Reviewerly/exHarmony huggingface dataset:
Learning Triple Sequence Patterns in Knowledge Graphs to Predict Inconsistencies

The current trend towards the Semantic Web and Linked Data has resulted in an unprecedented volume of data being continuously published on the Linked Open Data (LOD) cloud.Massive Knowledge Graphs (KGs) are increasingly constructed and enriched based on large amounts of unstructured data.However, the data quality of KGs can still suffer from a variety of inconsistencies, misinterpretations or incomplete information as well.This study investigates the feasibility of utilising the subject-predicate-object (SPO) structure of KG triples to detect possible inconsistencies.The key idea is hinged on using the Freebase-defined entity types for extracting the unique SPO patterns in the KG.Using Machine learning, the problem of predicting inconsistencies could be approached as a sequence classification task.The approach applicability was experimented using a subset of the Freebase KG, which included about 6M triples.The experiments proved promising results using Convnet and LSTM models for detecting inconsistent sequences.
THE STRUCTURAL-DYNAMIC FEATURES OF PEDAGOGUES’ PERSONAL POTENTIAL

The article examines the structural and dynamic characteristics of personal potential (PP) as a potential for pedagogues’ self-regulation. PP is considered as a system consisting of three functional structures: potential for self-determination, potential for realization, potential for preservation. The best formed basic components of the pedagogues PP are personal and social identity, copying strategies for problem solving, and conscious self-regulation of their activities. The indicators of life satisfaction, self-assessments of their happiness, tolerance to stress, reflexivity, self-organization, tolerance to uncertainty, meaningful life and the social support-seeking coping strategy are lower. The best developed structural components are the potential for preservation, which indicates that educators are better able to cope with pressure and threats than with situations requiring self-determination and goal implementation. The level of development and configuration of the structural components determine the overall typological PP range, which includes 7 types. Each type characterizes a certain stage of PP development, and their combination in the form of a dynamic cycloid model reflects PP structural and functional dynamics. The performed empirical study proves that PP growth and disclosure is related to the external situational context and occurs at two types of situation: either at increase of pressure, threats, continuous and increased uncertainty, restrictions on the way to the goals, associated with pre-crisis and crisis situations, or at pressure and threat reduction, uncertainty overcoming, emergence of possibilities for goal realization, associated with&nbsp; crisis overcoming, post-crisis or post-traumatic growth. In general, both types of conditions are characterized by existing transitional periods of personal development or development as personal changes. The level of personal potential development remains stable in the dichotomous situation of “realization - self-determination” against the background of low pressure and threats for a long time, i.e. in the conditions of stable development of a personality. &nbsp
The Potential Of Tertiary Learning Environments For Novice Teachers’ Training

The article maintains that information-based learning environment enables the integration of program environment, electronic environment, communication environment, and pedagogical environment with educational technologies, information support and documentation. Studying in an information-based learning environment, students are involved into learning activities and are encouraged to actively self-monitor their academic achievements. The authors underline the importance of ensuring proper pedagogical support of students when they master network technologies and explore the information-based learning environment of modern universities. The authors highlight the significance of involving students into active learning which promotes the development of their communication skills. The authors stress the necessity of securing students’ access to information and enabling two-way communication between teachers and students. To ensure proper training of novice teachers, it is essential that tertiary learning environments promote advanced information systems, students’ and teachers’ accounts, students’ personal portfolios where students can store their works in different formats. The Internet is responsible for the major part of information exchange in an education institution. The Internet serves both as a source and a means and a holder of information. The results of one’s professional activities are stored in the Internet in the form of presentations, graphical files, audios, videos, hypertexts, and hypermedia documents. Interactive online training involves master classes, distance learning courses, webinars, Internet projects, and iMind maps.
Deep Salient Object Detection with Fuzzy Superpixel Extraction and Controlled Filter Convolution

Deep salient object detection (DSOD), which leverages the popular deep learning techniques, is a promising new branch of salient object detection (SOD). By training on large-scale public datasets, DSOD methods showed significant performance improvement while avoiding the involvement of manually designed visual features and prior knowledge of specific datasets. This paper proposes a novel superpixel-based DSOD method based on fuzzy superpixel extraction (FSE), a neural network-based differentiable superpixel extraction method, and controlled filter convolution (CFC), a modified convolution operation that accepts two input feature maps and can balance their influences without hand-picked coefficients. Different from other superpixel-based methods, by using FSE, the proposed method is able to include superpixel extraction in the training process, which optimizes the superpixel representations according to the datasets. Then, the CFC layers combine two different parts of the information possessed by the superpixels, which are intrasuperpixel features and intersuperpixel features, to generate a unified feature map. In the experiments conducted on 5 widely used public datasets, the proposed method significantly outperformed state-of-the-art models, which proved its effectiveness and generalization ability.
UJI AKTIFITAS TABIR SURYA NANO-TITANIUM OKSIDA UNTUK MENDUKUNG FORMULA KOSMETIK ANTIAGING KHUSUS MENGHAMBAT PENUAAN AKIBAT SINAR MATAHARI

