Reviewerly/exHarmony huggingface dataset:
Creative Industry Development in Pesantren Tebuireng

Film, short movie, and animation are one of the sub-sectors in creative industry which are importantly considered for development of Indonesia’s creative economy. Pesantren (Islamic boarding school) have opportunity to get involve it. Kopi Ireng (Komunitas Photography Tebuireng, Tebuireng Photography Community) inspired Tebuireng santri to establish production house, namely Maksi (Rumah Produksi Tebuireng, Tebuireng Production House). Kopi Ireng has won many competitions while Maksi has produced two movies both Binar and Sakinah. Both Kopi Ireng and Maksi are an effort to regenerate the progress of film industry in Indonesia, especially in pesantren. Film in pesantren is something new which is sometimes considered unusually. Some of pesantren claimed that film activity will break the old tradition of santri and pesantren in studying Islamic studies. Both Kopi Ireng and Maksi believe that film is the way to spread dakwah and origin culture of Indonesia in the era of 4.0 industrial revolution. Keyword: 4.0 era, creative industry, film, Pesantren Tebuireng
A Hybrid Multifarious Clustering Algorithm for the Analysis of Memmogram Images

A number of clustering algorithms were used to analyze many databases in the field of image clustering.The main objective of this research work was to perform a comparative analysis of the two of the existing partitions based clustering algorithms and a hybrid clustering algorithm.The results verification done by using classification algorithms via its accuracy.The performance of clustering and classification algorithms were carried out in this work based on the tumor identification, cluster quality and other parameters like run time and volume complexity.Some of the well known classification algorithms were used to find the accuracy of produced results of the clustering algorithms.The performance of the clustering algorithms proved meaningful in many domains, particularly k-Means, FCM.In addition, the proposed multifarious clustering technique has revealed their efficiency in terms of performance in predicting tumor affected regions in mammogram images.The color images are converted in to gray scale images and then it is processed.Finally, it is identified the best method for the analysis of finding tumor in breast images.This research would be immensely useful to physicians and radiologist to identify cancer affected area in the breast.
Hierarchical Knowledge Squeezed Adversarial Network Compression

Deep network compression has been achieved notable progress via knowledge distillation, where a teacher-student learning manner is adopted by using predetermined loss. Recently, more focuses have been transferred to employ the adversarial training to minimize the discrepancy between distributions of output from two networks. However, they always emphasize on result-oriented learning while neglecting the scheme of process-oriented learning, leading to the loss of rich information contained in the whole network pipeline. Whereas in other (non GAN-based) process-oriented methods, the knowledge have usually been transferred in a redundant manner. Observing that, the small network can not perfectly mimic a large one due to the huge gap of network scale, we propose a knowledge transfer method, involving effective intermediate supervision, under the adversarial training framework to learn the student network. Different from the other intermediate supervision methods, we design the knowledge representation in a compact form by introducing a task-driven attention mechanism. Meanwhile, to improve the representation capability of the attention-based method, a hierarchical structure is utilized so that powerful but highly squeezed knowledge is realized and the knowledge from teacher network could accommodate the size of student network. Extensive experimental results on three typical benchmark datasets, i.e., CIFAR-10, CIFAR-100, and ImageNet, demonstrate that our method achieves highly superior performances against state-of-the-art methods.
Corpus based Amharic sentiment lexicon generation

Sentiment classification is an active research area with several applications including analysis of political opinions, classifying comments, movie reviews, news reviews and product reviews. To employ rule based sentiment classification, we require sentiment lexicons. However, manual construction of sentiment lexicon is time consuming and costly for resource-limited languages. To bypass manual development time and costs, we tried to build Amharic Sentiment Lexicons relying on corpus based approach. The intention of this approach is to handle sentiment terms specific to Amharic language from Amharic Corpus. Small set of seed terms are manually prepared from three parts of speech such as noun, adjective and verb. We developed algorithms for constructing Amharic sentiment lexicons automatically from Amharic news corpus. Corpus based approach is proposed relying on the word co-occurrence distributional embedding including frequency based embedding (i.e. Positive Point-wise Mutual Information PPMI). Using PPMI with threshold value of 100 and 200, we got corpus based Amharic Sentiment lexicons of size 1811 and 3794 respectively by expanding 519 seeds. Finally, the lexicon generated in corpus based approach is evaluated.
A Blind Watermarking Scheme Using Adaptive Neuro-Fuzzy Inference System Optimized by BP Network and LS Learning Model

