Reviewerly/exHarmony huggingface dataset:
Deep Features and Extreme Learning Machines based Apparel Classification

With the development of the new technologies, e-commerce, automated recommendation systems, smartphone applications have become inevitable tools of our lives. An apparel recommendation application highly attracts both women and men. An apparel recommendation application initially needs to determine the clothe types in a given image and then searches the internet for similar potential clothes. This task is quite challenging. With the development of the deep learning technologies, researchers were encouraged to propose solutions for such challenging problems. In this paper, features from pre-trained deep convolutional neural networks (CNN) and extreme learning machines (ELM) are used for apparel classification. To do it, AlexNet, VGGNet and ResNet models are considered. Fc6, fc7 and fc1000 layers of the pre-trained CNN models are used for feature extraction. These features are either used individually or concatenated form. These features are then classified with the ELM classifier. Classification accuracy is used to evaluate the achievements of the feature vectors. According to the obtained results, ResNet features obtain 60.42% accuracy score on apparel classification system (ACS) dataset. We also compare the obtained results with some of the published results on the same dataset. The comparisons show that our proposed scheme outperforms.
A New Time–Frequency Attention Tensor Network for Language Identification

Abstract In this paper, we aim to improve traditional DNN x-vector language identification performance by employing wide residual networks (WRN) as a powerful feature extractor which we combine with a novel frequency attention network. Compared with conventional time attention, our method learns discriminative weights for different frequency bands to generate weighted means and standard deviations for utterance-level classification. This mechanism enables the architecture to direct attention to important frequency bands rather than important time frames, as in traditional time attention methods. Furthermore, we then introduce a cross-layer frequency attention tensor network (CLF-ATN) which exploits information from different layers to recapture frame-level language characteristics that have been dropped by aggressive frequency pooling in lower layers. This effectively restores fine-grained discriminative language details. Finally, we explore the joint fusion of frame-level and frequency-band attention in a time–frequency attention network. Experimental results show that firstly, WRN can significantly outperform a traditional DNN x-vector implementation; secondly, the proposed frequency attention method is more effective than time attention; and thirdly, frequency–time score fusion can yield further improvement. Finally, extensive experiments on CLF-ATN demonstrate that it is able to improve discrimination by regaining dropped fine-grained frequency information, particularly for low-dimension frequency features.
Exhibiting <i>Mutator VR</i>: Procedural Art Evolves to Virtual Reality

Abstract Organic Art (William Latham and Stephen Todd, 1987) was reincarnated by the same authors with programmer and artist Peter Todd as Mutator2 (2013); Mutator2 was transitioned into VR as Mutator VR (2016) with programmer and artist Lance Putnam. The authors describe the graphics and audio systems of these works, particularly the procedural generation and visual effects and the creative exploitation of these effects within an art installation. The authors mix “real” and “unreal” features and effects, inspired by surrealist art, to create highly immersive psychedelic organic experiences for the viewer-participant. Interface simplicity and discoverability is critical for VR exhibitions, as is the balance between an experience constrained by an artist's choices and a freer (but riskier) one with greater public choice. Public gallery installation of Mutator VR creates special challenges.
Inspecting Unification of Encoding and Matching with Transformer: A Case Study of Machine Reading Comprehension

Most machine reading comprehension (MRC) models separately handle encoding and matching with different network architectures.In contrast, pretrained language models with Transformer layers, such as GPT (Radford et al., 2018) and BERT (Devlin et al., 2018), have achieved competitive performance on MRC.A research question that naturally arises is: apart from the benefits of pre-training, how many performance gain comes from the unified network architecture.In this work, we evaluate and analyze unifying encoding and matching components with Transformer for the MRC task.Experimental results on SQuAD show that the unified model outperforms previous networks that separately treat encoding and matching.We also introduce a metric to inspect whether a Transformer layer tends to perform encoding or matching.The analysis results show that the unified model learns different modeling strategies compared with previous manually-designed models.
Proof-Carrying Network Code

Computer networks often serve as the first line of defense against malicious attacks. Although there are a growing number of tools for defining and enforcing security policies in software-defined networks (SDNs), most assume a single point of control and are unable to handle the challenges that arise in networks with multiple administrative domains. For example, consumers may want want to allow their home IoT networks to be configured by device vendors, which raises security and privacy concerns. In this paper we propose a framework called Proof-Carrying Network Code (PCNC) for specifying and enforcing security in SDNs with interacting administrative domains. Like Proof-Carrying Authorization (PCA), PCNC provides methods for managing authorization domains, and like Proof-Carrying Code (PCC), PCNC provides methods for enforcing behavioral properties of network programs. We develop theoretical foundations for PCNC and evaluate it in simulated and real network settings, including a case study that considers security in IoT networks for home health monitoring.
Wikipedia in Virtual Reality and HowText-based Media can be Explore in Virtual Reality

