Reviewerly/exHarmony huggingface dataset:
Pengaruh Tanggapan Di Media Sosial Terhadap Pilihan Koleksi Bagi Pengunjung Interactive Art Museum Bali, Kota Denpasar

Interactive Art Museum Bali is the new one of tourists attraction in Denpasar City. Although classified as a new tourist attraction, but tourists visit the Interactive Art Museum Bali is always increasing every month. Based on that situation, the research is conducted for analyzing the influence of responses in social media on the collection options for visitors at the tourist attraction of Interactive Art Museum Bali, Denpasar City. &#x0D; The data used in this research is quantitative data. Data analysis techniques used in this study are correlation analysis. The collected data in this study through observation, literature study, questionnaire, and visual material.Informant determination technique used is convenience sampling techniquewith the number of samples determined by the Slovin formula. &#x0D; The results of this study obtained that the social media used by visitors who come to visit the Interactive Art Museum Bali is Instagram.The influence of responses in social media on collection options for visitors is seen from 3 classifications.First, according to the observing senses, has a correlation analysis of 0653 and 0.598.The second classification is according to the occurrence of having correlation analysis results of 0.627 and 0.668.The third characteristic is according to the environment has the result of correlation analysis of 0.564 and 0.585.&#x0D; Keywords: Social Media, Response, Visitor, Collection, Interactive Art Museum Bali
Structured Programming as a Means for Improving the Computer Training of Bachelors

The article addresses the problems associated with computer training of engineers at higher schools, which are stemming from insufficiently clear understanding of the main training goal. If this training is seen as a part of the overall mathematical education with defining its goal as educating a student to solve problems on a computer, this goal is achieved by teaching to structured programming, which is a modern method of solving problems on a computer. Conceptually computer programming has presently been fully shaped as a science, a circumstance that fundamentally changes the very approach to education for programming. The structured programming philosophy forms the basis for understanding the programming process as a formalized problem solution process. Structured programing is based on the basic programming language PASCAL which was developed for teaching to computer programming. This language clearly reflects the capabilities of a computer as a tool; they are necessary and sufficient for solving problems of any complexity because the PASCAL language strictly defines the set of data types supporting the problem solving process. Education for structured programming is learning the technology of solving problems on a computer which is the basis of computer literacy of any higher school graduate. The top-down program development method, which includes the rules for aggregation and decomposition of sub-problems, has been laid at the heart of this technology. In learning computer programming as a technological process for solving problems, a student should gain skills of solving problems on a computer and develop an algorithmic way of thinking already at the initial stage of his or her education. This experience will be useful for the future engineer, a specialist on technologies in his or her profession.
Análisis de la estructura curricular de los Aprendizajes Clave para la Educación Integral

The results of the research projects carried out by the academic community have been and will continue to be recognised, valued, published and disseminated by scholarly journals, regardless of their format. The role Higher Education Institutions (HEI) as publishers of scholarly journals is fundamental. Methods: The purpose of this article is to analyse the basic editorial features of the scholarly journals published by Mexican teacher-training colleges. The study uses a descriptive quantitative approach to examine a sample of journals based on the editorial criteria established by Latindex, the Regional Online Information System for Scholarly Journals from Latin America, the Caribbean, Spain and Portugal. Results and conclusions: The study has revealed that no research has been carried out on this type of publications in Mexico; that the number of publications published by Mexican teacher-training colleges is small, and that the analysed publications do not meet basic editorial requirements. The results of this diagnosis can help Mexican teacher-training colleges to make the necessary decisions to increase the quality, visibility and chances of survival of their journals.
Real Time Translation of Malayalam Notice Boards to English Directions

Neural Machine Translation (NMT) is an emerging technique depicting impressive performance, better than traditional machine translation methods.It is observed that NMT models have a strong efficacy to learn language constructs, improving performance.Considered as one of the toughest Indian languages to learn and comprehend, Malayalam is extensively used in Road Signs and Notice Boards in Kerala as it increasingly becomes India's tourism hub.In this paper, the barrier faced by the tourists is resolved by providing real-time translation to English.The results obtained show that accuracy can be improved by incorporating Deep Learning and Natural Language Processing (NLP) in translation.This paper is envisioned to not only convert notice boards but also translate Malayalam that is written and printed on all mediums.
Dynamic Path-Decomposed Tries

A keyword dictionary is an associative array whose keys are strings. Recent applications handling massive keyword dictionaries in main memory have a need for a space-efficient implementation. When limited to static applications, there are a number of highly-compressed keyword dictionaries based on the advancements of practical succinct data structures. However, as most succinct data structures are only efficient in the static case, it is still difficult to implement a keyword dictionary that is space efficient and dynamic. In this article, we propose such a keyword dictionary. Our main idea is to embrace the path decomposition technique, which was proposed for constructing cache-friendly tries. To store the path-decomposed trie in small memory, we design data structures based on recent compact hash trie representations. Experiments on real-world datasets reveal that our dynamic keyword dictionary needs up to 68% less space than the existing smallest ones, while achieving a relevant space-time tradeoff.
Hierarchical Attribute-based Signatures: Short Keys and Optimal Signature Length.

