Reviewerly/exHarmony huggingface dataset:
An ensemble detection method for shilling attacks based on features of automatic extraction

Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA (marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings' distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA (marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.
A Comparative Analysis of Causality Between Institutional Structure and Economic Performance for Developed and Developing Countries

The aim of the present study is to investigate the relation between countries' structural transformation and institutional factors.The causality relationship between 'economic complexity level' representing structural transformation and six institutional indicators revealed by the World Bank (government effectiveness, political stability, control of corruption, rule of law, regulation quality, voive and accountability) was investigated via Dumitrescu-Hurlin causality test for both E-7 and G-7 countries for the period of 1996-2017.The research question of the study is that whether there is a difference among developed and developing countries in terms of the interaction and causal relationship among institutional indicators and economic performance.On this purpose, E-7 country group is selected to represent developing countries whereas G-7 community is selected to represent developed one.As a result of the analysis, it was determined that institutional factors in E-7 countries comprised of developing countries displayed greater causality relationships with economic complexity level representing structural transformation in comparison with G-7 countries.Besides, causal relationships among institutional indicators themselves are also greater in E-7 countries than G-7 countries.This finding suggests that economic performance of developing countries are more sensitive to institutional structure in comparison to developed ones.That's why developing countries are required to pay more attention to institutional factors with greater rigorously.
DEVELOPMENT OF A HIGH-SPEED VIDEOGRAMMETRIC MEASUREMENT SYSTEM WITH APPLICATION IN LARGE-SCALE SHAKING TABLE TEST

Abstract. The high-speed videogrammetric measurement system, which provides a convenient way to capture three-dimensional (3D) dynamic response of moving objects, has been widely used in various applications due to its remarkable advantages including non-contact, flexibility and high precision. This paper presents a distributed high-speed videogrammetric measurement system suitable for monitoring of large-scale structures. The overall framework consists of hardware and software two parts, namely observation network construction and data processing. The core component of the observation network is high-speed cameras to provide multiview image sequences. The data processing part automatically obtains the 3D structural deformations of the key points from the captured image sequences. A distributed parallel processing framework is adopted to speed up the image sequence processing. An empirical experiment was conducted to measure the dynamics of a double-tube five-layer building structure on the shaking table using the presented videogrammetric measurement system. Compared with the high-accuracy total station measurement, the presented system can achieve a sub-millimeter level of coordinates discrepancy. The 3D deformation results demonstrate the potential of the non-contact high-speed videogrammetric measurement system in dynamic monitoring of large-scale shake table tests.
Parametric face drawings: A demographically diverse and customizable face space model

We introduce a novel face space model—parametric face drawings (or PFDs)—to generate schematic, though realistic, parameterized line drawings of faces based on the statistical distribution of human facial features. A review of existing face space models (including FaceGen Modeller, Synthetic Faces, MPI, and active appearance model) indicates that current models are constrained by their reliance on ethnically homogeneous face databases. This constraint has led to negative consequences for underrepresented populations, such as impairments in automatized identity recognition of certain demographic groups. Our model is based on a demographically diverse sample of 400 faces (200 female, 200 male; 100 East Asian/Pacific Islander, 100 Latinx/Hispanic, 100 black/African-American, and 100 white/Caucasian) compiled from several face databases (including FERET face recognition technology and the Chicago Face Database). Each front-view face image is manually coded with 85 landmark points that are then normalized and rendered with MATLAB (MathWorks, Natick, MA) tools to produce a smooth, parameterized face line drawing. We present data from two behavioral experiments to validate our model and demonstrate its applicability. In Experiment 1 we show that PFDs produce a reliable “inversion effect” in short-term recognition, a hallmark of holistic processing. In Experiment 2, we conduct a celebrity recognition task, comparing performance on PFDs to performance on untextured renderings from FaceGen Modeller. Participants successfully recognized approximately 50% of celebrity faces based on the PFD models, comparable to performance based on FaceGen Modeler (also 50% correct). We highlight a range of potential applications of our model, list some limitations, and provide MATLAB resources for researchers to utilize our face space, including the ability to customize the demographic makeup of the face space, add new faces, and produce morphs and caricatures.
Printed Versus Electronic Texts in Inclusive Environments: Comparison Research on the Reading Comprehension Skills and Vocabulary Acquisition of Special Needs Students

