Reviewerly/exHarmony huggingface dataset:
Open Domain Context-Based Targeted Sentiment Analysis System

Massive amount of opinions is available nowadays on the internet thought social networks. Availability of such wealth of opinions in the social media motivated researchers to develop automated systems for opinion mining, also known as sentiment analysis. A very recent direction currently addresses the problem of detecting the targets, i.e., topics, in the micro-blogs (e.g., tweets) and identifying the sentiment polarity toward them. This research direction is referred to as open domain sentiment classification. In this work, we developed an approach for identifying the context of a given set of micro-blogs and analyzing their sentiments within that context. We propose a new context-based analysis system for open domain targeted sentiment analysis. Based on our knowledge, the proposed system differs from status quo followed in developing systems for open domain targeted sentiment analysis. Existing systems detect opinions in each micro-blog individually. While, our proposed approach helps in developing a context-based analysis system among a set of micro-blogs. The proposed system detects context patterns among a set of micro-blogs by detecting targets and identifying sentiment polarities towards categorized topics that describe the detected targets. Experimental results shown in this paper illustrate performance of applying the proposed system to Arabic and English datasets. Some comparisons between the proposed system and other existing system are included in this work as well.
Developing a Model Guidelines Addressing Legal Impediments to Open Access to Publicly Funded Research Data in Malaysia

The objective of this study is to develop a model guidelines addressing legal impediments to open access to publicly funded research data in Malaysia. Previous studies have identified legal impediments to open access arising from intellectual property, confidentiality, privacy, national security, patent and tort laws. The legal impediments have not been fully addressed by public research funding agencies in Malaysia, thus the need for a model guidelines to be developed. This study conducted a comparative analysis of the principles/policies/guidelines on open access to research data of the civil society, government bodies, research funding agencies and research institutions in Australia, Canada, the EU, the UK and the USA. This comparative analysis attempts to identify the appropriate measures to address the legal impediments to open access to research data. This model guidelines is of international standard and suitable for adoption by public research funding agencies and research institutions in Malaysia. Hence, the model guidelines can become a benchmark in pursuing the objective of enabling open access to publicly funded research data in Malaysia.
Directions for improvement of the federal state educational standard of secondary education

Н. В. Кочергина, А. А
The Effects of Ability Tracking on the Academic Performance in the Secondary School: Evidence from South Korea

Ability tracking creates a homogenous learning environment within the classroom, by segregating high-performing and low-performing students into different groups. This paper exploits a unique policy change in South Korea combined with comprehensive micro dataset to measure the causal consequences of ability tracking, compared to the allocating students to mixed ability groups. Before the policy change, high schools selected students according to their prior academic achievement, whereas under the new policy, students are randomly assigned to high schools. Variations in the timing of the policy implementation across districts provide a difference-in-difference research design. We find that tracking benefits high performing students who study more and gain from a superior learning environment, with the expense of the academic achievement of the low-performing students. The transition to the ability mixing system caused a reduction in the students’ probability of getting into top-tier colleges. The distribution of the academic performance of the students under the non-tracking system converged to the mean, and the variance in college entrance test score across students declined. The underlying mechanism behind these results is that the positive benefits of having better peers dominate the tailored guidance of teachers. This paper emphasizes the importance of the design of the education policy in human capital development of a country.
Optimality Implies Kernel Sum Classifiers are Statistically Efficient

We propose a novel combination of optimization tools with learning theory bounds in order to analyze the sample complexity of optimal kernel sum classifiers. This contrasts the typical learning theoretic results which hold for all (potentially suboptimal) classifiers. Our work also justifies assumptions made in prior work on multiple kernel learning. As a byproduct of our analysis, we also provide a new form of Rademacher complexity for hypothesis classes containing only optimal classifiers.
A Novel Steganographic Approach for Embedding Secret Text in Multiple Images with Saw Tooth Pattern

Steganography is the science of sending the data secretly with the use of effective carrier.The ultimate focus of steganography is to hide the secret data from eavesdroppers.At the same time, this approach should keep the carrier medium's quality and it should be hard to hack the information by the eavesdroppers.This paper introduces a novel approach to embed the secret text in multiple image files as frame by frame.Initially the encrypted secret text will be spli t in to multiple frames as per the number of carrier images.Then each frame is rearranged as per Saw toot h pattern to increase the complexity for the eavesdroppers.The rearranged frames with the prefix of frame number will be embedded in carrier images one by one.As the reverse process, the receiver receives the set of Stego -images and the system will collect all the frames from the stego-images and arrange as per the frame order, then it decrypts using the secret key and produce the secret text.
PRINCIPALES PROBLEMAS METODOLÓGICOS EN LA ENSEÑANZA DEL SUBSISTEMA FONÉTICO-FONOLÓGICO ESPAÑOL EN EL ÁMBITO DE LOS ESTUDIOS UNIVERSITARIOS POLACOS