To maintain a trade-off between robustness and imperceptibility, as well as secure transmission of digital images over communication channels, a digital image blind watermarking scheme on the basis of adaptive neuro-fuzzy inference system (ANFIS) is proposed in this study. To achieve better results, an optimized ANFIS (OANFIS) combines a back propagation neural network and a least-square (LS) hybrid learning model. Each 3 × 3 non-overlap block of the original host image is selected to form a sample dataset, which is trained to establish a model of nonlinear mapping between the input and the output. As a sequence, the host image is decomposed by wavelet transform to obtain a low frequency subband. Finally, each watermark signal is adaptively embedded into the low frequency subband by OANFIS. Results show that our scheme is robust to various attacks while effectively maintaining satisfying transparency.
A Constant-Size Signature Scheme with a Tighter Reduction from the CDH Assumption

We present a constant-size signature scheme under the CDH assumption. It has a tighter security reduction than any other constant-size signature scheme with a security reduction to solving some intractable search problems. Hofheinz, Jager, and Knapp (PKC 2012) presented a constant-size signature scheme under the CDH assumption with a reduction loss of O(q), where q is the number of signing queries. They also proved that the reduction loss of O(q) is optimal in a black-box security proof. To the best of our knowledge, no constant-size signature scheme has been proposed with a tighter reduction (to the hardness of a search problem) than that proposed by Hofheinz et al., even if it is not re-randomizable. We remark that our scheme is not re-randomizable. We achieve the reduction loss of O(q/d), where d is the number of group elements in a public key.
Treating Artificial Neural Net Training as a Nonsmooth Global Optimization Problem

We attack the classical neural network training problem by successive piecewise linearization, applying three different methods for the global optimization of the local piecewise linear models. The methods are compared to each other and steepest descent as well as stochastic gradient on the regression problem for the Griewank function.
An Efficient Robotic Manipulator Trajectory Planning Using Modified Firefly Algorithm

A novel modified firefly algorithm (MFA) for optimal and accurate robotic trajectory planning has been proposed in this paper. In order to demonstrate the entire trajectory planning problem, an industrial 4-DOF SCARA robot manipulator has been chosen. An error function which is a combination of both error in position and error in orientation coordinates is defined as total objective function for the trajectory optimization assignment. In order to accomplish an efficient optimal trajectory, the best cost of the error objective fitness function, algorithm convergence speed, the error in positions and orientations are chosen as performance parameters. The objective function has been executed using bat algorithm (BA), cuckoo-search algorithm (CS), firefly algorithm (FA), artificial bee colony (ABC) algorithm and the proposed MFA. The effectiveness of the proposed MFA is validated by carrying out a comparative review of the acquired trajectory results from the MFA against the other considered existing algorithms. The ranks of the algorithms have been evaluated by conducting Friedman test for the mean solutions of error in positions and orientation. The comparative study of the implemented algorithms shows that MFA executes superior to the other algorithms with less computational effort for the same boundary conditions.
Fast ROI Detection for Speed up in a CNN based Object Detection

Fast operation of a CNN based object detection is important in many application areas.It is an efficient approach to reduce the size of an input image.However, it is difficult to find an area that includes a target object with minimal computation.This paper proposes a ROI detection method that is fast and robust to noise.The proposed method is not affected by a flicker line noise that is a kind of aliasing between camera and LED light.Fast operation is achieved by using down-sampling efficiently.The accuracy of the proposed ROI detection method is 92.5% and the operation time for a frame with a resolution of 640 x 360 is 0.388msec.
A Two-Stage Temporal-Like Fully Convolutional Network Framework for Left Ventricle Segmentation and Quantification on MR Images

Automatic segmentation of the left ventricle (LV) of a living human heart in a magnetic resonance (MR) image (2D+t) allows to measure some clinical significant indices like the regional wall thicknesses (RWT), cavity dimensions, cavity and myocardium areas, and cardiac phase. Here, we propose a novel framework made of a sequence of two fully convolutional networks (FCN). The first is a modified temporal-like VGG16 (the “localization network”) and is used to localize roughly the LV (filled-in) epicardium position in each MR volume. The second FCN is a modified temporal-like VGG16 too, but devoted to segment the LV myocardium and cavity (the “segmentation network”). We evaluate the proposed method with 5-fold-cross-validation on the MICCAI 2019 LV Full Quantification Challenge dataset. For the network used to localize the epicardium, we obtain an average dice index of 0.8953 on validation set. For the segmentation network, we obtain an average dice index of 0.8664 on validation set (there, data augmentation is used). The mean absolute error (MAE) of average cavity and myocardium areas, dimensions, RWT are 114.77 mm $$^{2}$$ ; 0.9220 mm; 0.9185 mm respectively. The computation time of the pipeline is less than 2 s for an entire 3D volume. The error rate of phase classification is 7.6364 $${\%}$$ , which indicates that the proposed approach has a promising performance to estimate all these parameters.
Joint rain and atmospheric veil removal from single image