Aging one of problems in social healthy especially in cosmetics community. UV-protection material was important because photo-aging give dominant effect at aging process. The development anti-aging material need exploration sunscreen material too. Utilization of Titanium Oxide in cosmetic formulation have been done. In this research Titanium Oxide was develop to form Nano-Titanium Oxide. It was important to get data that Nano-Titanium Oxide still have sunscreen activity. Spot, wrinkle and decrease of skin moisture were fast condition of aging if face skin get so many sun light especially UV-light with strong energy. In this research used nanomaterial of Titanium Oxide because nanomaterial give so many advantages. The method that used was measure UV absorption this material with UV-visible spectrophotometer instrument. The result of nanomaterial Titanium Oxide that still had sunscreen activity than support anti-aging in cosmetics formulation especially inhibit photo-aging. The impact of this research was the potential using of nanomaterial Titanium Oxide increase as anti-aging material that low level of cost in the future.
Online Encoder-decoder Anomaly Detection using Encoder-decoder Architecture with Novel Self-configuring Neural Networks &amp; Pure Linear Genetic Programming for Embedded Systems

Recent anomaly detection techniques focus on the use of neural networks and an encoder-decoder architecture.However, these techniques lead to trade offs if implemented in an embedded environment such as high heat management, power consumption and hardware costs.This paper presents two related new methods for anomaly detection within data sets gathered from an autonomous mini-vehicle with a CAN bus.The first method which to the best of our knowledge is the first use of encoder-decoder architecture for anomaly detection using linear genetic programming (LGP).Second method uses self-configuring neural network that is created using evolutionary algorithm paradigm learning both architecture and weights suitable for embedded systems.Both approaches have the following advantages: it is inexpensive regarding resource use, can be run on almost any embedded board due to linear register machine advantages in computation.The proposed methods are also faster by at least one order of magnitude, and it includes both inference and complete training.
Ontology-Based Event Detection for Wastewater Treatment.

Event detection systems can help detecting incidents of interest that occur within data streams generated by interconnected sensor devices. Corresponding personnel need to be notified about the incidents so that appropriate actions can be taken to address them. One of the typical use cases of event detection systems is monitoring the quality of recycling water during wastewater treatment. Existing event detection systems in this domain usually focus on dealing with low level primitive events and thus are not capable of detecting more complex events which contain relations and background knowledge. In this paper, we present a framework for detecting complex events with background knowledge in event streams, by using ontology and query rewriting techniques.
“Witness Protection: An Important Measure For The Effective Functioning Of Criminal Justice Administration”

The efficacy of criminal justice administration can be adjudged by the ability of courts to punish the wrongdoer and impart justice to the victim. During the process of finding the guilt, courts rely upon the evidence adduced by the parties in the oral or documentary form. The oral evidence is submitted by the means of versions of witnesses. Witness act as a valuable source of information for the courts to bring out the guilt of the accused. The versions of witness facilitate the courts to arrive at a judicious decision. Therefore, the witness plays a prominent role in criminal justice administration. For this reason, Whittaker Chambers said, “In search of truth, he plays that sacred role of the sun, which eliminates the darkness of ignorance and illuminates the face of justice, encircled by devils of humanity and compassion.”[1] However, it is a bitter truth that the condition of witnesses is turning pathetic in the justice administration system day by day. The reports of inducement, threatening, harassment and intimidation of witnesses are coming every day on a rolling basis. All these incidences create fear in the mind of witnesses due to which witness hesitate to come forward to cooperate in the court process and often turn hostile. Moreover, a witness also falters due to lack of witness protection mechanism in our country. Law Commission of India in its various reports highlighted the problems faced by the witness during the course of the trial and recommended to incorporate a comprehensive policy on witness protection. Hon’ble Supreme Court of India through its judgement revamped the urge for witness protection regime in the Indian judicial system. In this research paper, the researcher seeks to highlight the problem faced by the witnesses in the criminal justice system and discuss the issue of witness protection in India.&#x0D; &#x0D; [1] https://www.scconline.com/blog/post/tag/sit-and-cbi-probe/
Adaptive Adversarial Example Generating Network for Object Detection

