Reviewerly/exHarmony huggingface dataset:
Pemahaman Struktur Dan Makna Kosakata Kanji Jukugo Pada Mahasiswa Tingkat III Pendidikan Bahasa Jepang Tahun Ajaran 2017/2018

This research discuss the understanding of jukugo kanji vocabulary’s structure and meaning on III grade of students of Japanese language education of Riau University 2017/ 2018. The purpose of this research is to measure students ability and comprehension on jukugo kanji. Then, to explain the difficulty of the jukugo kanji comprehension. This research used quantitative descriptive method by using survey as the research design. The data were collected by using test and interview the respondents. This research was done as a quantitative research in the field of education. All data were analyzed using statistic method and the result explained by descriptive method to describe the result. Based on the results of the test, it can be seen that the overall level of students' understanding of the jukugo kanji comprehension is at level B+ or Good, at the interval 75-79,99with an average of 76.24. The test results show that the highest score is 96 and the lowest score is 30. The test result also supported by the interview result, respondents are always have a problem in comprehension of jukugo kanji　cause respondents are difficult to remind of kanji have been studied. Moreover, based on the interview result respondents are the lack of jukugo kanji vocabulary mastery cause of training less.Keywords : understanding, structure, meaning, jukugo kanji
Classification of High Risk Prostate Cancer using Deep Learning

Prostate cancer is one of the most common types of cancer for men, making proper diagnostic essential. Using machine learning as a tool to help in digital pathology has become increasingly popular and helps to limit the high intra observer variability between pathologists. Due to the many cases of prostate cancer and the large differences between tumours, treatments have to be individualized for each patient. The Active Surveillance was introduced for patients with low risk prostate cancer were treatment in the form of surgery or radiation was deemed too invasive for the cancers current state. Instead the progression is supervised and when, if ever, a certain threshold is surpassed further treatment is discussed. In this thesis it is investigated if a Convolutional Neutral Network (CNN) can be trained to find high risk patients before pathologists can see cancer progression and if benign tissue holds vital information about future development. A CNN was trained on two different datasets, the first containing all of the available data and the second only including the biopsies from the latest examination in a patient's timeline. The results indicate that the problem is hard and the biggest struggle has been to limit the data without introducing new biases. The variability within each class was seemingly large in relation to the possible underlying patterns containing clues about the cancer making the accuracy low. Generalization was overall bad but it was found that when combing the results to make a patient grading, instead of grading individual biopsies, accuracies increased. Peak performance was found when only training on the last biopsies and was for the patient grading 67%. Although no outstanding results were found further research has to be done in the area of predictive prostate cancer classification in order to draw any final conclusions.
Research of Multi-Chromosomal Genome Median Calculation Based on Improved Particle Swarm Optimization

With the rapid development of genetic technology in our country, the importance of multi-chromosome genome median calculation is getting higher and higher. Therefore, this paper presented a multi-chromosome genome median calculation model based optimized particle swarm optimization algorithm. In the study of chromosome sequences, we further calculated the median of the gene sequences by adding the optimized particle swarm algorithm to improve the computational efficiency. In the process of testing the particle swarm optimization algorithm, the efficiency and functionality of the algorithm were tested to show that our research was feasible.
Real Time Face Swap Based on Patch Warping

Face swap is one of the popular machine vision problems recently. Important problems in real-time face swap are the need for high computation power and the decrease in success of applications in case of face pose variations. In this paper, an original face swap algorithm which can work in real time is proposed. After detecting two faces in the input image, 68 landmark points of each face are localized. Using these points, a facial model has been formed which separates the faces into 23 quadrilateral planar regions. Homographies are calculated between the quadrilaterals formed by the same points on both sides in order not to disrupt the holistic structure of the selected regions. Face swap is performed by warping the face patches with calculated homographies. With the proposed method, the calculation resources used in the algorithm have been used efficiently and identity information such as eye, mustache and eyebrow has been preserved. The symmetries of the visible regions are used for the invisible parts in the viewing angles that the camera cannot see the entire face. The success of our study has been tested with different camera resolutions and faces at different angles and qualitative results are given.
CD-ABM: Curriculum Design with Attention Branch Model for Person Re-identification

