Reviewerly/exHarmony huggingface dataset:
AVOCAD: Adaptive Terrorist Comms Surveillance and Interception using Machine Learning

VoIP traffic classification is becoming increasingly important in the real-time detection and disruption of drug distribution and terrorism communications. To reduce waste and inefficiency, it is important that a VoIP call detection system is highly accurate and adaptive in order to distinctly differentiate VoIP and non-VoIP calls accurately and to detect newer versions of VoIP calls if a user uses a different VoIP application to avoid detection. However, a consistent disadvantage of VoIP classifiers is that they are only useful on the VoIP version or product that they were trained on. These systems are therefore unable to accurately classify previously unseen versions of the different VoIP products. In this paper we introduce Avocad, a novel methodology for detecting different VoIP traffic products such as Skype with high accuracy. Our approach uses machine learning classifiers and network statistical features and has comparable detection accuracy and speed to existing VoIP detection applications. The uniqueness of our method is that it detects untrained versions of a VoIP application, thus helping to optimise real-time detection of VoIP traffic. Results show that our method can record an F-Measure score of up to 0.996.
Intellectualized Data Collection and Fault Diagnosis for Ships Information System

As the level of intelligent ships has been increasing rapidly, the amount information which is transmitted among systems can be quite extensive. The probability of malfunctions occurring or going to happen goes up during information transit. Therefore, the demand for fault diagnosis and fault prediction in information system would definitely grow gradually. In this paper, aimed to meet the above demand, we developed a data acquisition device based on various interfaces. The device can collect the data from ships information system and provide an excellent platform for data processing and data analysis. Combining with fault feature library and using big data mining and machine learning, it can predict and diagnose faults automatically. This device provides daily reliable protection for the system.
Name Authority Work in the Linked Data Environment

Linked data and the semantic web have been the focus of library metadata research for years, in particular, the question of how cultural heritage and library communities can leverage this technolog...
Analysis of Time Series Anomalies Using Causal InfoGAN and Its Application to Biological Data

Data generation is an inverse function of the data recognition, which is a map from a low dimensional latent space to a high dimensional data. Generator of normal data acquires the data distribution in its latent space, and is expected to generate any normal data, but unable to generate abnormal data. Therefore, the generator can be used for the anomaly detection. We propose a model of Generative Adversarial Nets (GANs) with an encoder to characterize the data anomaly. GANs are trained by normal data, and the encoder is trained to infer the latent space of the trained generator. Then, by combining the encoder and the generator, anomaly of the data input to the encoder is characterized by the data reconstruction error and by its latent representation. If any interpretable representation is obtained for the latent space, we can characterize the abnormality by the latent variable. In this study, Causal InfoGAN is trained by normal time series data to acquire temporal state variables in the latent space. Then, a temporal abnormality can be characterized by an abnormal transition in the state space. Proposed method is applied to a simple toy data, which mimics a cell division in the biological system, and it is observed that the state transition which expresses the normal time development is obtained in the latent space.
Physical Data Acquisition from Virtual Android Phone Using Genymotion

With the large scale proliferation of mobile technology and smart phones, there has been an exponential rise in the cases of digital frauds and cybercrimes. With smart phones becoming powerful day by day with enhanced security features, smart phone forensics has become very important for every organisation. But the expensive forensic tools are a big impediment which leads to a no of crimes going undetected. Towards this there is a need to undertake mobile forensics without the use of commercial software’s. This paper proposes manual data extraction technique using virtual phones created in an emulator (Genymotion) for android OS. Deleted data recovery using physical acquisition is done with the help of open source tool Autopsy and then a comparative analysis is done using another forensic tool Foremost.
Tasks for the Future Education of Elementary Schools in the Face of a Decrease in the Population

본 연구의 목적은 학령 인구감소에 따른 초등교육을 위한 이슈를 발굴하고 이를 토대로 ‘학령 인구감소에 따른 초등 학교교육을 위한 정책 플레이’를 수립하고자 하는 것이다. 이를 위하여 2차에 걸쳐 델파이를 실시하고 허쉬코비치 분석과 Borich 요구분석을 한 결과는 다음과 같다. 최우선 순위로 차별화 교육과정: 학생 수준과 요구에 따른 차별적인 교육 제공이 선정되었고 이어 미래형 교사의 역할 세분화, 다양한 학교설립운영: 소규모 학교 이슈를 해결하기 위한 초중학교 연계 학교(예, 기초교육학교로 통합), 지역사회 연계학교 등과 이를 위한 행정지원체제 변화(행정인력의 정원 및 행정시스템의 변화) 등이 선정되었다. 이상의 연구 결과를 기반으로, 16개의 이슈 중 학령인구 감소에 따라 우선적으로 개발해야 하는 이슈가 분명히 존재하는 것으로 나타났으며 이에 따라 교육 정책 차원에서 고려할 수 있는 시사점을 제시하였다.The purpose of this study is to predict potential issues in elementary education due to the decline in school age population and establish a policy for elementary school education due to the decline in the aforementioned school age population. For this, the Delphi method was conducted twice, and Hershkowitz analysis and Borich needs analysis were also carried out. The results are as follows. Differentiated curricula: Provision of differentiated education according to students’ levels and needs was selected as the top priority, which was followed by subdivision of the roles of future teachers(learning coach, online teacher, one-to-one tutor, online learning manager, parent counselor, education software developer, etc.), the establishment of various schools: elementary and middle school connected education(e.g., integration as fundamental education schools), community connection schools to cover the issues of small schools, and a change in the administrative support system(a change in the number of administrative personnel and the general administrative system). Based on these results, it was found that there were several key issues due to the decline on school age population that needed to be preferentially developed out of sixteen finkings, and accordingly, this study suggested considerable implications from a perspective of educational policy.
Distributed Network Data Security Protection for SG-eIoT

