Reviewerly/exHarmony huggingface dataset:
Efficient Way of Result Publication System by Using Cryptography

Nowadays almost all exam results are published online in Bangladesh. Online publication systems work very well in most cases. But when there is a large number of candidates whose results need to be published, result servers become very slow and unusable. We have solved this problem in a cost-effective way. After the final preparation of results, we encrypt the result using symmetric key cryptography and send it to the individual students email addresses. On the publication date authority only needs to publish the decryption password. We have also developed an android application by which students can easily view the result after decryption. It is possible to implement this project in existing result publication systems.
A Framework to Handle Uncertainties of Machine Learning Models in Compliance with ISO 26262

Assuring safety and thereby certifying is a key challenge of many kinds of Machine Learning (ML) Models. ML is one of the most widely used technological solutions to automate complex tasks such as autonomous driving, traffic sign recognition, lane keep assist etc. The application of ML is making a significant contributions in the automotive industry, it introduces concerns related to the safety and security of these systems. ML models should be robust and reliable throughout and prove their trustworthiness in all use cases associated with vehicle operation. Proving confidence in the safety and security of ML-based systems and there by giving assurance to regulators, the certification authorities, and other stakeholders is an important task. This paper proposes a framework to handle uncertainties of ML model to improve the safety level and thereby certify the ML Models in the automotive industry.
Evaluation of Data Imbalance Algorithms on the Prediction of Credit Card Fraud

Credit card fraud has been on the rise for some years now after the introduction of card payment systems. To curb this menace, computational methods have been proposed. Unfortunately, the data available for such a study is highly skewed resulting in the data imbalance problem. In this study, the authors investigate the performance of some selected data imbalance algorithms employed in the prediction of credit card fraud. A dataset from Kaggle containing 284,315 genuine transactions and 492 fraudulent transactions was used for the evaluation. The machine learning algorithms deployed for the study is logistic regression, naïve bayes, and the k-nearest neighbour algorithm with F1 score and precision-recall area under the curve (PR AUC) as the metric. Numerical assessment of the performance of the adopted algorithm gave a rate of 82.5% and 81%, respectively, using neighbourhood cleaning rule for undersampling.
A Blockchain-Based Sentiment Analysis Framework for Reliable Feedback System

In many institutions, feedback collection is either carried out manually or in a generic centralized manner, both of which are prone to manipulation. To provide a legitimate and practical feedback system is still being explored in the area of industry and information security. With an increase in demand for user feedback, concerns on authenticity and privacy of information have grown. This article describes a web-based application implemented on a decentralized approach for building a reliable system that can ensure legitimate feedback. This feedback system provides an online platform to accomplish the whole procedure which is a research field of cryptography with the basics of encryption, hashing and signature algorithms. Further, sentiment analysis has been explored for projecting the cumulative feedback and providing veritable results. This application can be applied in corporate offices and educational institutions where students and employees will be required to submit feedback for the concerned authorities.
Clustering-Aided Graph Signal Sampling and Reconstruction for Large-Scale Sensor Networks

In this paper we develop a clustering-aided signal sampling and reconstruction method for data acquisition in large-scale sensor networks. Using the localization feature of a large network, we exploit the vertex-domain locality by the localized operator of each vertex on the graph, and develop a clustering method that sequentially selects cluster heads and their corresponding members by the use of the overlap factor of each vertex. On this basis, we apply greedy sampling set selection for each cluster in a distributed manner. By combining all local sampling sets, the global sampling set is selected and signals over the whole graph is then efficiently reconstructed. Simulation results over various large networks show that compared with existing sampling set selection methods, the proposed method can reduce the computational complexity while achieving acceptable reconstruction accuracy.
Regulation TL;DR

Short on time with a reduced attention span, people disengage from reading long text with a "too long, didn't read" justification. While a useful heuristic of managing reading resources, we believe that "tl;dr" is prone to adversarial manipulation. In a seemingly noble effort to produce a bite-sized segments of information fitting social media posts, an adversity could reduce a long text to a short but polarizing summary. In this paper we demonstrate an adversarial text summarization that reduces Federal Register long texts to summaries with obvious liberal or conservative leanings. Contextualizing summaries to a political agenda is hardly new, but a barrage of polarizing "tl;dr" social media posts could derail the public debate about important public policy matters with an unprecedented lack of effort. We show and elaborate on such example "tl;dr" posts to showcase a new and relatively unexplored avenue for information operations on social media.
Perception as Expression: Virtual Reality in the Classroom

