Reviewerly/exHarmony huggingface dataset:
Transfer Learning by Learning Projections from Target to Source

Using transfer learning to help in solving a new classification task where labeled data is scarce is becoming popular. Numerous experiments with deep neural networks, where the representation learned on a source task is transferred to learn a target neural network, have shown the benefits of the approach. This paper, similarly, deals with hypothesis transfer learning. However, it presents a new approach where, instead of transferring a representation, the source hypothesis is kept and this is a translation from the target domain to the source domain that is learned. In a way, a change of representation is learned. We show how this method performs very well on a classification of time series task where the space of time series is changed between source and target.
Optimization of Community Reading Garden Management in Improving Reading Culture

TBM is more accurately called a reading facility in the middle of the community (community-based library) and is managed simply, and independently by the community concerned. This action research aims to (1) describe the management pattern of Community Reading Gardens as part of Omah Passionaon empowerment programs; (2) Explain the optimization of TBM management in its efforts to improve reading culture. Action research with a focus on optimizing TBM management is carried out using a diagnosis, planning, implementing action and evaluation model with Omah Passinaon managers as research subjects. Data or information is collected by interviewing techniques such as FGDs, observations on aspects of program management, activities to obtain a comprehensive picture of the potential, problems, needs for improvement and forms of action to optimize TBM management. The results of this study indicate that (1) TBM management emphasizes the competence of capable and skilled managers in building public awareness of the importance of reading culture, as well as the realization of TBM that has community appeal as a source of information in Bejiharjo tourism area. (2) Actions for optimizing TBM management in research include (a) Raising public awareness of reading fondness at Omah Passionaon, (b) partnerships with academics, universities, and private parties, (c) mentoring for pioneering TBMs that attract public interest in reading, (d) evaluation. TBM management can be effective must have partnerships from private or government institutions, besides that the manager also has good competence. The benefit of this research is to provide awareness to the community to read and motivation for TBM managers to seek partnerships with private and government institutions.
Counting the Number of Shortest Chamfer Paths in the Square Grid

In this paper, the number of shortest paths between any point pairs of the square grid, based on weighted distances, is computed.We use two types of steps on the gridlines and diagonal steps.Consequently, we use an 8-adjacency square grid, that is, one where a first weight is associated with the horizontal and vertical movements, while a second weight (not necessarily different from the first) is assigned to the diagonal steps.The chamfer distance of two points depends on the numbers and weights of the steps in a 'shortest path'.In special cases, the cityblock and the chessboard distances, the two most basic and widely used digital distances (they are also referred as L 1 and L  distances, respectively) of the two-dimensional digital space occur.Although our combinatorial result is theoretical, it is closely connected to applications, such as communication networks, path counting in digital images, traces and trajectories in 2D digital grids.We consider all seven cases with non-negative weights and also the case when negative weights are allowed.
Authentication by Mapping Keystrokes to Music: The Melody of Typing

Expressing Keystroke Dynamics (KD) in form of sound opens new avenues to apply sound analysis techniques on KD. However this mapping is not straight-forward as varied feature space, differences in magnitudes of features and human interpretability of the music bring in complexities. We present a musical interface to KD by mapping keystroke features to music features. Music elements like melody, harmony, rhythm, pitch and tempo are varied with respect to the magnitude of their corresponding keystroke features. A pitch embedding technique makes the music discernible among users. Using the data from 30 users, who typed fixed strings multiple times on a desktop, shows that these auditory signals are distinguishable between users by both standard classifiers (SVM, Random Forests and Naive Bayes) and humans alike.
The Challenges of Measuring Scientific Research Productivity and Impact in Developing Economies

Scientific research is a difficult investment to measure.On the one hand, many developing countries around the world use scientific research as a mean to improve their economy and has found way to reduce their dependency on their natural resources.On the other, scientific research is an expensive endeavor.In this paper, we look at the different factors impacting scientific research and consider the relationship between governments and researchers, the impact of businesses on basic research funding, and the methods through which the performance scientific research is measured.This paper also considers the impact of scientific research funding measures on researchers' careers and briefly discuss the common public research funding mechanisms around the world.We also discuss knowledge-based economy and Solow's theory of economic growth and its later development made by Romer and some the elements that are not addressed by them.In this review, we have considered developing economies and the difficulties they face in moving away from their dependence on their natural resources towards become economies dependent on producing knowledge and implementing this knowledge into their business practices.This paper takes a closer look at the state of Qatar since it is a developing country trying to move away from its economy's dependence on fossil fuel production to a knowledge-based economy.
Towards a more flexible language of thought: Bayesian grammar updates after each concept exposure