The consensus of most recent researches is that rich training data which includes more different object forms in different situations is beneficial to improve the performance of deep networks. Deep learning is very dependent on data diversity. So, we provide a novel and principle method called adaptive adversarial example generating network (AAEGN) to generate examples with occlusions for object detection. AAEGN can generate occlusion examples which are rare in current dataset and are difficult to localize and classify. Compared to the traditional adversarial network for the example generation, AAEGN employs several innovations to simplify the architecture of adversarial network and improve the example generation and training speed. What’s more, AAEGN can adjust the hyper-parameters of the adversarial network in the process of training adaptively. The results of our experiments show that using AAEGN to train Fast-RCNN pipeline can get a 1.8% boost on VOC2007 dataset and 3.8% boost on VOC2012 dataset.
Correlation Filter Tracking Method via Metric Learning and Adaptive Multi-stage Appearance

In the complex tracking environment such as background clutter, generally there are multiple peaks in the response map of the correlation filter. It is difficult to distinguish the real object from the interference; and using the fixed learning rate to update the appearance model, it is not only difficult to maintain the sample diversity, but also easy to introduce noise information. Aiming at this problem, this paper proposes a correlation filter tracking method via metric learning and adaptive multi-stage appearance. By introducing metric learning to discriminate candidate samples corresponding to multiple peaks in the response map, the influence of multi-peak response map on tracking results in complex environments such as background clutter is eliminated; the Gaussian mixture model is used to divide the object appearance samples into groups and assign corresponding weights according to the duration, and the redundant information is eliminated while maintaining the diversity of the appearance model samples. The experimental results on OTB100 and VOT2017 datasets show that the overall precision score obtained by the algorithm in this paper is 0.866. The overall success plot score is 0.628, and the expected average overlap score is 0.211, which is better than most existing tracking methods.
Implementasi Model Pembelajaran STAD Berbantuan LKM Untuk Meningkatkan Hasil Belajar Akuntansi Sektor Publik

This study aims to determine the improvement of student learning outcomes through the implementation of STAD learning model assisted with LKM. This study was a Classroom Action Research (CAR) which was conducted in 3 cycles. Time of study in odd semester Academic Year 2018/2019, with a sample of accounting students at the Faculty of Economics and Business, University of Musamus, Merauke. The data analysis technique in this study is descriptive quantitative data analysis with percentages. The results of the study show that the Implementation of STAD Learning Model Assisted with LKM can improve Public Sector Accounting Learning Outcomes. The increase can be seen from the average value of student learning outcomes on the cycle of 50,36, increasing in the first cycle of 61,02, increasing in the second cycle of 73.07, and increasing in the third cycle of 85,31.
Forensic analysis with hacking tools on android devices

Currently, the use of mobile terminals is becoming a necessity for a high number of people around the world which has driven the development of devices with Android operating system; In addition to this, the same indiscriminate access to resources of internet and weak local and international regulations on the use, they have become vulnerable to attacks on the network - injection of malware, ransomware-, among many others. Therefore, this article reviews the hacking tools for the forensic investigation of mobile terminals, proposing from the documentary research a forensic information acquisition model to determine tangible and significant evidences as a probative material.
A Method for Facial Kinship Verification Based on Deep Learning

The analysis of facial information is always an important hotspot issue in the field of computer vision and pattern recognition and kinship verification by facial image is a challenging problem. Facial kinship verification has wide application range and important research value not only in the field of biometrics analysis but also in the social fields, such as analysis of mining social network data, searching work for scattered family members and so on. At present, with the development of computer vision especially the deep learning and metric learning, face recognition has made great achievements in recent years. In this thesis, a framework for kinship verification based on deep learning is proposed. Comparing with the current research methods that focus on metric learning, we use a deep learning network model to replace the two processes of feature extraction and metric learning. The effectiveness of the method is verified in KinFaceW datasets and TSKinFace datasets. The experimental results show that the accuracy is about 91% in KinFaceW datasets, and about 89.5% in TSKinFace datasets.
Anytime Autonomous English MALL App Engagement

