Reviewerly/exHarmony huggingface dataset:
Error-Correcting Neural Network.

Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable in real applications. This paper proposes an error-correcting neural network (ECNN) that combines a set of binary classifiers to combat adversarial examples in the multi class classification problem. To build an ECNN, we propose to design a code matrix so that the minimum Hamming distance between any two rows (i.e., two codewords) and the minimum shared information distance between any two columns (i.e., two partitions of class labels) are simultaneously maximized. Maximizing row distances can increase the system fault tolerance while maximizing column distances helps increase the diversity between binary classifiers. We propose an end-to-end training method for our ECNN, which allows further improvement of the diversity between binary classifiers. The end-to-end training renders our proposed ECNN different from the traditional error-correcting output code (ECOC) based methods that train binary classifiers independently. We empirically demonstrate that our proposed ECNN is effective against the state-of-the-art white-box attacks while maintaining good classification accuracy on normal examples.
Compact Hardware of Running Gaussian Average Algorithm for Moving Object Detection Realized on FPGA and ASIC

Real-time detection of moving object is essential to traffic monitoring, video surveillance and many other vital applications.The effectiveness of real-time detection hinges on background subtraction.This paper proposes a hardware-efficient architecture for the running Gaussian average (RGA) technique of background subtraction.The architecture was designed based on the field programmable gate array (FPGA) and application specific integrated circuit (ASIC).The FPGA was realized using Digilent ZedBoard, while the ASIC was implemented using Cadence Genus, Innovus, and Assura tools at 45 nm process technology.A prototype of our architecture was tested with a video containing multiple moving objects, and another containing a single moving object.The results show that the proposed architecture is efficient in terms of area, power and timing.Therefore, this paper provides a background subtraction approach that uses hardware resources effectively without sacrificing the detection accuracy.
Automatic detection of outdated comments in open source Java projects

Source code comments are significant assets to retain the logic designed during coding. In addition, they aid by communicating the intent of the code to other programmers, maintainers or even to oneself when checked at a later stage. Nevertheless, commenting code and keeping up-to-date comments are often disregarded by programmers. Some common reasons being, the extra work of commenting doesn’t deliver much value to the writer, time constraints, and various other reasons. As a first step to aid the process of updating comments following a code change, we present DocRevise, a tool that can automatically detect outdated Javadoc comments of open source Java projects at a fine-grain level. We utilized 30 Java methods from well-known and documented open source Java repositories from Github. Our approach involved inspecting the relationships between the code and the comments and fetching the identifiers that were altered. Experimental results show that DocRevise reaches 80.49% of accuracy when it comes to identifying relationships between the comments and the code. In addition, the results demonstrate that DocRevise can assist developers to locate outdated comments in prior versions of the existing projects.
Extraction and Annotation of ‘Location Names’

Introduced as part of the Message Understanding Conferences dedicated to information extraction, Named Entity extraction is a well-studied task in Natural Language Processing. The recognition and the categorisation of person names, location names, organisation names, etc., is regardedas a fundamental process for a wide variety of natural language processing applications dealing with content analysis and many research works are devoted to it, achieving very good results. One of our objectives is the identication and automatic (or semi-automatic) annotation of location names in order to apply the most appropriate information extraction methods. Then the main objective concerns the combination and interoperability between symbolic and statistical NLP (Natural Language Processing) methods (symbolic rules, machine learning, and data mining). Our work consisted of recognising named entities and in particular locations with Unitex, annotating them with Brat, and correcting them manually. The recall and accuracy rates are very encouraging but the question remains: What is a location name?
An Image Enhancement Optimization Method Based on Differential Evolution Algorithm and Cuckoo Search Through Serial Coupled Mode

Image enhancement based on Beta function is a widely used method for it is able to fit multiple transformation curves, which is a significant step for image analysis. The key step for the method is to find the appropriate parameters to determine the grayscale transformation function. However, it needs a lot of time to seek applicable parameters when enumeration is used and random optimization algorithms often have failures within a limited time and are prone to fall into the local optimum. In order to solve the problems a serial coupled mode of stochastic optimization algorithms is investigated in the paper. According to the model, the differential evolution algorithm and cuckoo search algorithm are tried in image enhancement through serial coupling mode and compared with the traditional optimization algorithm. The experimental results reveals that the proposed approach is feasible and the performance is more balanced, which has a good performance on the image enhancement.
Fuzzy relational equations employing Dragonfly operations

