Reviewerly/exHarmony huggingface dataset:
A Calculus of Chaos in Stochastic Compilation

An unexpected outcome from an open project to develop a ‘chaotic’ compiler for ANSI C is described here: a trace information entropy calculus for stochastically compiled programs. A stochastic compiler produces randomly different object codes every time it is applied to the same source code. This calculus quantifies the entropy introduced into run-time program traces by a compiler that aims for the maximal possible entropy, furnishing a definition and proof of security for encrypted computing (Turing-complete computation in which data remains in encrypted form throughout), where the status was formerly unknown.
Improving Image Captioning with Language Modeling Regularizations

Inspired by the recent work in language modeling, we investigate the effects of a set of regularization techniques on the performance of a recurrent neural network based image captioning model. Using these techniques, we achieve 13 Bleu-4 points improvements over using no regularizations. We show that our model does not suffer from loss-evaluation mismatch and also connect the model performance to dataset properties by running experiments on MSCOCO dataset. Further, we propose a human in the loop image captioning system as an alternative way to improve the model performance. Using only the first two tokens of a reference sentence of an image, we improve Bleu-4 score of our best model by 57 points with this hybrid system.
Browsing Visual Sentiment Datasets Using Psycholinguistic Groundings

Recent multimedia applications commonly use text and imagery from Social Media for tasks related to sentiment research. As such, there are various image datasets for sentiment research for popular classification tasks. However, there has been little research regarding the relationship between the sentiment of images and its annotations from a multi-modal standpoint. In this demonstration, we built a tool to visualize psycholinguistic groundings for a sentiment dataset. For each image, individual psycholinguistic ratings are computed from the image’s metadata. A sentiment-psycholinguistic spatial embedding is computed to show a clustering of images across different classes close to human perception. Our interactive browsing tool can visualize the data in various ways, highlighting different psycholinguistic groundings with heatmaps.
The Value of Library Resources and Services Through the Lens of Discovery Tools

Objective. The objective of the paper is to show the discovery service from the perspective of the library and user values as a means of access to information. Methods. The literature review on the subject of value approach to the library functioning is provided. In the paper were used data from the user experience study, statistical reports and anecdotal experience. The results show the issues of customization of discovery services functionalities and resources management required. The users’ evaluation of the discovery service as a tool for finding and retrieval of information is also given. Conclusions. Discovery services have special functions and are powerful tools for identifying the required information, which is placed in directories and databases, online libraries, and external full-text databases. The study found that finding relevant information was of paramount importance to users who highly appreciated the usefulness of this service for their research.
Taxonomia nas áreas da Biblioteconomia e da Ciência da Informação: uma revisão sistemática

We present the results of the research whose objective is to identify the presence of taxonomy in the scientific literature of the areas of Librarianship and Information Science.It is an exploratory research with an approach of quantitative and qualitative analysis in which through the systematic review of the literature published in the Reference Database of Journal Articles in Information Science (BRAPCI), in the period 2016-2019 and in Scopus, with coverage between 2017--2019 all conducted in the first half of this year.At BRAPCI, the search strategy was: taxonomy AND bibliot, at Scopus we adopted: taxonomy AND a string librar*.We used the prefix "librar*" to retrieve all variations of the word "library" and "librarian".The results showed 15 articles covering taxonomy related to support, tool, method, service, instruction and information organization.We concluded that the applicability of taxonomy in these areas is also related to the patterns of classification and organization of information in various organizational segments.
Generalized Simulated Annealing with Sequentially Modified Cost Function for Combinatorial optimization Problems

Recent efforts to develop hybrid quantum-classical algorithms for solving combinatorial problems have rekindled interest in revisiting heuristic classical optimization algorithms and exploring possibilities for improving them. A popular approach for finding good solutions to combinatorial problems is local search. In spite of its efficiency, if the search space is rugged, local search often gets trapped in unsatisfactory local optima. On the other hand, global search meta-heuristic algorithms, such as classical simulated annealing, guarantee asymptotic convergence in probability distribution to global optima. Despite its theoretical appeal, practical performance of classical simulated annealing is often sensitive to implementation details. In this paper, we revisit classical simulated annealing and propose a generalization in which the annealing is guided by a sequentially modified cost function. We prove asymptotic convergence to global optima and give an example choice of the modified cost function. We test the proposed algorithm with this example modified cost function on the traveling salesman problem. Numerical results suggest that the performance of this method is more robust to the initial temperature choice. Furthermore, the method demonstrates a significant efficiency gain without compromising its performance.
Picture What You Read

