Reviewerly/exHarmony huggingface dataset:
A Recommender System for Telecommunication Operators’ Campaigns

Achieving a complete client characterization to attribute better and customized products is a trend on the rise. This information filtering process, known as “recommendation system”, increases companies revenues, and improve client quality-of-experience. Unfortunately, current state-of-the-art focus mostly on data sets with explicit client feedback regarding the advised product, or are more focused on case studies such as e-commerce, or movies recommendation. Our main goal is to understand the feasibility of a recommendation approach in one specific scenario: recommendation of telecommunication operators’ campaigns. We aim to determine the extent to which it is possible to characterize the clients, using implicit feedback and state-of-the-art recommendation algorithms. We resorted to a data set supplied by an European Telecommunication Operator, with two years of advertised campaigns to a specific group of clients, and more than forty-six million lines of raw data. Having this data set, we applied pre-processing methods and analysed several algorithms to understand the feasibility of such approach in this real-world scenario. Results show that our approach can indeed infer the best product or service to advertise to the client – specially when we have historical data about past client adherence –, thus showing that these algorithms can improve recommendations in the context of a telecommunication operator.
The Relationship among Vitality, Self-regulation and Optimism of Elderly Participate in Sports for All: In Case of The Kookhak-Kigong

The purpose of this study was to verify the relationship among vitality, self-regulation and optimism of the elderly participating in sports for all. To this end, the survey was conducted through distributing questionnaires to the elderly people in their age of 60 or more who attended Koohak-Kigong 2017. In total, 655 valid responses were collected. Statistical analysis was performed using SPSS 18.0, and results were derived from frequency analysis, factor analysis, reliability analysis, correlation analysis and multiple regression analysis. The results are as follows. First, vitality had a significant influence on self-regulation. Second, self-regulation had a significant influence on optimism. Third, vitality had a significant influence on optimism. Fourth, self-regulation mediated between vitality and optimism. Thus, this study shows the structural relationship between vitality, self-regulation and optimism of the elderly. The enhancement of vitality and self -regulating ability through sports for all can be helpful for positive life in old age by maintaining optimism.
Deep Neural Network Compression for Image Classification and Object Detection

Neural networks have been notorious for being computationally expensive. This is mainly because neural networks are often over-parametrized and most likely have redundant nodes or layers as they are getting deeper and wider. Their demand for hardware resources prohibits their extensive use in embedded devices and puts restrictions on tasks like real-time image classification or object detection. In this work, we propose a network-agnostic model compression method infused with a novel dynamical clustering approach to reduce the computational cost and memory footprint of deep neural networks. We evaluated our new compression method on five different state-of-the-art image classification and object detection networks. In classification networks, we pruned about 95% of network parameters. In advanced detection networks such as YOLOv3, our proposed compression method managed to reduce the model parameters up to 59.70% which yielded 110X less memory without sacrificing much in accuracy.
Multilingual named entity recognition based on the BiGRU-CNN-CRF hybrid model

Uyghur, Kazak, and Kyrgyz (UKK languages) are agglutinative and low-resource languages with rich morphological features. Determining how to obtain a better general entity recognition method without relying on artificial features and resources is a problem that remains to be solved. In this paper, a hybrid neural network model based on bidirectional GRU (BiGRU)-CNN-CRF is proposed. This model uses concatenated vectors including affix vectors, part of speech vectors, and word vectors as inputs and constructs a deep neural network of BiGRU-CRF suitable for the recognition of UKK named entities. Finally, the global optimal labelling sequence is outputted by the conditional random field (CRF) layer. The experimental results show that this model can solve the problem of automatic recognition of named entities. In addition, the model has good robustness. The F1 value of UKK named entity recognition reached 93.11%, 90.29%, and 89.22% for the Uyghur, Kazak, and Kyrgyz languages, respectively.
Human Action Recognition Using GLAC Features on Multi-view Binary Coded Images

This paper presents a human action recognition framework by focusing on the auto-correlation features extracted on the binary coded motion and static information images of depth action video clips. At first, the action video clips are passed to the 3D Motion Trail Model (3DMTM) in order to generate the 2D motion and static information images. Clearly, for a depth video clip, the 3DMTM yields a set of three motion information images and a set of three static information images about the front, side and top projections of all the action video frames. Next, those images are transformed into the binary coded images by the Local Binary Pattern (LBP) operator. Finally, the Gradient Local Auto-Correlation (GLAC) algorithm is employed on those binary coded images for representing depth actions through the auto-correlation features. To classify the multiple actions with the gained features, the Extreme Learning Machine (ELM) is adopted here with the kernel trick. The introduced approach is extensively validated on the Microsoft Research Action3D (MSRAction3D) database. The experimental result demonstrates, the classification outcome of our system is better in comparison with the state-of-the-art systems.
Translation Course 4.0 Redefined: Enhancing Work Efficiency and Meaning Accuracy Using AEGISUB 3.2.2 Subtitling Software