“It is the real, and not the map, whose vestiges persist here and there in the deserts that are no longer those of the Empire, but ours: The desert of the real itself.” (Baudrillard in Simulacra and Simulation, ). VR is a new medium that is helping to dissolve the boundaries between technology and imagination. The VR market is expanding at a fast rate with worldwide growth rates in 2019 of up to 69% per year (Sinclair in AR/VR spending to jump 69% in 2019-IDC, ). In this paper, we will examine how virtual reality with fully immersive head-mounted display systems help students to develop creative confidence and improved attitudes. The research will extend existing research on VR to examine content creation apps; with a focus on Google’s Tilt Brush. In the first section, this paper will discuss the educational applications of VR, and the second section will include the statistical analyses of VR in the art classroom. Finally, we will conclude and evaluate whether VR is a useful tool in fostering creativity in the classroom environment. This research project makes use of a mixed methods research platform to investigate students’ perceptions and learning outcomes. Quantitative data is dominant in the study where surveys and sculptural artifacts are analyzed.
A Survey on Harmony Search for Task Scheduling

Harmony Search (HS) nests in a class of population based evolutionary algorithms which are a classification of meta-heuristic algorithm that resembles the improvisation of musical process by a team comprising of different musicians. It dominates the other existing algorithms belonging to this category by its impressive performance in terms of the convergence speed. This paper is a survey on the collection of different existing Harmony Search algorithms that are able to address computational issues especially in the field of task scheduling.
Semantic Artificial Intelligence

As it is known, the main difference between humans and animals is the presence of the second signaling system. The introduction of the second signaling system into the artificial intelligence system including the linguistic model of the world that works in conjunction with the extralinguistic model of the world, makes it possible to separate the presentation and processing of specific information into lower-level and upper-level representations. Thus, along with the detailed description, the model of the world also contains a generalized representation, which makes it easy for the user to interact with the system using natural language.
cofad: An R package and shiny app for contrast analysis

Contrast analysis offers a powerful method to test a hypothesis about means in factorial designs.Despite many advantages, the method is unknown to most researchers.For instance, even if researchers have a specific hypothesis for group means in an experimental design, they usually run an omnibus test or multiple post-hoc tests.The first approach reduces power while the second increases the type I error because of multiple testing.Contrast analysis is always the better alternative as it provides a single specific test with high power.The method is efficient, informative and shines with its simplicity (Furr, 2004).To revive and popularize contrast analysis, a software is needed that is flexible but also intuitive to use.
Secret Image Share hiding using Steganography

An epic methodology of steganography used to conceal the mysterious picture utilizing sharing dependent on a (k,n)- limit is proposed. First, Wavelet Transform is applied to the secret picture. Second, utilize Lagrange's Polynomial Interpolating Scheme to partition the picture information into n shares and select any k (k<n) shares. At last, conceal the mysterious offers inside the picture utilizing LFSR (Linear Feedback Shift Register) procedure, which has the best random properties.
Controlling Probe Frequency with Network Traffic Dynamics: An Experience Report

Fault detection and localization in modern networks is done with probing techniques. Probes generate additional traffic which compete for its share of bandwidth. In this paper we propose to select a set of probes such that probe paths avoid congested routes and yet can identify all non probe station node failures in the network. In order to select a set of probes, we use historic Round Trip Time (RTT) statistics. Once the probes are selected, subsequently probing is done by adjusting the probing frequency in response to the instantaneous changes in traffic intensity along the probe path. We use RTT as a measure of changing traffic dynamics in the network to adjust the probing frequency. We propose to decrease the probing frequency with increase in RTT and vice versa. We experiment with simulations and real networks and show that probing frequency can be dynamically adjusted with traffic dynamics.
Hybrid feature tweaking

When using prediction models created from data, it is in certain cases not sufficient for the users to only get a prediction, sometimes accompanied with a probability of the predictive outcome. Instead, a more elaborate answer is required, like given the predictive outcome, how can this outcome be changed to a wished outcome, i.e., feature tweaking. In this paper we introduce a novel hybrid method for performing feature tweaking that builds upon Random Forest Similarity Tweaking and utilizing a Constraint Logic Programming solver for the Finite Domain (CLPFD). This hybrid method is compared to only using a CLPFD solver and to using a previously known feature tweaking algorithm, Actionable Feature Tweaking. The results show that the hybrid method provides a good balance between the distances, comparing the original example and the tweaked example, and completeness, the number of successfully tweaked examples, compared to the other methods. Another benefit with the novel method, is that the user can specify a prediction threshold for feature tweaking and adjust weights of features to mimic the real-world cost of changing feature values.
The profession of bibliographer: Prospects in the digital era