Person re-identification (re-ID) is a challenging problem due to background clutter, illumination and pose variation, occlusion, and pedestrian misalignment. Current state-of-the-art methods commonly extract discriminative information by deep networks based on one-stage training. Though straightforward, using one-stage learning, the presence of pedestrian misalignment in practical applications may significantly degrade the performance of the learned model. To address this issue, we propose a novel model for person re-ID, called CD-ABM. It adopts a curriculum design to proceed training from easy to hard samples and generates an attention map in a supervised manner to further facilitate discriminative feature extraction. Compared with existing methods, CD-ABM has the following advantages: (1) The curriculum design can gradually improve the model capability through progressive learning. (2) The attention map enables the local branch to be associated with the global branch and better exploits both local and global information. Experiments on three benchmark datasets show that, CD-ABM can achieve competitive performance with the state-of-the-arts. Noteworthily, on the most challenging dataset MSMT17, it surpasses state-of-the-art methods by 15.9% in Rank-1 and 21.0% in mAP.
Cross-modal Representation Learning with Nonlinear Dimensionality Reduction

In many problems in machine learning there exist relations between data collections from different modalities. The purpose of multi-modal learning algorithms is to efficiently use the information present in different modalities when solving multi-modal retrieval problems. In this work, a multi-modal representation learning algorithm is proposed, which is based on nonlinear dimensionality reduction. Compared to linear dimensionality reduction methods, nonlinear methods provide more flexible representations especially when there is high discrepancy between the structures of different modalities. In this work, we propose to align different modalities by mapping same-class training data from different modalities to nearby coordinates, while we also learn a Lipschitz-continuous interpolation function that generalizes the learnt representation to the whole data space. Experiments in image-text retrieval applications show that the proposed method yields high performance when compared to multi-modal learning methods in the literature.
Seedless Fruit is the Sweetest: Random Number Generation, Revisited.

The need for high-quality randomness in cryptography makes random-number generation one of its most fundamental tasks.
Quality assurance in higher education: Russian peculiarities and international experience

The development of trends in modern society (with its fast expansion of innovative technologies and scientific-technical progress) requires education to be more efficient when training highly qualified specialists. It has become relevant to provide high-quality educational services in order to ensure that the training of personnel complies with the labor market’s requirements and needs. In the article, the authors have determined factors influencing the quality of educational services in a university and the goals for which university students get a higher education. Moreover, the authors have identified students’ satisfaction with the quality of education in higher education institutions. The authors have analyzed the assessment of educational services provided to university students. In the article, the authors characterize the meaning and functions of institutions at various levels in assuring the quality of higher education. Special attention has been paid to European institutions involved in the assessment and quality assurance of higher education.
Effect of Visual Context Information for Super Resolution Problems

In this study, the effect of visual context information to the performance of learning-based techniques for the super resolution problem is analyzed. Beside the interpretation of the experimental results in detail, its theoretical reasoning is also achieved in the paper. For the experiments, two different visual datasets composed of natural and remote sensing scenes are utilized. From the experimental results, we observe that keeping visual context information in the course of parameter learning for convolutional neural networks yields better performance compared to the baselines. Moreover, we summarize that finetuning pre-trained parameters with the related context yet fewer samples improves the results.
Binary Stochastic Filtering: a Method for Neural Network Size Minimization and Supervised Feature Selection

Binary Stochastic Filtering (BSF), the algorithm for feature selection and neuron pruning is proposed in this work. The method defines filtering layer which penalizes amount of the information involved in the training process. This information could be the input data or output of the previous layer, which directly leads to the feature selection or neuron pruning respectively, producing \textit{ad hoc} subset of features or selecting optimal number of neurons in each layer. Filtering layer stochastically passes or drops features based on individual weights, which are tuned with standard backpropagation algorithm during the training process. Multifold decrease of neural network size has been achieved in the experiments. Besides, the method was able to select minimal number of features, surpassing literature references by the accuracy/dimensionality ratio.
LEARNING RELATED DEVICE USAGE OF GERMAN AND INDIAN STUDENTS

This paper investigates learning related device usage of German and Indian students. For that purpose, an exploratorysurvey of students at the University of Hildesheim and the Symbiosis College of Arts and Commerce in Pune is executed.The aim of
Towards humane digitization: a wellbeing-driven process of personas creation

Digital transformation is a process of digitizing the working and living environment in which people are at the center of digitization. In this paper, we present a personas-based guideline for system developers on how the humanization of digital transformation integrates into the design process. The proposed guideline uses the positive personas from the beginning as a basis for the transformation of the working environment into the digital form. We used the literature research as a preliminary study for the process of wellbeing-driven digital transformation design, consisting of questions for structuring the required information in the positive personas as well as a potential method that could be integrated into the wellbeing-based design process.
Counterfeit IC Detection Research Based on Wavelet Transform and SVM