This article studies solvability of system of fuzzy relational equations where the antecedents and the consequents may suffer from missing values. We use the operations from the Dragonfly algebra that we have recently proposed as a specific algebra for partial fuzzy set theory for dealing with missing values especially when constructing fuzzy relational compositions. To investigate the solvability of the new system, we build an implicative model mirroring the shape of the standard implicative model and then we focus on the possible difference between the inferred outputs using such a model and the consequents appearing in the system of fuzzy relational equations. Furthermore, assuming the solvability of the system and some additional conditions, we show that it is possible to determine a solution of the system derived from the built implicative model by slight modifications.
Wildfire Risk Map Based on DBSCAN Clustering and Cluster Density Evaluation

Wildfire risk analysis can be based on historical data of fire hotspot occurrence. Traditional wildfire risk analyses often rely on the use of administrative or grid polygons which has their own limitations. This research aims to develop a wildfire risk map by implementing DBSCAN clustering method to identify areas with wildfire risk based on historical data of wildfire hotspot occurrence points. The risk ranks for each area/cluster were then ranked/calculated based on the cluster density. The result showed that this method is capable of detecting major clusters/areas with their respective wildfire risk and that the majority of consequent fire occurrences were repeated inside the identified clusters/areas.Keywords: wildfire risk map; clustering; DBSCAN; cluster density;
Protecting composite IoT server by secure secret key exchange for XEN intra virtual machines

Challenges presented by ever increasing volumes of smart objects are huge in terms of security and privacy of data generated out of these objects. These challenges range from the usage scenario with other objects and dedicated servers which act as a middleman among various networks such as internet and wireless sensor network. In this paper, attempt has been made to highlight a simple use case of a composite server in a XEN-based virtualised system. Security threats like sniffing and spoofing are isolated and analysed with respect to a novel key transfer method between host and guest operating system in XEN-based virtualised system. In addition, secure and trust model to cater the said security threats like sniffing and spoofing is also presented. The performance of the proposed system in terms of CPU usage and network bandwidth is also shown.
Deterrence approach on the compliance with electronic medical records privacy policy: the moderating role of computer monitoring

This study explored the possible antecedents that will motivate hospital employees' compliance with privacy policy related to electronic medical records (EMR) from a deterrence perspective. Further, we also investigated the moderating effect of computer monitoring on relationships among the antecedents and the level of hospital employees' compliance intention.Data was collected from a large Taiwanese medical center using survey methodology. A total of 303 responses was analyzed via hierarchical regression analysis.The results revealed that sanction severity and sanction certainty significantly predict hospital employees' compliance intention, respectively. Further, our study found external computer monitoring significantly moderates the relationship between sanction certainty and compliance intention.Based on our findings, the study suggests that healthcare facilities should take proactive countermeasures, such as computer monitoring, to better protect the privacy of EMR in addition to stated privacy policy. However, the extent of computer monitoring should be kept to minimum requirements as stated by relevant regulations.
A novel hybrid atlas-free hierarchical graph-based segmentation of newborn brain MRI using wavelet filter banks

Objective: The newborn brain MRI (magnetic resonance imaging) tissue segmentation plays a vital part in assessment of primary brain growth. In the newborn stage (nearly less than 28 days old), in T1- as well as T2-weighted MR images similar levels of intensity are exhibited by WM and GM, makes segmentation of the tissue extremely challenging. In this newborn stage for tissue segmentation, very few methods are developed. Hence the development of accurate brain tissue segmentation of neonate is prime objective of this paper.Methods: In this research work, we propose a novel hybrid atlas-free hierarchical graph-based tissue segmentation method for newborn infants. Wavelet filter banks are a class of deep models wherein filters and local neighborhood processes are used alternately for efficient segmentation on the raw input images, and fuzzy-based SVM (support vector machine) is used for precise tissue classification.Results: Specifically, from T1, T2 images multimodality information are used as inputs and then as outputs the segmentation maps are generated. The proposed approach considerably outperforms preceding methods of tissue segmentation as reflected in results. With this approach, the newborn MRI images that are even suffered from noise, poor resolution or the low contrasted images are also segmented more effectively with precision of 90% and sensitivity 98%.Conclusion: In addition, our findings indicate that the incorporation of multi-modality image led to significant improvements in performance. Thus, the proposed work effectively tackles the unreliability as well as the other issues faced with the prior methodologies with an interactive accurate segmentation outline.
It Runs in the Family: Searching for Similar Names using Digitized Family Trees.

