Reviewerly/exHarmony huggingface dataset:
A depth-based nearest neighbor algorithm for high-dimensional data classification

Nearest neighbor algorithms like k-nearest neighbors (kNN) are fundamental supervised learning techniques to classify a query instance based on class labels of its neighbors. However, quite often, huge volumes of datasets are not fully labeled and the unknown probability distribution of the instances may be uneven. Moreover, kNN suffers from challenges like curse of dimensionality, setting the optimal number of neighbors, and scalability for high-dimensional data. To overcome these challenges, we propose an improvised approach of classification via depth representation of subspace clusters formed from high-dimensional data. We offer a consistent and principled approach to dynamically choose the nearest neighbors for classification of a query point by i) identifying structures and distributions of data; ii) extracting relevant features, and iii) deriving an optimum value of k depending on the structure of data by representing data using data depth function. We propose an improvised classification algorithm using a depth-based representation of clusters, to improve performance in terms of execution time and accuracy. Experimentation on real-world datasets reveals that proposed approach is at least two orders of magnitude faster for high-dimensional dataset and is at least as accurate as traditional kNN.
New Bounds on the Partial Hamming Correlation of Wide-Gap Frequency-Hopping Sequence Sets With Low Hit Zone

In frequency-hopping systems, there should be a wide frequency-hopping gap between two adjacent frequencies in case the received signals are prone to be interfered by staying at one frequency point for a long time. In this paper, a bound on the maximum partial Hamming autocorrelation of wide-gap frequency-hopping sequences (WGFHSs), two bounds on the maximum partial Hamming correlation of WGFHS sets, two bounds on the maximum Hamming correlation of WGFHS sets with low hit zone and two bounds on the maximum partial Hamming correlation of WGFHS sets with low hit zone are derived. Moreover, the proposed bounds are tighter than the Eun-Jin-Hong-Song bound, Zhou-Tang-Niu-Udaya bounds, Peng-Fan-Lee bounds and Niu-Peng-Liu bounds in the sense that they can be achieved by the parameters of some WGFHSs. To the best of our knowledge, these bounds are obtained for the first time in the literature.
Information and Communication Technology in a Global World

This study takes its departure from ongoing debate about teachers’ (collective) “continuing professional development” (CPD). The overall aim is to highlight active teachers’ perceptions on developing a self-understanding of their complex role in daily practice by using digital tools. The following research questions guided the study: How can teachers make schools more relevant and engaging? How can students’ achievement increase? In what way can teachers provide high-quality education for all students? Three perspectives will be guiding the study: Information and Communication Technology (ICT), learning, and special education. The participants in the study are 21 staff members in preschool, primary school, and a recreation center with whom we conducted structured individual interviews and focus group discussions. Field notes were also taken during the interview and discussion sessions. The findings reveal the importance of the teachers’ deeper understanding of students’ creativity, critical thinking, communication, and collaboration. Furthermore, the most powerful thing teachers can do is to design engaging, meaningful, and authentic work and technology-enhanced learning experiences. In order to improve learning in a digital world, the teachers must be engaged and supported by professional learning opportunities to continually improve and strengthen their digital competencies and teaching practices. The theoretical standpoint is the norm model as an analytical tool to understand the teachers’ perceptions. The concept of “norm” is a collective term for the factors and structures that are regarded as a normal balance between the aspects of value/will, system conditions/possibilities, and cognition/knowledge.&#x0D;
Related-Key Differential Cryptanalysis of Full Round CRAFT

CRAFT is a lightweight tweakable block cipher introduced in FSE 2019. One of the main design criteria of CRAFT is the efficient protection of its implementations against differential fault analysis. While the authors of CRAFT provide several cryptanalysis results in several attack models, they do not claim any security of CRAFT against related-key differential attacks. In this paper, we utilize the simple key schedule of CRAFT to propose a systematic method for constructing several repeatable 2-round related-key differential characteristics with probability $$2^{-2}$$. We then employ one of these characteristics to mount a key recovery attack on full-round CRAFT using $$2^{31}$$ queries to the encryption oracle and $$2^{85}$$ encryptions, and $$2^{41}$$ 64-bit blocks of memory.. Additionally, we manage to use 8 related-key differential distinguishers, with 8 related-key differences, in order to mount a key recovery attack on the full-round cipher with $$2^{35.17}$$ queries to the encryption oracle, $$2^{32}$$ encryptions and about $$2^6$$ 64-bit blocks of memory. Furthermore, we present another attack that recovers the whole master key with $$2^{36.09}$$ queries to the encryption oracle and only 11 encryptions with $$2^7$$ blocks of memory using 16 related-key differential distinguishers.
PENGARUH MODEL PEMBELAJARAN JIGSAW TERHADAP HASIL BELAJAR IPS SISWA KELAS 4 SEKOLAH DASAR