Today, there are quite a few ways of promoting coming-soon movies to various target audiences. Putting up clips of movie trailers on Youtube channel is one of a kind promotion publicly. A good movie is always equipped with its movie trailer to gain as many movie-goers' interest as possible. However, there is one shortcoming of a movie trailer clip found on Youtube channel. The shortcoming is that almost every single movie trailer has no subtitles of the target audience's. Only closed-captions dialogues in the source language are currently available instead of the written dialogues in the target audience's language. This study discusses three matters on movie trailers. Firstly, it aims at knowing the working process of operating the AEGISUB software by the English department students of Universitas Dian Nuswantoro. Secondly, it discusses the work efficiency of the software dealing with subtitling matters. Thirdly, it describes meaning accuracy in translating idiomatic expressions of the SL with the software. From the data analysis, it is found that all the students made a good understanding by following all the subtitling procedures with the software. The rendering process was found more efficient due to the subtitling rules. Each text line in the TL was made efficient according to the rules in terms of the number of characters, time duration for one line to appear on the screen, and the number of text lines in one subtitle. Each idiomatic expression was accurately rendered in the target audience's language. It was made possible because the software allows the user to identify any idiomatic expression found in one text line.
Managing the Implementation and Evaluation of the School Reading Program

Aims: Non-reading phenomenon among students is a worldwide problem. This problem continues to balloon despite the various governments’ program. This study was conducted to determine the positive and negative experiences met by the school managers during the implementation and evaluation of the reading program in their respective area.&#x0D; Study Design: Qualitative- phenomenology.&#x0D; Place and Duration of Study: Digos City, Davao del Sur, Philippines, School Year 2018-2019.&#x0D; Methodology: Purposive; Focus-Group Discussions; Validated Researcher-made questionnaire.&#x0D; Results: Nine themes surfaced from the data analysis; implementation of the program; appointment of reading coordinator; amelioration of reading teachers; initiative of school administration; availability of reading materials; involvement in SIP/AIP creation; variations on identified challenges; heartening situations; satisfaction with co-workers and DepEd personnel; and recommendations for improvement.&#x0D; Conclusion: Contextualization of the reading program allows learners to connect and interact with the texts. School heads need to be consistent with their policies. They need to involve the teachers concern not only those chosen few because it creates partition and conflict. Teachers’ dedication and initiative to the program is essential; however, school managers need to provide relevant materials for its smooth implementation. Further, success of any program depends on the cooperation among the implementers. Unity and understanding among workers are essential in order to achieve the purpose of any endeavor.
A Many-to-Many Demand and Response Hybrid Game Method for Cloud Environments

In this article, we design a service mechanism for profits optimization between multiple cloud providers and multiple cloud customers ( <i>many-to-many</i> ). We explore this problem from the perspective of game theory but take a different approach compared with existing cloud resource pricing game methods. First, we regard the relationships among multiple cloud customers as an evolutionary game, and formulate the competitions among the multiple cloud providers as a noncooperative game. Eventually, we form a hybrid game model in which the strategy of each customer and each cloud provider is affected not only by the other side but also by customers or cloud providers other than themselves. Second, based on the hybrid game model, we simulate the bargaining process between cloud providers and customers by controlling supply and demand allocation, and try to ultimately achieve a balanced supply and demand state, i.e., a win-win situation. For each cloud customer and provider, we design a utility function. A customer’s utility involves net profits and the cloud providers’ bidding strategies, and a cloud provider’s utility involves net profits and the cloud customers’ demand strategies. Both sides attempt to maximize their own profits under the influences of each other. We prove that our proposed strategies enable each of the two games to converge to their own equilibrium. Finally, the strategies of cloud customers and providers can be implemented through an iterative proximal algorithm ( <inline-formula><tex-math notation="LaTeX">$\mathcal {IPA}$</tex-math></inline-formula> ) and a distributed iterative algorithm ( <inline-formula><tex-math notation="LaTeX">$\mathcal {DIA}$</tex-math></inline-formula> ). The experimental results validate our methods and show that the proposed method can benefit both multiple cloud providers and customers.
CREATIVITY IN PSYCHOLOGICAL AND SCIENTIFIC TERMS. PERSONALITY DETERMINANTS OF CREATIVITY

