Reviewerly/exHarmony huggingface dataset:
Mouse Behavior Analysis Based on Artificial Intelligence as a Second-Phase Authentication System

Nowadays, a wide variety of computer systems use authentication protocols based on several factors in order to enhance security. In this work, the viability of a second-phase authentication scheme based on users’ mouse behavior is analyzed by means of classical Artificial Intelligence techniques, such as the Support Vector Machines or Multi-Layer Perceptrons. Such methods were found to perform particularly well, demonstrating the feasibility of mouse behavior analytics as a second-phase authentication mechanism. In addition, in the current stage of the experiments, the classification techniques were found to be very stable for the extracted features.
The Implementation of Problem-Based Learning on Elementary School Students of Sukoharjo

This research aimed to describe the implementation of the problembased learning model in elementary school students of Sukoharjo.The research method was qualitative research.The data were collected using observation, interview and documentation.The data were analysed using interactive data analysis.The research results describe that the teacher has not applied the problem-based learning model to the elementary school students of Sukoharjo maximally.The learning was still teacher-centred.Only certain students were active, which resulted in the gap in the learning process among the students.Students did not work together well in groups and they looked bored in learning.
Image Quality Assessment through Contour Detection

This paper presents a study on objective image quality assessment from the segmentation perspective. Instead of modeling the image distortion or degradation process, the assessment is achieved from the end user's point of view, i.e. how the image is interpreted by segmentation operation. The accessibility of informative features in an image subject to certain degradation is evaluated by comparing the contour of input and reference images. A new quality index is defined with the F-measure in the contour mapping measure framework. The demonstrative experiments illustrate how the index works for the prepared images and the preliminary results prove the effectiveness of the proposed approach.
Iterative Budgeted Exponential Search

We tackle two long-standing problems related to re-expansions in heuristic search algorithms. For graph search, A* can require Ω(2ⁿ) expansions, where n is the number of states within the final f bound. Existing algorithms that address this problem like B and B’ improve this bound to Ω(n²). For tree search, IDA* can also require Ω(n²) expansions. We describe a new algorithmic framework that iteratively controls an expansion budget and solution cost limit, giving rise to new graph and tree search algorithms for which the number of expansions is O(n log C*), where C* is the optimal solution cost. Our experiments show that the new algorithms are robust in scenarios where existing algorithms fail. In the case of tree search, our new algorithms have no overhead over IDA* in scenarios to which IDA* is well suited and can therefore be recommended as a general replacement for IDA*.
Cross-domain mapping learning for transductive zero-shot learning

Zero-shot learning (ZSL) aims to learn a projection function from a visual feature space to a semantic embedding space or reverse. The main challenge of ZSL is the domain shift problem where the unseen test data has a large gap with the seen training data. Transductive ZSL based methods alleviate this problem by learning from both labeled data and unlabeled data to capture their common semantic information. In this paper, we propose a framework to learn a robust cross-domain mapping for transductive ZSL with an extremely efficient algorithm for model optimization. Combining with a deep model, we formulate the cross-domain mapping as a general loss function that optimizes both the projection function and discriminative visual features simultaneously in an end-to-end manner. Extensive experiments on five benchmark datasets show that the proposed Cross-Domain Mapping (CDM) model outperforms the state-of-the-art.
A Comparative Study between TPS and LRD Strategy to Enhance Students’ Reading Comprehension

The purpose of this study is to find out if there is any significant difference in students’ reading comprehension enhancement between those who were taught using Listen-Read-Discuss strategy and those who were taught using Think-Pair-Share strategy. This study is a quantitative research method using comparative design by using pre-test and post-test. This study was designed to find out the answer to the following question: Is there any significant difference in students’ reading comprehension enhancement between those who were taught by Think-Pair-Share strategy and those who were taught by Listen-Read-Discuss strategy. The sample of this study was two classes of grade VIII SMPN 10 Cimahi, Bandung. Grade VIII F was taught using Think-Pair-Share and grade VIII was taught using Listen-Read-Discuss. The instrument used for this study was a reading comprehension test, which contained 39 multiple-choice questions. The result of this study showed from the mean score that both classes had an enhancement in their reading comprehension. In addition, there was no significant difference between those who were taught using TPS and those who were taught using LRD strategy. However, it meant that both strategies were efficient in enhancing students’ reading comprehension.
Investigation of Passive Voice Occurrence in Scientific Writing

