Reviewerly/exHarmony huggingface dataset:
The Significance of ‘Things’ in Cybercrime: How to Apply Actor-network Theory in (Cyber)criminological Research and Why it Matters

In recent years computer technologies and digital devices have become ubiquitous in all facets of human existence, including crime and deviant behavior. Various forms of criminality have emerged in which technical entities play a substantial role. It can be argued that such a development urges criminologists and anthropologists to draw more attention to the significance of things in crime. Latour’s (2005) actor-network theory (ANT), which considers non-human entities as active participants of the social, could be a useful approach for extending our analytical focus to the non-human. The article will not only asses why, but also how we can apply ANT as a more-than-human methodology in qualitative research, by discussing three ANT-based methodological principles: ‘follow the tool’, ‘follow the hybrid’ and ‘follow the network.’ In this scope, this article draws on earlier conducted qualitative ANT case studies on different forms of high-tech cybercrime. In a more general vein, the article aims to show that innovations in qualitative research methods can be also informed by theory.
Pengaruh Metode Permainan Kata Teka-Teki Silang dan Anagram terhadap Penguasaan Kosakata Siswa Kelas IV

&lt;div align="center"&gt;&lt;table width="645" border="1" cellspacing="0" cellpadding="0"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td valign="top" width="439"&gt;&lt;p&gt;&lt;strong&gt;Abstract:&lt;/strong&gt; Vocabulary mastery is crucial to be taught to elementary school students because it significantly affects the student’s language skills. This research was aimed to explain the influence of wordplay method such as crosswords and anagramss toward the vocabulary mastery of elementary school students. This study used a pretest posttest control group design with a sample of 36 students. The hypothesis test carried out using anacova test. The result show that there is a significant effect on the wordplay method include crosswords and anagrams to vovabulary mastery with the sig. value of 0,000 &amp;lt; 0,05.&lt;/p&gt;&lt;p class="Abstract"&gt;&lt;strong&gt;Abstrak:&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;Penguasaan kosakata sangat penting untuk diajarkan kepada peserta didik di sekolah dasar karena hal tersebut sangat memengaruhi keterampilan berbahasa peserta didik. Tujuan penelitian ini adalah untuk menjelaskan pengaruh metode permainan kata teka-teki silang dan Anagram terhadap penguasaan kosakata siswa sekolah dasar. Penelitian ini menggunakan &lt;em&gt;pretest-posttest control group design &lt;/em&gt;dengan sampel 36 peserta didik. Uji hipotesis dilakukan dengan uji Anacova. Hasil penelitian menunjukkan bahwa terdapat pengaruh signifikan metode permainan kata teka-teki silang dan Anagram terhadap penguasaan kosakata dengan nilai Sig. 0,000 &amp;lt; 0,05.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;
PENGARUH PERBEDAAN KONSENTRASI GELATIN CEKER AYAM KAMPUNG TERHADAP KARAKTERISTIK FISIK EDIBLE FILM

THE EFFECT OF DIFFERENCES CONCENTRATION OF NATIVE CHICKEN CLAW GELATIN ON THE PHISYCAL CHARACTERISTICS OF EDIBLE FILM. This study aims to determine the effect of differences concentration on the physical characteristics of edible film from native chicken claw gelatin. This study used a completely randomized design with the treatment of chicken claw gelatin concentration 5%, 10%, 15 and 20%, each treatment repeated four times. The results showed that the differences concentration of native chicken claw gelatin had significantly different effect (P&lt;0.05) on the value of tensile strength, elongation and thickness of edible film. Based on the results of discussion, it can be concluded that the concentration of native chicken claw gelatin from variables with 10% gelatin concentration had good physical characteristics of edible film. Keywords: Native chicken claw, Edible film Gelatin
An Experimentation to Simulate Mouse Movement using Hand Gestures based on Human Computer Interaction

This paper proposes a new approach for controlling mouse movement using a real-time camera.Most of the existing approaches involve changing mouse parts such as adding more buttons or changing the position of the tracking ball.Instead, it proposes to change the hardware design.The method uses a camera and computer vision technology, such as image segmentation and gesture recognition, to control mouse tasks (left click and right click, selection, scroll and drag).Hand gestures are acquired using a Web camera based on colour detection.In this research work, three colour tapes are used on fingers.The tapes will be used for clicking events of the mouse.Through a Web camera, the real-time video is captured.Image processing is performed on each frame of that video to detect the colour and mouse tasks are performed.
WEB SERVICE UNTUK INTEGRASI DATA MATERI PELAJARAN SEKOLAH MENENGAH PERTAMA (SMP) SECARA TERDISTRIBUSI