In this research, an effort is made to compare the effectiveness of reading texts presented through electronic books in a computer environment and regular (printed) texts, in terms of the development of the reading comprehension and vocabulary acquisition skills of students with special needs within inclusive educational environments. The research was designed with ‘Adaptive Alternating Applications’, and the study group of the research was formed by using the ‘purposive sampling’ method. As a requirement of the research design used, two special needs students were studied. According to the results of the study, the vocabulary acquisition levels of both students produced more effective results in presentations made with electronic texts, and in addition, electronic texts were found to be more effective in improving reading comprehension skills than printed texts.
The visual selectivity of the default mode network

The default mode network (DMN) is a large scale brain network with correlated cortical activities in frontal, parietal and temporal nodes. These nodes deactivate when participants are actively engaged in perceptual tasks, and activate for high-level cognition tasks such as mind wandering and autobiographical memory. While recent findings indicate that activations and deactivations of the DMN carry visual information related to memory, the functional role of its deactivations remains a mystery. Using population receptive field methods (pRF) applied to the Human Connectome Project retinotopy dataset (181 participants, 7T), we show that voxels in the DMN selectively deactivate as a function of the position of a visual stimulus. Moreover, we demonstrate the DMN’s functional representation of visual space by successfully decoding the location of a visual stimulus in a cross-validated analysis of another 7T fMRI dataset. Together, our results establish for the first time the DMN as a visually selective network and open up the possibility of using vision-science paradigms to understand the role of this network in high-level cognition.
Dividing the Neighbors is Not Enough: Adding Confusion Makes Local Descriptor Stronger

Descriptors with learned features, other than descriptors with handcrafted features, have been popular in the research community. In this paper, we propose a novel piecewise loss function to improve the performance of learned descriptor, which is inspired by the nearest neighbor distance ratio matching strategy (the matching criterion for SIFT). In our strategy, if the distance ratio between positive and closest negative example is larger than a threshold, the loss will be chosen as HardNet loss; If the ratio is smaller than the threshold, the loss will be confusion. Applying the proposed loss function to the L2Net and HardNet architecture, the proposed descriptor is named ConfusionNet. Specifically, we find that adding the confusion to train local descriptors can achieve better performance. Comparing with previous works, we show in the experiments that ConfusionNet achieves state-of-the-art results in patch retrieval, patch verification, and image matching.
Combination of Principal Component Analysis and Time-Frequency Representation for P-Wave Arrival Detection

Seismic events are phenomena which commonly occur in the mining industry. Due to their dangerous character, such information as the energy of the potential event, the location of hazardous regions with higher seismic activity is considered valuable. However, the acquisition of this information is almost impossible without the ability to detect the onset time of the seismic event. The main objectives of algorithms in finding P-wave are high accuracy, reasonable time of operation, and automatic detection of wave arrival. In this paper, an innovative method which incorporates principal component analysis (PCA) with time-frequency representation of the signal is proposed. Due to the significant difference between the spectra of recorded seismic wave and pure noise which precedes the event, time-frequency representation allows for better accuracy of signal change detection. However, with an additional domain, the complexity rises. Thus, the incorporation of PCA (which is known for high efficiency in lowering data dimensions while maintaining original information) seems to be recommended. In order to show the feasibility of the method, it will be tested on real data originating from monitoring system used in underground mine.
An Assessment of Cybersecurity Technologies in the Selected Universities in Southwestern Nigeria