The concept of creativity is ambiguous, and research on creativity is carried out in such fields of science as philosophy, anthropology, pedagogy and psychology. In ancient Greece, creativity was called the verb "to do." It referred only to poetry: At a time when Christianity was very much dominant in the world, creativity became the domain of God, which gives existence to things, only in the nineteenth century "creativity" as a term, she returned to the area of human activities, to the field of art. In the twentieth century, the described phenomenon was even more widely interpreted. Creativity began to be perceived in every area of human activity, for example in science, technology, self-creation etc. The starting element leading to understanding the phenomenon of creativity is to recognize it as a product of a product. In the described model of thinking, a creative product is characterized by novelty and value. There are two types of interpretation of the concept of creativity as an individual feature. One group of researchers assigns the creativity to exclusively highly talented people. The second type of interpretation of creativity in understanding it as an individual characteristic is called an egalitarian approach. In this approach, every man is creative, and creativity is a characteristic of a person standing next to intelligence or extraversion. People differ only in the intensity of this feature. Researchers sought to answer the issue of creative personality in the detection of typical personality traits that testify to creative abilities. These studies have led to the emergence of three groups of features related to the mechanisms of creation. The creator, therefore, is characterized by openness independence and perseverance.
Development of an Image Processing Module for Autonomous Underwater Vehicles Through Integration of Object Recognition With Stereoscopic Image Reconstruction

Abstract This study investigated the development of visual recognition and stereoscopic imaging technology, applying them to the construction of an image processing system for Autonomous Underwater Vehicles (AUVs). For the proposed visual recognition technology, an optical flow algorithm was used to detect the linear features and movement speeds of dynamic images; the proposed stereoscopic imaging technique employed a Harris corner detector to estimate the distance of the target. A physical AUV was constructed with a wide-angle lens camera and a binocular vision device mounted on the bow to provide image input. Subsequently, a simulation environment was established in Simscape Multibody and used to control the post-driver system of the stern, which contained horizontal and vertical rudder planes as well as the propeller. Finally, the dynamic testing results were combined with a fuzzy controller to output the real-time responses of the vehicle regarding the angles, rates of the rudder planes, and the propeller revolution speeds at various distances.
Data Cleaning for Process Mining with Smart Contract

Process Mining (PM) is a special data mining technique that allows extracting information from data of critical transactions (i.e. event logs) carried out in Information Systems and monitors the patterns in these transactions. When we start to process event logs with process mining tools, we face with data quality problems such as incorrect and insufficient logging and timing. Thus, data cleaning operations must be applied to event logs before applying process mining on these logs. Being an innovative medium of distributed data processing and storage with the features of enhanced security, traceability, automated transaction verification and integration, Blockchain Technology and Smart Contracts might be a good option to process and store event logs for process mining. In this paper, we focused on the cleaning of the event logs by smart contract as data is flowing from the information systems into the blockchain, and used Hyperledger Composer by IBM to develop our solution. We tested our proposal on an open process data of 1555 records, and compared the cleaning performance of our proposal with that of DataWrangler by Stanford University. Our proposal not only cleaned all 1313 records identified and cleaned by DataWrangler, it also saved 12 additional records with a different date format that was caught and corrected by our smart contract implementation.
Scale-dependent Relationships in Natural Language

Natural language exhibits statistical dependencies at a wide range of scales. For instance, the mutual information between words in natural language decays like a power law with the temporal lag between them. However, many statistical learning models applied to language impose a sampling scale while extracting statistical structure. For instance, Word2Vec constructs a vector embedding that maximizes the prediction between a target word and the context words that appear nearby in the corpus. The size of the context is chosen by the user and defines a strong scale; relationships over much larger temporal scales would be invisible to the algorithm. This paper examines the family of Word2Vec embeddings generated while systematically manipulating the sampling scale used to define the context around each word. The primary result is that different linguistic relationships are preferentially encoded at different scales. Different scales emphasize different syntactic and semantic relations between words.Moreover, the neighborhoods of a given word in the embeddings change significantly depending on the scale. These results suggest that any individual scale can only identify a subset of the meaningful relationships a word might have, and point toward the importance of developing scale-free models of semantic meaning.
Investigation of the Structural Relationship among Factors That Affect Digital Reading