The problems concerning teaching methodology in the domain of phonetics and phonology of Spanish have not as yet been properly addressed within the considerably young Polish studies of the Spanish language. The author of the present article, based on his own research and teaching experience, suggests a number of solutions and practical remarks related to teaching methodology of contemporary Spanish phonetics and phonology within Spanish language and literature studies in Poland. The so-called ”comprehensive approach” has been verified for compliance with the most recent achievements of modern Spanish phonetic and phonological studies.
The simple location-based authentication method using multi-layer display in Korea

The importance of selecting an environment appropriate authentication method is the most crucial decision in designing secure systems.In particular, authentication such as login process is more considering with security threat.This paper introduces the method of location-based authentication using multi-layer display which is the ability to authenticate pc and mobile users based on location-based information they would carry out anyway.The system generates authentication information on multi-layer display by users' current location information captured by smartphones, PCs, time, and weather information to authenticate users.We develop a simple model and application for how to perform location-based authentication, which is working on the multi-layer display, describe the benefits of our method.Our preliminary findings support that this is a meaningful approach, whether used to increase usability or increase security and simplicity.
Face Image Retrieval Using Discriminative Ternary Census Transform and Spatial Pyramid Matching

Face image retrieval is a process to efficiently select one or more faces from face databases which are similar to the query face image. This study proposes a new face image retrieval method where images are indexed using Discriminative Ternary Census Transform Histogram (DTCTH) which captures the discriminative structural properties of an image by avoiding unwanted background information. It encodes local micro structures of an image such as line, edge, corners etc. and uses a dynamic threshold during image transformation thus makes it more stable against intensity fluctuation. Global structure of the Discriminative Transformed image is captured using Spatial Pyramid representation during histogram index computation. The computed histogram index is used as face image descriptor. The computed histogram index along with the face image is stored in the database. In this way face image database is built. In the retrieval phase, when a query image is given, the histogram index is computed using the same process and then distance between the feature vectors of query images and feature vectors of the database images are computed. Images with less distance are registered as output. The proposed solutions are experimentally evaluated on the standard constrained FRAV2D, JAFFE and FERET face image databases. Experimental result shows that the face retrieval method is very effective on databases containing face images with facial expression and intensity variations.
Genetic programming methods for reinforcement learning

Reinforcement Learning (RL) algorithms can be used to optimally solve dynamic decision-making and control problems. With continuous-valued state and input variables, RL algorithms must rely on function approximators to represent the value function and policy mappings. Commonly used numerical approximators, such as neural networks or basis function expansions, have two main drawbacks: they are black-box models offering no insight in the mappings learnt, and they require significant trial and error tuning of their meta-parameters. In addition, results obtained with deep neural networks suffer from the lack of reproducibility. In this talk, we discuss a family of new approaches to constructing smooth approximators for RL by means of genetic programming and more specifically by symbolic regression. We show how to construct process models and value functions represented by parsimonious analytic expressions using state-of-the-art algorithms, such as Single Node Genetic Programming and Multi-Gene Genetic Programming. We will include examples of nonlinear control problems that can be successfully solved by reinforcement learning with symbolic regression and illustrate some of the challenges this exciting field of research is currently facing.
Hierarchical Relation Extraction Based On Bidirectional Long Short-Term Memory Networks

Relation extraction is an important task in the field of natural language processing (NLP). Most of the present methods extract each relation in isolation, without considering the hierarchical semantic information between relations. A novel loss function to optimize model of relation extraction based on hierarchical relation has been proposed in this paper. The experimental results show that the proposed model outperforms most of the present methods.
Aprendizaje cooperativo mediante herramientas digitales en el ámbito universitario