In natural rainy scenes, visibility is significantly degraded by two types of phenomena: specular highlights of nearby individual rain streaks and atmospheric veiling effect caused by distant accumulated rain. However, most existing deraining methods only take the first kind of degradation into consideration, which limits their potential application in heavy rain. In this study, a joint rain and atmospheric veil removal framework is proposed to address this problem. Since rain streaks and rain accumulation are entangled with each other, which is intractable to simulate, causing clean/rainy image pairs of real-world are hard to generate. Hence, after introducing a generalised rain model, which can represent both rain streaks and atmospheric veil physically, the authors do not learn the mapping function between image pairs using deep-learning architecture, but estimate the rain streaks, transmission, and atmospheric light via Gaussian mixture model patch prior and dark channel prior to solve the rain model instead. According to the comprehensive experimental evaluations, the proposed method outperforms other state-of-the-art methods in terms of both high visibility and vivid colour, especially in natural heavy rain scenario.
PROBLEMS OF SEARCH FOR INFORMATION IN ELECTRONIC CATALOGS OF LIBRARIES OF VARIOUS TYPES

The results of the study of electronic catalogs of the largest Russian libraries of various types are presented. Particular attention is paid at search capabilities of libraries’ electronic catalogs, their availability and problems of user’s perception of the information provided.
VARIATION ON THE NUMBER OF HIDDEN NODES THROUGH MULTILAYER PERCEPTRON NETWORKS TO PREDICT THE CYCLE TIME

Multilayer Perceptron Network (MLP) has a better prediction Multilayer Perceptron Network (MLP) has a better prediction performance compared to other networks since the structure ofthe MLP is suitable for training processes in solving prediction problems. However, to the best of our knowledge, there is no ruleof thumb in determining the number of hidden nodes within the MLP structure. Researchers normally test with various numbers of hidden nodes to obtain the lowest square error value for optimal prediction results since none of the approaches has yet to be claimed as the best practice. Thus, the aim of this study is to determine the best MLP network by varying the number of hidden nodes of developed networks to predict cycle time for producing a new audio product on a production line. The networks were trained and validated through 100 sets of production lots from a selected audio manufacturer. As a result, the 3-2-1 MLP network was the best network based on the lowest square error value compared to the 3-1-1 and 3-3-1 networks. The 3-2-1 predicted the best cycle time of 5 seconds to produce a new audio product. Hence, the prediction result could facilitate production planners in managing assembly processes on the production line.
The sandstone-hosted Osen lead deposit, Norway: new Pb isotope evidence for sourcing in the underlying granitoid basement

The sandstone-hosted Osen lead deposit, Norway: new Pb isotope evidence for sourcing in the underlying granitoid basement
The Concept of Freedom of Enterprise Through the Prism of Criminal Law Characteristics of Fraud

The research of issues connected with the analysis of business risks is relevant because of the problem of qualifying the actions of entrepreneurs under the fraud-related Articles of the Criminal Code of the Russian Federation. Besides, the development of technologies increases the number of frauds in the digital environment, which makes it necessary to determine key features of fraudulent actions connected with the changes in the economic organization of the society connected with the digital transformation of some branches of the world economy in general and Russian economy in particular, of the social sphere, and of the specifics of public administration of some areas of life. The responsiveness of lawmakers manifested in amending a group of Articles in the Criminal Code of the Russian Federation regarding the legal characteristics of fraud, shows that there are some problems in the legislative regulation of this sphere. The author believes that they are caused by an attempt to assess the degree of freedom of enterprise and the degree of involvement of each side of legal relations in the risk of investment. The analysis of legislation, the law enforcement practice, statistical data give reason to believe that most of the problems of legislative understanding of fraud in entrepreneurship are not connected with contradictions in the legal regulation, but rather with the drawbacks of the law enforcement practice, the prevalence of repressive methodology in classifying the actions of entrepreneurs and the inner conviction of the law enforcement employees that entrepreneurs intentionally strive to obtain negative results in any, and primarily entrepreneurial, activities. The author argues that further improvement of the Criminal Code of the Russian Federation will not yield any tangible results, which testifies to a considerable transformation of the fraud-related Articles in the last 15 years. Changes in the practice of enforcement of the criminal law’s articles regarding fraud are only possible after the principles of such work are worked out by the Supreme Court of the Russian Federation, who at present pays much attention to this issue, although some clauses of the Plenary Session of the Supreme Court of the Russian Federation require further analysis and improvement.
Visual Question Answering System for Identifying Medical Images Attributes

