Reviewerly/exHarmony huggingface dataset:
Learning to Measure Stereoscopic S3D Image Perceptual Quality on the Basis of Binocular Rivalry Response

Blind perceptual quality measurement of stereoscopic 3D (S3D) images has become an important and challenging issue in the research field of S3D imaging. In this paper, a blind S3D image quality measurement (IQM) method that does not depend on examples of distorted S3D images and corresponding subjective scores is proposed. As the main contribution of this work, we replace human subjective scores with a quality codebook of binocular rivalry responses (BRRs); this allows blind S3D-IQM methods to be learned without evaluation performance loss. Our results, using the publicly accessible LIVE S3D dataset, confirm that our method is highly robust and efficient.
Interactive foreground segmentation and shape reconstruction from RGBD images

Three dimensional shape reconstruction of objects in complex scenes has important applications in intelligent transportation systems and medical image processing. This paper aims to segment and locate the foreground object through the interactive segmentation with multiple cues including saliency, depth and color. The desired foreground object is obtained by using the foreground and background information provided by saliency map and heatmap. Then, the multi-view point cloud information of the foreground object is recovered by depth information, and a multi-view point cloud registration algorithm based on color information is proposed. The three-dimensional shape model of the object is reconstructed through a multi-view point cloud registration algorithm combined with motion averaging and low-rank sparse matrix decomposition. The validity of the framework is empirically assessed with comparative experiments.
Forecasting of Changes in Service System during the Launch Period of the New Automobile Lineup

The purpose of the research is developing tools for forecasting changes in the service system during the period when new models of automotive vehicles are being introduced to the market.Research objectives: development of the structure and composition of decision support systems to improve the competitiveness of the service system, development of a mathematical model of a dealer-service network and a simulation model of the network of the Republic of Kazakhstan.The simulation model takes into account the following parameters: the percentage of satisfaction for applications for spare parts directly from the warehouse, the number of posts, the number of workers, the distribution of the flow of applications from the concentration of vehicles in the service center, the mode of operation of the service center.The following research methods were used: methods of system analysis and mathematical statistics, elements of the theory of reliability, simulation, computer experiment.After verification and validation of the developed model, a computer experiment was conducted with the current parameters.The obtained simulation values are reliable at a 5% significance level.The following result was achieved: a simulation model was built for the functioning of the dealer-service network that can be used in strategic planning for expanding the service network and entering new markets.
Augmented, virtual and robotics reality for distance learning and internship in telematics Sport and Exercise Science degrees in Italy

Sport and Exercise Science degrees has special purposes because the curricula provide, in relation to specific aims, the deepening of some lessons and activities, with internship traditionally playing in facilities, training and sport organizations, businesses, administration structures and laboratories, in addition to visits of study at other national and foreign universities, including in the framework of international exchange about teaching and research.The distance education methods in e-learning university has been continuously developed but doesn't provide the whole solution for the physical and sport activities in Exercise and sport sciences degrees, including the internship and works.The aim is to search and carry out the technological products to help the development of peculiarity aspects of the practice to achieve the target.Utilizing the archive research method and the technological based evidences could be make a hypothesis to improve the current vision with augmented, virtual and robotics reality applied to education distance.In this way the main technologies, such as Nintendo, PS, Xbox and others, could be utilized for enlarge the education opportunities for special needs people, included the disabled people.For the law aspects have to consider the specific mode to validate the activity in the same manner to play currently with an appropriate rule modification.
A novel hardware-efficient spatial orientation tree-based image compression algorithm and its field programmable gate array implementation

Set partitioning in hierarchical trees (SPIHT) has become a popular research topic for more than a decade now.This is because it is simple, besides having compression efficiency close to the state-of-the-art JPEG2000 standard.The main drawback of SPIHT is that it uses three lists to store addresses of coefficients during its operation.These lists are dynamic and in worst cases need to store more number of addresses than total coefficients.In this work, a novel hardware-efficient spatial orientation tree-based algorithm is proposed and its hardware implementation is carried out.The wavelet transformed image is partitioned into 2 × 2 blocks.Each node of spatial orientation tree (SOT) represent a block of coefficients, rather than a single coefficient.Two small state-tables are used in this algorithm to store the status of each block.In addition to this, two extremely small lists are used to store the node addresses of a single SOT tree.To store the state-tables and lists for 512 × 512 image, only 0.88% of the memory needed by SPIHT is required for five levels of dyadic decomposition.The peak-signal-to-noise ratio (PSNR) gain of 0.1 dB to 0.3 dB at low bit rates (below 1 bpp) and 0.6 dB to 1.2 dB at high bit rates (above 1.75 bpp) in comparison to SPIHT is observed for test image Lena.A field programmable gate array (FPGA) implementation targeted for Xilinx Zynq Z-7020 is presented in the paper.The proposed architecture saves 90% of the FPGA area used by SPIHT.The hardware efficiency of the proposed architecture is better in comparison to different existing architectures.
Killing spinors from classical r-matrices