Searching for a person's name is a common online activity. However, Web search engines provide few accurate results to queries containing names. In contrast to a general text which has only one correct spelling, there are several legitimate spellings of a given name. Today, most techniques used to suggest synonyms in online search are based on pattern matching and phonetic encoding, however, they frequently have poor performance. As a result, there is a need for an effective tool for improved synonym suggestion. In this paper, we propose a revolutionary approach for tackling the problem of synonym suggestion. Our novel algorithm titled GRAFT utilizes historical data collected from genealogy websites, along with network algorithms. This is a general algorithm that suggests synonyms based on the construction of a graph-based on names derived from generated digitized ancestral family trees. Synonyms are extracted from this graph constructed using generic ordering functions that outperform other algorithms that suggest synonyms based on a single dimension, a factor that limits their performance. We evaluated GRAFT's performance on forenames and surnames, including the utilization of a large-scale online genealogy dataset with over 17 million profiles and more than 200,000 unique forenames and surnames. For comparison, we compared its performance at suggesting synonyms to nine algorithms, including phonetic encoding, string similarity algorithms, and machine and deep learning techniques. The results show that GRAFT found superior to the evaluated algorithms with respect to both forenames and surnames and demonstrate its use as a tool to improve synonym suggestion.
Optimal page ranking system for web page personalisation using kernel-based fuzzy C-means and gravitational search algorithm

In this personalised web search (PWS), we utilise a kernel-based FCM for clustering a web pages. For effective personalised web search, queries are optimised using GSA with respect to clustered query sessions. In offline processing, initially preprocess the input information taken from consumer visited web pages and are transformed in to numerical matrix. These matrices are gathered with the help of kernel-based FCM method after produce a vector for consumer query and detect a minimum distance as centroid values these values are input to the GSA algorithm. It will engender these links given top N web pages from cluster. In online processing, the user query is engaged as input then extract some web pages from Google, Bing, Yahoo also extract content and snippet from web pages. Finally, detect a sum of contents and snippets and web pages would be considered in descending order.
A Markov chain representation of the “5 E’s” instructional treatment

Formulation of the problem.The socio-constructive theories of learning have become very popular during the last decades for teaching mathematics.The "5 E's" is an instructional model based on the principles of social constructivism that has recently become very popular, especially in school education, for teaching mathematics.Each of the 5 E's describes a phase of learning which begins with the letter "E" -Engage, Explore, Explain, Elaborate, Evaluate.Depending on the student reactions, there are forward or backward transitions between the three middle phases (explore, explain, elaborate) of the 5E's model during the teaching process.The "5 E's" model allows students and teachers to experience common activities, to use and build on prior knowledge and experience and to assess their understanding of a concept continually.Materials and methods.Probabilistic methods of analysis are used. Results. The mathematical representation of the "5 E's" model is attempted by applying an absorbing Markov chain on its phases. A MarkovChain (MC) is a stochastic process that moves in a sequence of steps (phases) through a set of states and has a one-step memory.A finite MC having as states Si the corresponding phases Ei, i = 1, 2,…, 5, of the "5 E's" instructional model is introduced.A classroom application is also presented illustrating the usefulness of this representation in practice.The following application took place recently at the Graduate Technological Educational Institute of Western Greece for teaching the concept of the derivative to a group of fresher students of engineering.Conclusions.The Markov chain representation of the "5 E's" model provides a useful tool for evaluating the student difficulties during the teaching process.This is very useful for reorganizing the instructor's plans for teaching the same subject in future.
Analisis Algoritma Apriori untuk Mendukung Strategi Promosi Perguruan Tinggi

&lt;p&gt;&lt;em&gt;Every company and organization that wants to survive needs to determine the effectiveness of the right promotion strategy. Determination of the right promotion strategy will be able to reduce the cost of promotion and achieve the right promotional goals. One way that can be done to determine the promotion strategy is to use data mining techniques. Data mining techniques used in this case are using the Apriori algorithm. A priori algorithm is one of the classic data mining algorithms. A priori algorithms are used so that computers can learn the rules of association, look for patterns of relationships between one or more items in a dataset. This study is conducted by observing several research variables that are often considered by universities in determining their promotion goals, namely school, region, and department. The results of this study are in the form of interesting patterns resulting from data mining which is important information to support the right promotion strategy in getting new students.&lt;/em&gt;&lt;/p&gt;
Study of the equilibrium of labor resources in differential games of supporting the projects’ joint investment processes