This study investigated passive voice use in theses of Graduate EFL students, Universitas Negeri Malang. The occurrences of passive voice in research method chapter in two time periods, 1985 - 2000 and 2002 – 2015 were compared. Passive voice occurrence in chapter of research method was also investigated then it was compared with the active. Passive voice occurrence in theses before 2002 investigated which was 1458, and 1171 in theses published after 2000 indicated the fact that today passive voice use in scientific writing indeed less frequent compared to the past. In addition, unlike the findings of some previous studies that claimed passive voice occurrence is still more dominant than the active in method section of scientific writing, this study found that, even in chapter of research method, active voice today occurs more frequently. It was 1883 occurrences of active voice and 1171 of passive voice were found.
Criminological Characteristics of Drug Addiction in the Republic of Kazakhstan: Basic Concepts, Signs and General Approaches to Their Assessment

The relevance of the study is due to the need to consider such antisocial social phenomenon as &amp;ldquo;narcotism&amp;rdquo;, which includes two components - &amp;ldquo;drug addiction&amp;rdquo; and &amp;ldquo;drug business&amp;rdquo;. The researchers came to the conclusion that the concept of narcotism itself has not yet acquired an unambiguous or more or less well-established semantic status, and does not have wide recognition in international legal acts and documents of authoritative international organizations. In foreign vocabulary, the concept of &amp;quot;narcotism&amp;quot; is difficult to differentiate and separate from the concept of &amp;quot;drug addiction&amp;quot;. Therefore, it is necessary to achieve the highest possible consistency in approaches to understanding the essence of this phenomenon.&#x0D; &#x0D; The content of the article is aimed at identifying the key point denoting the main core around which complex processes are taking place in the desired field related to the concept of drug addiction. Reflecting and arguing, the authors, in essence, were based on an analysis of a well-known range of historical, theoretical information, international legal and national regulatory documents, using the methods of critical analysis, synthesis and comparison of theoretical and legal sources. This made it possible to consider the phenomenon under study from the standpoint of different conceptual approaches.&#x0D; &#x0D; The article revealed that drug addiction affects the widest range of social problems - from deviant social behavior to national security issues. The attention is focused on the fact that drug addiction from the position of law is characterized as an antisocial social phenomenon associated with the illegal consumption and distribution of drugs, their appearance and the existence of goods and services in consumer circulation. A basic definition has been formulated that outlines the scope or distribution of narcotism. It was concluded that &amp;ldquo;narcotism&amp;rdquo; should be a differentiated designation of a negative social phenomenon and contain a description of the whole diversity of social manifestations and processes that are directly related to drugs that are in a state of contradiction with the interests of society. In turn, drug trafficking, while remaining the largest component of drug addiction, is its most dynamic part. The last sign is largely associated with a direct effect - the drug business, which is a generating or producing principle in narcotic drugs. The materials of the article are of practical value for researchers and law enforcement officials dealing with the problems of drug addiction and narcotism.
OPEN ACCESS TO SCIENTIFIC INFORMATION AS A FORM OF INFORMATION AND ANALYTICAL SUPPORT OF SCIENTIFIC ACTIVITIES AND COMMUNICATION

The relevance of the issue is associated with the massive publication of scientific research results in the network, the creation of electronic scientific repositories, and building a specific informational scientific environment.Interpretations of the concepts "open access", "open educational resources" (OER), "open knowledge", "electronic open access journal systems" are given.The advantages of using open access to scientific information, features of the "green" and "golden" ways are determined, as well as the electronic platforms that host Ukrainian scientific open access e-journals are analyzed, which constitute the purpose of the article.It is emphasized that the key factors in the emergence of the movement to open access are intensive informatization of society, the emergence and rapid spread of the Internet, as well as the relatively high and ever-increasing price of a subscription to scientific journals.As a result, at the beginning of the 21st century, several international documents appeared, they declared free online access to scientific literature and introduced the concept of open access.Examples of information projects and portals that provide open access to Ukrainian scientific sources: "Scientific periodicals of Ukraine at OJS", "Science of Ukraine: Access to Knowledge" are given.It is noted that the legal framework for open access is provided by Creative Commons licenses.Examples of open electronic journaling systems used in national scientific institutions are given: Open Journal System (OJS), Joomla!The conclusions prove that awareness of the policy of open access to scientific information and the study of the work principles of the open access scientific electronic publications are important parts of the scientific activity of higher education institution teachers, employees of scientific institutions, doctoral students, and post-graduate students.
An Improved Hardware Accelaration Architecture of Binary Neural Network With 1T1R Array Based Forward/Backward Propagation Module

An improved hardware acceleration architecture of RRAM based Binary Neural Networks (BNNs) is proposed and demonstrated. In the architecture, a 1T1R array-based propagation module is introduced and designed to realize the computing acceleration of fully parallel Vector-Matrix Multiplication (VMM) in both forward and backward propagation. By using the propagation module, high acceleration is achieved in both training (50×) and inference (273×) process.
Aplikasi Prediksi Kelulusan Mahasiswa Berbasis K-Nearest Neighbor (K-NN)