Visualization refers to our ability to create an image in our head based on the text we read or the words we hear. It is one of the many skills that makes reading comprehension possible. Convolutional Neural Networks (CNN) are an excellent tool for recognizing and classifying text documents. In addition, it can generate images conditioned on natural language description. In this work, we utilize CNNs capabilities to generate realistic images representative of the text illustrating the semantic concept. We conducted various experiments to highlight the capacity of the proposed model to generate representative images of the text descriptions used as input to the proposed model.
One Class of Clusterization Problems in Network Models

One clustering problem, which arises in design of search systems, is considered. NP-hardness of the general case of the problem is proved. A complexity status is determined for the cases with certain particular structures of the directed graph, such as: Complete Acyclic graph, Path graph, OutTree and InTree graphs. For the cases of Path and InTree graphs the exact algorithms with quadratic complexity are proposed.
Differentially Private Event-Triggered Sampling

This paper describes a differentially private event-triggered sampling mechanism to select measurement samples from a data sequence whose dynamics can be modelled by a stochastic linear system. The mechanism produces subsequences that can be used to reestimate the original sequence relatively accurately and the differential privacy constraint guarantees that these subsequences are insensitive to certain variations in the input sequence. The subsampling process can be motivated by the presence of communication bandwidth constraints, but also provides an additional tool to explore achievable privacy-utility tradeoffs in privacy-preserving signal processing and control. Event-triggered sampling can offer benefits over periodic subsampling by attempting to select the most useful samples, but the fact that it leaks information when no sampling occurs must be carefully taken into account to meet the differential privacy requirement. We propose a design using a stochastic sampling threshold, leveraging the "sparse vector technique" from differential privacy to incur a privacy loss only when samples are actually released. This design includes a suboptimal but tractable recursive finite-dimensional estimator that can also be used to re-estimate the original sequence from the differentially private noisy subsequence.
Oblique and Mixed Decision Trees

The most popular decision tree systems are based on greedy, top-down induction, and they mainly use univariate tests. The trees generated by such systems are frequently overgrown and unstable [1]. Globally induced decision trees are smaller, as shown in the previous chapter, but still, for certain problems, especially when decision borders are not axis-parallel, room for improvement exists. However, this requires making a decision tree representation more flexible by introducing oblique tests in non-terminal nodes.
Multi-scale Spatial Location Preference for Semantic Segmentation

This paper proposes a semantic segmentation network which can address the problem of adaptive segmentation for objects with different sizes. In this work, ResNetV2-50 is firstly exploited to extract features of objects, and then these features are fed into the reconstructed feature pyramid network (FPN), which includes multi-scale preference (MSP) module and multi-location preference (MLP) module. Aiming at objects with different sizes, the receptive fields of kernels need to be adjusted. MSP module concatenates feature maps of different receptive fields, and then combines them with the SE block in SE-Net to obtain scale-wise dependencies. In this way, not only multi-scale information can be encoded in feature maps with different degree levels of preference adaptively, but also multi-scale spatial information can be provided to MLP module. The MLP module combines the channels containing more accurate spatial location information with preference to replace traditional nearest interpolation upsampling in FPN. At last, the weighted channels equip with scale-wise information as well as more accurate spatial location information and yield precise semantic prediction for objects with different sizes. We demonstrate the effectiveness of the proposed solutions on the Cityscapes and PASCAL VOC 2012 semantic image segmentation datasets and our methods achieve comparable or higher performance.
Formation and Processing of GMW-Similar Sequences Based on the Dual Basis in Digital Information Transfer Systems

Algorithms for processing and determining, using a dual basis, the initial states of shift registers that form sequences similar to Gordon–Mills–Welch (GMW) sequences, which are characterized by their large number and higher structural secrecy comparing with the widely used M-sequences, are presented. It is shown that the proposed algorithms, unlike the known ones, make it possible to determine arbitrary initial states of shift registers and produce encoding of useful information, which expands the possibilities of using GMW-like sequences to solve various problems in the transmission of digital information in communication systems under electronic countermeasures.
Augmented Reality—A Tool for Mediated Communication: A Case Study of Teen Pregnancy in Contexts like India

