Reviewerly/exHarmony huggingface dataset:
NeuralCubes: Deep Representations for Visual Data Exploration

Visual exploration of large multi-dimensional datasets has seen tremendous progress in recent years, allowing users to express rich data queries that produce informative visual summaries, all in real time. Techniques based on data cubes are some of the most promising approaches. However, these techniques usually require a large memory footprint for large datasets. To tackle this problem, we present NeuralCubes: neural networks that predict results for aggregate queries, similar to data cubes. NeuralCubes learns a function that takes as input a given query, for instance, a geographic region and temporal interval, and outputs the result of the query. The learned function serves as a real-time, low-memory approximator for aggregation queries. Our models are small enough to be sent to the client side (e.g. the web browser for a web-based application) for evaluation, enabling data exploration of large datasets without database/network connection. We demonstrate the effectiveness of NeuralCubes through extensive experiments on a variety of datasets and discuss how NeuralCubes opens up opportunities for new types of visualization and interaction.
Do interactions between formal and informal institutions matter for productive entrepreneurship?

Motivation: Institutional and Entrepreneurship theory pays attention to institutions and their influence on productive, unproductive and destructive entrepreneurship in transition economies. However, according to the literature, it is not only institutions that matter for productive entrepreneurship, but also the interaction between formal and informal institutions. Moreover, transition economies needs the productive entrepreneurship ‘to catch-up’ with world leaders.Aim: The aim of this paper is to advance institutional research through the development of a better understanding of the relation between formal and informal institutions and their influence on productive entrepreneurship in transition economies like CEECs.Results: The paper demonstrates that strong formal and informal institutions, the ‘invisible hand’ of the state, as well as the complementary and accommodating relations between formal and informal institutions are necessary for productive entrepreneurship in transition economies.
CDMF: A Deep Learning Model based on Convolutional and Dense-layer Matrix Factorization for Context-Aware Recommendation

We proposes a novel deep neural network based recommendation model named Convolutional and Dense-layer Matrix Factorization (CDMF) for Context-aware recommendation, which is to combine multi-source information from item description and tag information.CDMF adopts a convolution neural network to extract hidden feature from item description as document and then fuses it with tag information via a full connection layer, thus generates a comprehensive feature vector.Based on the matrix factorization method, CDMF makes rating prediction based on the fused information of both users and items.Experiments on a real dataset show that the proposed deep learning model obviously outperforms the state-of-art recommendation methods.
Design and Implementation of Web-Based English Autonomous Learning System

This paper attempts to solve the following problems of traditional Internet Eng-lish learning websites: the emphasis on the teaching process over the timely feed-back of the students, the bottleneck on the autonomous learning ability of the stu-dents, and the delay in the update of relevant resources. For this purpose, the au-thor established a web-based English autonomous learning platform. Specifically, the conceptual model was analysed by the object-oriented technology, the brows-er/server (B/S) architecture was set up to send data to the ADO.NET-based backend database, and displayed as webpages. The research results show that the design and implementation of the proposed system offers a preliminary solution to the lack of collaborative communication in traditional webpage learning, and widens the range of resource sharing. The system is in line with the English learners’ demand for and interests in Internet learning, and arouses the students’ interests and enthusiasm of English learning.
Naimark extension for the single-photon canonical phase measurement

We address the implementation of the positive-operator-valued measure (POVM) describing the optimal $M$-outcome discrimination of the polarization state of a single photon. Initially, the POVM elements are extended to projective operators by the Naimark theorem; then the resulting projective measure is implemented by a Knill-Laflamme-Milburn scheme involving an optical network and photon counters. We find the analytical expression of the Naimark extension and the detection scheme that realizes it for an arbitrary number of outcomes $M={2}^{N}$.
Enhanced two-phase residual network for single image super-resolution

Recent research on deep residual neural network has made breakthrough in single image super-resolution. However, these studies ignore persistent memory of information in the propagation, which fail to infer plausible high-frequency sufficiently. To solve this problem, we propose a novel enhanced two-phase residual network (ETRN). The proposed ETRN progressively generate high-resolution image in two phases so that we can recover accurate texture. We also present dense residual unit to capture hierarchical features in the local residual learning phase. With the help of these high-level representations, we use global residual learning to improve reconstruction quality in the second phase. In order to reduce cumbersome computation, between two phases we introduce dilated convolution which enlarges receptive field without increasing additional parameters. Comprehensive experiments show that ETRN has a significant improvement in both quantitation and visual perception.
E Resources vs Prints Usages and Preferences by Undergraduates in a Private University Nigeria