Students are important assets for an educational institution and for this reason, it is necessary to pay attention to the student's graduation rate on time. Presentation of the ups and downs of students' ability to complete their studies on time is one of the elements of campus accreditation assessment. Based on data from the Study Program Section in the last 3 years the student graduation presentation is only 25% of the total students who can complete their studies on time. In this study using the K-Nearest Neighbor algorithm which aims to be able to identify student graduation in new cases by adapting solutions from previous cases that have closeness to new cases. This algorithm has the role to get the value of the closeness of the new case to the old case, which in turn the most population in area K with the closest value obtained by the student is predicted whether to pass on time or not on time. This study uses Roger S. Pressman's waterfalll method, namely Communication, Planning, Modeling, and Construction. Based on the tests carried out using K-Fold Cross Validation, the highest accuracy in the third model was 80% when folded 4th and 61% when the K value = 1. While testing using the Confusion Matrix obtained the highest accuracy of 98% at K = 1 for classification "Timely", and 98% at K = 2 for classification "Not Timely"
Code Generator from Mockups

The automation of tasks is increasingly a current practice in the organizational environment, and this practice reduces the need for manpower and often reduces the errors associated with the human factor. In the present document a solution will be presented to automatically generate the source code of a mockup, having as input an image corresponding to the prototype. In the development of this project techniques of Deep Learning will be used, especially Convolutional Neural Networks for the detection and classification of objects in images. The developed solution provides the code base of a mockup in less than 60 seconds, with an average error rate 15.85%.
A visible human body slice segmentation method framework based on OneCut and adjacent image geometric features

As a recent research hot issue, obtaining the accurate 3 D organ models of Visible Human Project (VHP) has many significances. Therefore, how to extract the organ regions of interest (ROI) in the large-scale color slice image data set has become an urgent issue to be solved. In this paper, we propose a method framework based on OneCut algorithm and adjacent image geometric features to continuously extract the main organ regions is proposed. This framework mainly contains two parts: firstly, the OneCut algorithm is used to segment the ROI of target organ in the current image; secondly, the foreground image (obtained ROI) is corroded into several seed points and the background image (other region except for ROI) is refined into a skeleton. Then the obtained seed points and skeleton can be transmitted and mapped onto the next image as the input of OneCut algorithm. Thereby, the serialized slice images can be processed continuously without manual delineating. The experimental results show that the extracted VHP organs are satisfactory. This method framework may provide well technic foundation for other related application.
Central Similarity Hashing via Hadamard matrix.

Hashing has been widely used for efficient large-scale multimedia dataretrieval. Most existing methods learn hashing functions from data pairwisesimilarity to generate binary hash codes. However, in practice we find onlylearning from the local relationships of pairwise similarity cannot capture theglobal distribution of large-scale data, which would degrade thediscriminability of the generated hash codes and harm the retrievalperformance. To overcome this limitation, we propose a new global similaritymetric, termed as \emph{central similarity}, to learn better hashing functions.The target of central similarity learning is to encourage hash codes forsimilar data pairs to be close to a common center and those for dissimilarpairs to converge to different centers in the Hamming space, whichsubstantially improves retrieval accuracy. In order to principally formulatethe central similarity learning, we define a new concept, \emph{hash center},to be a set of points scattered in the Hamming space with a sufficient distancebetween each other, and propose to use Hadamard matrix to constructhigh-quality hash centers efficiently. Based on these definitions and designs,we devise a new hash center network (HCN) that learns hashing functions byoptimizing the central similarity w.r.t. these hash centers. The centralsimilarity learning and HCN are generic and can be applied for both image andvideo hashing. Extensive experiments for both image and video retrievaldemonstrate HCN can generate cohesive hash codes for similar data pairs anddispersed hash codes for dissimilar pairs, and achieve noticeable boost inretrieval performance, i.e. 4\%-13\% in MAP over latest state-of-the-arts. Thecodes are in: \url{https://github.com/yuanli2333/Hadamard-Matrix-for-hashing}
Scalable Similarity-Consistent Deep Metric Learning for Face Recognition