The author examines the vectors for bibliographer profession within the professional infrastructure of digital economy. The need for interdisciplinary study of staffing in bibliography is substantiated, and the author’s approach is introduced. The predictive vectors for the bibliographer profession are discussed. The factors of bibliography professionalization are specified. The competence transformation and profession prospects due to digitalization are identified. The author applies her own methods to analyze information profession: the methods have been introduced in her preceding works. She investigates into the nature of socially transforming bibliographic activities, their limits, and interaction with other professions. Criteria of structural and functional stability of the profession, mechanisms and dynamics in the social professional structure are revealed. The author dissects and analyzes the concept of “digital bibliographer” as the new profession model. The tasks for bibliography on the stage of artificial intellect ideology integration are specified. Changing limits of the profession due to the bibliographical method universality are discussed.
Passive Ranging for Infrared Search and Track System Mounted on an Aircraft

This paper presents two passive ranging algorithms viz. i. Extended Kalman Filter (EKF) in Modified Spherical Coordinates (MSC), ii. Pseudo Linear Estimator (PLE) for an Infrared Search and Track (IRST) system mounted on an aircraft. Detailed mathematical formulations for both algorithms are presented. Target-Aircraft relative simulation scenarios are generated, and the performance of algorithms has been presented. The robustness of the algorithms for sensor noise levels and aircraft maneuvers has been clearly brought out.
Low Cost Equipment for Military Vehicle Location in Forest Areas

Knowing the location of military personnel and vehicles plays an essential role in military operations, especially when these operations take place in jungles or places of dense vegetation. In this article, the use of the GPS network for the positioning of military vehicles is proposed, in addition, this location is reported through the Iridium satellite network. After the tests, the location of the device is sent, received and stored, with an approximate error of 2m using geodesic points to verify the accuracy of the equipment.
Deep Learning and Prediction of Survival Period for Breast Cancer Patients

With the rise of deep learning, cancer-specific survival prediction is a research topic of high interest. There are many benefits to both patients and caregivers if a patient’s survival period and key factors to their survival can be acquired early in their cancer journey. In this study, we develop survival period prediction models and conduct factor analysis on data from breast cancer patients (Surveillance, Epidemiology, and End Results (SEER)). Three deep learning architectures - Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) are selected for modeling and their performances are compared. Across both the classification and regression approaches, deep learning models significantly outperformed traditional machine learning models. For the classification approach, we obtained an 87.5% accuracy and for the regression approach, Root Mean Squared Error of 13.62% and \({R}^{2}\) value of 0.76. Furthermore, we provide an interpretation of our deep learning models by investigating feature importance and identifying features with high importance. This approach is promising and can be used to build a baseline model utilizing early diagnosis information. Over time, the predictions can be continuously enhanced through inclusion of temporal data throughout the patient’s treatment and care.
Semantic Text Analysis Technology Application in Assessing Current Threats and Software Vulnerabilities

The paper provides a brief overview of the most common open databases (classification systems) of computer attacks, information security threats and software vulnerabilities. The advantages of using the methods of semantic analysis of texts in natural language (Text Mining) for working with textual descriptions of typical attacks and their components contained in the above classification systems are noted. An automated method is proposed for assessing current (i.e., potentially most dangerous) vulnerabilities in industrial control system (ICS) software using semantic analysis methods of descriptions in order to determine a list of threats that are relevant to software vulnerabilities identified at a specific object using security scanners. An example of the proposed techniques application for assessing vulnerabilities of the application software of industrial oil production facility automation subsystem is considered, followed by the formation of a list of relevant threats.
Elderly care——STM32-based Intelligent anti-fall vest for the elderly

According to a survey, the proportion of elderly population in China continues to rise, and most old people fall due to diseases, aging, visual impairment or other problems frequently. In this regard, the "Intelligent Anti-fall Vest For The Elderly" proposed a feasible solution. The product is composed of three modules: anti-collision, fall monitoring, positioning and rescuing. The functional circuit of the product is based on the STM32F103rct6 microcontroller, which controls the HC-SR04 ultrasonic sensor to measure the distance between the wearer and the obstacle to realize the anti-collision function, controls the MPU6050 six-axis sensor to measure the wearer’s body acceleration vector amplitude and attitude angle to realize the fall monitoring function, and controls the GPS to obtain the wearer’s position information, controls the Wi-Fi module networking, and use the MQTT protocol , through the single-chip microcomputer and Alibaba Cloud platform for data interaction, to realize the positioning and rescuing function.
Exploiting Multi-granular Features for the Enhanced Predictive Modeling of COPD Based on Chinese EMRs