The article presents a mathematical model for the study of the equilibrium of labor resources. The problem of finding a sufficient equilibrium condition in differential games with many participants is solved. The article deals with a differential game of X persons with terminal gain, and sufficient conditions are derived in the class of positional strategies. The task is to find a sufficient equilibrium condition. The results obtained are adjacent to the results of [1,2,5], in which sufficient optimality conditions for optimal control problems and antagonistic differential games are stated.
LEGAL ANALYSIS OF SEXTORTION CRIME IN THE COMPERATIVE LAW AND TURKISH LAW

Due to technological developments, cybercrime techniques evolve day by day. Nowadays, innocent users have been the target of cybercriminals who want to make money or extort sexual favor in the context of sextortion crime. This paper discusses the definition of sextortion with the techniques used by the criminals, legal framework of aforementioned crime in Turkey and discusses how to prevent and solve past crimes.
Item-focused trees approach in differential item functioning (DIF) analysis: a case study of an EFL reading comprehension test

Recent years have witnessed an increasing interest in providing new insights into modern language testing method targeting test bias. In this study, item-focused trees (IFT) approach was applied to identify uniform and non-uniform differential item functioning (DIF) of an English as a foreign language (EFL) reading comprehension test. The multistage cluster sampling method was employed to randomly choose a large sample of 4937 students who took the entrance exam of MA program in English studies. The reading comprehension section of the general English test including 20 items was selected for the IFT analysis. Three categorical and continuous DIF source variables including gender and academic background were concomitantly taken into account for the IFT analysis, which is capable of handling more than one variable with both binary and continuous measurement. Then, in the final stage of IFT analysis within a logistic regression framework, uniform and non-uniform DIF was analyzed using DIF tree package of R. The results showed that 10 items had uniform DIF in which 2 items had 2 joint DIF predictor variables (2 splits) and 8 items had only one split. Additionally, 6 splits and 5 non-uniform DIF items were found in non-uniform DIF analysis in which only 1 item had 2 simultaneous DIF source variables. Furthermore, gender and background knowledge had significant relationships with EFL reading comprehension. This study promises practical implications for addressing gender and background knowledge differences in EFL reading comprehension studies on the one hand, and impacting language testing methodology on the other.
Lessons Learned from the Agile Transformation of an Aeronautics Computing Center

The aim of this paper is to share the main lessons learned in more than one year of work in the Agile Transformation of the Brazilian Aeronautics Computing Center of São José dos Campos. At the beginning of works, the intention was to support the implementation of Scrum as a framework for software development. The first faced challenge was the paradox between Scrum, for its horizontality in the interaction between individuals and the military hierarchy. An update in the Internal Regulations of this Computing Center included in its organizational structure a Project Management Office focused on advising Scrum Teams. The Scrum Teams were also included in the organizational structure, with no hierarchical link to any sector of the Organization. Over time, there has been an increase in people’s engagement to this work. The year 2019 began with the reorganization of the teams, following the Scrum Guide, and with two success stories: the construction of a Flight Simulator and the development of a system that controls the overflight of foreign aircraft in brazilian airspace.
EFEKTIFITAS PENGGUNAAN MEDIA PEMBELAJARAN E-LEARNING PADA MATA KULIAH QOWAIDULLUGHOH DI PRODI PAI FAI UHAMKA

E-learning based learning media is technology based learning where learning material is presented in the form of audio visual such as video. This type of research is an experimental study to determine the effectiveness of learning media that is treated. The study population was all semester II PAI UHAMKA students in South Jakarta. The sample used in this study was taken randomly, namely the PAI classes A, B and C. The data collected included primary data. The results of this study were obtained from primary data. Primary data were obtained from pre-test and post-test results of students. There is an increase in the pre-test and post-test results in the experimental class and a decrease in the pre-test and post-test results in the control class. The average results obtained from the experimental class from 59.36 to 71.76 and in the control class from 72.82 to 70.64. The difference in mean results shows an increase in pre-test and post-test results and it can be concluded that the e-learning-based learning media is effectively used in learning.