&#x0D; &#x0D; &#x0D; Electronic and print resources usages and preferences among undergraduates of Landmark University, Omu-Aran, Kwara State, Nigeria has been examined. The study population consist of all undergraduates of the University, from which a sample of 250 that is 10 percent of the population was selected. The instrument used for data collection was a questionnaire. 231 copies of the questionnaire were completed and returned, representing 92.45 per cent response rate. Results from the study revealed that 110 (47.6 %) of the respondents preferred print resources, while interestingly, the same number 110 (47.6 %) preferred electronic resources; 11(4.8 %) of the participants did not provide any response to the question. It therefore implies that both print and electronic resources are complementary in meeting the information needs of undergraduates. However, majority of the respondents indicated that they usually supplement print materials with electronic resources or vice versa. The test of hypotheses revealed that user preferences were influenced by demographic factors such as age, course and level of study. Libraries should maintain hybrid collections, as both resources were found to be relevant sources of information for teaching, learning and research is recommended.&#x0D; &#x0D; &#x0D;
Memristor based ring oscillators true random number generator with different window functions for applications in cryptography

&lt;span&gt;Cryptographic applications require numbers that are random and pseudorandom. Keys must be produced in a random manner in order to be used in common cryptosystems. Random or pseudorandom inputs at different terminals are also required in a lot of cryptographic protocols. For example, producing digital signatures using supporting quantities or in verification procedures that requires generating challenges. Random number generation is an important part of cryptography because there are flaws in random number generation that can be taken advantage by attackers that compromised encryption systems that are algorithmically secure. True random number generators (TRNGs) are the best in producing random numbers. This paper presents a True Random Number Generator that uses memristor based ring oscillators in the design. The designs are implemented in 0.18 µm complementary metal oxide semiconductor (CMOS) technology using LT SPICE IV. Different window functions for the memristor model was applied to the TRNG and compared. Statistical tests results of the output random numbers produced showed that the proposed TRNG design can produce random output regardless of the window function.&lt;/span&gt;
Penggunaan LKM Untuk Menunjang Kegiatan Laboratorium Virtual Fisika Inti

To improve the quality of the Nuclear Physics lectures in addition to understanding the material as knowledge aspect, students are also expected to practice for the skills. The Nuclear Physics lecture material is microscopic so it requires laboratory activities, but there is no suitable practical tools available in the laboratory of Physics Department, mathatics and science faculty, universitas negeri padang. Therefore, a virtual laboratory model was developed in this research which was supported by students' worksheet. The valid and practical students' worksheet have been obtained. Then, it was tried out to students who take the Nuclear physics lecture to see whether the students' worksheet are effective to support virtual laboratory activities. The research conducted is included in the type of quasi-experimental research. The population is students who take part in the Nuclear Physics lecture in semester January-June 2018. The samples were taken using Cluster Random Sampling techniques. Data on knowledge and skills competencies were analyzed using the t test at a real level of 0.05. Based on the results of data analysis, there were significant differences in the two sample classes. The results of the regression analysis indicate that there is a strong correlation between the use of students' worksheet to support virtual laboratory activities for knowledge and skills competencies.Keywords: Nuclear Physics, Students Worksheet, Virtual Laboratory
Utilization &amp; Quality of Information System for Administration Services Based on ICT In Patehan, Kraton, Yogyakarta

Development of technology and application of e-government at city, sub-district, and village become the factor of emergence of Information and Communication Technology-based information service system innovation. Yogyakarta has implemented e-government as one of the strategies used to achieve the slogan of Yogyakarta Smart City. One of the areas that have implemented the concept is Patehan, Kraton, Yogyakarta. This research aims to describe the system of online administrative services, identify of utilization by the community, and measure the quality of its services. The research method used was qualitative and quantitative methods. Data collected are primary data and secondary data with analytical technique used is descriptive qualitative and quantitative. Based on the results of the research can be seen that the administrative service system in Patehan (SiWarga Patehan) is a system made by citizens, for citizens, and from citizens to take care of online certificate from RT, RW until the village that serve as one alternative for the community has high mobility. Based on 60 respondents, it can be concluded that the administrative system of SiWarga Patehan included into the category of good with the service index of 79.97.
Malaria Parasite Detection from Microscopic Blood Smear Image: A Literature Survey