In this article we summarize the interplay between supersymmetry and integrable deformations of superstring theory backgrounds.We start from a brief review of a recipe for the preserved Killing spinors in TsT deformed backgrounds.Then a few examples in TsT deformations of flat space, AdS 5 × S 5 , and AdS 7 × S 4 are presented.We also comment on the general Killing spinor formula expressed only in terms of the bi-vector Θ, obtained by the Seiberg-Witten map.
Deriving Workflow Privacy Patterns from Legal Documents

The General Data Protection Regulation (GDPR) has strengthened the importance of data privacy and protection for enterprises offering their services in the EU.An important part of intensified efforts towards better privacy protection is enterprise workflow (re)design.In particular, the GDPR as strengthen the imperative to apply the privacy by design principle when (re)designing workflows.A conforming and promising approach is to model privacy relevant workflow fragments as Workflow Privacy Patterns (WPPs).Such WPPs allow to specify abstract templates for recurring data-privacy problems in workflows.Thus, WPPs are intended to support workflow engineers, auditors and privacy officers by providing pre-validated patterns that comply with existing data privacy regulations.However, it is unclear yet how to obtain WPPs systematically with an appropriate level of detail.In this paper, we introduce our approach to derive WPPs from legal texts and similar normative regulations.We propose a structure of a WPP, which we derive from pattern approaches from other research areas.We also introduce a framework that allows to design WPPs which make legal regulations accessible for persons who do not possess in-depth legal expertise.We have applied our approach to different articles of the GDPR, and we have obtained evidence that we can transfer legal text into a structured WPP representation.If a workflow correctly implements a WPP that has been designed that way, the workflow automatically complies to the respective fragment of the underlying legal text.
Indonesian EFL students’ reading motivation and writing achievement across gender

The inseparable linkage between reading and writing has gained a crucial emphasis on language learning. It has been regarded as a constructive issue for the improvement in teaching English skills. However, little attention is given to the underlying relationship between students’ motivation in reading and their achievement in writing. This current research aimed at the correlation between students' reading motivation and their writing achievement across gender. It involved 50 third-year EFL students enrolled essay writing course divided into two classes in the ELT department. The quantitative data were collected through the use of the questionnaire on motivation in reading. Correlation analysis completed using Pearson product-moment revealed that the students' motivation in reading significantly positive influences their writing scores. It described that changes in students' reading motivation are a very weak correlation with the changes in their writing score. Another finding demonstrated that female and male students’ reading motivation and their writing achievement are significantly different. It signified that the differences correlation coefficient of both male and female students. Finding teaching implication is one of the areas that future researchers are suggested to investigate. Regarding the research findings, it indicates that there are more extensive areas offered for further studies in the relationship between reading and writing.
Detecting Spam in Twitter and Email using Machine Learning Approach

Online social Networks have turned out to be increasingly famous in the global. Individuals utilize these social platforms (e.g., Facebook, Twitter, etc.) for storing and sharing their personal information, activities, views, documents, posts and so on. At the same time, Social spam turned into an incessant issue nowadays which is incredibly hazardous to both individual clients and companies. Therefore, it is an imperious need to develop more accurate and powerful spam recognition models. This Paper essentially centers around machine Learning technique for distinguishing spam in Twitter and Email so as to make it increasingly secure. We choose twitter and email as our study targets. We apply Naive Bayes algorithm for discovery of spam using techniques such as preprocessing, word extraction by taking emails and real-time tweets as input to our project. The outcomes show an efficacious classification of spam and non-spam. Finally, we verify the perceptibility of spam tweets and mails as well as accounts through evaluation. Our proposed method could accomplish better outcomes contrasted with other existing supervised machine learning methods.
Facial Expression Information in Deep Convolutional Neural Networks Trained for Face Identification