Teamwork is a special relevance for professionals in the educational field.One of the problems in the students is the management of the groups, which prevents the success of the projects and the adequate resolution of the tasks.Information and Communication Technologies (ICT) in Education are presented as an alternative to improve this problem.In this sense, digital skills are also essential learning for the student´s professional performance.Therefore, this project integrates both aspects, cooperative work, digital competence, developing theory and practices activities in various subjects of Pedagogy Degree.Specifically, we want to offer training that improves the student´s motivation and participation in the classroom.So that they become apprentices through interaction with others, sharing objectives, distributing responsibilities, etc.At the same time, we want to enhance the role of ICT, facilitating these interaction processes and the joint resolution of problems.The main of this study was to design materials, resources, and activities in the virtual classrooms of various subjects for an order to develop cooperative learning.For this, technologies and tools were used, such as Trello or Drive.In addition, resources have been introduced such as Nearpod, Padlet, and Socrative.Finally, the questionnaires of motivation, learning styles, cooperation, and satisfaction have been designed.
TOAD: A Tool for Recommending Auto-Refactoring Alternatives

Developers often face usability problems when trying to adopt refactoring tools. We replicate a user study to identify and categorize these problems, and we propose a tool that guides the developer to select the correct piece of code for Extract Method refactoring. Our tool works during the workflow of refactoring activities and selects candidate selections that (i) are syntactically correct and (ii) meet the necessary preconditions for Extract Method.
Co-DesignMR: An MR-based Interactive Workstation Design System Supporting Collaboration

Digital prototyping and evaluation using 3D modeling and digital human models are becoming more practical for customizing products to the preference of a user. However, the 3D modeling is less accessible to casual users, and digital human models suffer from insufficient body data and less intuitive illustration on how people use the product or how it accommodates to their body. Recently, VR-supported 'Do It Yourself' design has achieved real-time ergonomic evaluation with users themselves by capturing their poses, however, it lacks reliability and quality of design. In this paper, we explore a multi-person interactive design approach that enables designer, user, and even ergonomist to collaborate to achieve the effective and reliable design and prototyping tasks. Mixed Reality that utilizes Hololens and motion tracking devices had been developed to provide instant design feedback and evaluation, and to experience prototyping in physical space. We evaluate the system based on the usability study, where casual users and designers are engaged in the interactive process of designing items with respect to the body information, the preference, and the environment.
A Consumer Protection Approach to Platform Content Moderation

Congress should consider legislation to regulate the content moderation practices of platforms. Failure to act will leave platform users unprotected and will allow other countries, notably the European Union and China, to seize global leadership in yet another area of tech policy. But a law requiring content rules against the most salient kinds of harmful platform content including hate speech, terrorist material and disinformation campaigns would not pass constitutional muster under the First Amendment. In contrast a consumer protection approach to content moderation might be effective and pass First Amendment scrutiny. The Federal Trade Commission, on its own or with authorization from Congress, could treat the failure to establish and maintain a procedurally adequate content moderation program as an unfair practice. This would effectively require platforms to have a content moderation program in place that contains content rules, enforcement procedures and due process protections including disclosure, mechanisms to ask for reinstatement and an internal appeals process, but it would not mandate the substance of the platformâ€™s content rules. It would respond to strict First Amendment scrutiny as a narrowly crafted requirement that burdens speech no more than necessary to achieve the compelling government purpose of preventing an unfair trade practice. In addition, or alternatively, the FTC might be authorized to use its deception authority to require platforms to say what they do and do what they say in connection with content moderation programs. The FTC would treat failure to disclose key elements of a content moderation program as a material omission, and the failure to act in accordance with its program as a deceptive or misleading practice. Its First Amendment defense would rest on the compelling government interest in preventing consumer deception. The unfairness version would be more effective but less likely to survive a constitutional challenge. The pure disclosure version would be less effective, but more likely to be found consistent with current First Amendment jurisprudence. One additional advantage of this consumer protection approach is that it does not require controversial modification of Section 230 immunities for platforms.
Phase-coded computational imaging for depth of field extension

We present a computational imaging approach, combining a phase-coded computational camera with a corresponding CNN-based deblurring network, that enables extended depth of field images. The simulations demonstrate promising results achieving significant depth of field extension.
Training time reduction for network architecture search using genetic algorithm

Designing the optimal architecture of neural networks is an important issue. However, since this is difficult even for experienced experts, automatic optimization of the network architecture is required. In this study, we regard this issue as a combinatorial optimization problem, and utilize genetic algorithm to optimize the network architecture. Because training the networks, which are represented by individuals in GA, takes a long time, a novel method to reduce the training time by inheriting the weights of the trained network is proposed. From experimental results, our proposed method achieved the time reduction and higher accuracy than a conventional method.
Trilho Verde: Enhancing field trips with mobile learning