Mediated communication has shrunk the world into “customized cottages”. At the click of a button, we laugh, work, and even think together. Such digital interaction allows a fantastic mix of media usage. The best appears to be the combination of folk and mass media, where we have the intimacy and familiarity of the former and the outreach of the latter. Yet, challenges like illiteracy and problems of denial and deprivation of marginalized existence rampant in developing contexts necessitate the adoption of the futuristic “immersive technology” to communicate critical social messages.
AI(인공지능)시대 미래교육의 배움학적 제언

This study discussed the direction of education in the New Frontier era. The Fourth Industrial Revolution is expected to have widespread impact across the enterprise and society. Education is no exception. In the era of the fourth industrial revolution, human beings should live by practicing lifelong education. This era will require active lifelong learning, not passive learning. Differences in motivation for learning will cause big changes in the future. Human beings must develop their own power in accordance with changing times. Eachness competency are the ability to know themselves right away, creative competency, problem-solving and critical thinking. A future society is a society that lives together. Therefore, it is a society that needs empathy competency, collaboration competency, and communication competency. These changes are already welcoming us into the future. In the era of the Fourth Industrial Revolution, we must strive to develop our own learning skills.
Neural Hidden Markov Model

Hidden Markov models are tractable to capture long-term dependencies but intractable to compute the transition probabilities of higher-order process. We propose a neural hidden Markov models to compute the transition probabilities of higher-order hidden Markov model by a neural network and reduce the cost of computation. It is applied for time-aware recommender systems to show the benefits from the hybrid of combining neural network and hidden Markov model. We implement the recommender system and experiment on real datasets to demonstrate better performances over the existing recommender systems.
Blockchain Based Confidential Communication and Authorization Model for IoT Devices

The Internet of things (IoT) has many unexplored potentials. Features such as low purchasing costs, low energy consumption, and small size make the Internet of things indispensable and irreplaceable. However, the fact that they only have enough processor power to run particular tasks has led to the failure of implementation of many traditional security measures to the IoT environment, which are developed to date. Furthermore, the producers bring early products to the market due to the market share concerns. Moreover, the lack of knowledge of security measures to be taken by developers creates lots of security loopholes and causes unauthorized persons to seize a lot of valuable information, which undermines the trust of the users. Likewise, the unpredictable and rapid growth of IoT devices highlights the importance of scalability, authorization and encryption issues. Although partial solutions are being suggested to address these concerns, a generally accepted solution still does not exist. Thanks to its tamper-proof, decentralized, distributed, programmable structure, blockchain can be used as a solution to these security concerns. Blockchain-based authentication and access control systems can be used to strengthen IoT security. Until recently, it has been thought that communication over the blockchain network is not possible for resource-constrained devices because the communication depends on using not only complex but also memory hungry cryptography algorithms. In this paper, our application aims resource-constrained device authorization through the blockchain network that is presented together with the analysis and test results related to this study.
Geological Context Mapping of Batouri Gold District (East Cameroon) from Remote Sensing Imagering, GIS Processing and Field Works

The Batouri area is located in the Adamawa-Yade domain in East Cameroon region, and has a high geological potential as a host for gold deposits.It is covered by thick forest where outcrops are sometime scarce.The purpose of this study is to generate and combine different geological information which makes up the specificity of the Batouri gold District, in order to contribute to the better knowledge of its geological setting.From satellite imageries, GIS tools and field data; lithological units, lineament and density maps have been dressed at regional-scale of 1/400.000.The mapping has enabled the discovery of spatial and topologic relationships between shear zones, lineaments, gold occurrences and often mineralized granitic intrusions.According to the field data, lithological and lineament maps, the lithology of the Batouri gold District is characterized by alkali granitoids (tonalite, granodiorite, syenomonzo-granite, alkaline granite) hosted by orthogneisses and migmatites as gold mineralization hosts; while the lineaments show a major shear zones trending NE-SW defined by presence of mylonites.The shear zones crosscut all lithologies, mostly granodiorite where majority of gold occurrences is observed; locally, nearest these shear zones, rocks are transformed to the mylonites and gold is concentrated along.From density map, it is shown that the high gold mineralization zone corresponds to highest lineaments density.All those data suggest that gold mineralization in the Batouri district is controlled by tectonic and lithology.It is conclusive that Batouri gold deposit is epigenetic gold set emplaced in orogenic setting, during the post-collisional stage of the Central African Fold Belt (CAFB) and the Congo Craton (CC).
Students’ knowledge test control in Physics