The National Education Standards Agency's (BSNP) curriculum requires in principle the active participation of students in collecting information from various media, including one on the Internet. The role of schools, especially teachers, in providing sources of materials is also very necessary. The Internet is one of the means of information essential to the learning process. Therefore, setting up a digital hardware service network can be an effective learning tool. Digital hardware services can be mutually integrated between schools to obtain a variety of physical resources. The existence of a web service can integrate subject data into each school and can provide distributed information to many providers and service users according to the needs of each user. The Web service is different from a search engine system that does not provide the necessary data integration on hardware service systems that connect data between schools to be distributed to users according to their needs. The web service technology that is built is able to integrate the subject's service material and can be distributed to many users with different needs.
Tradeoffs in the Efficient Detection of Sign Language Content in Video Sharing Sites

Video sharing sites have become keepers of de-facto digital libraries of sign language content, being used to store videos including the experiences, knowledge, and opinions of many in the deaf or hard of hearing community. Due to limitations of term-based search over metadata, these videos can be difficult to find, reducing their value to the community. Another result is that community members frequently engage in a push-style delivery of content (e.g., emailing or posting links to videos for others in the sign language community) rather than having access be based on the information needs of community members. In prior work, we have shown the potential to detect sign language content using features derived from the video content rather than relying on metadata. Our prior technique was developed with a focus on accuracy of results and are quite computationally expensive, making it unrealistic to apply them on a corpus the size of YouTube or other large video sharing sites. Here, we describe and examine the performance of optimizations that reduce the cost of face detection and the length of video segments processed. We show that optimizations can reduce the computation time required by 96%, while losing only 1% in F1 score. Further, a keyframe-based approach is examined that removes the need to process continuous video. This approach achieves comparable recall but lower precision than the above techniques. Merging the advantages of the optimizations, we also present a staged classifier, where the keyframe approach is used to reduce the number of non-sign language videos fully processed. An analysis of the staged classifier shows a further reduction in average computation time per video while achieving similar quality of results.
Application of Augmented Reality (AR) in T-Shirt Catalog

Augmented Reality (AR) is a visual technology that able to provide users with a semi-real time experience. The advancement of mobile phone and camera technology have make the potential of AR emerged. Companies wants high product sales and look more advanced than their competitors. One of the effort is to provide customers with correct experience of their products. Catalog is a tool to market a product. However, paper printing catalog is two dimensional, and has limitation in fully presenting the product. This work, presents the adoption of AR in T-shirt catalog. The application utilized Android mobile phone to view the 3D model of the t-shirt. The user scan the catalog using the android handphone on the paper printing catalog, and they able to view in 3D the different angle of the T-shirt. This application has successfully developed and testing has been made on compatibility and functionality test.
Salient Object Subitizing

As early as the nineteenth century, it was observed that humans can effortlessly identify the number of items in the range of 1–4 by a glance.
SciDetector: Scientific Event Discovery by Tracking Variable Source Data Streaming

We present the design of and a demonstration for SciDetector, a system of scientific research for online analysis. SciDetector can efficiently online analyze scientific events on large-scale newly arriving data. In modern scientific research, especially astronomy survey, with the development of the observation infrastructure, Short-Timescale and Large Field-of-view (STLF) sky survey has been the major topic. However, existing scientific system focus on offline analysis of long-term historical data, not real-time analysis of large-scale scientific data. Using real data, we demonstrate how SciDetector processes the data and generate the scientific product.
A Comparative Study on Despeckling Techniques in Intravascular Ultrasound Images

Intravascular ultrasound (IVUS) imaging is a diagnostic imaging technique for tomographic visualization of coronary arteries and studying atherosclerotic diseases. These medical images are generally corrupted by multiplicative speckle noise due to the interference of the signal with the backscattered echoes. Speckle noise is an inherent property of medical ultrasound imaging, and it generally tends to reduce the image quality; thus, removing noise from the original medical image is a challenging problem for diagnosis applications. Trying to reduce speckle assists experts for better understanding of some pathologies and diagnosis purposes. Recently, several techniques have been proposed for effective suppression of speckle noise in ultrasound B-Scan images. In this paper, we overview the denoising techniques in both spatial and transform domain, and propose a new despeckling method based on Shearlet transform and ant colony optimization. To quantify the performance improvements of the speckle noise reduction methods, various evaluation criteria in addition to the visual quality of the denoised images are used. Our results showed that in general Shearlet transform with three different types of threshold selection is faster and more efficient than other techniques. In the case of accurate estimation of the variance of noise, Shearlet transform based on ACO yields acceptable results in comparison with others methods. This technique can obtain a favorable signal-to-noise ratio and successfully improve the quality of images and preserve edges and curves as well.
Reactive Power Compensation with Fuzzy Logic in Electrical Facilities