With Attribute-based Signatures (ABS) users can simultaneously sign messages and prove compliance of their attributes, issued by designated attribute authorities, with some verification policy. Neither signer’s identity nor possessed attributes are leaked during the verification process, making ABS schemes a handy tool for applications requiring privacy-preserving authentication. Earlier ABS schemes lacked support for hierarchical delegation of attributes (across tiers of attribute authorities down to the signers), a distinct property that has made traditional PKIs more scalable and widely adoptable.
Ensemble Super-Resolution with A Reference Dataset

By developing sophisticated image priors or designing deep(er) architectures, a variety of image Super-Resolution (SR) approaches have been proposed recently and achieved very promising performance. A natural question that arises is whether these methods can be reformulated into a unifying framework and whether this framework assists in SR reconstruction? In this paper, we present a simple but effective single image SR method based on ensemble learning, which can produce a better performance than that could be obtained from any of SR methods to be ensembled (or called component super-resolvers). Based on the assumption that better component super-resolver should have larger ensemble weight when performing SR reconstruction, we present a Maximum A Posteriori (MAP) estimation framework for the inference of optimal ensemble weights. Specially, we introduce a reference dataset, which is composed of High-Resolution (HR) and Low-Resolution (LR) image pairs, to measure the super-resolution abilities (prior knowledge) of different component super-resolvers. To obtain the optimal ensemble weights, we propose to incorporate the reconstruction constraint, which states that the degenerated HR image should be equal to the LR observation one, as well as the prior knowledge of ensemble weights into the MAP estimation framework. Moreover, the proposed optimization problem can be solved by an analytical solution. We study the performance of the proposed method by comparing with different competitive approaches, including four state-of-the-art non-deep learning based methods, four latest deep learning based methods and one ensemble learning based method, and prove its effectiveness and superiority on three public datasets.
Analysis of chi-squared divergence changes by filtering of stego images formed according to UNIWARD embedding methods

Counteraction to sensitive information leakage is topical task today. Special interest is taken on early detection of hidden (steganographic) information transferring by data transmission in communication systems. Message (stego data) embedding is provided by alteration of cover files, such as digital images, according to used steganographic algorithm. Reliable detection of formed stego images requires usage of targeted stegdetector that needs a priori information about specific distortions (signatures) of cover due to data hiding. It makes detection systems vulnerable to zero-day attack – usage by malefactors the previously unknown embedding algorithms. Therefore it is required development of universal (blind) stegdetectors that are capable to reliable revealing of stego images even in case of limited or absence information about used embedding method. Creation of blind stegdetector requires determination of cover image parameters that are sensitive to any alteration caused by message hiding. As such parameters it is proposed to use information-theoretic estimations (chi-square divergence) of pixels brightness distribution distortion due to stego data embedding. For amplification of these distortions it is used image pre-processing with median and Wiener filters. The case of adaptive messages hiding in cover images according to UNIWARD methods is considered. It is revealed that usage of chi-square divergence allows reliably detection of small alteration of cover image even in case of low cover payload (less than 10\%). Different character of chi-square divergence changes for filtered images by information hiding in spatial and JPEG domains allows determine type of used embedding domain.
Research Guides: Harmony Library Services: Borrowing from other libraries

Learn how to use CCC Library services at the Harmony Campus. Request materials from other libraries through Summit or ILL.
Problem of Maximum Matching in Non-Bipartite Graph Using Edmonds’ Cardinality Matching Algorithm and Its Applicationin the Battle of Britain Case