With the development of deep learning, deep metric learning (DML) has achieved great improvements in face recognition. Specifically, the widely used softmax losses in the training process often bring large intra-class variations, and feature normalization is only exploited in the testing process to compute the pair similarities. To bridge the gap, we impose the intra-class cosine similarity between the features and weight vectors in softmax loss larger than a margin in the training step and extend it from four aspects. First, we explore the effect of a hard sample mining strategy. To alleviate the human labor of adjusting the margin hyper-parameter, a self-adaptive margin updating strategy is proposed. Then, a normalized version is given to take full advantage of the cosine similarity constraint. Furthermore, we enhance the former constraints to consider the intra-class and inter-class constraints simultaneously in the exponential feature projection space. The extensive experiments on the labeled face in the wild (LFW), youtube faces (YTF), and IARPA Janus benchmark A (IJB-A) datasets demonstrate that the proposed methods outperform the mainstream DML methods and approach the state-of-the-art performance.
Vision beyond Pixels: Visual Reasoning via Blocksworld Abstractions

Deep neural networks trained in an end-to-end fashion have brought about exceptional advances in computer vision, especially in computational perception. We go beyond perception and seek to enable vision modules to reason about perceived visual entities such as scenes, objects and actions. We introduce a challenging visual reasoning task, Image-Based Event Sequencing (IES) and compile the first IES dataset, Blocksworld Image Reasoning Dataset (BIRD). Motivated by the blocksworld concept, we propose a modular approach supported by literature in cognitive psychology and children's development. We decompose the problem into two stages - visual perception and event sequencing, and show that our approach can be extended to natural images without re-training.
Creation of a corpus with semantic role labels for Hungarian

In this article, an ongoing research is presented, the immediate goal of which is to create a corpus annotated with semantic role labels for Hungarian that can be used to train a parser-based system capable of formulating relevant questions about the text it processes.We briefly describe the objectives of our research, our efforts at eliminating errors in the Hungarian Universal Dependencies corpus, which we use as the base of our annotation effort, at creating a Hungarian verbal argument database annotated with thematic roles, at classifying adjuncts, and at matching verbal argument frames to specific occurrences of verbs and participles in the corpus.
Causal Embeddings for Recommendation: An Extended Abstract

Recommendations are commonly used to modify user’s natural behavior, for example, increasing product sales or the time spent on a website. This results in a gap between the ultimate business ob- jective and the classical setup where recommenda- tions are optimized to be coherent with past user be- havior. To bridge this gap, we propose a new learn- ing setup for recommendation that optimizes for the Incremental Treatment Effect (ITE) of the policy. We show this is equivalent to learning to predict recommendation outcomes under a fully random recommendation policy and propose a new domain adaptation algorithm that learns from logged data containing outcomes from a biased recommenda- tion policy and predicts recommendation outcomes according to random exposure. We compare our method against state-of-the-art factorization meth- ods, in addition to new approaches of causal rec- ommendation and show significant improvements.
Using Fuzzy Cognitive Mapping to Collate Expert Knowledge: Diverse impacts of renewable energy on biodiversity and the UN Sustainable Development Goals

The European Union (EU) is committed to tackling the issue of climate change, which poses serious risks to the global environment and human well-being. Supporting renewable energy is a key policy direction for the EU to lower its contributions to climate change. However, renewable energy technologies have diverse effects on the environment and on society. These effects can be considered a complex system of interacting elements and are challenging to assess. Conceptual models are a way of synthesizing this information to obtain an overview of the system and essential insights. We present the results of an activity to assess the impacts of EU renewable energy policies on overseas biodiversity and the United Nations (UN) Sustainable Development Goals (SDGs). This was carried out as part of the EKLIPSE (EKLIPSE (Establishing a European Knowledge and Learning Mechanism to Improve the Policy-Science-Society Interface on Biodiversity and Ecosystem Services) mechanism to synthesise environmental knowledge in response to specific requests by decision-makers at the European level. We carried out a participatory process to collate expert knowledge into a conceptual model using a Fuzzy Cognitive Mapping Approach (Özesmi and Özesmi 2004), with the Mental Modeler software for mapping (Gray et al. 2013). The participants were guided to connect significant EU policies associated with renewable energy, the technologies they support, and known impacts of these technologies on biodiversity and the SDGs, drawing on a preliminary review of the literature. The individual models obtained were integrated into a single model (see Suppl. material 1 for images). This was then subject to network analysis, which reveals the collective effects of different renewable energy technologies (RETs) on the wider socioecological system. Our findings highlight that RETs have complex and at times disparate effects on biodiversity and the SDGs, acting through a variety of mediating processes. They benefit the SDGs on balance, particularly climate-related SDGs. Mitigation of biodiversity impacts remains a concern, and processes such as habitat change were found to be influential here. Our results suggest that policymakers must focus on implementing appropriate environmental impact assessments, guided by these mediating processes. This would minimize any negative environmental impacts of RETs, while maximizing the benefits.