Reviewerly/exHarmony huggingface dataset:
Improved unwrapped phase retrieval method of a fringe projection profilometry system based on fewer phase-coding patterns

In this paper, based on two additional phase-coding patterns, an improved phase demodulation method is proposed. First, six equally spaced coding phases in the interval [$ - \pi $-π, $\pi $π] are embedded in different periods of the coded fringes following a certain sequence. Subsequently, since a group of phase orders can be uniquely determined by the four adjacent coding phases, the phase-order map of the object can be generated. To ensure the accuracy of decoding results, the interference coding numbers should be corrected in advance. In the meantime, the connected regions exhibiting the same orders are classified and then labeled for simplifying the decoding process. The simulation results verify the feasibility of the proposed method. By two groups of 3D imaging experiments, the applicability of this method to multiple objects and discontinuous objects is confirmed.
Diacritic segmentation technique for arabic handwritten using region-based

Arabic is a broadly utilized alphabetic composition framework on the planet, and it has 28 essential letters. The letters in order was first used to compose messages in Arabic, most prominently the Qur'an the holy book of Islam. However, Arabic language has diacritics in the word or letters which are not something extra or discretionary to the language, rather they are a vital piece of it. By changing some diacritics may change both the syntax and semantics of a word by turning a word into another. However, the current researches address the foreground image and consider the diacritics as noises or secondary images. Thus, it is not suitable for Arabic handwritten. The diacritics will be removed from the image and this will lead to losing some good features. Furthermore, to extract the diacritics, the region-based segmentation technique is used. The image will be measured based on the region properties by first finding the connected component in binary image, and then we will determine the best area range measurement in that region for each image. The proposed technique region based has been tested in nine different images with different handwritten style, and successfully extracted secondary foreground images (diacritics) for each image.
A Complete Classification and Clustering Model to Account for Continuous and Categorical Data in Presence of Missing Values and Outliers †

Classification and clustering problems are closely connected with pattern recognition where many general algorithms have been developed and used in various fields. Depending on the complexity of patterns in data, classification and clustering procedures should take into consideration both continuous and categorical data which can be partially missing and erroneous due to mismeasurements and human errors. However, most algorithms cannot handle missing data and imputation methods are required to generate data to use them. Hence, the main objective of this work is to define a classification and clustering framework that handles both outliers and missing values. Here, an approach based on mixture models is preferred since mixture models provide a mathematically based, flexible and meaningful framework for the wide variety of classification and clustering requirements. More precisely, a scale mixture of Normal distributions is updated to handle outliers and missing data issues for any types of data. Then a variational Bayesian inference is used to find approximate posterior distributions of parameters and to provide a lower bound on the model log evidence used as a criterion for selecting the number of clusters. Eventually, experiments are carried out to exhibit the effectiveness of the proposed model through an application in Electronic Warfare.
A quantum three pass protocol with phase estimation for many bits transfer

In this research, we propose a protocol for many bits transfer which is improved from quantum three pass protocol by using quantum phase estimation algorithm. In which, each party encrypt and decrypt the message by their own key. In addition, instead of attached in the carrier qubits, the key distribution is attached into the phase. Therefore, in the proposed protocol, the phase estimation is used to estimate the key distribution. Finally, the comparison between proposed protocol and original one is given to show the better performance.
Three-Weight Codes over Rings and Strongly Walk Regular Graphs

We construct strongly walk-regular graphs as coset graphs of the duals of codes with three non-zero homogeneous weights over $${\mathbb {Z}}_{p^m},$$ for p a prime, and more generally over chain rings of depth m, and with a residue field of size q, a prime power. In the case of $$p=m=2,$$ strong necessary conditions ( diophantine equations) on the weight distribution are derived, leading to a partial classification in modest length. Infinite families of examples are built from Kerdock and generalized Teichmüller codes. As a byproduct, we give an alternative proof that the Kerdock code is nonlinear.
Penggunaan Feature Selection di Algoritma Support Vector Machine untuk Sentimen Analisis Komisi Pemilihan Umum

At this time sentiment analysis is very widely used by people to see the extent of people's sentiments towards an object. Objects that can be used in sentiment analysis can be various kinds, for example about the product regarding receipt by consumers, agencies or institutions regarding the performance of the agency. Whereas for this study taking sentiment analysis of the State Institution namely the General Election Commission (KPU) about the sentiments of the implementation of the ELECTION simultaneously and also the results of the implementation of the ELECTION which have become the subject of discussion by netizens on social media. So this research takes retweet data and retention comments from Twitter social media users. The algorithm used in this study is Support Vector Machine (SVM), with optimization of the use of Weight by Correlation Feature Selection (FS). The results of cross validation SVM without FS are 66.49% for accuracy and 0.716 for AUC. Whereas SVM with FS is 81.18% for accuracy and 0.943 for AUC. Very significant improvement with the use of Weight by Correlation Feature Selection (FS).
A Multi Purpose and Large Scale Speech Corpus in Persian and English for Speaker and Speech Recognition: the DeepMine Database