Controlling learners’ educational achievements in our time performs the most important function of learning. Scientists and methodologists argue that the test control of students’ academic achievement meets the requirements of quantitative and objective measurement of learners’ knowledge, skills and abilities. The relevance of the test controlling method of learners’ academic achievements is dictated also by the introduction of the independent external evaluation (IEE) as a final certification of school leavers. With the emergence of computer classes, the use of tests became available and appropriate. The use of computers in the testing process greatly enhances the benefits of this type of control. This paper is aimed at studying experimentally the impact of the controlling testing on enhancing learners’ educational achievements in Physics and managing learners’ cognitive activity. We have been prepared the system of test questions on the following topics: “We begin to study Physics”, “Mechanical work. Units of work”, “Power and its units”, “Electric current. Conductor Resistance”, “Serial and Parallel Conductor Connection”, “Ideal Gas Laws”, “Atomic Physics. Spectrums”. The tests were developed in order to identify the students’ acquisitions of the main issues constituting the educational material / programme, as opposed to revealing some amount of information from the learner's side. In our opinion, it is important for students to master the level of education that would become the basis for further self-improvement of their own education, their own opportunities to overcome the challenges that the life postures to him / her in today's society. Therefore, the main task while developing the test tasks was the formation of certain subject and life-oriented competences. We conducted a pedagogical experiment to introduce the test control of learners' knowledge in Physics. A series of lessons were conducted using the test survey in the experimental class, but no tests were used in the control class. Using the example of testing 8th grade schoolchildren in Physics it was detected that the use of a systematic test control of knowledge acquisition at corresponding lessons Physics allowed the students to demonstrate better knowledge of certain topics at sufficient and high levels. Keywords: testing control, learners’ self-educational competence in Physics, creative thinking.
Comparison of Data Mining Techniques in the Cloud for Software Engineering

Mining software engineeringSoftware engineering data has recently become an important research topic to meet the goal of improving the software engineeringSoftware engineering processes, software productivity, and quality. On the other hand, mining software engineeringSoftware engineering data poses several challenges such as high computational cost, hardware limitations, and data management issues (i.e., the availability, reliability, and security of data). To address these problems, this chapter proposes the application of data miningData mining techniques in cloudCloud, the environment on software engineeringSoftware engineering data, due to cloudCloud computing benefits such as increased computing speed, scalability, flexibility, availability, and cost efficiency. It compares the performances of five classificationClassification algorithms (decision forestDecision forest, neural networkNeural network, support vector machineSupport vector machine, logistic regressionLogistic regression, and Bayes point machine)Bayes point machine in the cloudCloud in terms of both accuracy and runtime efficiency. It presents experimental studies conducted on five different real-world software engineeringSoftware engineering data related to the various software engineeringSoftware engineering tasks, including software defect prediction, software quality evaluation, vulnerability analysis, issue lifetime estimation, and code readability prediction. Experimental results show that the cloudCloud is a powerful platform to build data miningData mining applications for software engineeringSoftware engineering.
Pedagogical conditions for the formation of professional competence of future Specialists Majoring in information and communication technologies

The article describes the creation of pedagogical conditions for the formation of professional competence of the future specialists majoring in information and communication technologies. The purpose of this article is to investigate the conditions facilitating the formation of professional competence of future specialists majoring in information and communication technologies, to create and use application software aimed at increasing the efficiency as well as the degree of automation for providing educational services. According to the purpose, the following tasks have been assigned: pedagogical conditions for the formation of the professional competence of future specialists majoring in information and communication technologies have been proposed; the complex of circumstances necessary for ensuring positive dynamics in the formation of the professional competence of future specialists majoring in information and communication technologies has been studied; some ways of improving the formation of the professional competence of future specialists majoring in information and communication technologies have been considered; the ways of raising the professional skills level (proficiency level) of higher education applicants through the development of software applications for educational purposes have been analysed; the project alongside with the software that automates and optimizes the tasks of the subjects of the educational process as well as provides an opportunity to receive relevant and timely information concerning the organization of the educational process have been elaborated; a client-centred education schedule for students of higher education institutions has been developed, taking into account the specificities of the optimal online route. The project includes the following stages: preparatory (cognitive-information), selection of days, time, audience (issues), hierarchyzation of these issues, selection of starting and ending points of the route, after which the route scheme and its optimization are developed. The development of the route scheme and the elaboration of its software are inextricably linked parallel processes. Keywords: information system, specialists, software, higher education institution, information and communication technologies.