In this paper, we propose a new Visual Question Answering (VQA) System which is able to give an answer in natural language about an image and a natural language question. In our System, we have employed a Convolutional Neural Network (CNN) for visual input processing and Recurrent Neural Network (RNN) in Encoder-Decoder model which consists of an LSTM to encode sequential input which is an assembling between image and question vectors, and another LSTM to decode the states for predicting target answers in output, this one will be generated in natural language using greedy search algorithm.
Stress production by Cebuano learners of Arabic: A metrical analysis

Stress is one of the most neglected components of the Arabic language in classrooms (Lin, 2018; Ryding, 2013).This study is devoted to analyzing stress production in Arabic as produced by Cebuano learners in order to highlight the challenges so that teachers can address them in the best way. The data have been examined within the metrical theory of word stress elaborated in Hayes (1995). A sample of 100 words has been considered, spoken by six non-native speakers of Arabic, three females and three males, whose first language is Cebuano, the national language of the Philippines. Data analysis shows that native Cebuano speakers have an iambic foot, where the foot involves left-to-right parsing, satisfies the End Rule Right Principle by which the main stress lands on the head of the rightmost visible foot, and imposes a weak ban on the degenerate foot. Intriguingly, foot iambicity observed in the produced words is regarded as a reflection of the speakers’ source language (L1) that has an iambic foot. Arabic words spoken by Cebuano non-natives conform to the bimoraic condition for the minimal phonological word that takes the primary stress, and is repaired only through vowel lengthening; whereas gemination, as a main strategy for creating bimoraicity, is totally absent. Similarly, vowel lengthening is seen as a transfer effect of L1, where stress always attracts a long vowel. The results point to the great importance of prosody in teaching Arabic as a foreign language, since prosodic features significantly contribute to the communication intelligibility.
Research Using Big Data in Gastroenterology: Based on the Outcomes from Big Data Research Group of the Korean Society of Gastroenterology

The Big Data Research Committee of the Korean Society of Gastroenterology conducted activities and researches with three goals. First, it provides the basis for proper and cost-effective treatment of digestive diseases in Korea. Second, it carries out population-level global research by establishing a system of big data analysis related to gastroenterology. Third, it provides the members of the Korean Society of Gastroenterology with the opportunity to plan and assess the public interest related to big data. The studies published by the committee members in this paper were carried out under these objectives, and the findings are believed to have achieved the public interest goals that may be helpful in the current medical and health policy. The construction of the big data infrastructure for digestive drugs is also underway, and we expect to see meaningful results pertaining to important digestive drugs. Research using public health medical big data, such as the National Health Insurance Corporation data base, should ultimately provide a basis for reflecting public messages and policies for the public. To this end, it is necessary for Korean researchers to lead efforts to lower the barriers and to approach relevant information and opportunities using big data research.
Formation of Russian magistracy: implementation of the digital economy requests in the educational process

The state of Russian magistracy in the transition to a digital economy has been studied. Some aspects of Russia’s accession to the Bologna process have been affected – problems of transition associated with a lack of understanding of the essence of transformations by the main stakeholders have been noted. The issues of socialization of students of the «digital» generation have been analyzed, the most characteristic of them – the desire for constant change, intolerance of the same type of problems, good knowledge of information technology. It has been noticed, that Russian magistracy is faced with the need to adapt to the needs of the digital economy, as well as the «generational» characteristics of representatives of generation Z. The results of an empirical sociological study “Birth of a Russian magistracy”, carried out as part of the project of the Higher School of Economics Institute of Education, have been presented. The purpose of this study is to analyze the state of modern Russian magistracy and define key processes in which the main Acknowlegements. The article reflects the results of the research in the framework of the project “Birth of the Russian master’s degree”, implemented by the HSE Institute of education, the winner of the Vladimir Potanin Scholarship program of The V. Potanin Charitable Foundation. © The Author(s), 2019. This is an open access article under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/). stakeholders (students, teachers and employers) are involved. The results of a survey of teachers of magistracy (713) in Russian regions, as well as undergraduates (1 140), have been reflected in the article. As the main results, information on the use of modern educational technologies in the preparation of Russian undergraduates has been given – a conclusion has been made about the small distribution of on-line education and network programs in universities. The research data characterizing the degree of digital competencies of Russian graduate students have been adduced: information search and processing, use of IT technologies, information security, ability to work with «big» data. It has been noted, that these skills are well developed among modern students, but the participation of universities in their development is low. It has been concluded, that Russian magistracy is characterized by a «transitional» period in solving the problems of the digital economy.