Reviewerly/exHarmony huggingface dataset:
Universal Resource Management and Logistics Using Blockchain Technology

This article is part of a series of articles that describe an Integrated Decision Making Platform modeled after the human anatomy. In the first articles we presented the main components of the platform and the way they integrate with each other to form a cohesive and heterogeneous system. A resources is a quantifiable source of value and represents the scope or object of any decision making process. The allocation, management and distribution of resources is the main reason why a good decision making process becomes the essential factor that determines the success of any person, corporation, institution or state. The difference between success and failure is given by the way we decide to spend our resources. For this reason one vital component for a good decision making platform or system is the resource management component. The resource management and logistics component we will describe uses the blockchain as a robust, transparent and distributed ledger. The blockchain will not only allow us to keep track of our resources but using stored blockchain procedures, popularly called smart contracts, we can create the logistics for how we add, allocate, distribute and spend our resources.
Indexing k-mers in Linear-space for Quality Value Compression

Many bioinformatics tools heavily rely on k-mer dictionaries to describe the composition of sequences and allow for faster reference-free algorithms or look-ups.Unfortunately, naive k-mer dictionaries are very memory inefficient, requiring very large amount of storage space to save each k-mer.This problem is generally worsened by the necessity of an index for fast queries.In this work we discuss how to build an indexed linear reference containing a set of input k-mers, and its application to the compression of quality score in FASTQ files.Most of the entropy of sequencing data lies in the quality scores, and thus they are difficult to compress.Here, we present an application to improve the compressibility of quality values while preserving the information for SNPs calling.We show how a dictionary of significant k-mers, obtained from SNPs databases and multiple genomes, can be indexed in linear space and used to improve the compression of quality value.Availability: the software is freely available at https://github.com/yhhshb/yalff.
Fast and Discriminative Semantic Embedding

The embedding of words and documents in compact, semantically meaningful vector spaces is a crucial part of modern information systems.Deep Learning models are powerful but their hyperparameter selection is often complex and they are expensive to train, and while pre-trained models are available, embeddings trained on general corpora are not necessarily well-suited to domain specific tasks.We propose a novel embedding method which extends random projection by weighting and projecting raw term embeddings orthogonally to an average language vector, thus improving the discriminating power of resulting term embeddings, and build more meaningful document embeddings by assigning appropriate weights to individual terms.We describe how updating the term embeddings online as we process the training data results in an extremely efficient method, in terms of both computational and memory requirements.Our experiments show highly competitive results with various state-of-the-art embedding methods on different tasks, including the standard STS benchmark and a subject prediction task, at a fraction of the computational cost.
AMOGAP: Defending Against Man-in-the-Middle and Offline Guessing Attacks on Passwords

Passwords are widely used in online services, such as electronic and mobile banking services, and may be complemented by other authentication mechanism(s) for example in two-factor or three-factor authentication systems. There are, however, a number of known limitations and risks associated with the use of passwords, such as man-in-the-middle (MitM) and offline guessing attacks. In this paper, we present AMOGAP, a novel text password-based user authentication mechanism, to defend against MitM and offline guessing attacks. In our approach, users can select easy-to-remember passwords, and AMOGAP converts currently-used salted and hashed password files into user tokens, whose security relies on the Decisional Diffie-Hellman (DDH) assumption, at the server end. In other words, we use a difficult problem in number theory (i.e., DDH problem), rather than a one-way hash function, to ensure security against offline password guessing attackers and MitM attackers. AMOGAP does not require any change in existing authentication process and infrastructure or incur additional costs at the server.
МЕТОДИКА ЗАСТОСУВАННЯ ІНФОРМАЦІЙНО-КОМУНІКАЦІЙНИХ ТЕХНОЛОГІЙ У ПІДГОТОВЦІ ДО ПРОЕКТНОЇ ДІЯЛЬНОСТІ МАЙБУТНІХ УЧИТЕЛІВ ТРУДОВОГО НАВЧАННЯ

The ways of solving the problem of using modern information and communication technologies at different stages of project activity of students are outlined. The specific feature of the professional activity of craft and technology teachers enables the use of modern communication tools at different stages of creative projects, combining technical means with the latest educational technologies. The methodical features of ICT application during information search, project development, evaluation and self-evaluation are highlighted. The variants of expedient application of public computer programs and means of communication with the purpose to increase the professionalism of projects execution by students, optimization of feedback between the teacher and subjects of training, innovative approach in the implementation of complex tasks are offered.The mastery of instrumental artistic and technical software should become a base for the methodical system of teaching at the Pedagogical University. They form the basis of information technology and artistic and graphic competence of future technology teachers and is the basis for the development of the person's ability to professional activity, allowing the future teacher of technology to professionally use the mastered computer resources in pedagogical and artistic and creative activities. It has been proved that the training of future technology teachers is effective on the basis of ICT (programming language, ready-made software products, Microsoft Office software package, cloud services, Internet resources, WEB-site), implemented in the educational process of special (designing and modeling of clothing, design of clothing ) and psychological and pedagogical (the method of profile education, the theory and methodology of teaching technology, the method of educational work) disciplines.
What If Quantum Computer Combined with Artificial Intelligence?