Matching is a part of graph theory that discusses pair. A matching M is called to be maximum if M has the highest number of elements. A blossom which is encountered in non-bipartite graph can cause failure in process of finding the maximum matching in non-bipartite graph. One of the algorithms that can be used to find a maximum matching in non-bipartite graph is Edmonds’ Cardinality Matching Algorithm. Shrinking process is done in each blossom Bi that is encountered to become pseudovertex bi, in a way that each blossom does not interfere the process of finding a maximum matching in non-bipartite graph. In order to accelerate the finding, simple greedy method is used to perform initialization of matching and BFS algorithm is also used in constructing an alternating tree in a non-bipartite graph.The research discussed the finding of maximum matching in non-bipartite graph using Edmonds’ cardinality matching algorithm. In addition, this research gave a sample of its application in the resolution of The Battle of Britain case. The result obtained is a maximum matching in non-bipartite graph. The maximum matching obtained is a solution to the case of The Battle of Britain.
The importance of human computer interaction in the development process of software projects

Today, software industry has a rapid growth. In order to resist the competition increased by this growth, software projects need to be developed with higher quality and especially user friendly. Therefore, the importance of human-computer interaction emerges clearly. In design and development phases of software projects, the properties of human which is an important agent for interaction -- such as behavioral, cognitive, perceptive, efficiency and physical factors have to be considered. This study aims to express the importance of developing softwares by taking into consideration the human-computer interaction applications. In this context, firstly a wide literature review is made to examine software development process and human-computer interaction in detail, the results obtained by using design methods in this process are explicated and the importance of said interaction is openly expressed with the exemplary applications in the literature. According to the results of the research, especially in software life cycle, it is observed that rules of interaction must be implemented before software development, however, these methods are usually included in software life cycle in the latter stages of software development process. This situation causes the developed softwares to be user unfriendly and of low quality. Furthermore, it is observed that when the design methods used in the scope of human-computer interaction are integrated into software development process during the life cycle, the developed projects are more successful, have better quality and are more user friendly.
WILL STUDENTS BE ABLE TO MANAGE THEIR LIVES?

This research article aims at observing the results of studying Russian students’ life priorities, social sustainability, their readiness to manage their life and professional activities, and at developing on this basis a set of educational technologies supposed to improve the graduates of Russian universities’ competitiveness. There are analyzed the works of Russian and foreign authors (I. B. Tselyugina, E. A. Sokolova, V. P. Podvoisky, A. A. Laskin, Yu. O. Kustikova, S. Е. Metelev, E. V. Kudryashov, V. D. Panachev, O. N. Knyazkova, Zh. V. Puzanova, A. G. Tertyshnikova, V. A. Tishkov, R. E. Barash, V. V. Stepanov, Zee Marjolein, Peter F. de Jong, M. Y. Helma, I. Cunningham, C. Longobardi, N. O. Iotti, T. Jungert, M. Settanni, R. S. Adams, H. Bradbury), devoted to the problems of students. Within the study, the methods of questionnaire, expert survey, comparison, decomposition, and grouping have been used. The research referred to students from Russian regional universities. The sample included more than 400 students from 13 state universities located in 5 federal districts of the Russian Federation, and involved 36 experts. There are analyzed Russian students’ life guidelines and prospects for increasing their readiness to manage their own lives. Changes in the organization of education are shown to be affecting the students’ livelihood. According to the results of the survey, it is the understanding of the importance of personal and professional development for successful future employment and professional career that has the priority influence over the applicants’ desire to study at the university. The motives for getting higher education are its necessity for the chosen profession, demand for it at the labor market, prospects for improving their material well-being. The main factor that influences the choice of profession is considered to be the students’ abilities. The research is original, as it studies the important problem of students’ social sustainability, their readiness to manage their own lives. This problem has not been previously considered by domestic and foreign authors. The results of the monitoring indicate the necessity and possibility of purposeful work within our universities on more active involving students in the processes of educational, scientific and social activities, on preparing them for future professional activities and independent life. The article presents the experience of working out and using special educational technologies, which should increase students’ social sustainability when solving problems identified within the research. The results of the research should show to the Russian students the ways to increase their social sustainability and to accelerate their readiness for independent life and activities.
Accurate decoding of materials using a finger mounted accelerometer

Sensory feedback is the fundamental driving force behind motor control and learning. However, the technology for low-cost and efficient sensory feedback remains a big challenge during stroke rehabilitation, and for prosthetic designs. Here we show that a low-cost accelerometer mounted on the finger can provide accurate decoding of many daily life materials during touch. We first designed a customized touch analysis system that allowed us to present different materials for touch by human participants, while controlling for the contact force and touch speed. Then, we collected data from six participants, who touched seven daily life materials-plastic, cork, wool, aluminum, paper, denim, cotton. We use linear sparse logistic regression and show that the materials can be classified from accelerometer recordings with an accuracy of 88% across materials and participants within 7 seconds of touch.
Electro-Thermal Model of Liver Tissue and its Approximation