THE INFLUENCE OF JIGSAW LEARNING MODEL IN INCREASING THE LEARNING OUTCOME SOCIAL STUDIES (IPS) SUBJECT FOR STUDENTS IN CLASS 4 ELEMENTARY SCHOOL. The purpose of this research was to re-analyze the use of cooperative learning type jigsaw model in increasing the learning outcome in social studies subject. The research method was meta-analysis. The research began with formulating research problems, then trancing relevant research result to be analyzed. The data was collected by browsing article, electronic journals, and essay using “learning outcomes”, “jigsaw model”, and “Social Studies of Primary Education”. The searching result showed that there were 12 articles, electronic journals, and essay but only 10 relevant articles, electronic journals, and essay. The data were analyzed using qualitative descriptive analysis. The analysis result showed that cooperative learning type jigsaw model could improve student’s learning from the lowest 9,89% to the highest 85,56% and the mean was 46,09%
Perceived Usefulness of Multimodal Voice Assistant Technology

Smart speaker devices are appealing to consumers, but the perceived usefulness of the multimodal voice experience is not fully understood. The purpose of this study was to evaluate the extent to which cognitive load, the relevance of visual information, and personality influence the perceived usefulness of multimodal voice assistant technology in a within-subjects repeated measures design. A multimodal voice prototype was created to answer the question, “What are some extreme weather conditions?” Nine variants, including 3, 5 and 7 system responses with relevant, irrelevant or no information presented on a screen were included. Three tasks were embedded within each condition (Stroop task, sort M&amp;Ms and no task). Perceived usefulness score, recall, personality score, and fluctuations in galvanic skin response (GSR) values were the subjective and objective measures. The findings suggest that when there’s a smaller number of responses/words for the participant to attend to, and subsequently recall, in addition to relevant visual feedback to aid in that recall, they perceive the voice assistant experience to be more useful, while task conducted exhibits marginal significance in determining PU. Scores of conscientiousness, openness to experience, agreeableness, and neuroticism were successful in predicting some variation in the PU responses, while GSR data was not. It is highly recommended that UX designers of the multimodal interface create succinct voice responses with relevant visual feedback to accompany it, and to keep the main use cases of these products in mind to increase the experience’s PU and subsequent behavioral intention to use the product.
Quotation Detection and Classification with a Corpus-Agnostic Model

The detection of quotations (i.e., reported speech, thought, and writing) has established itself as an NLP analysis task.However, state-of-the-art models have been developed on the basis of specific corpora and incorporate a high degree of corpus-specific assumptions and knowledge, which leads to fragmentation.In the spirit of task-agnostic modeling, we present a corpus-agnostic neural model for quotation detection and evaluate it on three corpora that vary in language, text genre, and structural assumptions.The model (a) approaches the state-of-the-art on the corpora when using established feature sets and (b) shows reasonable performance even when using solely word forms, which makes it applicable for non-standard (i.e., historical) corpora.
Deep intrinsic decomposition trained on surreal scenes yet with realistic light effects

Estimation of intrinsic images still remains a challenging task due to weaknesses of ground-truth datasets, which either are too small or present non-realistic issues. On the other hand, end-to-end deep learning architectures start to achieve interesting results that we believe could be improved if important physical hints were not ignored. In this work, we present a twofold framework: (a) a flexible generation of images overcoming some classical dataset problems such as larger size jointly with coherent lighting appearance; and (b) a flexible architecture tying physical properties through intrinsic losses. Our proposal is versatile, presents low computation time, and achieves state-of-the-art results.
Named Entity Recognition in Information Security Domain for Russian

In this paper we discuss the named entity recognition task for Russian texts related to cybersecurity.First of all, we describe the problems that arise in course of labeling unstructured texts from information security domain.We introduce guidelines for human annotators, according to which a corpus has been marked up.Then, a CRF-based system and different neural architectures have been implemented and applied to the corpus.The named entity recognition systems have been evaluated and compared to determine the most efficient one.
Voice Conversion With CycleRNN-Based Spectral Mapping and Finely Tuned WaveNet Vocoder