Building a predictive model to understand a patient’s status and the future progression of disease is an important precondition for facilitating preventive care to reduce the burden of chronic obstructive pulmonary disease. Recently, unsupervised feature learning has become the dominant approach to predictive modeling based on EMRs (electronic medical records), which usually adopts word and concept embeddings as distributed representations of clinical data. By observing that (1) semantic discrimination is limited by the inherent small feature granularity of words and concepts and (2) clinical decision-making is conducted based on a set of attribute-value pairs, e.g., a clinical laboratory test (the attribute) with a numerical or categorical value, this paper proposes a novel predictive modeling approach based on EMRs. In this approach, multi-granular features, i.e., words, concepts, concept relations and attribute-value pairs, are extracted through three subtasks (concept recognition, relation extraction and attribute-value pair extraction), then, combined to derive representations and predictive models of chronic obstructive pulmonary disease (COPD). The approach itself is highly generic, it can be used for different disease, but limited in Chinese EMRs. In this paper, we focus on the COPD risk prediction, and conduct extensive experiments on real-world datasets for a comparison study. The results show that our approach outperforms baselines, which demonstrates the effectiveness of the proposed model.
Multi-mode Network Analysis under Differential Privacy

Abstract With the advent of the big data era and the advancement of social network analysis, the public is increasingly concerned about the privacy protection in today’s complex social networks. For the past few years, the rapid development of differential privacy (DP) technology, as a method with a reliable theoretical basis, can effectively solve the key problem of how to “disassociate” personal information in social networks. This paper focuses on the multi-mode heterogeneous network model which has attracted a lot of attention in the field of network research. It introduces differential privacy and its application in big social networks briefly first, and then proposes a centrality-analysis method based on DP in a typical social network, i.e. the multi-mode network. The calculation principle and applicable scenarios are discussed. Then, its utility is analyzed and evaluated through experimental simulation. Possible improvement of DP algorithm in multi-mode networks above is prospected in the end.
Scientific and Design Understandings of Aesthetics

User Experience (UX) extends the construct of usability by an additional focus on emotion, motivation and aesthetics. An emphasis on aesthetics has been undertaken to a greater extent by design disciplines than by science. The present review examines both design and scientific approaches to aesthetics in order to integrate the two approaches and identify research opportunities that could result in science based design principals. The review of design approaches to aesthetics indicates the primary importance of balance as an element of design. Accordingly, research on the role of balance in producing aesthetic responses from users is a reasonable starting point for a program of research. Additionally, the analysis of aesthetic metrics and individual differences in aesthetic preferences in scientific research are discussed as possible collaboration areas for designers.
Performance Evaluation of Modified AES Algorithm in comparison with Advanced Symmetric Key Cryptographic Algorithms

Cryptography plays a very important role in safeguarding data and personal information in every field of the digital world. The problems faced  in the field of the information security are resolved by using these cryptographic algorithms but these algorithms consume a lot of processing power and  memory and  overcome these problems symmetric key cryptographic algorithms are developed. This work deals with the modified Advanced Encryption Algorithm by constructing the dynamic S Box  and making it robust as in comparison with the advanced symmetric key cryptographic algorithms  like AES, DES, and 3DES, Blowfish, RC4, IDEA and TEA. Performance analysis of the of the modified AES was conducted on the java programming language to check space and complexity other variables that depend on space-time complexity such as devices performance, software usage frameworks, clock speed, etc. are calculated by this experiment . In the experimental results it shows that MAES and IDEA are effective and  take the less encryption time while as MAES and AES are efficient in terms of decryption time and memory usage. Overall comparison was done on the basis of key length, rounds, modifications, structure, flexibility and security. The findings obtained will be used  in securing applications and services to improve the efficiency of the information security environment.
Classification and Identification of Dog Breed Using CNN

This paper presents an image recognition problem using deep learning. This system identifies the breed of a dog by just taking one image as an input. This system makes use of deep learning and uses a convolutional neural network (CNN) to process the image. The proposed system identifies the dog breed by using a CNN algorithm where scanning of the image happens pixel wise. This model is trained and evaluated on the Stanford dataset. We use the pre-trained model and learned weighs to extract the feature from the data-set that contains the breed information of more than 120 dogs. Web application is used to display the output to the user. It contains a client and a server, which includes libraries for evaluating on a neural network in both offline and online systems. In this paper, we have come up with an idea to identify the dog breed and obtain its related breed information more efficiently and accurately using the Xception model architecture.