Malaria is a mosquito-borne parasitic disease that is caused by the parasite of the genus Plasmodium.This infectious disease is transmitted via the bite of infected Anopheles mosquitoes.This allows the parasite to enter the human body, and they get matured in the liver and affect the RBC.Malaria is usually found in tropical regions and sub-tropical regions where the climate is suitable for its growth.Every year millions of people are affected by Malaria.In this paper, we focus on the study of different image processing methods for the detection of Malaria infection in humans.A comparison study is made among these methods.
URL PHISHING DATA ANALYSIS AND DETECTING PHISHING ATTACKS USING MACHINE LEARNING IN NLP

Nowadays people using the internet for shopping, banking, mailing etc. Phishing is one of the major attacks on the website which people are facing in their day to day life.A phishing attack is one of cybercrime because it is the illegal attempt that gets sensitive information of the user such as username, password, and credit card detail.Too aware of such phishing attacks taken online so in this paper have to detect phishing Uniform Resource Locator (URL), that is, we loading the URL data from the Kaggle open source website which is an online community of data scientists and machine learning, owned by Google Limited Liability Company( LLC).In most of the phishing website, the attackers use a malicious URL which will display to the user like an authorized URL.Different algorithms like Naive Bayes, Random Forest, K nearest neighbor are performed in detection of the URL, by using algorithm their accuracy level will be different.So in this paper can adopt the best classification machine learning algorithm with SVM (Support Vector Machine), this predicts the phishing or non-phishing status of the given URL and it is the best algorithm in classification (based on the features of given data) and regression (is the continuous prediction of uniform data) from which we have to improve our accuracy level.
Detection of Voice Transformation Spoofing Based on Dense Convolutional Network

Nowadays, speech spoofing is so common that it presents a great challenge to social security. Thus, it is of great significance to recognize a spoofed speech from a genuine one. Most of the current researches have focused on voice conversion (VC), synthesis and recapture which mimic a target speaker to break through ASV systems by increased false acceptance rates. However, there exists another type of spoofing, voice transformation (VT), that transforms a speech signal without a target in order `not to be recognized' by increased false reject rates. VT has received much less attention. Thus, in this paper, we investigate the model of VT and propose a method using a very deep dense convolutional network with 135 layers to detect VT spoofed speeches from genuine speeches. The experimental results show that the average accuracies over intra-database and cross-database outperform the reported state-of-the-art methods.
Moment invariant-based features for Jawi character recognition

&lt;p&gt;Ancient manuscripts written in Malay-Arabic characters, which are known as "Jawi" characters, are mostly found in Malay world. Nowadays, many of the manuscripts have been digitalized. Unlike Roman letters, there is no optical character recognition (OCR) software for Jawi characters. This article proposes a new algorithm for Jawi character recognition based on Hu’s moment as an invariant feature that we call the tree root (TR) algorithm. The TR algorithm allows every Jawi character to have a unique combination of moment. Seven values of the Hu’s moment are calculated from all Jawi characters, which consist of 36 isolated, 27 initial, 27 middle, and 35 end characters; this makes a total of 125 characters. The TR algorithm was then applied to recognize these characters. To assess the TR algorithm, five characters that had been rotated to 90o and 180o and scaled with factors of 0.5 and 2 were used. Overall, the recognition rate of the TR algorithm was 90.4%; 113 out of 125 characters have a unique combination of moment values, while testing on rotated and scaled characters achieved 82.14% recognition rate. The proposed method showed a superior performance compared with the Support Vector Machine and Euclidian Distance as classifier.&lt;/p&gt;
Design and Development of Job Recommendation System Based On Two Dominants On Psychotest Results Using KNN Algorithm

Employees are an important factor in the progress of a company. Employees with good performance will certainly provide positive results for the company. One that can determine employee performance is the right placement in the job. To find out the right placement in a job, one way can be done psychologically. Psikotes can help to know the nature of an employee and suitable work based on their nature. The construction of a job recommendation application system was created to help prospective employees know their true identity and suitable work so that they can apply according to their expertise. This system is built with the programming language PHP, Javascript, HTML for web-based platforms and the KNN algorithm as the method. The KNN algorithm is used to measure the closest distance between training data and test data to produce job recommendations. Training data is taken from expert, and book references. System trials are given to users by filling in psychological tests and questionnaires regarding the satisfaction of system use. After getting feedback from users, the value of system satisfaction reached 85%. This states that the system can provide job recommendations that are in accordance with the psychological test results of the user.
MATLAB/Simulink’s Variant Manager vs SESToPy

This paper describes how a complex case study for variability modeling and simulation from the documentation of MATLAB/Simulink can be remodeled with the extended System Entity Structure and Model Base (eSES/MB) approach using the Phython-based tool SESToPy and the accompanying modelbuilder SESMoPy.
Comparison of Technical Terms and Consciousness of Blended Classes in ‘AI Technology’ and ‘Artificial Intelligence'

