader Selection in the Self-Organizing Migrating Algorithm

In this article, a novel leader selection strategy for the self-organizing migrating algorithm is introduced. This strategy replaces original AllToOne and AllToRand strategies. It is shown and statistically tested, that the new strategy outperforms the original ones. All the experiments were conducted on well known CEC 2014 benchmark functions according to the CEC competition rules and reported here.
Towards a Compositional Analysis of German Light Verb Constructions (LVCs) Combining Lexicalized Tree Adjoining Grammar (LTAG) with Frame Semantics

Complex predicates formed of a semantically 'light' verbal head and a noun which contributes the major part of the meaning are frequently referred to as 'light verb constructions' (LVCs).In the paper, we present a case study of LVCs with the German posture verb stehen 'stand'.In our account, we model the syntactic as well as semantic composition of such LVCs by combining Lexicalized Tree Adjoining Grammar (LTAG) with frames.Starting from the analysis of the literal uses of posture verbs, we show how the meaning components of the literal uses are systematically exploited in the interpretation of stehen-LVCs.The paper constitutes an important step towards a compositional and computational analysis of LVCs.We show that LTAG allows us to separate constructional from lexical meaning components and that frames enable elegant generalizations over event types and related constraints.
Thread: Towards fine-grained precision reconfiguration in variable-precision neural network accelerator

In this work, we propose a neural network accelerator that supports finer-grained precision tuning for both activations and weights. To the best of our knowledge, this is the first neural network accelerator supporting arbitrary bit widths within 8 bits for both neural weights and activations. Our accelerator combines the features of bit-parallel and bit-serial design methods so that we can accomplish high arithmetic-unit utilization and precision tuning flexibility simultaneously. According to the cycle accurate simulation and the synthesized result, the proposed accelerator achieves up to 1.77x energy-efficiency improvement over the bit-serial design of Stripes for the evaluated workloads.
Design and Implementation of a Teaching Assistance Platform for College Students Based on ASP.NET

Teaching assistance platforms for college students greatly promote the quality and resource utilization of college teaching, marking the direction of information-based modern higher education. This paper designs a student-centered, teacher-led college student teaching assistance platform. Following the object-oriented design method, the SQL was taken as the database, the ASP.NET was selected as the server-end language, and the ADO.NET was adopted as the database access technology. The platform structure and the e-Homework module were cited as examples to detail the design and implementation of the platform. The analysis shows that the platform can fully support all aspects of teaching and satisfy the specific requirements of college teaching. The research findings provide impetus to scientific teaching in colleges.
Computer Systems in Analytical Applications

The academic research aims to expand the knowledge of advanced analytical software.Applications innovate over time, and in this generation, computer software has made it possible to live and carry out day-to-day chores within a click of a button.Being in a computer engineering field, it is vital to obtain an analytical to be resourceful of technological solutions.Through innovation, advancements in technology have continued to lend humankind an extended hand of ease into progression.This extended hand of ease is marked through applications that offer convenience.The components to obtain an analytical application are the sensor, rough set theories, spatial images, and artificial intelligence.
S (for Syllogism) Revisited

&#x0D; &#x0D; &#x0D; In 1978, the authors began a paper, “S (for Syllogism),” henceforth [S4S], intended as a philosophical companion piece to the technical solution [SPW] of the Anderson-Belnap P–W problem. [S4S] has gone through a number of drafts, which have been circulated among close friends. Meanwhile other authors have failed to see the point of the semantics which we introduced in [SPW]. It will accordingly be our purpose here to revisit that semantics, while giving our present views on syllogistic matters past, present and future, especially as they relate to not begging the question via such dubious theses as A →’ A. We shall investigate in particular a paraconsistent attitude toward such theses. &#x0D; &#x0D; &#x0D;
Implementasi Algoritma K-Nearest Neighbor untuk Melakukan Klasifikasi Produk dari beberapa E-marketplace

E-marketplace has gained popularity with the Indonesian society resulting in the increment of products offered. Consequently, customers require more effort to search for products. In this study, we classified products from several e-marketplaces. The classification was carried out using TF-IDF method for the weighting, cosine similarity to calculate product similarity distance, and k-nearest neighbor algorithm. Based on the first testing result using 150 product data, the k-nearest neighbor method with k=5 successfully classified 146 data with 4 data classified into the wrong class. This k=5 value gives the best result for this case, with an accuracy of 97.33%. The second testing result using 150 mixed brand product data, the k-nearest neighbor method successfully classified 145 data with 5 data classified into the wrong class. The accuracy of the second testing is 96.67%.
INFORMATION AND COMMUNICATION TECHNOLOGIES MANAGING THE QUALITY OF EDUCATIONAL ACTIVITIES OF A UNIVERSITY

The article highlights the results of the practical application of modern information and communication technologies in the management of institutions of higher education on the example of the Kyiv National University of Technology and Design (KNUTD). The constituents of the quality management system of educational activity are considered. The subsystems of the automated control system of educational activity of the university have been characterised and analysed. The key elements of the information systems use dare the following electronic databases: students and their academic performance; results of employment; students’ satisfaction with educational programs; distribution of the teaching load; teachers’ performance; learning and teaching resources, facilities, equipment and their cost; other indicators of university activity. In addition to the popular application packages, such as “iZETA”, "The System of Financially-Oriented Management of Higher Education Institutions", and "Anti-Plagiarism", the software products developed by KNUTD are described. Namely, the "Schedule" package allows one to form directories focused on the drawing up of class schedules which are accessible to all divisions. The software "Electronic Journal" provides monitoring of learning outcomes and prompt response to the failure of individual students; it serves for the formation of final control mark sheets, the evaluation of academic success rates of university students, etc. The electronic rating system "RTS rating" is used for the evaluation of research and teaching staff activity according to the results of their work in the reported year. The modular environment of the University's educational process, based on the Moodle distance learning platform, is described. It accumulates the teaching and methodological support necessary for conducting educational activities. The results of the research are of practical value since the implementation of the considered software products is capable of improving the quality and effectiveness of the management of educational activities of higher education institutions.
Learning Robust Features for Planar Object Tracking

Visual tracking of planar objects with multiple degrees of freedom is a core component for numerous vision-based applications. Generally, since direct methods use all raw pixels in the region of interest to estimate the motion model directly, these methods are sensitive to illumination changes, partial occlusion, and motion blur. Recently, the deep convolutional network has demonstrated remarkable ability in visual tracking via learning robust deep features. In this paper, deep features are used for improving the robustness of direct methods. To learn suitable features in an end-to-end fashion, we employ a novel network architecture, the efficient second-order minimization (ESM) layer, which performs the ESM algorithm on deep feature maps. We train and validate the convolutional features on a synthetic dataset generated from the MS-COCO dataset and evaluate the tracking performance on two challenging, real-world datasets. The experimental results show that the proposed approach outperforms most state-of-the-art methods in various tracking challenges.