Recent approaches to human concept learning have successfully combined the power of symbolic, infinitely productive rule systems and statistical learning to explain our ability to learn new concepts from just a few examples. The aim of most of these studies is to reveal the underlying language structuring these representations and providing a general substrate for thought. However, describing a model of thought that is fixed once trained is against the extensive literature that shows how experience shapes concept learning. Here, we ask about the plasticity of these symbolic descriptive languages. We perform a concept learning experiment that demonstrates that humans can change very rapidly the repertoire of symbols they use to identify concepts, by compiling expressions that are frequently used into new symbols of the language. The pattern of concept learning times is accurately described by a Bayesian agent that rationally updates the probability of compiling a new expression according to how useful it has been to compress concepts so far. By portraying the language of thought as a flexible system of rules, we also highlight the difficulties to pin it down empirically.
Peningkatan Kemampuan Membaca Huruf Hijaiyah Melalui Kartu Huruf Hijaiyah di PAUD

Early age is often referred to as the golden age (Golden Age) during which all aspects of development in children experience very significant development and this is the right time for parents and teachers to lay the foundations of the skills they will develop. To introduce letters hijaiyah to children after early childhood in some PAUD institutions are still applying conventional methods by using the book Iqro '. Introduction of hijaiyah letters can be varied by using hijaiyah letter cards. Learning model in the conventional way. This study wants to see the effectiveness of the use of hijaiyah letters in improving the ability to read hijaiyah letters in early childhood in PAUD. The research method used in this study is Classroom Action Research (CAR). The results showed that the ability to read hijaiyah letters in B2 class PAUD Arrahma Siak Hulu increased by using hijaiyah letter media development. Evidenced by the results of cycle 1 that 50% complete reading hijaiyah letters while in increased to 93.75%
RSA algorithm using key generator ESRKGS to encrypt chat messages with TCP/IP protocol

This study aims to analyze the performance and security of the RSA algorithm in combination with the key generation method of enhanced and secured RSA key generation scheme (ESRKGS). ESRKGS is an improvement of the RSA improvisation by adding four prime numbers in the property embedded in key generation. This method was applied to instant messaging using TCP sockets. The ESRKGS+RSA algorithm was designed using standard RSA development by modified the private and public key pairs. Thus, the modification was expected to make it more challenging to factorize a large number n into prime numbers. The ESRKGS+RSA method required 10.437 ms faster than the improvised RSA that uses the same four prime numbers in conducting key generation processes at 1024-bit prime number. It also applies to the encryption and decryption process. In the security testing using Fermat Factorization on a 32-bit key, no prime number factor was found. The test was processed for 15 hours until the test computer resource runs out.
Problems of Development of a Cloud-Oriented Educational Environment of the University

The article shows that at the current stage of development of modern universities, it is necessary to ensure the functioning of the educational environment, which will fully realize the conditions for academic mobility, communication and cooperation between research and teaching staff (RTS) and students.That is, it is necessary to introduce a cloud-oriented educational environment of universities (COEE) for more efficient organization of the academic community, which brings together both RTS and students.It is shown that for the effective implementation of COEE in universities it is necessary to develop new models that reflect the main components of COEE, processes and the relationships between them.There is proposed a multi-cloud COEE model based on the principles of modular arithmetic.The proposed modification of COEE will increase the reliability of storage and processing of data shared by the academic community of several universities at once, both for scientific work and for exchanging experience in introducing advanced teaching methods and new educational and methodological developments of university research and teaching staff who participate in the project.There are analyzed the models of reliable storage of large volumes of data for COEE.There are shown the advantages of models based on modular arithmetic over models based on a positional number system.So, for example, the probability of COEE failure on the basis of an excess system of residual classes (RCS) is approximately 4,5-5 times less than for a system based on replica storage.
Evaluation of Learning Approaches Based on Convolutional Neural Networks for Mammogram Classification

Mammography is still considered the best screening method for detection, diagnosis and follow-up of breast cancer. A correct classification of mammographic findings demands a high expertise level of the clinician observer. For this, different Computer-aided Diagnosis systems have been developed to support the diagnosis tasks and reduce the inter or intra-observer variability caused by the complex visual information contained in mammograms. However, the classification of some findings (masses, calcifications) is still a difficult task. This work presents a methodological approach to evaluate the performance of the training process for different convolutional neural network configurations of the VGG16 Convolutional Neural Network architecture, designed to perform mammographic classification. For doing that, the impact of different learning strategies (focal loss, to deal with highly unbalance datasets, gradient clipping and learning transfer) is evaluated.The proposed method was two-fold evaluated. First, the performance for classifying between normal and abnormal Regions of Interest (ROIs) extracted from the DDSM and CBIS-DDSM datasets was explored. After that, a multi-class problem was addressed, for which a set of 5-class was included according to well-known BI-RADS classification. The obtained results reported an average accuracy of 0.92 for the binary classification and a rate of accuracy of 0.85 for the 5-class classification (with 30 epochs), reducing the convergence time (23 and 30 epochs for both binary and multi-class classification tasks, respectively).
Detecting Textual Information in Images from Onion Domains Using Text Spotting