This paper deals with the proposal of the approximation method of complex organic tissues (in this example liver) for electro-thermal simulation. The main objective of the proposed approximation is to reduce the required computational power for electro-thermal simulation of given issue. The approximated parameters are meant to be used in three-dimensional finite element simulation models. Verification of proposed approximation is given by the comparison of electrical current distribution within complex model of liver tissue and approximated one.
Diverse personalized recommendations with uncertainty from implicit preference data with the Bayesian Mallows Model

Clicking data, which exists in abundance and contains objective user preference information, is widely used to produce personalized recommendations in web-based applications. Current popular recommendation algorithms, typically based on matrix factorizations, often have high accuracy and achieve good clickthrough rates. However, diversity of the recommended items, which can greatly enhance user experiences, is often overlooked. Moreover, most algorithms do not produce interpretable uncertainty quantifications of the recommendations. In this work, we propose the Bayesian Mallows for Clicking Data (BMCD) method, which augments clicking data into compatible full ranking vectors by enforcing all the clicked items to be top-ranked. User preferences are learned using a Mallows ranking model. Bayesian inference leads to interpretable uncertainties of each individual recommendation, and we also propose a method to make personalized recommendations based on such uncertainties. With a simulation study and a real life data example, we demonstrate that compared to state-of-the-art matrix factorization, BMCD makes personalized recommendations with similar accuracy, while achieving much higher level of diversity, and producing interpretable and actionable uncertainty estimation.
Review of Evaluation of software understandability using software matrices

In this, we discussed mainly the previous work, which was done on the Evaluation of understandability. For that, we have gone through different techniques like the Fuzzy set approach. After that, we have concentrated on a particular metric like Spatial complexity. Here they consider the factors like the Understandability of documentation, Understandability of structure, Understandability of components, Understandability of source code, and Understandability of data. In order to explain these factors, they considered a few metrics to measure it. When a worker needed to reconstruct one software system as many times until he/she correctly reconstructs it, it can be considered the software system is difficult for him/her to understand.
NAHSL: NAHSL Conference 2019: Planning Committee

The official website for NAHSL 2019: November 8, 2019, located at Sheraton Springfield at Monarch Place, Springfield, MA. A list of planning committee members
Application of remote sensing for detection of tectonic structures and localisation of ore deposits within Biryusinsky and Krepsky-Tumanshetsky ore clusters (Eastern Sayan)

As a result of decoding of the materials of the Russian space survey “Resource-P” (ShMSA) on Biryusinskaya area, the features of the tectonic structure of the horst-anticlinal uplifts and the graben-synclines separating them were determined, the large role of sublatitudinal disjunctions was shown in the structural diagram of the area under study, ore prospective sites were isolated. The study of the Krepsko-Tumanshetskaya area was carried out using space multispectral and panchromatic high-resolution surveys “Resource-P” (Geoton), which made it possible to determine the structural position of the ore deposits. On the Krepsko-Tumanshetskaya area, the ore-controlling structures are small along the length of sublatitudinal strike faults and the nodes of their intersection with the northwestern and northeastern disjunctions.
LED2Net: Deep Illumination-aware Dehazing with Low-light and Detail Enhancement

We present a novel dehazing and low-light enhancement method based on an illumination map that is accurately estimated by a convolutional neural network (CNN). In this paper, the illumination map is used as a component for three different tasks, namely, atmospheric light estimation, transmission map estimation, and low-light enhancement. To train CNNs for dehazing and low-light enhancement simultaneously based on the retinex theory, we synthesize numerous low-light and hazy images from normal hazy images from the FADE data set. In addition, we further improve the network using detail enhancement. Experimental results demonstrate that our method surpasses recent state-of-theart algorithms quantitatively and qualitatively. In particular, our haze-free images present vivid colors and enhance visibility without a halo effect or color distortion.
EvalUMAP 2019 Chairs' Welcome &amp; Organization

Research in the areas of User Modelling, Adaptation and Personalization faces a number of significant scientific challenges. One of the most significant of these challenges is the issue of comparative evaluation. It has always been difficult to rigorously compare different approaches to personalization, as the function of the resulting systems is, by their nature, heavily influenced by the behavior of the users involved in trialing the systems. Developing comparative evaluations in this space would be a huge advancement as it would enable shared comparison across research. This workshop aims to develop initial shared task(s) that will be published ahead of UMAP 2020, providing opportunity for participants to test and tune their systems and complete the task in order for comparative results and associated publications to be prepared for and presented at UMAP 2020.