The study identified the forms of cybercrimes in selected universities in Southwestern Nigeria.It determined the cyber security techniques and polices adopted by the selected Universities.It examined the effectiveness of cyber securities adopted by the selected Universities.Hence, primary data were obtained through the administration of questionnaire on 33 ICT administrators from each of the selected nine (9) universities in Southwestern Nigeria, making 297 respondents.Secondary data were sourced from publications.Data collected were analysed using descriptive and inferential statistics.The result showed that common types of cybercrimes in the selected institutions were hacking 66.3%), credit card fraud (58.5%), spamming (52.2%), software piracy (60.7%), identity theft (55.2%), sweet heart swindle (53.3%) and malicious programme/virus (54.4%).The results also showed that most cybercriminals used password cracker (83%), network sniffer (50.4%) and key logger (44.4%) to perpetrate their illicit acts.Also, the results indicated the adopted cyber securities mechanism in the selected universities which include identity (ID) and Password (100%), Public Key Cryptography (33.3%),Biometric Authentication (29.6%) and Digital Signature (18.5%).There was a significant (F=7.043;p<0.05) relationship between frequency of cyber-attack on Servers/Web Services and the deployment of Multi-Layer Authentication, Digital Signature (F=16.611,p<0.05) and Public Key Cryptography (F=6.750,p<0.05).The study concluded that major technologies used for cybercrimes in the universities in Southwestern Nigeria were password cracker, key logger and network sniffer.The study also concluded that the common types of cybercrime in the universities were hacking, credit card fraud, spamming, software piracy, identity theft, pornography, sweet heat swindle and malicious programmes (virus).The study further concluded that authentication protocols deployed majorly by universities in Southwestern Nigeria were Kerberos, internet protocol (IP) securities, CHAP and MS-CHAP.The study concluded that all the universities deployed identity (ID) and password authentication for security access control while privacy policy, network security policy and employee training regarding confidential information were the cyber security policy adopted by most of the universities in Southwestern Nigeria.The study concluded that the deployment of Cyber securities such as public key cryptography and digital signature inhibits threats on servers and services of the universities.This study concluded that universities in Southwestern Nigeria that did not deploy multi-layer authentication, digital signature and public key cryptography on their internet servers are susceptible to cybercrime frequently.
An image retrieval method of mammary cancer based on convolutional neural network

This paper designs a brand-new image retrieval method of mammary cancer based on convolution neural network. This method simulates VLAD layer in CNN network structure, designs a trainable universal VLAD layer-NET VLAD layer, reduces dimensions and optimizes VLAD descriptors, applies structure from motion algorithm to automatically label samples, and obtains the minimum loss function value by a new training program of weakly supervised ranking loss. Experiments show that this method has improved retrieval performance compared with similar retrieval methods and non-network structure retrieval methods.
Educational potential of “Foreign Language” course to develop soft skills of technical students

Currently the relevance of the development of students’ universal competencies (soft skills) along with the formation of their professional (hard skills) skills is determined by employers’ requirements, rapidly changing labor market and terms of professional activity. The article deals with the students of technical majors soft skills developmentin course foreign language instruction. The authors analyze the essence and the concept of soft skills in frames of foreign language university training. The authors highlight that soft skills development occurs through focused and purposeful work on critical, system and analytical thinking skills. A special course of the discipline "Foreign Language" is designed by the authors. The preliminary results of the experimental work demonstrate that positive dynamics of skills development has been observed.
TESTING OF V3.SUN MODULE PROTOTYPE FOR SOLAR RADIATION MODELLING ON 3D OBJECTS WITH COMPLEX GEOMETRIC STRUCTURE

Abstract. High-resolution solar radiation modelling requires the three-dimensional geometric structure of the landscape to be respected. Currently, remote sensing methods such as laser scanning and close-range photogrammetry are most commonly used for detailed mapping. The output is detailed 3D models containing buildings, trees, relief and other landscape features. The raster approach allows modeling solar energy for relief, but it is unsuitable for landscape objects such as buildings and trees. The polygonal features vector approach is mainly designed for buildings. Our goal is to create a freely available tool for highly detailed solar radiation modelling for geometrically complex 3D landscape objects. In the paper, we present a prototype of the v3.sun module. We propose a solution of solar radiation modeling designed for all landscape features based on TIN data structure. In the paper, tests of the proposed algorithmic solution for various types of 3D data obtained from the above-mentioned collection methods are demonstrated.
Beam drift reduction by straightness measurement based on a digital optical phase conjugation

One of the greatest challenges of long distance measurement is the beam drift caused by the air refractive index gradient. It has been established in many researches that optical phase conjugation (OPC) can be used to compensate for the beam bending. However, this method is limited to responding speed, phase conjugate reflectivity, flexibility, and specific source and medium. To reduce beam drift, instead of OPC, this study applies a digital OPC (DOPC) method, which is also creatively applied to collimation and flatness measurements. The main devices in the wavefront correction unit are the spatial light modulator and the Shack&#x2013;Hartmann wavefront sensor. For the straightness measurement unit, the collimation and flatness of the optical rail are measured through the prism system and a position-sensing detector. After wavefront compensation, the root mean square is decreased from 0.0029&#x03BB; to 0.0005&#x03BB;. The beam drift is decreased from 1.22&#x00A0;mm to 0.70&#x00A0;mm in the x direction and from 2.49&#x00A0;mm to 1.55&#x00A0;mm in the y direction. The experimental data indicate that the straightness measurement system based on DOPC can effectively decrease the beam drift.
De-Professionalization of Cataloging and Classification Practitioners in the Digital Environment: Retooling and Reskilling Issues in Academic Libraries in Nigeria

This article discusses issues of de-professionalization that is fast eroding cataloging and classification practices in academic libraries in Nigeria. It focuses on the factors that led to de-professionalization, its effect on cataloging and classification practices, and the need to re-strategize by retooling and reskilling the cataloging librarian in order to continue to remain relevant in the digital environment.
An efficient technique for identification of leukemia in microscopic blood samples using image processing.