With the continuous advancement of power system reform, grid enterprises relying on new technologies such as big data, cloud computing, artificial intelligence, internet of things (IoT) and blockchain are transforming towards more open energy internet enterprises, which brings more challenges to network security. The construction of SG-eIoT has promoted the circulation of data in all aspects of the system, cleaning up the barriers of data islands and effectively improving the operational efficiency of power company. However, due to the large scale of data access points, complex business operations, and huge amount of upstream and downstream enterprises in the industry chain, security risks may exist in the information circulation process such as being tampered and untrustworthy. Aiming at the problem of information security and credibility in the distributed network environment of power enterprises, this paper studies the application of blockchain technology in business information flow, and constructs a new data management mode of weak service connection to strong data interoperability based on the characteristics of blockchain technology, such as non-tampering property, collective maintenance and transaction non-repudiation, which achieves accurate data protection, and fully spports the construction of world-class energy internet company with excellent competitiveness.
Game “Sepak Tekong” As A Method for Increasing the Ability of Reading Situation in Primary School

In every country have a game that fulfills the loaded cultural literacy.Indonesia also has a few tradisional games.One of the games that have loaded the literacy culture that is sepak tekong in the Minangkabau.The purpose of this study is so that make sepak tekong games become learning methods to improve the ability to read the situation of primary school students and preserve sepak tekong games in everyday life.The game of sepak tekong is one of the traditional Minangkabau games.sepak tekong games are carried out in groups where one person keeping of the tekong and the rest is hiding.Those in hiding read the situation from guards to be able kicking tekong.The ability to read situations can increase creativity and sensitivity of students in the scope of interpretive skills to predicting or deciding an action.This study is a library research where the implementation uses literature.The hope of researchers is to make sepak tekong games as a method of learning to read fun for primary school students, especially improving the ability to read the situation.
Building a Semantic RESTFul API for Achieving Interoperability between a Pharmacist and a Doctor using JENA and FUSEKI

Abstract Interoperability within different healthcare systems (clinics/hospitals/pharmacies) remains an issue of further research due to a barrier in sharing of the patient’s Electronic Health Record (EHR) information. To solve this problem, cross healthcare system collaboration is required. This paper proposes an interoperability framework that enables a pharmacist to access an electronic version of the patient’s prescription from the doctor using a RESTFul API with ease. Semantic technology standards like Web Ontology Language (OWL), RDF (Resource Description Framework) and SPARQL (SPARQL Protocol and RDF Query Language) were used to implement the framework using JENA semantic framework tool to demonstrate how interoperability is achieved between a pharmacy and a clinic JENA was used to generate the ontology models for the pharmacy called pharmacy.rdf and clinic called clinic.rdf. The two models contain all the information from the two isolated systems. The JENA reasoner was used to merge the two ontology models into a single model.rdf file for easy querying with SPARQL. The model.rdf file was uploaded into a triple store database created using FUSEKI server. SPARQL Endpoint generated from FUSEKI was used to query the triple store database using a RESTFul API. The system was able to query the triple store database and output the results containing the prescription name and its details in JSON and XML formats which can be read by both machines and humans.
FPGA Implementation of Directional Peer-Group Image Filter

Peer group is known as one of the simplest methodologies for image denoising in the spatial domain. Unfortunately, its filtering effectiveness will rapidly degrade while the noise density increasing. This paper introduces a modified directional peer-group filter for better restoration of images corrupted by random impulse noises. In addition, a low complexity FPGA architecture for the implementation of this simple algorithm is also demonstrated. Simulation results show that the pro-posed approach can effectively reconstruct images and preserve edges even for the image suffered from high-density noises.
Data Brushes: Interactive Style Transfer for Data Art