Deep convolutional neural networks (DCNNs) are state-of-the-art learning algorithms inspired by the primate visual system (e.g., Fukushima, 1988; Krizhevsky et al., 2012). Face identification DCNNs produce a top-layer representation that supports face identification across changes in pose, illumination, and expression. Counter-intuitively, this representation also contains information not relevant for identification (e.g., viewpoint, illumination) (Parde et al., 2016). We asked whether DCNN identity codes also retain information about facial expression. Facial expressions are a type of identity-irrelevant information, though there are opposing neuropsychological theories about the independence of facial identity and facial expression processing (Fox and Barton, 2007; Calder and Young, 2005). Using the Karolinksa database (KDEF), a controlled dataset of expressions (Lindqvist et al., 1998), we examined whether the top-layer features of a high-performing DCNN trained for face recognition (Ranjan et al., 2017) could be used to classify expression. The KDEF dataset contains 4,900 images of 70 actors posing 7 facial expressions (happy, sad, angry, surprised, fearful, disgusted, and neutral), photographed from 5 viewpoints (90- and 45-degree left and right profiles, and frontal). All images were processed by the DCNN to generate 512-element face representations comprised of the top-layer DCNN features. Linear discriminant analysis revealed that the tested expressions were predicted accurately from the features, with happy expressions predicted most accurately (72% correct), followed by surprise (67.5%), disgust (67.5%), anger (65%), neutral (55.5%), sad (51%), and fearful (39%); (chance = 14.29%). We also examined the interaction between viewpoint and expression using a cosine similarity measure between the representations of different images in the dataset. Heatmaps and histograms of inter-image similarity indicated that there is a higher cost to identification with viewpoint change than with expression change. These results reveal a potential link between facial identity and facial expression processing and encoding in artificial neurons.
Security-oriented agile approach with AgileSafe and OWASP ASVS

In this paper we demonstrate a security enhancing approach based on a method called AgileSafe that can be adapted to support the introduction of OWASP ASVS compliant practices focused on improving security level to the agile software development process. We also present results of the survey evaluating selected agile inspired security practices that can be incorporated into an agile process. Based on the survey’s results, these practices were used as an input to AgileSafe method as well as to demonstrate their potential to comply with OWASP ASVS requirements.
Penerapan Aturan Asosiasi Algoritma Apriori Terhadap Penjualan Thai Tea Pada PT Nyonya Besar Lestari

Competition in the business world, especially in the increasingly lively beverage sales, requires the sellers to find strategies to increase sales. More and more sales data every day can be used to develop marketing strategies if processed correctly. To find out the most sales Apriori Algorithm is needed to be able to find out and with the help of Rapidminer tools, products with the most sales can be known. Apriori algorithm including the type of association that has raised the attention of many researchers to produce an efficient algorithm is the analysis of high frecuency patterns (frequent pattern mining) in this study can be implemented on the interest of buying drinks with the data used are 31 transactions with a minimum support or 30% and minimum confidence 60%.
Pengaruh Aplikasi Gliserin pada Kekerasan Resin Komposit Nanofiller dengan Perendaman Cuka Apel

Composite resin is a type of filling material that is often used because it has good bonds with dentine and enamel and has a high aesthetic value. Nanofiller composite resin has high hardness, good polishing, color accuracy, and good translucency. The hardness of composite resins can change caused by low pH beverage. Purpose: To determine the differences in effect of glycerin application on hardness of nanofiller composite resins with immersion of apple cider vinegar. The type of research used was experimental laboratories with a pretest and post test group design. Samples were 24 immersion in apple vinegar for 60 and 120 minutes. Measurement of hardness using the Vicker Hardness Tester to determine the hardness value. Data analysis was performed using One Way Anova and Post-Hoc LSD tests.There is a significant difference in the value of composite resin hardness in the entire study group. The highest hardness value was found in the group with the application of glycerin and without immersion, while the lowest value was found in the group without the application of glycerin and immersion in 120 minutes.
Public Integrity Auditing for Shared Dynamic Cloud Data with Group User Revocation

Now daily's distributed computing is one of the top innovation idea. Distributed storage servers assume a significant job in the buzz distributed computing innovation where customers can store their information at cloud servers and can get to this information from anyplace and whenever. This opens up the danger on the information which is the customer is putting away on the cloud as it needs to store and access from any gadget through web. This Paper manages the information Security which opens up part of zones like information respectability check, confirmation of customers and information security saving. In the proposed framework for the most part concentrate is on information respectability zone which is worried about information security and the Third Party Auditors (TPA) is used to check the legitimacy of the information without really getting to the information on cloud.
DenoisingNet: An Efficient Convolutional Neural Network for Image Denoising