In view of the non-stationary characteristics of the electromagnetic (EM) side channel signal, this paper uses the EM probe and oscilloscope to collect the EM side channel signal of the chip, analyzes the signal by wavelet transform, extracts the wavelet coefficient as the signal feature, and uses the support vector machine to complete the classification of the four chip types. Experiments show that this method can identify the chip model quickly, stably and low-cost. The experiment achieved the expected results. The average correct rate reaches 91.625%.
An Empirical Study on Employee Health and FIRO-B Relationship

The Fundamental Interpersonal Relations Orientation-Behavior (FIRO-B) instrument was created in the late 1950s by William Schutz. He developed the FIRO-B theory to aid in the understanding and predicting of how high-performance military teams would work together. This article focus on deterioration of employee's health due to the poor relationship among employees. Convenience sampling techniques is used to collect data for the analysis. The study shows the importance of FIRO B relationship among the employees.
Augmented Unlocking Techniques for Smartphones Using Pre-Touch Information

Smartphones store a significant amount of personal and private information, and are playing an increasingly important role in people's lives. It is important for authentication techniques to be more resistant against two known attacks called shoulder surfing and smudge attacks. In this work, we propose a new technique called 3D Pattern. Our 3D Pattern technique takes advantage of a new input paradigm called pre-touch, which could soon allow smartphones to sense a user's finger position at some distance from the screen. We implement the technique and evaluate it in a pilot study (n=6) by comparing it to PIN and pattern locks. Our results show that although our prototype takes about 8 seconds to authenticate, it is immune to smudge attacks and promises to be more resistant to shoulder surfing.
PENGEMBANGAN GIS HYDRANT KOTA BATAM (BATAM WEB-GISH)

The development of information technology is currently very rapid, thus it is possible to be used for positive things such as helping human work. One of human's jobs is as firefighting personnel. Fire disasters often occur in places, cities, and countries. Batam City is one of the most densely populated cities where residents live in housing, lots and illegal houses (RULI) and their land contains bauxite. This condition is one of the factors causing the fire disaster. The Batam City Government has provided several hydrant facilities as a source of water for fire disaster management. Hydrants in Batam City are very adequate, but the management has not utilized information technology, so that if a fire occurs in one location, firefighters must manually search for the hydrant location. Based on this problem, researchers are interested in building a web Geographic information system hydrant (web-GISH) so that hydrants managed by the Batam city government are mapped on the web-GISH. The development of this website will be done by collecting hydrant location data in the form of latitude and longitude data, designing website interfaces, building a hydrant database and building web-based applications by utilizing google maps service as a provider of hydrant location information. With the existence of a special map of hydrant locations in the city of Batam, it indirectly helps firefighting personnel in handling fire disasters.
A New Approach on Emotion Analogy by Using Word Embeddings

According to the Plutchik emotion classification, complex emotions consist of different combinations of eight basic emotions. The word embeddings in the literature are described as vector space where the word meanings are represented numerically. In this space, word analogies dealing with the similarities of vectors can be carried out. In this study, “emotion analogy” is proposed as a new method to create complex emotion vectors in case there is no learning data for complex emotions. In this respect, 12 complex feeling vectors were obtained by combining the word vectors of the basic emotions by the purposed method. The similarities between the obtained combinational vectors and the word vectors belonging to the complex emotions were investigated. As a result of the experiments performed on GloVe and Word2Vec word embeddings, it is found that the results of word analogy and emotion analogy are similar at 0.82 on average.
Transforming space and time, a compositional method by Pablo Rubio-Vargas using digital tools to remodel acoustic and spatial features into a musical interpretation.