This paper introduces Data Brushes, an interactive web application to explore neural style transfer using models trained on data visualizations. Our application includes two distinct modes that invite casual creators to engage with deep convolutional neural networks to co-create custom artworks. The first mode, `magic markers', mimics painting with a brush on a canvas, enabling users to paint a style onto selected areas of an image. The second mode, `compositing stamps', uses a real-time method for applying style filters to selected portions of an image. Specifically, we focus on style transfer networks created from canonical and contemporary works of data visualization and data art in order to investigate the versatility and flexibility of the algorithm. In addition to enabling a novel creative workflow, the process of interactively modifying an image via multiple style transfer networks reveals meaningful features encoded within the networks, and provides insight into the effects particular networks have on different images, or different regions within a single image. To evaluate Data Brushes, we gathered expert feedback from participants of a data science symposium and ran an observational study, finding that our application facilitates the creative exploration of neural style transfer for data art and enhances user intuition regarding the expressive range of style transfer features.
INTELIGÊNCIA NÃO UNITÁRIA E SUAS CONTRIBUIÇÕES: UMA REVISÃO DA LITERATURA

Non-unitary intelligences argue directly and succinctly that there is more than one intelligence to be evaluated.This theory makes it possible to understand other existing ways of approaching intelligence and contemporary educational practices.In this sense, this article aims to discuss the importance of Howard Gardner's multiple intelligence and Robert J. Sternberg's Success Intelligence in the student's educational development.The research was conducted in books and online platforms that included 3 (three) books and 6 (six) articles that addressed this theme, the period from 2014 to 2018.The study on intelligences brings significant changes to the educational system, one of them being an integrative look at student skill development.A discussion on the subject is necessary, in order to develop best practices in the educational context integrating the development of diverse intelligences and the construction of a more democratic and more humane school.
Class-Specific Dictionary Based Semi-Supervised Domain Adaptation for Land-Cover Classification of Aerial Images

Supervised scene classification of aerial images plays an important role in land-cover classification. However, it is difficult and time-consuming to annotate the required number of samples. Moreover, the conventional classifiers cannot produce satisfactory results without sufficient labelled data. Semi-supervised domain adaptation methods can overcome this problem to some extent by transferring previously labelled data. But the feature distribution bias caused by different sensors, seasons or locations may lead to a lower performance. In order to reduce the feature distribution bias and keep the discriminative ability, we propose a novel class-specific dictionary-based semi-supervised domain adaptation (CDSDA) framework when newly labelled data are unavailable. The CDSDA first learns a discriminative class-specific dictionary in the source domain. Then the target dictionary are obtained by designing an objective function in an iterative process in order to reduce the feature distribution bias and keep the discriminative ability. The target and source features are both mapped to the new feature space by the learnt target dictionary for classification. The CDSDA method was tested on a large aerial image where two benchmark datasets serve as the training dataset. The experiments on a larger aerial image demonstrate that the CDSDA method performs better than some previous domain adaptation methods in the case of no target labelled data.
Early Reading and Writing by Using the Manakok Game as a Manifestation of Basic Literacy

Basic literacy was needed in the digital era.Given that low-class students are preoccupied with technology, it is feared that they do not recognize the Minangkabau children's game culture, one of which is the game manakok.While on the other hand, technology was increasingly making students' egocentric higher.Bringing the alpha generation closer to the culture of the manakok game in West Sumatra to succeed in the early reading and writing learning is the aim of this study.This research was conducted with literature studies.The results of the study show the emergence of the view that the manakok game as one of the cultures of Minangkabau children is assumed to be able to deliver early reading and writing learning in realizing basic literacy.Early reading and writing learning by using manakok games can be one of the new views in the world of education that learning can be realized with local wisdom.Manakok games can also build social attitudes among students by realizing familiarity in the game process.Although most students do not recognize the manakok game, familiarizing students with the culture of local wisdom is still better than the technological sophistication that shapes the ego slump.
Mathematical music theory of embodied acoustics of Ikoro music using beat-class theory

Through mathematical representation (beat-class theory) of embodied acoustics (psychoacoustics) the predominance of the musical tradition of the Ikoro drum with the Igbo’s can be traced from the past, into the present and forecasted into the future. The Ikoro music tradition has been viewed as an integral and indispensable part of Igbo culture at large (Onwubiko and Neilsen, 2019). The major musical instruments that accompany most Igbo music are percussional, such as, ichaka (beaded-gourd rattle), okpokolo (wooden claves), and igba (membrane drum) and are characterized by successions of rhythmic interchange unlimited to interesting pitch, timbre, rhythm and meter by employing shifted accents, non-accented rhythms and syncopations. In order to understand Ikoro music located in the listener’s experience (embodied psychoacoustics), we demonstrate how mathematical music theory (beat-class theory) provides the means to articulate the “mind and body” response to the stimulus of sound. By examining the aural tradition of Ikoro music of the Igbo’s through visualizations and sonifications of beat-class theory using ski-hill graphs and circular cyclic graphs, “hidden” musical structures are revealed which possess significant cultural significance.
Reliable lower bound for number of calling marine mammals in Chukchi Sea