Due to the efforts of different authorities in the fight against illegal activities in the Tor networks, the traders have developed new ways of circumventing the monitoring tools used to obtain evidence of said activities.In particular, embedding textual content into graphical objects avoids that text analysis, using Natural Language Processing (NLP) algorithms, can be used for watching such onion web contents.In this paper, we present a Text Spotting framework dedicated to detecting and recognizing textual information within images hosted in onion domains.We found that the Connectionist Text Proposal Network and Convolutional Recurrent Neural Network achieve 0.57 F-Measure when running the combined pipeline on a subset of 100 images labeled manually obtained from TOIC dataset.We also identified the parameters that have a critical influence on the Text Spotting results.The proposed technique might support tools to help the authorities in detecting these activities.
Creation of a Russian abstract-noun dictionary: Selection criteria for lexiсal entries

The article studies criteria for distinguishing between abstract and concrete vocabulary.This topic is the focus of academic interest due to the increasing number of studies in the field of text complexity.Among the basic markers of "difficult" texts is the uneven correlation of concrete and abstract vocabulary.However, the studies of this topic show inconsistencies in the definition of criteria for selecting abstract words.The similar problem is found in theoretical works on the issues of the modern Russian language.This article analyzes various approaches to the definition of concrete and abstract vocabulary, defines the main criterion, namely the semantic one, since the semantics of the considered lexical and grammatical categories determines their grammatical features.The analysis of the cognitive approach to defining characteristic features of abstract and concrete vocabulary allows us to conclude that the meaning of each lexical unit contains both abstract and specific components, which can be expressed to a greater or lesser extent depending on the context and features of perception.Thus, the review indicates the need for specifying criteria for abstract vocabulary and the importance of creating a Russian abstract noun-dictionary, which would contain numerical indicators of the degree of abstractness for each input unit.For the English language, such dictionary was created in the 20th century.The article proposes one of the possible methods for selecting vocabulary for an abstract-noun dictionary, which could play an important role in both theoretical and applied research.
Automated Locating Mining-Induced Microseismicity without Arrival Picking by Weighted STA/LTA Traces Stacking

The automatic location of the microseismic source is still a challenging endeavor in the microseismic field. Due to the complexity of the mining environment, the microseismic records collected by different channels vary, and generally have a low signal-to-noise ratio (SNR). Therefore, the automatic location algorithm is required to be robust and accurate. For microseismic records with low SNR, the stack-based method does not need to pick arrival, thus avoiding the large location error caused by picking arrival. However, the traditional stack-based method does not consider the influence of the waveform quality of different stations, which can bring some errors to the location result. In this paper, in order to improve the location accuracy of the traditional stack-based method, we propose a method for weighted STA/LTA traces stacking. First, we established evaluation indicators of waveform quality based on microseismic records. Then, the STA/LTA traces are given weight to stack according to the evaluation indicators. Finally, the maximum value of the stacking function is solved in the four-dimensional space to obtain the source coordinates. In the process of calculation, we use the weighted differential evolution (WDE) optimal algorithm instead of the full grid search method, which greatly improves the calculation efficiency. The blasting experiment and engineering application show that the proposed method is stable and effective, and the location accuracy is higher than the traditional stack-based method and the arrival-based method.
The factors that influence of Reading ability the Hijaiyah alphabet on pre-school children

This study was conducted in order to determine the factors that influence the read ability of Hijaiyah Alphabet in pre-school age children. These factors are the worship places distance, worship frequency, study duration, and parental education. The research was conducted with a quantitative approach using survey methods. The sample of the research was 131 students from Kindergarten ‘Aisyiyah Bustanul Athfal (ABA) Karangharjo Berbah, Yogyakarta, Indonesia. Data collection techniques in this research were using documentation and questionnaires. Data analysis with multiple regression analysis path analysis models. The results of data analysis showed that there was a large influence of the frequency of worship through length of study on the ability to read the Hijaiyah alphabet by 0.85. Distance of places of worship has little effect on the ability to read the Hijaiyah alphabet by 0.09. While parental education does not affect the ability to read the Hijaiyah alphabet with a coefficient of 0.04. Therefore, active participation from parents is needed to improve the read ability the Hijaiyah alphabet of pre-school children by always providing assistance and supervision in performing worship continuously with the help of technology that makes it easy for them to read the Hijaiyah alphabet.
The governance quality-growth nexus revisited: A new evidence from the Bayesian multilevel generalized linear model