With the rapid progress in computation, our ability to deal with mega data is enhanced dramatically. Quantum computer is the latest development that promises us with great contribution to the upcoming generation of artificial intelligence. The positive feedback cycle formed between quantum computer and artificial intelligence is the most eye-catching point that possesses the potential to reach the goal of self-learning and self-improvement. The new era of technology has approached, the only thing we need to do is to enhance and upgrade our knowledge to prevent being knocked out.
Directional relative total variation for structure–texture decomposition

Structure–texture decomposition is one of the fundamental branches of computer vision and image processing. It is not only beneficial to image understanding but also to subsequent object recognition and tracking. However, diversity and complexity of textures impede structure extraction. In this study, a simple yet effective structure–texture decomposition method is proposed based on directional relative total variation. More concretely, the directionality of relative total variation is taken into account by using the directional gradient operator instead of partial derivatives both in x and y directions. In addition, in order to explore the directional information on different scales, the non-subsampled pyramid filter bank is employed to obtain the multi-scale properties. Experiments on a wide variety of images reveal the efficacy of the proposed method and show its superiority over several state-of-the-art methods. Besides, the applicability of the proposed method to several image processing tasks is also demonstrated, such as edge extraction, defect detection, and so on.
Peran Taman Bacaan Masyarakat Mata Aksara dalam menumbuhkan minat baca pada masyarakat

People’s reading interest in Indonesia is 0.001% and is ranked third from the bottom, lagging far behind other countries that have an average percentage of 0.45% -0.62%. While for the percentage of reading interest based on province, Yogyakarta ranks third with a percentage of 91.00%. Based on these conditions, indeed, it requires a provider of information which is easy to access to increase reading interest. One of the information providers is Taman Bacaan Masyarakat (TBM)/Community Reading Park. To find out more about this, obviously, research needs to be conducted relating to the role of TBM in supporting information dissemination among the community. The research location was Mata Aksara TBM. The purpose of this study was to determine the role of Mata Aksara TBM for the community. This study used a qualitative approach through descriptive research methods, with Mata Aksara TBM, Yogyakarta as the study subject. The data collection technique used was observation and interviews. Results of the study indicated that Mata Aksara TBM acted as a place of learning and play, as a place of learning and practice, as a place of recreation and education, and as a place to preserve culture. The conclusion from this study is that the Community Reading Park is beneficial in empowering reading interest, especially among children, and has a positive impact on the community in the TBM environment in advancing the agricultural and livestock sectors of the society around the TBM.
Vista.ue at SemEval-2019 Task 5: Single Multilingual Hate Speech Detection Model

This paper shares insight from participating in SemEval-2019 Task 5.The main propose of this system-description paper is to facilitate the reader with replicability and to provide insightful analysis of the developed system.Here in Vista.ue,we proposed a single multilingual hate speech detection model.This model was ranked 46/70 for English Task A and 31/43 for English Task B. Vista.ue was able to rank 38/41 for Spanish Task A and 22/25 for Spanish Task B.
CLaC Lab at SemEval-2019 Task 3: Contextual Emotion Detection Using a Combination of Neural Networks and SVM

This paper describes the CLaC Lab system at SemEval 2019, Task 3 (EmoContext), which focused on the contextual detection of emotions in a dataset of 3-round dialogues.For our final system, we used a neural network with pretrained ELMo word embeddings and POS tags as input, GRUs as hidden units, an attention mechanism to capture representations of the dialogues, and an SVM classifier which used the learned network representations to perform the task of multi-class classification.This system yielded a micro-averaged F1 score of 0.7072 for the three emotion classes, improving the baseline by approximately 12%.
Hypnoteachingg: Solusi Siswa Learning Disorder

This study wants to analyze the extent to which the role of hypnoteaching is offered by the world to academics in the education fild to overcome the problem of learning disorder students. The reseach methode used is the reseach library. Based on the reseach it was concluded that hypnoteaching is the most accurate and relevant solution to create the effectiveness of learning disorder learning students, although sometimes only through simple strategies. By hypnoteaching, learning disorder students will feel more likely to be present and not cornered even though their essence is not the same as other normal humans. The implication og this study is that the theories that are conceptualized in hypnoteaching succeed in reducing students’ learning disorder anxiety while participating in learning activities.
An Improved Approach for Semantic Graph Composition with CCG