&lt;p style="text-align:justify"&gt;Target subject is a module called ‘AI Technology’, which applied the ideas of blended learning. Firstly, lecture-style teaching was conducted with presentation slides in order to explain the contents of a textbook. Secondly, students were required to do exercises and quizzes. By using the last eight weeks, they were asked to create presentation slides outside a class to introduce the up-to-date topics on artificial intelligence. These slides were mutually evaluated among them so that they developed their own slides based on the feedback before the tenth week of the course for the second round of mutual evaluations. Questionnaires concerning students’ understanding technical terms of the field and consciousness-raising towards competence were also conducted before and after the programs. The learning effects of a module in ‘AI Technology’ are compared with my previous research outcome of the module, ‘Artificial Intelligence’. The reasons of difference between both modules are discussed. This paper reports their results.&lt;/p&gt;
Normal and abnormal red blood cell recognition using image processing

&lt;p&gt;In medical field, the recognition of red blood cells (RBC) are used as an indicator to detect the type of diseases such as anaemia, malaria and leukaemia etc. The problems using manual detection of normal and abnormal RBCs under the microscope is tend to give inaccurate result and errors. This paper proposed a method to recognize the normal and abnormal shaped RBCs image by using Form Factor as feature descriptor. Detecting normal cells of RBCs indicate a healthy patient and abnormal cells indicate presence of disease. And is very important in medical field to detect abnormal condition in early stage because it saves and protects human lives. The patients waiting time for blood test is more because the time taking to generate the result of the patient is high due to high demand and less equipment this method is used in order to improve the accuracy of the existing one and 94% accuracy was achieved in the detection.&lt;/p&gt;
Hidden Markov Model for Identification of Different Marks on Human Body in Forensic Perspective

This paper proposes a computational forensic methodology which identify and classify different marks on the human body using Hidden Markov model.The methodology gives an efficient and effective computerized approach for the characteristics of different marks such as birthmarks, burntmarks, tattoos and weapons' wounds found on human body.This proposed method will be a computationally effective substitution for the traditional forensic method in identifying the body marks in crime investigation of homicidal cases.Hidden Markov Model (HMM) is statistical and logical tool suitable for this identification.The marks on human body describe different patterns with characteristics that are helpful in identification.The experimental results achieved for identification of different marks with an average accuracy of 94.6%, on the available database of 400 images that includes four categories: Birthmarks, Burntmarks, Tattoos and weapons' wounds (100 images of each marks).The methodology gives the better combination of features (color, texture and shape), which are extracted for the identification of marks on human body for the purpose of computational forensic science.
How Video Object Tracking Is Affected by In-capture Distortions?

Video Object Tracking -VOT- in realistic scenarios is a difficult task. Image factors such as occlusion, clutter, confusion, object shape, and zooming, among others, have an impact on video tracker methods performance. While these conditions do affect trackers performance, there is not a clear distinction between the scene content challenges like occlusion and clutter, against challenges due to distortions generated by capture, compression, processing, and transmission of videos. This paper is concerned with the latter interpretation of quality as it affects VOT performance. The contribution of this paper is two-fold. We have constructed a database of 537 surveillance videos containing different levels of authentic distortions such as low exposure and out-of-focus. It is available at https://tinyurl.com/DSVD-Test. Based on this database, we assessed seven state-of-the-art trackers with the A-R plot performance measure. We demonstrate that in-capture distortions severely hamper VOT methods performance in a non intuitive way.
Efficient Online Hyperparameter Adaptation for Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) has shown its extraordinary performance on a variety of challenging learning tasks, especially those in games. It has been recognized that DRL process is a high-dynamic and non-stationary optimization process even in the static environments, their performance is notoriously sensitive to the hyperparameter configuration which includes learning rate, discount coefficient, and step size, etc. The situation will be more serious when DRL is conducting in a changing environment. The most ideal state of hyperparameter configuration in DRL is that the hyperparameter can self-adapt to the best values promptly for their current learning state, rather than using a fixed set of hyperparameters for the whole course of training like most previous works did. In this paper, an efficient online hyperparameter adaptation method is presented, which is an improved version of Population-based Training (PBT) method on the promptness of adaptation. A recombination operation inspired by GA is introduced into the population adaptation to accelerating the convergence of the population towards the better hyperparameter configurations. Experiment results have shown that in four test environments, the presented method has achieved 92%, 70%, 2% and 15% performance improvement over PBT.