In this paper, we present a novel framework for a voice conversion (VC) system based on a cyclic recurrent neural network (CycleRNN) and a finely tuned WaveNet vocoder. Even though WaveNet is capable of producing natural speech waveforms when fed with natural speech features, it still suffers from speech quality degradation when fed with oversmoothed features, such as spectral parameters estimated from a statistical model. One way to address this problem is to introduce oversmoothed features while developing a WaveNet model. However, in a VC framework, providing oversmoothed spectral features of a target speaker for WaveNet modeling is not straightforward owing to the difference in the time-sequence alignment from that of a source speaker. To overcome this problem, we propose the use of a cyclic spectral conversion network, i.e., CycleRNN, capable of performing a conversion flow, i.e., source-to-target, and a cyclic flow, i.e., to generate self-predicted target spectra. The CycleRNN spectral model is trained using both conversion and weighted cyclic losses. To finely tune WaveNet, a pretrained multispeaker WaveNet model is optimized using the self-predicted features of the corresponding target speaker of a speaker conversion pair. The experimental results demonstrate that 1) the proposed CycleRNN-based spectral model for WaveNet fine-tuning significantly improves the naturalness of the converted speech waveforms, giving an overall mean opinion score of 3.50; and 2) the proposed model yields the highest speaker conversion accuracy, giving an overall speaker similarity score of 78.33%, which is a significant improvement compared with conventional WaveNet fine-tuning using natural target features.
Deep Dialog Act Recognition using Multiple Token, Segment, and Context Information Representations

Automatic dialog act recognition is a task that has been widely explored over the years. In recent works, most approaches to the task explored different deep neural network architectures to combine the representations of the words in a segment and generate a segment representation that provides cues for intention. In this study, we explore means to generate more informative segment representations, not only by exploring different network architectures, but also by considering different token representations, not only at the word level, but also at the character and functional levels. At the word level, in addition to the commonly used uncontextualized embeddings, we explore the use of contextualized representations, which are able to provide information concerning word sense and segment structure. Character-level tokenization is important to capture intention-related morphological aspects that cannot be captured at the word level. Finally, the functional level provides an abstraction from words, which shifts the focus to the structure of the segment. Additionally, we explore approaches to enrich the segment representation with context information from the history of the dialog, both in terms of the classifications of the surrounding segments and the turn-taking history. This kind of information has already been proved important for the disambiguation of dialog acts in previous studies. Nevertheless, we are able to capture additional information by considering a summary of the dialog history and a wider turn-taking context. By combining the best approaches at each step, we achieve performance results that surpass the previous state-of-the-art on generic dialog act recognition on both the Switchboard Dialog Act Corpus (SwDA) and the ICSI Meeting Recorder Dialog Act Corpus (MRDA), which are two of the most widely explored corpora for the task. Furthermore, by considering both past and future context, similarly to what happens in an annotation scenario, our approach achieves a performance similar to that of a human annotator on SwDA and surpasses it on MRDA.
Fuzzy Logic Applied for Pronunciation Assessment

Pronunciation teaching is an important stage in language learning activities. This article tackles the pronunciation scoring problem where research has demonstrated relatively low human-human and low human-machine agreement rates, which makes teachers skeptical about their relevance. To overcome these limitations, a fuzzy combination of two machines scores is suggested. The experiments were carried in the context of Algerian pupils learning to read Arabic. Although the native language of Algerian pupils is a dialect of Arabic, Modern Standard Arabic remains difficult for them with difficult sounds to master and letters close in their pronunciation. The article presents a fuzzy evaluation system including both oral reading fluency, and intelligibility. The fuzzy system has shown that despite the disparities between human ratings, its scores correspond at least to one of their ratings and most of the time its ratings are in favor of learners. Therefore, fuzzy logic, more favorable than thresholding systems, encourages learners to pursue their training.
Comparison of Machine Learning Approaches for Industry Classification Based on Textual Descriptions of Companies

This paper addresses the task of categorizing companies within industry classification schemes.The dataset consists of encyclopedic articles about companies and their economic activities.The target classification schema is build by mapping linked open data in a semi-supervised manner.Target classes are built bottom-up from DBpedia.We apply several state of the art text classification techniques, based both on deep learning and classical vectorspace models.
Multi-UAV Automatic Dynamic Obstacle Avoidance with Experience-shared A2C

With the increasing usage of UAV in reconnaissance, agriculture, logistics and entertainment, it's necessary for multi-UAV to automatically avoid the dynamic obstacles in order to ensure the safety of drones and livings in environment. The automatic obstacle avoidance is a classic multiple agent decision-making problem. Traditional algorithms, limited in the method of state classification and policy selection, are not applicable in such a complex scene including randomly dynamic scene and cooperative decision-making. In this paper, Advantaged Actor-Critic Algorithm is introduced to train multi-UAVs to automatically avoid obstacles and optimize avoidance decision-making model. Deep Q Learning, Actor-Critic (AC) and Advantaged Actor-Critic (A2C) algorithm are compared. And to further maximize the performance, we specifically improved A2C algorithm towards the multi-UAV scene by sharing experiences between UAVs to expedite the training process. Our experimental result shows our Experience-shared A2C (ES-A2C) algorithm leads to a higher performance and a shorter training period.
Quantum mechanics and speed limit of ultrafast local control in spin chains