This paper discusses the first prototype of Wikipedia VR. It discusses the challenges surrounding building a text-based media application in virtual reality such as eye strain issues and focuses on the creation of an environment that can parse existing web content for consumption within 3D space. Wikipedia is used as the basis for design of a program that can parse and display long form written content for perusal and manipulation within an interactive environment. Results of research have shown that limitations of current generation virtual reality hardware may render them unsuitable to text-based applications.
Sentence Simplification for Semantic Role Labelling and Information Extraction

In this paper, we report on the extrinsic evaluation of an automatic sentence simplification method with respect to two NLP tasks: semantic role labelling (SRL) and information extraction (IE).The paper begins with our observation of challenges in the intrinsic evaluation of sentence simplification systems, which motivates the use of extrinsic evaluation of these systems with respect to other NLP tasks.We describe the two NLP systems and the test data used in the extrinsic evaluation, and present arguments and evidence motivating the integration of a sentence simplification step as a means of improving the accuracy of these systems.Our evaluation reveals that their performance is improved by the simplification step: the SRL system is better able to assign semantic roles to the majority of the arguments of verbs and the IE system is better able to identify fillers for all IE template slots.
Pedagogical model to train specialists for Industry 4.0 at University

Л. Н. Овинова, Е. Г
Trust in Archives– Trust in Digital Archival Content Framework

Trust in information found online matters now more than ever. The proliferation of fake news and “post-truths” makes it difficult for people to determine what is trustworthy. People even question digital versions of basic identity documents such as birth certificates, the processes that archivists use to preserve them, and attestations of their authenticity. Given the current landscape, we need to better understand trust in archival materials found online. Drawing from relevant literature, I developed a model to examine the relationship between trust in archives and trust in digital archival content, the Trust in Archives–Trust in Digital Archival Content (TIA-TDAC) Framework. This article outlines an empirical test of the TIA-TDAC Framework. Using measures of both levels of trust, I designed a survey to evaluate archives users and potential archives users’ trust in a diverse range of digitized and born-digital materials from a broad range of archives in the United States. Results of the survey indicate both that the association of trust in archives with trust in digital archival content could be empirically measured and that the relationship between the two trust levels was positively correlated. The article also discusses future directions for research. RESUME La confiance accordee a l’information trouvee en ligne est maintenant plus importante que jamais. La proliferation des fausses nouvelles et des « post-verites » fait en sorte qu’il est difficile pour les gens de cerner ce qui est digne de confiance. On remet meme en question les versions numeriques des pieces d’identite de base, comme les certificats de naissance, les procedes utilises par les archivistes pour les preserver, et la certification de leur authenticite. Etant donne l’etat actuel des choses, nous devons mieux comprendre la confiance accordee au materiel archivistique qui se trouve en ligne. Puisant dans les ecrits pertinents, j’ai developpe un modele pour explorer la relation entre la confiance envers les archives et la confiance envers le contenu archivistique numerique, le cadre Confiance dans les archives-confiance dans le contenu archivistique numerique (CA-CCAN). Cet article souligne un test empirique du cadre CA-CCAN. En utilisant des mesures pour les deux niveaux de confiance, j’ai elabore un sondage pour evaluer la confiance accordee par des utilisateurs et des potentiels utilisateurs d’archives a une variete de materiel numerise et ne numerique provenant d’une large selection d’archives aux Etats- Unis. Les resultats du sondage demontrent a la fois que l’association entre la confiance accordee aux archives et la confiance accordee au contenu archivistique numerique peut etre mesure de facon empirique et que la relation entre les deux niveaux de confiance demontre une correlation positive. L’article aborde egalement des orientations de recherche eventuelles.
Penerapan Algoritma C4.5 Untuk Mengukur Tingkat Kepuasan Mahasiswa

Measuring student satisfaction is very important to note given the high level of competition in the education world as knowledge and technology develops. In measuring student satisfaction it is very important to consider whether the services expected by students are in accordance with what is received. Measuring student satisfaction will greatly help a university in improving the quality of service which will also have an impact on increasing the number of students. The system that is needed is the Application of Data Mining in Measuring Student Satisfaction. Where students will be objects that provide an assessment / opinion on variables that have characteristics, namely Tangiable, Reability, Responsivnes, Assurance, Empathy. This system was built with the C4.5 algorithm and Testing system with RapidMiner 7.5 application rocks
Learning dynamic polynomial proofs