A reliable lower bound is derived for the number of vocalizing bowhead whales and walruses in the Chukchi Sea from ten acoustic receivers deployed during NOAA's CHAOZ experiment in 2011. The lower bound is derived from extremely reliable 100% confidence intervals (CI) of the sound's locations. CI are derived from measurements of the Time Differences of Arrival (TDOA) of sound between receivers. Sequential Bound Estimation (SBE) is used to estimate the CI by assimilating the TDOA. SBE was successfully and independently evaluated by the U.S. Navy. It is a nonlinear non-Bayesian technique capable of explicitly accounting for all errors affecting uncertainty while being computationally efficient. To date, its CI always contain the true location of the source. The CI are affected by uncertainty of sound speed, TDOA, receiver location, and clock time. For two-dimensional models of location, i.e., longitude/latitude, it appears necessary to utilize isodiachrons instead of hyperbolas to obtain reliable CI because of the extreme non-homogeneity of sound speed needed to obtain correct location. [Supported by the North Pacific Research Board and Microsoft.]
Directivity of the musical instruments and voice in a real situation using beamforming techniques

Musical instruments are considered natural sound sources with specific directivities. Each instrument unique directivity needs to be identified and implemented in auralization of sound in room acoustic designs. This paper shows detailed directivity measurements on selected musical instruments, human voice, and impulse sources used in room acoustics design under realistic conditions. This work presents results that are measured using acoustic beamforming techniques. Measurements are based on simultaneous recording using a hemispherical array of 120 microphones. The directivity data are measured and analyzed in the time, and frequency domain, and effects in the near field of musical instruments are explored. Dynamic variations in the musical instruments’ directivity relative to sound power, loudness (intensity, pressure), projection, and the apparent size of the sound or volume are presented. Recorded data are presented in static or dynamic mode as well as in both 2- and 3-dimensional format using the beamforming software as well as Math lab algorithm. The results of this work are applicable toward improvement in the quality of auralization of musical instrument in simulation tools and vocal production studies and room acoustic sound filed. Evaluation of their projected radiations’ pattern contributes to a more realistic acoustic design simulations or virtual reality applications.
Effects of Cloud Computing Tools, Study Type and Task Difficulty on Cognitive Load and Performance

The purpose of this research is to investigate the effects of using cloud computing technologies, study type and task difficulty on cognitive load and students’ performance. The research was conducted as 2x2x2 complex mixed design. The two experiment groups are the first factor of design. In the first experiment group students used non collaborative cognitive tools (NCCT), in the second group students used collaborative cloud computing (CCCT) for learning tasks. Second and third factors of the research as repeated measures are study type (individual – group) and task difficulty (easy – difficult). During the experiment process four different types of tasks were given to students. These tasks were individual and easy task (1), individual and difficult task (2), in group and easy task (3) and in group and difficult task (4). The depended variables of the research are cognitive load, performance and instructional efficiency scores. The experiment group consists of 57 females and 57 males totally 114 university students. 55 students used NCCT and 59 students used CCCT for learning tasks. Three factors mixed design ANOVA were used to analyze the gathered data. Analyzes showed that cloud computing improves learning performance and instructional efficiency while does not affect cognitive load. According to the results especially for the difficult tasks using cloud computing is suggested to improve students’ collaboration and performance.
Mineral-phase paragenes in explosive products of modern emergencies of Kamchatka and Kuril volcanoes. Part 2. Minerals-satellites of tolbach type diamond

In the composition of the explosive-atmoelectrogenic paragenes studied by us, more than 100 mineral species, varieties and non-crystalline phases are found - carbon minerals, phases and compounds, native metals and alloys, carbides, silicides, nitrides, halides, chalcogenides, oxides, silicates and aluminosilicates, oxygen salts. The studied paragenes is characterized by an abnormally low level of mineralogical organization, which indicates the deep origin of the substance in the explosive facies of volcanic eruptions, including carbon minerals, phases and organic compounds.
ATHsENSe: An Experiment in Translating Urban Data to Multisensory Immersive Artistic Experiences in Public Space

This paper presents ATHsENSe, a multisensory interactive installation art project employing ubiquitous computing technologies, various sensors, virtual reality interfaces, multi-channel audio displays and interactive light structures. The project explores the concept of a smart city, through creatively translating the urban data produced by the environment of Athens and by its citizens. ATHsENSe is implemented through ubiquitous computing infrastructure, combining the use of sensors, location-based technology and mobile devices, in order to form a network of human and non-human sensors across the city. This structure enables the acquisition of rich urban data, which are eventually creatively translated in different artistic multisensory representations, displayed in physical and virtual space.