Since convolutional neural networks (CNNs) were used in image denoising field, they have showed state-of-the art denoising quality beyond traditional methods. However, the existing CNN denoising models have a high computational cost and require high memory capacity, which is impractical for embedded applications or mobile platforms. Inspired by the efficiency of the depthwise separable convolutions introduced in MobileNet, this paper proposes a novel network structure, named DenoisingNet, which designed specifically to minimize model size while maintaining image denoising performance. The resulting DenoisingNet model possesses a model size of 369 KB, which is an order of magnitude lighter than other state-of-the-art networks while maintaining the denoising quality. Extensive experimental results indicate that very small deep neural network architectures can be designed for real-time image denoising that are well suited for embedded scenarios. To the best of our knowledge, DenoisingNet is the first lightweight network architecture for image denoising.
Development of a Scale for Students in Evaluating 2017 Information Technology and Software Curriculum

The purpose of this study was to develop a valid and reliable instrument measuring the views of secondary school 5 th grade students on the curriculum of 2017 Information Technology and Software (ITS) course.The study was carried out with 528 5 th grade students (15 for pilot application, 320 for exploratory factor analysis (EFA), 203 for confirmatory factor analysis (CFA) studying in the spring semester of 2017-2018 school year.Implementation of the scale was realized with participants who were determined through the combination of criterion and convenience sampling.In order to determine the content validity and face validity of the scale, expert opinions were obtained, while both EFA and CFA were performed to assess the construct validity of the scale's measures.Analyses revealed one-factor structure which sheltered 30 items, which explained 39.92 percent of the total variance with a high internal consistency coefficient.The CFA on the one-factor structure revealed acceptable fit indices as well.Based on these findings, it could be concluded that the scale is an instrument that produces valid and reliable measures, and that can be used to determine fifth grade students' views on the curriculum evaluation of 2017 ITS course.For further studies, the scale can be implemented to a random sample form the population of 5 th grade students to reveal their views on the curriculum and studies can be conducted in order to investigate in terms of different variables.
Image Encryption Algorithm Based on a Hyperchaotic System and Fractional Fourier Transform

An image encryption scheme that combines a hyperchaotic system with standard weighted fractional Fourier transform theory is proposed. Simulation results showed that grayscale distribution of the encrypted image was balanced, correlation coefficients of adjacent pixels were highly irrelevant, and the encrypted image was highly sensitive to the secret key, offering good robustness against attacks and a larger key space.
Analysis on CBIR with Color and Texture-based Feature Extraction using SVM

In this technology era, images have become a major part of information processing.An Image plays an important role in Image registration (IR) processing for the extraction of information.There are various fields like in medical, tourism and geological, weather systems forecasting used image registration.In this paper, IR is presented based on Support Vector Machine learning in the content-based image retrieval system.A Support Vector Machine (SVM) for the purpose of retrieval of images similar to the query image.Using the SVM classifier, the system can retrieve more images relevant to the query in the database efficiently.There are many traditional techniques that have been used to retrieve images.One of the Content-based image retrievals has the most popular research area in the last few years.Image retrieval is a technique of finding out the most important features of the image.The main task of content-based image retrieval (CBIR) is to get a similar images as well as perfect and fast result.In this CBIR system, effective organization of the image database used to improve the performance of the system.The study of contentbased image retrieval (CBIR) technique has become an important research issue.In this way, studied and analyzed of various features as an individual or in combinations.Through the studied of various research papers after that conclude the color and texture-based feature extraction is the most important for imparting the best extraction and support vector machine makes this task more easy and effective.
Drug Abuse Detection via Broad Learning

Prescription drug abuse is one of the fastest growing public health problems in the USA. This work develops a broad learning method for Drug Abuse Detection (DAD). In this paper, we propose a new broad learning-based method named ILSTM, short for Improved Long Short-Term Memory, to study the data fusion and prediction from heterogeneous data sources for DAD. The algorithm utilizes the broad learning framework to handle data fusion broadly and information mining deeply simultaneously. Moreover, the effectiveness and prevalence of Holt-Winter inspire our work in the temporal property for DAD.
STRATEGI PERGURUAN TINGGI DALAM MENINGKATKAN PUBLIKASI HASIL PENELITIAN

Research without the publication has not provided meaning about benefits and research results. Research results need to be published or disseminated to benefit the community. The purpose of this study is to determine the strategy of universities in the acceleration of publication of research results. The research method used a qualitative approach. Sources of data consist of interviews with lecturers and team of journal publications, document studies, and reports on the achievements of publications of scientific papers. This study uses inductive thinking. This research was conducted on journal team and publication of Universitas Pendidikan Indonesia and research and publishing institute of UIN Sunan Gunung Djati Bandung. Based on the research obtained the following results: Universities make strategic plans and master plan research in achieving publication of research results; to form journal team and publication and term of reference (TOR) publication of research result