This paper builds on previous work using Combinatory Categorial Grammar (CCG) to derive a transparent syntax-semantics interface for Abstract Meaning Representation (AMR) parsing.We define new semantics for the CCG combinators that is better suited to deriving AMR graphs.In particular, we define relation-wise alternatives for the application and composition combinators: these require that the two constituents being combined overlap in one AMR relation.We also provide a new semantics for type raising, which is necessary for certain constructions.Using these mechanisms, we suggest an analysis of eventive nouns, which present a challenge for deriving AMR graphs.Our theoretical analysis will facilitate future work on robust and transparent AMR parsing using CCG.
ПОДАННЯ ІНФОРМАЦІЇ В КОМП’ЮТЕРНО ОРІЄНТОВАНИХ ОСВІТНІХ ТЕХНОЛОГІЯХ ТА ЇЇ ЕКСПЛІКАЦІЯ В ІМПЕРАТИВАХ ПОСТМОДЕРНІЗМУ

The article highlights the performance and features of the application of information and educational technologies, the embodiment of which presents education at the level of a strategic resource for social and economic development. The role of computerization of educational technologies in the era of modernity and postmodern is being compared; the boundaries of effective application of formalization of reality in algorithms are outlined. The thought of the conditionality of information content by specifics of computer representation, the requirements of logical verification is carried out. It is said that the absolute domination in the era of modernism in the educational information technology of formal-logical rationality has had a detrimental effect on the humanistic component of social intelligence. Postmodernist imperatives encourage methodologists of information provision of education to recognize a wide range of types of rationality, in the content of which great importance is the weakening of logical correction in favor of strengthening the irrational component, subjectivity as creativity. It is postulated that it is inappropriate to determine the evaluation models of information on which the educational process is based, based only on the classical rationality in marking the truth and its criteria. The educational process in the competence model is oriented towards pragmatism and is therefore characterized by methodological pluralism, where any methodology can become the leading one depending on the didactic purpose, and the forms and methods of cognition are chosen situationally, based on the parity of the rational and irrational components of knowledge. Postmodern intellectualism has grown on the diversity of forms of comprehension of reality, on the syncretism of computer and human intelligence in educational technologies. It is noted that every civilizational transformation causes a reassessment of the semantic priorities of information, the relativization of forms of cognition, rethinking the criteria for assessing the level of socio-intelligence. At present, the prospect of innovative development of education is seen in the modernization of methodological culture and the very way of thinking of educators. Education should evolve ahead of civilization challenges.
Exploiting Self-Embedding Fragile Watermarking Method for Image Tamper Detection and Recovery

The increasingly rapid use of digital images makes data security technology become an important issue to ensure the integrity and ownership of the received image.Watermarking technique is a technique that ensures data security including for checking the authenticity of an image.Current developments, not only can test the authenticity of an image, but also can determine the location of damage while fixing it.The method proposed in this paper is through using the block-based technique of self-embedding fragile watermarking, where the watermark is selected from feature images representing the entire image.Four authentication bits and eight recovery bits are generated in each 2x2 non-overlapping block.Authentication bits are embedded in the three least significant bits (LSBs) of the block itself while recovery bits are embedded in the three LSBs of the corresponding mapped block.Our method is efficient as it only uses simple parity check operations and comparison between average intensities as image tamper detection and use the five most significant bits (MSBs) of 2x2 pixels in average for recovery.The experimental results of the tests on cropped images show that the proposed methods allow image recovery with acceptable visual quality better than some of state-of-the-art schemes.
Command and Control vs self Management

Abstract It’s estimated that in 2017 there were 111 billion lines of new software code generated by developers and most lines were written within a team. Producing software is a creative activity and therefore very different from mass-production lines, because each algorithm tries to solve a different problem. For many years, traditional system development life cycle has been used as the main algorithm for how programmer teams should work to deliver software products. Waterfall methodology is successfully used in simple, unchanging projects but its main drawback is how it handles change. Scrum encourages the creativity of each member of the team, allowing all of them to take on the role they want during the project. Each person can be a developer, a tester, a designer, depending on the responsibilities he takes between two sprints. Considering the diversity of IT projects, it makes sense to understand in what kind of projects we should use the Scrum methodology and where there is a need for more explicit management and control.
ColumbiaNLP at SemEval-2019 Task 8: The Answer is Language Model Fine-tuning