DeepMine is a speech database in Persian and English designed to build and evaluate text-dependent, text-prompted, and text-independent speaker verification, as well as Persian speech recognition systems. It contains more than 1850 speakers and 540 thousand recordings overall, more than 480 hours of speech are transcribed. It is the first public large-scale speaker verification database in Persian, the largest public text-dependent and text-prompted speaker verification database in English, and the largest public evaluation dataset for text-independent speaker verification. It has a good coverage of age, gender, and accents. We provide several evaluation protocols for each part of the database to allow for research on different aspects of speaker verification. We also provide the results of several experiments that can be considered as baselines: HMM-based i-vectors for text-dependent speaker verification, and HMM-based as well as state-of-the-art deep neural network based ASR. We demonstrate that the database can serve for training robust ASR models.
A Spectral Clustering Based Filter-Level Pruning Method for Convolutional Neural Networks

Convolutional Neural Networks (CNNs) usually have millions or even billions of parameters, which make them hard to be deployed into mobile devices. In this work, we present a novel filter-level pruning method to alleviate this issue. More concretely, we first construct an undirected fully connected graph to represent a pre-trained CNN model. Then, we employ the spectral clustering algorithm to divide the graph into some subgraphs, which is equivalent to clustering the similar filters of the CNN into the same groups. After gaining the grouping relationships among the filters, we finally keep one filter for one group and retrain the pruned model. Compared with previous pruning methods that identify the redundant filters by heuristic ways, the proposed method can select the pruning candidates more reasonably and precisely. Experimental results also show that our proposed pruning method has significant improvements over the state-of-the-arts.
On Field Gesture-Based Robot-to-Robot Communication with NAO Soccer Players

Gesture-based communication is commonly used by soccer players during matches to exchange information with teammates. Among the possible forms of gesture-based interaction, hand signals are the most used. In this paper, we present a deep learning method for recognizing robot-to-robot hand signals exchanged during a soccer game. A neural network for estimating human body, face, hands, and foot position has been adapted for the application in the robot soccer scenario. Quantitative experiments carried out on NAO V6 robots demonstrate the effectiveness of the proposed approach. Source code and data used in this work are made publicly available for the community.
Game-Watching Should be More Entertaining: Real-Time Application of Field-Situation Prediction to a Soccer Monitor

This paper describes an extension to a soccer monitor used in the RoboCup Soccer Simulation 2D League. The aim of the extension is to make the experience of watching games more entertaining. The audio effects and the visualization are focused on this purpose. The extended soccer monitor simulates the supporters’ excitement in watching a game by estimating the time cycle until the next goal, which is called SituationScore. This paper describes how SituationScore is obtained using a machine learning model and also describes the resulting soccer monitor.
Multiagent System for Mutual Collaboration Classification for Cancer Detection

A multiagent system (MAS) is a mechanism for creating goal-oriented autonomous agents in shared environments with communication and coordination facilities. Distributed data mining benefits from this goal-oriented mechanism by implementing various distributed clustering, classification, and prediction techniques. Hence, this study developed a novel multiagent model for distributed classification tasks in cancer detection with the collaboration of several hospitals worldwide using different classifier algorithms. A hospital agent requests help from other agents for instances that are difficult to classify locally. The agents communicate their beliefs (calculated classification), and others decide on the benefit of using such beliefs in classifying instances and adjusting their prior assumptions on each class of data. A MAS model state and behavior and communication are then developed to facilitate information sharing among agents. Regarding accuracy, implementing the proposed approach in comparison with typically different noncommunicated distributed classifications shows that sharable information considerably increases the classification task accuracy by 25.77%.
Variability-Aware Datalog

Variability-aware computing is the efficient application of programs to different sets of inputs that exhibit some variability. One example is program analyses applied to Software Product Lines (SPLs). In this paper we present the design and development of a variability-aware version of the Souffl\'{e} Datalog engine. The engine can take facts annotated with Presence Conditions (PCs) as input, and compute the PCs of its inferred facts, eliminating facts that do not exist in any valid configuration. We evaluate our variability-aware Souffl\'{e} implementation on several fact sets annotated with PCs to measure the associated overhead in terms of processing time and database size.
Educational research into socio-economic dynamics of university graduate employment: Triple analogy-based physics-and-engineering approach to labor market oscillations