Author(s): Rubio-Vargas, Pablo | Advisor(s): Dunn, David D | Abstract: The methodology explored in this cycle of compositions conceptualizes physical space and sound as hybrid compositional elements. This work builds on previous experiments with spatial awareness through sound localization. For example, orchestral piece Terretektorh by composer Iannis Xenakis displays acoustic immersive acoustic design, or multimedia piece Great Animal Orchestra by sound artist Bernie Krause that shows a collection of recorded soundscapes in different locations. Three compositions — Hombre-Pajaro, ži’ilœ and Nat.er.ura — comprise the focus of the present study. Each piece engages physical space as a compositional tool, combining ecology and spontaneous improvisation. Hombre-Pajaro combines a collage of granulated soundscapes as a sound-installation with a trio improvising on the top of the soundscapes. The piece ži’ilœ uses an open score to be interpreted in multiple ways by the musicians. The melodic material in ži’ilœ is the result of an algorithmic process of pitch contour recognition applied to a soundscape. And finally Nat.er.ura, a composition that conglomerates musical improvisation with modulated soundscapes.
Blockchains, Private Ordering, and the Future of Governance

Ten years ago Satoshi Nakamoto published the Bitcoin white paper and launched the first Bitcoin protocol. Since then, blockchain’s potential to impact upon a wide variety of relationships and transactions has become evident. Blockchain-based governance is now possible, and as technologists, entrepreneurs, and legal professionals refine its implementation, it will likely become more mainstream. It is no exaggeration to say that a blockchain revolution has begun. Although not as attention-grabbing as blockchain’s earliest uses to facilitate cryptocurrency, the technology’s potential to transform the way business enterprises are governed is no less ground-breaking. This chapter considers blockchain-based governance from an American business law perspective. It argues that, despite the possibility that it will allow parties to rely less on the governance arrangements currently provided by traditional business organizations, it is nonetheless consistent with American business law’s ‘enabling’ approach and the significant room it leaves for private ordering. Furthermore, substitution of blockchain-based for traditional governance does not obviate the need for limited liability and the other advantages of separate legal personhood. Consequently, state-level efforts to accommodate blockchain-based governance in the context of limited liability entities are already appearing. The chapter concludes with a short discussion of some of the challenges that blockchain-based governance presents under American business law.
A Study on the Methods for Establishing Security Information &amp; Event Management

There has recently been emerging a global threat caused by the attack through multi-hacking technologies against the national infrastructure, industrial control system and enterprises, what’s called cyber-hacking and cyber-attack on the cyber space like cyber war, for the sake of the nation and organization. Besides, APT (Advanced Persistent Threat) attack utilizing complex types of attack in order to attack a certain target brings about a tremendous chaos on a national and social level. Under such a situation, a necessity for ESM (Enterprise Security Management) is emphasized to establish multi-network enterprise security systems for a defense against an attack from outside and an efficient management. However, ESM analyzed and collected data, with the main focus on information security system-based security event and network sensor-based harmful traffic event without carrying out a function to analyze a general system and application log-based event. As far as an effective security detection is concerned, strategies for a systemic preparation and execution to actively solve the security issue are necessary by utilizing enormous big data occurring throughout the enterprise IT infrastructure sectors. In this regard, this study is to present a security log analysis system utilizing SIEM (Security Information & Event Management) system to cope with an advanced attack that the existing ESM can hardly detect. SIEM is going to analyze an association between data and security event occurring in major IT infrastructure facility network, system, applied services and a great deal of information security systems, and then to present the methods for identifying, in advance, potential security threat.
Positive or Negative? A semantic orientation of financial news

Semantic orientation, also known as sentiment analysis, is now expanding its research area due to its importance in many areas such as finance, business and management. In addition to the financial statements, news about the fundamentals of a company, forums, blogs and social media posts have become important sources affecting investors' decisions. On the other hand, due to the difficulties in monitoring the relevant and important stories about a company and determining its semantic orientation within this huge volume of information, automatic opinion mining has become a necessity for investors to act in a timely manner. In this context, this study presents a solution to the problem of semantic orientation of financial news by applying a Naive Bayes Classifier on a data set consisting of 75000 news texts formed from the news between 1996 and 2018 and analyzes the results in detail.
Toward a GUI-Based Comprehension of Software Architecture

Architecture-level software comprehension is important to maintain a software system. In particular, an architecture of a software system can assist software engineers in comprehending the high-level structure of the system. However, the knowledge of an architecture could be disappeared and incomprehensible over time, and this leads to poor sustainability. Therefore, it is necessary to recover the knowledge from the artifacts that are available even over time. To achieve this goal, we focus on Graphical User Interfaces (GUI) of a software system, which are mostly available in the life-time of the system. In this paper, we propose an approach to recover the knowledge of the system’s architecture by using GUI. The case study is performed to evaluate the approach for an open-source software system and the results show that GUI provides useful evidences in identifying the high-level software structure.