Community Question Answering forums are very popular nowadays, as they represent effective means for communities to share information around particular topics.But the information shared on these forums is often not correct or misleading.This paper presents the ColumbiaNLP submission for the SemEval-2019 Task 8: Fact-Checking in Community Question Answering Forums.We show how fine-tuning a language model on a large unannotated corpus of old threads from Qatar Living forum helps us to classify question types (factual, opinion, socializing) and to judge the factuality of answers on the shared task labeled data from the same forum.Our system finished 4th and 2nd on Subtask A (question type classification) and B (answer factuality prediction), respectively, based on the official metric of accuracy.
An Adaptive Non-linear Statistical Salt-and-Pepper Noise Removal Algorithm using Interquartile Range

This paper presents a salt-and-pepper noise removal scheme using modified mean filter. The proposed method is based on a simple basic concepts of mean filter, where each mean value is calculated from the mathematical formula of interquartile range (IQR). It replaces the noisy pixels using IQR based mathematical formula applied on the filter window. Experimental results are presented to demonstrate the efficiency (quality of the image) of the method compared to other existing different types of impulse noise removal techniques.
Communicating Library Values, Mission, Vision, and Strategic Plans through Social Media

Social media communication needs to be aligned with your library’s broader communication plan so that user experience is consistent with messaging from other library venues. Ideally, social media along with all library communication points include language from your library’s values, mission, and vision statements and strategic plan. This article will outline two critical pieces to forming your social media communication strategy: Making a social media plan and applying social media optimization (SMO) to your library’s Web pages. Once set up, your plan and SMO can easily become part of your library’s routine when posting to social networks and creating and editing Web pages.
Clark Kent at SemEval-2019 Task 4: Stylometric Insights into Hyperpartisan News Detection

In this paper, we present a news bias prediction system, which we developed as part of a SemEval 2019 task.We developed an XG-Boost based system which uses character and word level n-gram features represented using TF-IDF, count vector based correlation matrix, and predicts if an input news article is a hyperpartisan news article.Our model was able to achieve a precision of 68.3% on the test set provided by the contest organizers.We also run our model on the Buz-zFeed corpus and find XGBoost with simple character level N-Gram embeddings to be performing well with an accuracy of around 96%.
Grunn2019 at SemEval-2019 Task 5: Shared Task on Multilingual Detection of Hate

Hate speech occurs more often than ever and polarizes society.To help counter this polarization, SemEval 2019 organizes a shared task called the Multilingual Detection of Hate.The first task (A) is to decide whether a given tweet contains hate against immigrants or women, in a multilingual perspective, for English and Spanish.In the second task (B), the system is also asked to classify the following subtasks: hateful tweets as aggressive or not aggressive, and to identify the target harassed as individual or generic.We evaluate multiple models, and finally combine them in an ensemble setting.This ensemble setting is built of five and three submodels for the English and Spanish task respectively.In the current setup it shows that using a bigger ensemble for English tweets performs mediocre, while a slightly smaller ensemble does work well for detecting hate speech in Spanish tweets.Our results on the test set for English show 0.378 macro F1 on task A and 0.553 macro F1 on task B. For Spanish the results are significantly higher, 0.701 macro F1 on task A and 0.734 macro F1 for task B.
Incorporating Real Projects Into a Software Engineering Undergraduate Curriculum

Software engineering researchers and practitioners are increasingly more concerned about non-technical issues like user involvement and interaction as a way to improve software development process efficiency. This issue is also present in software engineering education. The IEEE/ACM software engineering guidelines highlights that an undergraduate course in this matter should have a real-world basis. In this paper, we present an undergraduate program that connect students with real-world projects throughout their studies. To evaluate educational results, we performed a survey with 111 students from this software engineering program. The results indicate that students in the end of this program has a much better chance of taking users' desires into consideration instead of focusing on software implementation.
VS<sup>3</sup>‐NET: Neural variational inference model for machine‐reading comprehension

We propose the VS3-NET model to solve the task of question answering questions with machine-reading comprehension that searches for an appropriate answer in a given context. VS3-NET is a model that trains latent variables for each question using variational inferences based on a model of a simple recurrent unit-based sentences and self-matching networks. The types of questions vary, and the answers depend on the type of question. To perform efficient inference and learning, we introduce neural question-type models to approximate the prior and posterior distributions of the latent variables, and we use these approximated distributions to optimize a reparameterized variational lower bound. The context given in machine-reading comprehension usually comprises several sentences, leading to performance degradation caused by context length. Therefore, we model a hierarchical structure using sentence encoding, in which as the context becomes longer, the performance degrades. Experimental results show that the proposed VS3-NET model has an exact-match score of 76.8% and an F1 score of 84.5% on the SQuAD test set.