Better student understanding of the dynamic trends in graduate employment requires the development of the author's description of this multidisciplinary social problem.This educational paper is focused on an author-proposed engineering-friendly description of oscillatory dynamics in the employment market for university graduates.This didactical paper widely uses computational methods of oscillations theory, theory of electrical and hydraulic circuits as well as concepts of physical analogies and similarity.The generalized character of the employment-related oscillations in the studied social system of employees was didactically enhanced through the original introduction of two technical analogies with similar oscillations in the electrical system of an LC-field-effect transistor oscillator and the mechanical system of a hydraulic ram pump.The author-proposed triple physics-and-engineering analogy for the periodic oscillations in the socio-economic problem in graduate employment provides a broadening of the cross-disciplinary ideas of engineering students about oscillatory dynamics in the social, electrical and hydraulics systems. It was found in the case of the Donbass State Engineering Academy (Kramatorsk, Ukraine), that this original author's approach provides simultaneous enhancement of the cross-disciplinary undergraduate engineering curriculum in the courses of economics, management, higher education pedagogy, physics, hydraulics and electrical engineering.
Human Action Recognition Based on a Spatio-Temporal Video Autoencoder

Due to rapid advances in the development of surveillance cameras with high sampling rates, low cost, small size and high resolution, video-based action recognition systems have become more commonly used in various computer vision applications. Human operators can be supported with the aid of such systems to detect events of interest in video sequences, improving recognition results and reducing failure cases. In this work, we propose and evaluate a method to learn two-dimensional (2D) representations from video sequences based on an autoencoder framework. Spatial and temporal information is explored through a multi-stream convolutional neural network in the context of human action recognition. Experimental results on the challenging UCF101 and HMDB51 datasets demonstrate that our representation is capable of achieving competitive accuracy rates when compared to other approaches available in the literature.
v-trel: Vocabulary Trainer for TracingWord Relations - An Implicit Crowdsourcing Approach

In this paper, we present our work on developing a vocabulary trainer that uses exercises generated from language resources such as ConceptNet and crowdsources the responses of the learners to enrich the language resource.We performed an empirical evaluation of our approach with 60 non-native speakers over two days, which shows that new entries to expand Concept-Net can efficiently be gathered through vocabulary exercises on word relations.We also report on the feedback gathered from the users and an expert from language teaching, and discuss the potential of the vocabulary trainer application from the user and language learner perspective.The feedback suggests that v-trel has educational potential, while in its current state some shortcomings could be identified.
Autonomy Motivators, Serious Games, and Intention Toward ISP Compliance

The growing number of security breaches has become a major concern in organisations. Most often, such security breaches are related to internal employees due to their indirect or direct actions leading to information security policy (ISP) violations. Therefore, understanding employees’ security behaviour and intrinsic motivation towards ISP compliance with respect to autonomy is critical. This study aims to find out whether the autonomy intrinsic motivator can be influenced by the Decisions and Disruptions (D-D) table-top game to enhance security awareness and, in turn, reinforce behavioural intention towards ISP compliance. We developed pre- and post-assessment tests on intrinsic motivation to find out whether there is a significant improvement in test scores after participants experience D-D gameplay. Thirty postgraduate students participated in the study. Overall results confirmed that the autonomy intrinsic motivator is positively influenced by the game and has a positive effect on the behavioural intention to comply with ISPs.
Implementation of Augmented Reality of Android Based Animal Recognition using Marker Based Tracking Methods

Abstract Augmented Reality (AR) is a technology that combines 2 dimensions with 3 dimensions in realtime. Augmented reality can be applied in various things, one of them is in the field of education. In the world of augmented reality technology education can be used as a means of introduction of aquatic animals. Augmented reality serves to display 3-dimensional objects and their information by scanning markers. Markers made as scanning objects are carried out by the smartphone camera and will display objects in 3 dimensions. Based on testing the distance measurement of the camera against the marker, the optimal distance obtained from the camera can read the marker at a distance of 8 - 77 cm. This augmented reality application has been running well on smartphones and has featured 3-dimensional objects along with its information. Applications that have been made to facilitate the introduction of aquatic animals.
LA-ICP-MS in situ analyses of the pyrites in Dongyang gold deposit, Southeast China: Implications to the gold mineralization