Many empirical studies have been conducted so far on economic growth in the world.In these studies, the effect of various elements on the economic growth, such as public expenditure, inflation, labor, private investment, etc., has been examined.In this study, the effect of governance on the economic growth in 43 Asian countries is considered during the period from 2004 to 2016.Using the Bayesian multilevel generalized linear model, it is estimated that governance has a positive impact on economic growth with a probability of more than 80%.Based on this, policy implications are provided for improving the governance quality to promote economic growth.
Decoding of the Story Superimposed of Buddhist Sculpture unearth from Bharja and testifying its relation to this Silk-route area of Sirohi District, India

Rohida police station recovered an old sculpture, accidentally discovered from the cemetery of Bharja/Bhaja village. It is a broken bronze Buddhist Idol and very rare sculpture. It has exquisite Antique beauty with rust in red and corrosion in green. Sculpture of Buddha seated in Padma Asana (lotus posture) and his hand in Dhyan Mudra (meditation posture). Buddha is wearing the robe; the robe is decorated with the scene of Buddhacharita story, superimposed on the visible crust. This Sirohi district has the history of Jainism and Hinduism only, till the date there is no evidence regarding Buddhism practices in Sirohi district of Rajasthan. Sirohi is famous from its silk route in the valley, ancient Chandrawati city and Mount Abu, where thousands of temples of Jainism and Shivanism were built. Objective: what were the technique and technology used to manufacture sculpture? Where this artefact came from? What are the main characteristics &amp; features of this sculpture? What carving scene depicted in this sculpture? Research analysis: For analysis of this sculpture we carefully look sculpture and magnify scene to compare with the stories of Buddhacharita. Moreover, check out that this sculpture is indigenous work of ancestral craftsmen or not. Also compare superimposed stories of Buddha and his life. Scientific method: Buddha sculpture is hollow in nature but very heavy in weight; Craftsman used the lost wax method to manufacture it. In ancient time the science behind manufacturing sculpture is very time consuming, first sculptors need to imagine about the subject, draft and then mould through melting, condensing, chiselling, hammering and exquisite carving. One of the oldest methods of metal casting according to Archaeo-metallurgy is bee wax method; this technique is now termed as the lost wax method. Conclusion: In the end, we like to conclude that in the history of Sirohi exploration, first time unearths the Buddhist sculpture but we have certain doubts that it mustn’t belong to Sirohi district. This idol is required for further critical research like dating and detailed mould-casting technique used in the manufacturing of this sculpture.
Linguistic Fuzzy Rule Learning through Clustering for Regression Problems

The fuzzy rule-based system model is an important and active research line in the fuzzy logic community looking for compact and robust systems with a high level of accuracyinterpretability of trade-offs.On the other hand, fuzzy rules-based systems provide accurate and interpretable solutions that give the ability to handle Complex data and uncertainty.It has also been historically applied in the solution of classification and regression problems.In this paper, the authors present a new fuzzy approach for solving problems of regression based on linguistic fuzzy rule learning with subtractive clustering and linguistic modifiers.The proposed system includes two phases for getting linguistic fuzzy rules: Multi-granularity, fuzzy discretization of the linguistic variables and linguistic approximation of fuzzy rules learned.Regarding experiments, researchers used twelve real-world data sets to compare the proposed system with three of the most widely used simplified fuzzy genetic systems: FS e MOGFS+TUN e , A-METSK-HD e and FRULER.The results highlight the competitiveness of the model in terms of accuracy and its superiority in interpretability.
Motion-Aware Feature for Improved Video Anomaly Detection.

Motivated by our observation that motion information is the key to good anomaly detection performance in video, we propose a temporal augmented network to learn a motion-aware feature. This feature alone can achieve competitive performance with previous state-of-the-art methods, and when combined with them, can achieve significant performance improvements. Furthermore, we incorporate temporal context into the Multiple Instance Learning (MIL) ranking model by using an attention block. The learned attention weights can help to differentiate between anomalous and normal video segments better. With the proposed motion-aware feature and the temporal MIL ranking model, we outperform previous approaches by a large margin on both anomaly detection and anomalous action recognition tasks in the UCF Crime dataset.