The purpose of the study was to examine the structural relationship among factors that affect digital reading. PISA DRA data was analyzed to reveal 1) what factors (e.g., ICT factors, attitude, and navigation indices) affect Korean students’digital reading assessment scores in PISA DRA?; 2) What structural relationships are identified among factors (e.g., ICT factors, attitude and navigation indices)?. According to the research results, all three factors (ICT usage, positive attitude, navigation index) impact DRA achievement. When ranking the most influential variable, navigation index (.46) scored the highest followed by ICT usage at home for school (.22) and then positive attitude toward ICT (.10). Out of the pre-determined variables that affect digital reading achievement, navigation index showed to have the greatest impact. Following navigation, the next most influential factor affecting digital reading achievement was ICT usage at home for school. Since ICT usage is related to digital reading familiarity, an attempt was made to observe the direct and indirect effects of how ICT usage variables influence a student’s digital reading achievement. In summary, we empirically discovered that ICT usage, attitude toward ICT, webpage navigation and digital reading achievement are interconnected each other. Also, attitude toward ICT mediates between ICT usage and webpage navigation skills. Additionally, webpage navigation skills mediate between a positive attitude toward ICT and digital reading achievement. Based on these empirical evidences, a deliberate and thoughtful educational approach and policy should be made so to enhance students’ digital reading competencies.
Global and local information in structured derivative free optimization with BFO

A structured version of derivative-free random pattern search optimization algorithms is introduced which is able to exploit coordinate partially separable structure (typically associated with sparsity) often present in unconstrained and bound-constrained optimization problems. This technique improves performance by orders of magnitude and makes it possible to solve large problems that otherwise are totally intractable by other derivative-free methods. A library of interpolation-based modelling tools is also described, which can be associated to the structured or unstructured versions of the initial BFO pattern search algorithm. The use of the library further enhances performance, especially when associated with structure. The significant gains in performance associated with these two techniques are illustrated using a new freely-available release of BFO which incorporates them. A interesting conclusion of the results presented is that providing global structural information on a problem can result in significantly less evaluations of the objective function than attempting to building local Taylor-like models.
IDENTIFYING READING COMPREHENSION QUESTIONS OF NATIONAL EXAMINATION FOR SENIOR HIGH SCHOOL STUDENTS

Reading is the complex cognitive process of decoding symbols to derive meaning. It is a form of language processing. Success in this process is measured as reading comprehension. The aim of the researcher is to identify the types of question reading comprehension of the national examination of the textbook. The form of this research is descriptive by analyzing the reading question in the Sonar Eksplorasi USBN/UNBK’s book 2019. Indicators data are grouped based on qualitative. To maintain the results of research carried out to collect data using descriptive qualitative. Then the data can be seen means out of reading comprehension questions into five types, namely question of literal comprehension, a question involving reorganization or reinterpretation, the question of inference, the question of evaluation and question of personal responses. The question of literal comprehension gets 5 data (5,05%). The next types are the question involving reorganization or reinterpretation it gets 6 data (6, 06%). The question of inference gets 5 data (5,05%). The question of evaluation gets 4 data (4, 04%). The last type is the question of personal responses which gets 10 (10, 10%). Data obtained through book evaluation text by looking at the form and level of questions. Based on this result, it can be concluded that the dominant type of used test is multiple-choice items in testing the reading comprehension.
Whale Optimization Algorithm for Data Clustering

Issues relating to clustering today are computational techniques, optimization, and performance of clustering algorithms. In this research, a metaheuristic grouping method of Whale Optimization Algorithm with modification to weight changes based on the food hunting method and behavior of humpback whales was proposed. A total of ten datasets obtained from the learning repository of UCI machine were used to evaluate the performance of the proposed algorithm compared to other clustering algorithms. Whale Optimization Algorithm can provide optimal solutions and more stable clustering results because there is no dependence on initial cluster center initialization. Moreover, clustering using Whale Optimization Algorithm produces clusters that are better than clusters using Ant Colony Optimization, Isodata, and Forgy. This can be seen from silhouette coefficients, time examination, variance examination and sum of square error of a number of test datasets obtained. Overall, results show that clusters of Whale Optimization Algorithm and the other three methods have nearly identical variance, and each cluster produces high intra-class similarity and low inter-class similarity.
Proof complexity of systems of (non-deterministic) decision trees and branching programs

This paper studies propositional proof systems in which lines are sequents of decision trees or branching programs, deterministic or non-deterministic. Decision trees (DTs) are represented by a natural term syntax, inducing the system LDT, and non-determinism is modelled by including disjunction, ∨, as primitive (system LNDT). Branching programs generalise DTs to dag-like structures and are duly handled by extension variables in our setting, as is common in proof complexity (systems eLDT and eLNDT). Deterministic and non-deterministic branching programs are natural nonuniform analogues of log-space (L) and nondeterministic log-space (NL), respectively. Thus eLDT and eLNDT serve as natural systems of reasoning corresponding to L and NL, respectively.The main results of the paper are simulation and non-simulation results for tree-like and dag-like proofs in LDT, LNDT, eLDT and eLNDT. We also compare them with Frege systems, constant-depth Frege systems and extended Frege systems.