We study optimization of fidelity for ultrafast transformation of a spin chain via external control of a local exchange coupling. We show that infidelity of such a process can be dramatically decreased by choosing a proper control profile in nonadiabatic time domain, predict main features of this profile analytically, corroborate them numerically with a gradient search algorithm, and discuss the corresponding quantum speed limit. For ultrafast transformations, the qualitative features of the obtained optimal control are system independent. Moreover, the main restrictions on its shape do not depend on the transformation time and remain valid up to the adiabatic limit. Our results can be applied to control a broad variety of quantum systems.
Translationese Features as Indicators of Quality in English-Russian Human Translation

We use a range of morpho-syntactic features inspired by research in register studies (e.g.Biber, 1995;Neumann, 2013) and translation studies (e.g.Ilisei et al., 2010;Zanettin, 2013;Kunilovskaya and Kutuzov, 2018) to reveal the association between translationese and human translation quality.Translationese is understood as any statistical deviations of translations from non-translations (Baker, 1993) and is assumed to affect the fluency of translations, rendering them foreign-sounding and clumsy of wording and structure.This connection is often posited or implied in the studies of translationese or translational varieties (De Sutter et al., 2017), but is rarely directly tested.Our 45 features include frequencies of selected morphological forms and categories, some types of syntactic structures and relations, as well as several overall text measures extracted from Universal Dependencies annotation.The research corpora include English-to-Russian professional and student translations of informational or argumentative newspaper texts and a comparable corpus of non-translated Russian.Our results indicate lack of direct association between translationese and quality in our data: while our features distinguish translations and non-translations with the near perfect accuracy, the performance of the same algorithm on the quality classes barely exceeds the chance level.
A new solution of Dijkstra's concurrent programming problem

research-article Share on A new solution of Dijkstra's concurrent programming problem Author: Leslie Lamport Massachusetts Computer Associates, Inc. Massachusetts Computer Associates, Inc.View Profile Authors Info & Claims Concurrency: the Works of Leslie LamportOctober 2019 Pages 171–178https://doi.org/10.1145/3335772.3335782Published:04 October 2019Publication History 4citation188DownloadsMetricsTotal Citations4Total Downloads188Last 12 Months14Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Factors Contributing to the Success of Information Security Management Implementation

Information Security Management (ISM) concerns shielding the integrity, confidentiality, availability, authenticity, reliability and accountability of the organisation's information from unauthorised access in order to ensure business continuity and customers' confidence.The importance of information security (IS) in today's situation should be given due attention.Recognising its importance, organisations nowadays have devoted wide efforts in protecting their information.They establish information security policy, processes, and procedures as well as reengineer their organisational structures to align with ISM principles.Regardless of the efforts, security incidents continue to occur in many organisations.This phenomenon shows that the current implementation of ISM is still ineffective due to unaware of the factors contributing to the success of ISM.Thus, the objective of this paper is to identify ISM success factors and their elements through a large-scale survey.The survey involves 243 practitioners from statutory bodies, public and private organisations in Malaysia.The results of the survey indicate that top management, IS coordinator team, ISM team, IS audit team, employees, third parties, IS policy, IS procedures, resource planning, competency development and awareness, risk management, business continuity management, IS audit and IT infrastructure are the factors that contribute to the success of ISM implementation.These factors shall guide practitioners in planning and refining ISM implementation in their organisations.
Critical Success Factors to Improve the Adoption of Social Media in Teaching and Learning: A Case Study at a Traditional University

Mobile devices and social media tools are two emerging technologies that have positively influenced teaching and learning practices at traditional universities. Regardless of the popularity of social media, few lecturers make use of these tools for educational purposes. The objective of this paper was to identify critical success factors to improve the adoption of social media among lecturers at a traditional university in South Africa. The unified theory of acceptance and use of technology (UTAUT) was chosen as the theoretical foundation for this study. A quantitative, survey data collection method was uses to collect data. A questionnaire was distributed to all academics at the university, with a response rate of 39 %. From these, descriptive statistics were used to analyse data and the Pearson chi-square test was used to establish the association amongst different variables. The following CSFs were identified to enhance emerging technologies at traditional universities: management support; provide adequate ICT infrastructure and resources; provide sufficient training for lecturers, and introduce a champion to promote social media. The study, therefore, recommends that these four critical success factors must be considered by universities to adopt social media in teaching and learning.&lt;div&gt;&lt;hr align="left" size="1" width="33%" /&gt;&lt;div&gt; &lt;/div&gt;&lt;div&gt; &lt;/div&gt;&lt;div&gt; &lt;/div&gt;&lt;/div&gt;