Leukemia is a blood cancer which features through the ejection of manipulated and strange fabrication of white blood cells which is the way of bone marrow within the blood. The project aims at designing and developing an efficient technique for the detection of luekemia based on image segmentation techniques and nuclei analysis which incorporates the affected percentage and are compared and classified using KNN and SVM. The DNA of youngster cells, for the maximum detail white platelets, subsequently finally ends up harmed here and there. This version from the norm reasons platelets to increase and separate constantly. Sound platelets bypass on inevitably and are supplanted by approach of new cells, which might be brought in bone marrow.
APPLICATION OF MAMDANI FUZZY METHOD TO DETERMINE ROUND BREAD PRODUCTION AT PT VANESSA BAKERY

Mamdani Fuzzy is a method that interprets input values and makes conclusions based on IF-THEN rules and producing the output. In this research Mamdani fuzzy method is applied to determine the amount of round bread production at PT Vanessa Bakery. The step involve: determining the fuzzy system, the membership functions, as well as the fuzzy rules. The defuzzification process is applied to determine the amount of total production and to calculate the MAPE value of the Mamdani fuzzy method. The calculated MAPE as much as 5.94%, indicates this method has an excellent forecasting ability because the value is less than 10%. Thus, the Mamdani fuzzy method can be used at PT Vanessa Bakery.
Research on the Application of Cooperative Learning in College English Teaching

By collecting and sorting out relevant theoretical materials, the author analyzes the current situation of college English teaching, and by selecting experimental classes to demonstrate the obvious progress before and after the adoption of cooperative learning teaching model, the author holds that it is imperative to implement cooperative learning teaching model at the present stage, On this basis, this paper tries to put forward several effective cooperative learning strategies and suggestions, hoping to enlighten the front-line teacher’s engagement in college English teaching and researching.
P6V2G: a privacy-preserving V2G scheme for two-way payments and reputation

Abstract The number of electric vehicles (EVs) is steadily growing. This provides a promising opportunity for balancing the smart grid of the future, because vehicle-to-grid (V2G) systems can utilize the batteries of plugged-in EVs as much needed distributed energy storage: In times of high production and low demand the excess energy in the grid is stored in the EVs’ batteries, while peaks in demand are mitigated by EVs feeding electricity back to the grid. But the data needed for managing individual V2G charging sessions as well as for billing and rewards is of a highly personal and therefore sensitive nature. This causes V2G systems to pose a significant threat to the privacy of their users. Existing cryptographic protocols for this scenario either do not offer adequate privacy protection or fail to provide key features necessary to obtain a practical system. Based on the recent cryptographic toll collection framework P4TC, this work introduces a privacy-preserving but efficient V2G payment and reward system called P6V2G. Our system facilitates two-way transactions in a semi online and post-payment setting. It provides double-spending detection, an integrated reputation system, contingency traceability and blacklisting features, and is portable between EVs. The aforementioned properties are holistically captured within an established cryptographic security framework. In contrast to existing protocols, this formal model of a V2G payment and reward system allows us to assert all properties through a comprehensive formal proof.
ANALISA PERBANDINGAN FUZZY LOGIC METODE TSUKAMOTO, SUGENO, MAMDANI DALAM PENENTUAN KELUARGA MISKIN

The application of the Fuzzy Tsukamoto, Sugeno, and Mamdani methods in determining poor families in Sumedang Regency can be done by the three methods using poor family outcomes which differ from the results of all data calculations. From all data calculated using the fuzzy logic method of the Tsukamoto, Sugeno, and Mamdani methods, it can be concluded that there are 4 data classified as poor and 16 data classified as not poor in the Tsukamoto method, 8 data classified as poor and 12 data classified not poor in the Sugeno method, and 10 data classified as poor and 10 classified as not poor in the Mamdani method. These results indicate that each method has different results even though the input used is the same.