The inductive loads fed by the electrical installations are reflected as an additional load by pulling the reactive power from the mains.Inductive loads are an important and useful parameter in the formation of rotating field of motors, formation of magnetic field in transformers.However, it is a harmful parameter that decreases blackness in transmission and distribution lines.Therefore, in large-scale power plants, reactive power requirements are provided by compensator systems connected to the input terminals.Continuous control equipment and systems need to be developed to improve the continuity and efficiency of energy systems.In this study, reactive power compensation, fuzzy logic-controlled application was made.Thus, power factor correction, dynamic voltage control are provided, power transmission capacities of the transmission lines and losses caused by reactive power flow are minimized.However, the fact that this compensator system is not linear makes it difficult to mathematically model them by using system based control systems based on fuzzy logic theory.In this study, without a mathematical model of the system, a control process based on fuzzy logic applied to the result was realized by using the effective relationship between system output and input.
Model Matching: A Novel Framework to use Clustering Strategy to Solve the Classification Problem

It is a common practice to handle labeled data with classifiers and unlabeled ones with clusterings. The traditional Bayesian network classifiers (BNC$^{\mathcal {T}}\text{s}$ ) learned from labeled training set $\mathcal {T}$ directly map the unlabeled test instance into the network structure to calculate the conditional probability for the classification, which neglects the information hidden in the unlabeled data and will result in classification bias. To address this issue, we propose a novel learning framework, called model matching, that uses the “clustering” strategy to solve the classification problem. The labeled data is divided into several clusters according to the different class label to learn a set of BNC$^{\mathcal {T}}\text{s}$ and a corresponding set of BNC$^{p}\text{s}$ is built for each unlabeled test instance. To make a classification, the cross entropy method is applied to compare the structural similarity between BNC$^{\mathcal {T}}$ and BNC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sup> . The extensive experimental results on 46 datasets from the University of California at Irvine (UCI) machine learning repository demonstrate that for BNCs model matching helps improve the generalization performance and outperforms the several state-of-the-art classifiers like tree-augmented naive Bayes and Random forest.
PROFESSIONAL DEVELOPMENT OF FUTURE MASTERS OF MUSICAL ART IN THE CONTEXT OF THE ACSIOLOGICAL APPROACH

The article outlines the possibilities of applying the axiological approach to the study of the problems of professional development of future Masters of Musical Art. The analysis of theoretical works devoted to the study of the problem of the development of value orientations of Masters of Musical Art shows that it is considered by scholars mainly in philosophical, sociological, psychological and pedagogical aspects.Since the socio-cultural and educational crises are typical for the present, the presence of a value vacuum, the study of the axiological basis of the educational process is extremely relevant. At this point, the axiological approach is an effective research tool, through which the value-humanist basis of the professional development of future specialists should be ensured.From the standpoint of the axiological approach, the basis for the development of the personal qualities of the future Masters of Musical Art should be, above all, humanistic values, the formation of which manifests itself in the direction and outlook of the teacher-musician, in his knowledge, skills, and styles of thinking, cultural levels. Accordingly, the professional development of future Masters of Musical Art should be based on an understanding of the significance of the value of music, which prompts the person to «nternal dialogue» with himself, with the surrounding reality. Thus, the professional development of future Masters of Musical Art is impossible without taking into account their value orientations, which, in turn, should determine their behavior and attitudes towards the world and musical and pedagogical activities, as well as to fill their professional activity with value meaning and striving for creativity.
Autoethnography in Intrapersonal and Intra-Active Training of Pedagogues. Selected Aspects and Application Possibilities

The article presents autoethnography in the context of posthumanistic student education as a way not only for a deep self-knowledge, but also for understanding other people.In this context, the authors seek answers to questions about the essence of autoethnography as a research method and its significance for personalized intra-personal and intra-active pedagogical training.By pedagogues/educators we mean people who lead others to maturity.The article presents innovative research techniques conducive to auto-ethnographic cognition and examples of application of autoethnography in the education of pedagogical students.
Replica Conditional Sequential Monte Carlo

We propose a Markov chain Monte Carlo (MCMC) scheme to perform state inference in non-linear non-Gaussian state-space models. Current state-of-the-art methods to address this problem rely on particle MCMC techniques and its variants, such as the iterated conditional Sequential Monte Carlo (cSMC) scheme, which uses a Sequential Monte Carlo (SMC) type proposal within MCMC. A deficiency of standard SMC proposals is that they only use observations up to time $t$ to propose states at time $t$ when an entire observation sequence is available. More sophisticated SMC based on lookahead techniques could be used but they can be difficult to put in practice. We propose here replica cSMC where we build SMC proposals for one replica using information from the entire observation sequence by conditioning on the states of the other replicas. This approach is easily parallelizable and we demonstrate its excellent empirical performance when compared to the standard iterated cSMC scheme at fixed computational complexity.
Zoho at SemEval-2019 Task 9: Semi-supervised Domain Adaptation using Tri-training for Suggestion Mining