In this article we present a case study developed in the area of Mobile Learning where an interactive mobile application is used to support learning in the context of a field trip. This tool will guide students along a pre-established itinerary, notifying them of relevant locations found along the way. In each of these key places, a set of activities are presented in order to complement the information that can be inferred from the observation of some specific natural elements and/or to test the students' knowledge. With this work, we hope to contribute to the enrichment of the range of teaching/learning tools available to schools, especially those where resources are scarcer. The adopted developing methodology was the design science approach. The feedback received from the school during the Demonstration phase, where a first prototype was presented, was very positive, giving us the necessary motivation to improve the designed application and complete the last two phases of this approach in future work.
FACTORS INFLUENCING STUDENT’S ACHIEVEMENT IN LEARNING ENGLISH OF INDONESIAN DEPARTMENT AT STKIP YAYASAN ABDI PENDIDIKAN PAYAKUMBUH

The purpose of this study was to find out the factors influencing student’s achievement in learning English of indonesian department at STKIP Yayasan Abdi Pendidikan Payakumbuh. This study was carried out of descriptive qualitative research by employing questionnaire and peer debriefs to check the trustworthiness of the data. It was given to 25 students from indonesian department selected by using purposive sampling technique. The findings of this study show that the highest score of the factors that influence the learning achievement of the students is the Internal factor that is Psychology factors with the score 815. It can be concluded that the psychology factors as positively atmosphere which extrinsically motivated them in learning especially in English.Keywords
THE INFLUENCES OF REGIONAL CREDIT GUARANTEE (JAMKRIDA), WORKING CAPITAL, AND GOVERNMENT’S ROLE ON MICRO, SMALL AND MEDIUM ENTERPRISES (MSMEs) WELFARE IN DENPASAR CITY

The Micro Small and Medium Enterprises (MSMEs) in Denpasar city faced a various obstacles in developing their business such as limited access to capital or collateral despite having a feasible business, limited working capital conditions and lack of government's role in order to encourage the development of MSMEs.This study aims to analyze the effect of regional credit guarantee, working capital and government"s role on income and welfare of Denpasar"s MSMEs and welfare of Denpasar"s MSMEs through income.The research use Partial Least Square (PLS) approach analysis.The study was conducted on Denpasar city by examining 87 samples from 652 populations.Determination of sample with stratified random sampling.The results of the analysis show that regional credit guarantee, working capital and government"s role significantly affect income of Denpasar"s MSMEs.Regional credit guarantee, working capital and income affect welfare of Denpasar"s MSMEs while government"s role has no significant effect on welfare of Denpasar"s MSMEs.Regional credit guarantee, working capital and government"s role have an indirect effect on welfare of Denpasar"s MSMEs through income.
A Novel Frequent Pattern Mining Technique for Prediction of User Behavior on Web Stream Data

In recent years, as the size of the online databases increases, content in the web pages also increases, then the human behavior towards the online content has become a major issue for decision making.Hence, the need for extracting knowledge from high dimensional online content using automated techniques also increased.To address the decision making issues, a novel dynamic model is required to discover the required knowledge from these databases.In this paper, a novel filter based user navigation pattern mining model is designed and implemented on the large online steaming databases.Experimental results proved that the present filtered based frequent pattern mining model efficiently predicts the user navigation patterns with high accuracy and less runtime.
A Portfolio Optimization Algorithm Using Fuzzy Granularity Based Clustering

Clustering algorithms are applied to numerous problems in multiple domains including historic data analysis, financial markets analysis for portfolio optimization and image processing. Recent years have witnessed a surge in use of nature inspired computing (NIC) techniques for data clustering to solve various real world optimization problems. Granular Computing (GC) is an emerging technique to handle pieces of information, known as information granules. In this paper, an ensemble of fuzzy clustering using Particle Swarm Optimization and Granular computing for stock market portfolio optimization. The model is then tested on stocks listed in Hong Kong Stock Exchange. Experimental results suggested that clusters formed through Fuzzy Particle Swarm Optimization (FPSO) with Granular computing are well suited and efficient for portfolio optimization. For comparison, we have used a benchmark index of Hong Kong Stock Exchange called as Hang Sang Composite Index (HSCI). Results proved that results of proposed approach are better in comparison to benchmark results of HSCI.
Results from experimental and design thinking driven programs

This issue includes a number of papers from the Innovation 4 Change program led by Silvia Petocchi, Chief Executive Officer, and Raghu Movva, Director, at the College des Ingenieurs, in collaboration with Politecnico di Torino during the spring of 2019. Tuuli Utriainen has also been involved in the program as a facilitator from CERN IdeaSquare.