Polynomial inequalities lie at the heart of many mathematical disciplines. In this paper, we consider the fundamental computational task of automatically searching for proofs of polynomial inequalities. We adopt the framework of semi-algebraic proof systems that manipulate polynomial inequalities via elementary inference rules that infer new inequalities from the premises. These proof systems are known to be very powerful, but searching for proofs remains a major difficulty. In this work, we introduce a machine learning based method to search for a dynamic proof within these proof systems. We propose a deep reinforcement learning framework that learns an embedding of the polynomials and guides the choice of inference rules, taking the inherent symmetries of the problem as an inductive bias. We compare our approach with powerful and widely-studied linear programming hierarchies based on static proof systems, and show that our method reduces the size of the linear program by several orders of magnitude while also improving performance. These results hence pave the way towards augmenting powerful and well-studied semi-algebraic proof systems with machine learning guiding strategies for enhancing the expressivity of such proof systems.
Financial Inclusion, Institutional Quality and Financial Development: Empirical Evidence from OIC Countries

This unique study examines the moderation effect of institutional quality (IQ) on the relationship between financial inclusion (FI) and financial development (FD) of 45 Organization of Islamic Coop...
Validation of the Online Security Behaviors and Beliefs Questionnaire with College Students in the United States

The Online Security Behaviors and Beliefs Questionnaire is a 75-item self-report inventory assessing awareness, attitudes, behaviors, and beliefs toward various aspects of cybersecurity best practices. The questionnaire was originally constructed and validated for use with adult employees in corporate settings, with the measure assessing 13 latent constructs. The goal of this study was to adapt the questionnaire to implement it with a college sample and examine if the identified factor structure for use with this population. Data were collected from 735 students at two public universities in the Southeastern region of Virginia, 676 of whom were retained for analyses. Confirmatory factor analysis using the means and variances adjusted weighted least squares method was conducted using Mplus 8.1 to assess goodness of model fit. The 13-factor model failed to meet criteria for exact or approximate model fit: χ2 (2701) = 44569.85, p < 0.001; root mean square error of approximation = 0.07, 95 percent confidence interval [0.06–0.07]; confirmatory fit index = 0.86; Tucker Lewis index = 0.85. The results suggest significant differences between cybersecurity awareness, attitudes, and beliefs between the corporate workplace and college student populations that ultimately influenced the reliability and validity of the questionnaire when adapted for use with college students in the United States. It is also possible that the theoretical underpinnings of this questionnaire may not apply, or they may apply differently, in this sample of predominantly African American and Caucasian students.
A Study of Medical Problem Extraction for Better Disease Management.

This study focuses on the extraction of medical problems mentioned in electric health records to support disease management. We experimented with a variety of information extraction methods based on rules, on knowledge bases, and on machine learning, and combined them in an ensemble method approach. A new dataset drawn from cancer patient medical records at the University of Utah Healthcare was manually annotated for all mentions of a selection of the most frequent medical problems in this institution. Our experimental results show that a medical knowledge base can improve shallow and deep learning-based sequence labeling methods. The voting ensemble method combining information extraction models outperformed individual models and yielded more precise extraction of medical problems. As an example of applications benefiting from acurate medical problems extraction, we compared document-level cancer type classifiers and demonstrated that using only medical concepts yielded more accurate classification than using all the words in a clinical note.
Universal Adversarial Perturbations to Understand Robustness of Texture vs. Shape-biased Training

Convolutional Neural Networks (CNNs) used on image classification tasks such as ImageNet have been shown to be biased towards recognizing textures rather than shapes. Recent work has attempted to alleviate this by augmenting the training dataset with shape-based examples to create Stylized-ImageNet. However, in this paper we show that models trained on this modified dataset remain as vulnerable to Universal Adversarial Perturbations (UAPs) as those trained in ImageNet. We use UAPs to evaluate, compare, and understand the robustness of CNN models with varying degrees of shape-based training. We also find that a posteriori fine-tuning on ImageNet negates features learned from training on Stylized-ImageNet. This study reveals an important current limitation and highlights the need for further research into robustness of CNNs for visual recognition.
Large-Scale Sparse Subspace Clustering Using Landmarks