The Dongyang gold deposit is a newly discovered epithermal deposit in Fujian Province, Southeast China, along the Circum-Pacific metallogenic belt. Herewith, the authors present mineralogical, scanning electron microscope, and laser ablation inductively coupled clasma mass spectrometry analysis to reveal the relations between Au and Te, As, S, Fe, etc., and discuss the gold precipitation process. The pyrites in this deposit are Fe-deficient, and are enriched in Te and As. The authors infer that As was mainly in form of As-complexes, and Te-Au-Ag inclusions/solid solution also exsits in the Py I. Along with the depletion of Te and As, they were less active chemically in the Py II, and Au may be incorporated into As-rich and Fe-deficient surface sites by chemisorption onto As-rich growth surfaces. Because of the incorporation of new fluid, Te and As became the most active chemically in the Py III, which was the main elements precipitation stage, and As dominantly substituted for S in the lattice of pyrite, due to the more reducing condition. The authors propose Au was in form of invisible gold, and the incorporation of gold can be considered as post-pyrite event, while the Au-bearing minerals were result of post incorporation of gold in arsenian pyrite.
Entry application software to identify the development of reading and writing vocabulary

Abstract The results of the reading literacy test for Indonesian children at national and international levels were low. Literacy tests are carried out by measuring aspects of understanding, using, and reflecting the results of reading in writing that contains vocabulary. Vocabulary is one of the important things that supports the development of student literacy. Thus, the vocabulary is important to study. This study identified the development of reading and writing vocabulary of junior and senior high school students. This research is descriptive quantitative. The data of this study were in the form of vocabulary derived from books of high school Indonesian students grade VII to grade XII based on the 2013 curriculum and the results of the writing of junior and high school students from grade VII to grade XII. Data were collected by entry application software. For data analysis, the linkage technique of comparing and contrasting was used. The results showed that the development of the number of Indonesian language entries in junior and high school student books was not good, tending to be stagnant. Compared with the results of similar studies in other countries, Indonesian language student books with the number of entries slightly less support literacy development include critical, creative, communicative, and collaborative thinking skills of junior and high school students. Conversely, the development of written vocabulary of middle school students has increased significantly.
Prefigurative Post-Politics as Strategy: The Case of Government-Led Blockchain Projects

Critically engaging with literature on post-politics, blockchain and algorithmic governance, and drawing also on knowledge gained from undertaking a three-year empirical study, the purpose of this article is to better understand the transformative capacity of government-led blockchain projects.Analysis of a diversity of empirical material, which was guided by a digital ethnography approach, is used to support the furthering of the existing debate on the nature of the post-political as a condition and/or strategy.Through these theoretical and empirical explorations, the article concludes that while the post-political represents a contingent political strategy by governmental actors, it could potentially impose an algorithmically enforced post-political 'condition' for the citizen.It is argued that the design, features and mechanisms of government-led projects are deliberately and strategically used to delimit a citizens' political agency.In order to address this scenario, we argue that there is a need not only to analyse and contribute to the algorithmic design of blockchain projects (i.e. the affordances and constraints they set), but also to the metapolitical narrative underpinning them (i.e. the political imaginaries underlying the various government-led projects).
Who is to blame? Exploring accountability in fraud victimisation

Purpose The purpose of this paper is to examine the discourses surrounding accountability as it relates to fraud victimisation. Design/methodology/approach This paper is based upon interviews with 31 professionals across the fraud justice network (FJN) in the UK and Canada. Findings The paper highlights the complexities that surround participant’s perspectives of liability when it comes to fraud. Professionals articulated responsibility falling across the spectrum of victims, offenders and third parties. Further, it is evident that perspectives of accountability are largely influenced by the various types of frauds that exist and the ways in which victims incur losses. Research limitations/implications Interviews with selected FJN professionals may not be representative of those across the broader sector in each country. Despite this, there was still a diversity in views which highlights the tensions that currently exist as to where accountability is positioned. Practical implications The findings clearly indicate that accountability is not directed at any one party, rather there appears to be an interplay across offenders, victims and third parties. While the offender is arguably the one who should be held most accountable for their actions, a lack of official responses to fraud offending means that the offender is largely invisible. For those who place accountability on the victim, there is evidence of neoliberal discourses that argue for prudential citizens, or those who take responsibility for their own actions. This is in contrast to those who believed that victims could not be held accountable for actions beyond their control, and instead third parties were accountable, and should increase their role in education and awareness. Originality/value This paper articulates the discourses of accountability that exist for fraud, and how the current thinking can contribute to interactions with victims, as well as current responses to victimisation. Further work is required to better identify the criteria against which victims are being held accountable, as well as better understand who bears responsibility with responses to fraud victimisation.