Mobile assisted language learning (MALL) apps are often said to be 'Anytime' activities. But, when is 'Anytime' exactly? The objective of the paper is to provide evidence for the when of MALL activity around the world. The research method involved the collection and analysis of an EFL app’s time data from 44 countries. The findings were surprising in the actual consistency of usage, 24/7, across 43 of the 44 countries. The 44th country was interesting in that it differed significantly in terms of night time usage. The research also noted differences in Arab, East Asian and Post Communist country usage, to what might be construed to be a general worldwide app time usage norm. The results are of interest as the time data findings appear to inform the possibility of a potentially new innovative pedagogy based on an emerging computational awareness of context and opportunity, suggesting a possible future language learning niche within the Internet of Things (IoT), of prompted, powerful, short-burst, mobile learning.
Needs Analysis of Training Web Exe CMS (Content Management System) and Arduino “Two In One” For Physics Teacher

Most of physics teachers in Purworejo Regency use conventional teaching media. This is due to the lack of understanding and information about the latest It-based teaching media. They need new breakthroughs and methods to improve the quality of physics learning, especially those related to the development of teaching media. Therefore CMS (Content Management System) web exe and Arduino-based training needs analysis are conducted for Purworejo Regency physics teachers. This effort are tested by research descriptions are by giving questionnaires to respondents. The results showed that Purworejo district physics teachers needed continuous and periodic training with guided monitoring and needed training related to the latest IT-based teaching media such as e learning and microcontroller-based teaching media such as Arduino.
Micro-lecture Design Based on User Requirements

Micro-lecture is the product of the deep integration of information technology and subject teaching.The purpose of micro-lecture teaching is to help students improve their learning efficiency.Therefore, at the beginning of the design of micro-lecture, students' attitudes and specific needs towards micro-lecture should be understood, and the arrangement and design of micro-lecture teaching content should be carried out on this basis.According to the statistical analysis of 241 questionnaires of undergraduate students, undergraduates have an objective demand for micro-lecture teaching and basically have the conditions and ability to learn by using micro-lecture.However, the actual understanding and utilization of micro-lecture still need to be improved.The demand of students for micro-lectures is reasonable time length, clear explanation by teachers, and self-arrangement of learning time.Based on the above user needs, teachers should follow the scientific design concept to design micro-lectures, pay attention to the connection and coordination with traditional classroom teaching, and strengthen the evaluation of students' micro-lecture learning effect.
A 2D U-Net for Automated Kidney and Renal Tumor Segmentation

Kidney and renal tumor segmentation are critical aspects to the diagnosis process.However, segmentation is a time consuming and tedious task, especially for volume segmentation.To help with this issue, we test a simple two-dimensional U-Net are architecture for automating the segmentation process for both regions of interests.In doing so, we found that the vanilla U-Net was able to achieve a local tumor-kidney test dice of 0.91 and tumor-only dice of 0.25 and leaderboard scores of 0.85 and 0.22.
A Real-Time Identification System for VoIP Traffic in Large-Scale Networks

Recently, VoIP applications win most of the users' favor with their high service quality and low price. However, owing to the flexible of VoIP services, they will cause incalculable damage if these services are used improperly. In order to make VoIP applications serve humans better, it is important to keep VoIP applications under supervision. The upgrading of VoIP technology makes the traditional identification methods inefficient, it becomes more difficult to identify VoIP traffic. For the challenge of identifying VoIP traffic, the deep learning technology provides us a new solution. With supervised learning method, we can build feature set for identifying VoIP traffic. This way is not only able to find the most useful features, but also it can get rid of human effort in exploring features. In this paper, we adopt CLNN(Convolutional Neural Networks, Long Short-Term Memory) to extract features for accurate VoIP application identification. In addition, we design a real-time identification system to capture VoIP traffic in a large-scale network and identify their application types with the features we trained. The evaluation results verify that our system can identify VoIP traffic timely and accurately.
KEEFEKTIVAN PENGGUNAAN MODEL DISCOVERY LEARNING DALAM PEMBELAJARAN MENULIS TEKS CERPEN PADA SISWA SMP

This research is based on the curiosity of the researchers in the use of the Discovery Learning model for learning to write short story texts in class IX A Cipongkor 2 Junior High School. Based on the background of the research, the researcher can formulate a problem, namely 1) Is the Discovery Learning model effective in learning to write short story texts? and 2) What is the description of student performance during the implementation of learning with the Discovery Learning model? And so the researcher aims to find out the implementation of the discovery learning model in writing short text students of class IX A of Cipongkor Junior High School 2 with an object of 30 students as samples. The method used was quasi-experiment in the form of One Group Pretest and Posttest Design. Data were analyzed by descriptive analysis using the initial and final tests. The criteria used are the suitability of the theme, the element of building short stories and linguistic rules. Based on the results of data verification there was a significant increase between the results of the initial test which amounted to 47% to 74% in the final test. Then it can be concluded that the Discovery Learning model is effectively used.