Subspace clustering methods based on expressing each data point as a linear combination of all other points in a dataset are popular unsupervised learning techniques. However, existing methods incur high computational complexity on large-scale datasets as they require solving an expensive optimization problem and performing spectral clustering on large affinity matrices. This paper presents an efficient approach to subspace clustering by selecting a small subset of the input data called landmarks. The resulting subspace clustering method in the reduced domain runs in linear time with respect to the size of the original data. Numerical experiments on synthetic and real data demonstrate the effectiveness of our method.
Towards Balance and Boundaries in Public Discourse: Expressing and Perceiving Online Hate Speech (XPEROHS)

This study presents an overview and preliminary findings from the XPEROHS-project on hate speech in online contexts. The data is extracted from large-scale Facebook and Twitter corpora, while comparing linguistic instantiations of hate speech in the Danish and German languages. Findings are based on four sub-projects involving the semantics and pragmatics of denigration, the covert dynamics of hate speech, perceptions of spoken and written hate speech, and rhetorical hate speech strategies employed in online interaction. The results demonstrate both overt and covert hate speech towards minority groups, especially Muslims, that are symptomatic of larger societal othering processes and stigmatization.
Handling Occlusions in Augmented Reality for Mobile Devices

The coherent display of the spatial relationship between real and virtual elements is a desirable feature for realistic Augmented Reality (AR) systems. The system must be able to automatically estimate the depths of the real and virtual elements that compose the scene and display in the foreground of the frame the element closest to the camera. In other words, real and virtual elements must hide (totally or partially) each other according to their depths in the frame. This paper presents a method for occlusion treatment of scene elements for mobile applications. The solution presented uses pixel proportions, relative to the total pixels of the frame, occupied by the real and virtual elements to estimate their depths. Then, a skin color model based segmentation method is applied to extract the real element in frames in which it is in the foreground of the scene and, as a result, must overlap the virtual object. The results of the evaluation of the proposed method showed its superiority to those found in the literature when a set of important features are compared.
Group Re-Identification via Transferred Representation and Adaptive Fusion

Group re-identification (G-ReID) is a less-studied task. Its challenges include not only appearance changes of individuals which have been well-investigated in general person re-identification (ReID), but also group layout changes and group membership changes which are newly introduced by G-ReID. The key task of G-ReID is to learn representations robust to these changes. To address this issue, we design a Transferred Single and Couple Representation Learning Network (TSCN). The merits are two aspects: 1) Due to the lack of training samples, existing methods exploit unsatisfactory hand-crafted features. To obtain the superiority of deep learning models, we treat a group as multiple persons and transfer the labeled ReID dataset to the G-ReID dataset style to learn the single representation. 2) Taking into account neighborhood relationship in a group, we also propose the couple representation, which maintains more discriminative features in some cases. We also exploit an unsupervised weight learning method to adaptively fuse the results of different views together according to the result pattern. Extensive experimental results demonstrate the effectiveness of our approach, outperforming the state-of-the-art methods on two public datasets.
Dynamics of Proposition and Funding of Lecturers’ Research Proposal at Private Universities in Higher Education Service Institutes Region XII – Indonesia

The problem in this study was the gap between the target of the Indonesian Minister of Research, Technology and Higher Education (Menristekdikti) in the field of publication who wishes to be number one in ASEAN, meanwhile, the Higher Education Service Institute (LLDikti) Region XII of Maluku and North Maluku, which is one of the ministries' work units, has a number of lecturers who are not very active in the field of research with publication impact.This can be seen from the very small number of research proposals funded by the Directorate of Research and Community Service (DRPM) of Ministry of Research, Technology and Higher Education (Kemristekdikti) in the last 3 (three) years (2017, 2018 and 2019).Speaking of which, the objective of this study was to find the inhibiting factors that underlie the lack of involvement of lecturers at private universities (PTS) in LLDikti XII.This type of research was a qualitative study that seeks to ideologically raise the phenomenon of the low level of proposition and the pass of lecturers' research proposals in LLDikti XII.The research site was overlapping with the the Higher Education Services Institute (LLDikti) Region XII, namely Maluku Province and North Maluku Province.Data were collected through document search, interviews, and focus group discussions.The analysis process was carried out both in the middle of and post data collection process.The steps to analyze the data were: (1) data reduction, (2) data "display", (3) understanding, interpretation, (4) drawing conclusions and verification.This study found that of 2,445 lecturers in LLDikti XII, the lecturers involved in the research did not reach a quarter of the total.Likewise with the number of Private Universities involved, which is from a total of 44 Private Universities, only one third participated in research funded by DRPM.This finding indicated that the average ability of lecturers (in this case also Private Universities) in LLDikti Region XII is still very limited and requires capacity building.