This paper describes our submission for the SemEval-2019 Suggestion Mining task. A simple Convolutional Neural Network (CNN) classifier with contextual word representations from a pre-trained language model was used for sentence classification. The model is trained using tri-training, a semi-supervised bootstrapping mechanism for labelling unseen data. Tri-training proved to be an effective technique to accommodate domain shift for cross-domain suggestion mining (Subtask B) where there is no hand labelled training data. For in-domain evaluation (Subtask A), we use the same technique to augment the training set. Our system ranks thirteenth in Subtask A with an F1-score of 68.07 and third in Subtask B with an F1-score of 81.94.
Tom Jumbo-Grumbo at SemEval-2019 Task 4: Hyperpartisan News Detection with GloVe vectors and SVM

In this paper, we describe our attempt to learn bias from news articles.From our experiments, it seems that although there is a correlation between publisher bias and article bias, it is challenging to learn bias directly from the publisher labels.On the other hand, using few manually-labeled samples can increase the accuracy metric from around 60% to near 80%.Our system is computationally inexpensive and uses several standard document representations in NLP to train an SVM or LR classifier.The system ranked 4th in the SemEval-2019 task.The code is released for reproducibility 1 .
A Power Network Illegal Access Monitoring Method Based on Random Forests

With the development of network technology, people have begun to pay more and more attention to the impact of illegal access on the power network, and have tried to take measures to monitor whether the power network is illegally accessed. People try to use machine learning to monitor the power network. Because of the high classification accuracy and high efficiency of the random forest algorithm, we proposed a random forest-based power network monitoring algorithm. Comparison experiments with other algorithms have proved that our algorithm is more superior both in terms of time consumption and accuracy.
Examining the Use of Geographical Information System (GIS) on the Cadastral Data Study

From the perspective of system design and development, this paper combines GIS and cadastral management to analyze and study the system, and expounds the overall design, functional design and database design of the system.This paper introduces the design principles, system goals and system structure of the system.From the perspective of functional design it introduces the function of the system.From the perspective of database structure, the storage methods of different data in the system are introduced.Finally, this paper puts forward the views on the construction and development of the system.
RGCL-WLV at SemEval-2019 Task 12: Toponym Detection

This article describes the system submitted by the RGCL-WLV team to the SemEval 2019 Task 12: Toponym resolution in scientific papers.The system detects toponyms using a bootstrapped machine learning (ML) approach which classifies names identified using gazetteers extracted from the GeoNames geographical database.The paper evaluates the performance of several ML classifiers, as well as how the gazetteers influence the accuracy of the system.Several runs were submitted.The highest precision achieved for one of the submissions was 89%, albeit it at a relatively low recall of 49%.
Fair Use and Machine Learning

There would be a beaten path to the maker of software that could reliably state whether a use of a copyrighted work was protected as fair use. But applying machine learning to fair use faces considerable hurdles. Fair use has generated hundreds of reported cases, but machine learning works best with examples in greater numbers. More examples may be available, from mining the decision making of web sites, from having humans judge fair use examples just as they label images to teach self-driving cars, and using machine learning itself to generate examples. Beyond the number of examples, the form of the data is more abstract than the concrete examples on which machine learning has succeeded, such as computer vision, viewing recommendations, and even in comparison to machine translation, where the operative unit was the sentence, not a concept that could be distributed across a document. But techniques presently in use do find patterns in data to build more abstract features, and then use the same process to build more abstract features. It may be that such automated processes can provide the conceptual blocks necessary. In addition, tools drawn from knowledge engineering (ironically, the branch of artificial intelligence that of late has been eclipsed by machine learning) may extract concepts from such data as judicial opinions. Such tools would include new methods of knowledge representation and automated tagging.If the data questions are overcome, machine learning provides intriguing possibilities, but also faces challenges from the nature of fair use law. Artificial neural networks have shown formidable performance in classification. Classifying fair use examples raises a number of questions. Fair use law is often considered contradictory, vague, and unpredictable. In computer science terminology, the data is “noisy.” That inconsistency could flummox artificial neural networks, or the networks could disclose consistencies that have eluded commentators. Other algorithms such as nearest neighbor and support vectors could likewise both use and test legal reasoning by analogy. Another approach to machine learning, decision trees, may be simpler than other approaches in some respects, but could work on smaller data sets (addressing one of the data issues above) and provide something that machine learning often lacks: transparency. Decision trees disclose their decision-making process, whereas neural networks, especially deep learning, are opaque black boxes. Finally, unsupervised machine learning could be used to explore fair use case law for patterns, whether they be consistent structures in its jurisprudence, or biases that have played an undisclosed role. Any possible patterns found, however, should be treated as possibilities, pending testing by other means.