Reviewerly/exHarmony huggingface dataset:
Detection of Wangiri Telecommunication Fraud Using Ensemble Learning

Fraudsters can manipulate telecom regulatory systems to their advantage, and to the disadvantage of the telecom operator, in ways that are difficult to detect, trace, and prosecute. A subscriber whose network has been compromised will often refuse to pay large fraudulent charges, leaving the operator to cover the bill resulting in revenue losses. Moreover, attacks frequently happen over holidays and weekends, when networks are often less monitored closely. This calls for an intelligent automated solution for telecom fraud detection. Wangiri (Japanese term) telecom fraud also referred to as “one ring and cut fraud” relies on this single ring method for a quick way to make money. Missed calls from unknown callers entice subscribers to call back unknowingly premium numbers where they are deceived to stay on the line for as long as possible in an effort to inflate their bill. This paper proposes the use of varied ensemble classifiers to overcome the highly biased dataset and help make a more precise classification in an efficient and effective manner. Extreme Gradient Boosting algorithm was found to have the best results in terms of correctness and performance.
Mutation based self regulating and self perception particle swarm optimization for efficient object tracking in a video

Object tracking is a pre-eminent task in many computer vision application.This paper addresses a realistic object tracking problem, considering single as well as multiple objects with different challenging perspectives. In view of this, a Mutation-based Self Regulating and self-Perception Particle Swarm Optimization (MSRSP-PSO) algorithm is proposed in order to explore the effective sampling strategy as well as to estimate and localize the optimum position of the target object. In conjunction with the optimization technique, an effective appearance model is also used for the reinforcement of tracker to achieve better accuracy. The appearance model is constructed with the joint distribution of the Local Binary Pattern (LBP)/Local Contrast Measure (LCM) based texture description and the Ohta color features. In pursuance of validating the efficacy of the proposed tracking algorithm, performance of other state-of-the-art methods such as, Particle Swarm Optimization (PSO) based tracking, Sequential Particle Swarm Optimization (SPSO) based tracking, and Adaptive Gaussian Particle Swarm Optimization (AGPSO) based tracking algorithms are compared with the proposed tracking algorithm. Both the quantitative and qualitative evaluation of the experiments, validate the effectiveness of the proposed technique.The performance measures include the convergence rate, center location error and the coverage test (PASCAL SCORE). Furthermore, the non-parametric statistical analysis using the Sign test and Wilcoxon signed rank test, is also figured out to validate the statistical superiority of the proposed MSRSP-PSO algorithm over others. The experimental results reveal that the proposed algorithm robustly track single as well as multiple similar objects with complex interaction.
Evaluating learning approaches for product assembly

Augmented Reality (AR) as an assistive technology is a promising tool for novice operators to learn assembly processes. This experiment compared an AR instruction method to display based electronic working instructions (EWI) for product assembly, to assess learning during the first repetitions of the products. In addition, two types of work instructions were used, i.e., standard and chunk instructions. In this experiment a chunk instruction consists of six assembly steps. Effects of the instruction method and type on the learning phase were evaluated with 24 novice operators building two products i.e.. Operators were then asked to build the same products without instructions in order to assess learned skills and establish effects on the recall phase, also as a result of instruction method and type. Task completion time (TCT), product quality, operator workload and learning curve were measured. The learning curve, as indicated by the TCT, took place during the first three repetitions of product assembly. Instruction method and instruction type had no effect on the learning curve. Product quality was high and no differences were found between learning conditions. Operator workload revealed that chunking of the instruction increased workload during the learning phase. During the recall phase, the AR group's TCT increased 19.2%, but only for the first product's repetition without instruction. Product quality remained the same during the recall phase, however operator workload was reduced for chunk learned products. This study indicates that chunking of instructions should be avoided for novice workers. Both EWI and AR can be used for teaching new assembly procedures. While AR and EWI are useful during the learning phase, there are indications that these methods might hinder the operator once they required the necessary skills and knowledge to assemble the product. A possible solution is making instructions more adaptive to fit the skill proficiency of the operator.
Exploring (Dis-)Similarities in Emoji-Emotion Association on Twitter and Weibo

Emojis have gained widespread acceptance, globally and cross-culturally. However, Emoji use may also be nuanced due to differences across cultures, which can play a significant role in shaping emotional life. In this paper, we a) present a methodology to learn latent emotional components of Emojis, b) compare Emoji-Emotion associations across cultures, and c) discuss how they may reflect emotion expression in these platforms. Specifically, we learn vector space embeddings with more than 100 million posts from China (Sina Weibo) and the United States (Twitter), quantify the association of Emojis with 8 basic emotions, demonstrate correlation between visual cues and emotional valence, and discuss pairwise similarities between emotions. Our proposed Emoji-Emotion visualization pipeline for uncovering latent emotional components can potentially be used for downstream applications such as sentiment analysis and personalized text recommendations.
Efficient and Secure Key Exchange Protocol Based on Elliptic Curve and Security Models

nowadays, the success of many online applications relays on keeping the data sent through the global network secure and far away from hackers. To carry out this task, the two communicating parties must exchange keys during their session. Some of the key exchange protocols are called key agreement protocols. The Elliptic Curve-Diffie Hellman (ECDH) is one of the most efficient algorithms for securing data. The ECDH is more efficient than other traditional techniques such as Rivest-Shamir-Adleman (RSA) in terms of key size, computation and network bandwidth. The Authenticated Key Agreement (AKA) protocol is used for establishing a common session key between the two communicating parties. The common session key is used for subsequent cryptography goals. Most of the key agreement protocols (e.g. Menezes-Qu-Vanstone(MQV) family) generate one key per session therefore increasing the opportunities for guessing the session key. In this paper, we focus on developing an enhanced multiple sessions key which is based on ECDH. We propose an efficient and secure AKA protocol which is based on the ideas of the hashed MQV (HMQV), the YAK protocol as a robust key agreement based on public key authentication and multiple session keys. The proposed protocol generates multiple common keys per a session, where the generated common key depends on the static and ephemeral keys. Furthermore, the proposed protocol overcomes the attacks on the HMQV and YAK protocols and provides desirable security properties as compared with the related works in this paper.
Remote-Sensing Image Usability Assessment Based on ResNet by Combining Edge and Texture Maps

Authentic remote-sensing images suffer non-uniform complex distortions during acquisition, transmission, and storage. Clouds, light, and exposure also affect local quality. This paper constructs a usability-based subjective remote-sensing image dataset and gives a definition of usability for images with non-uniform distortion, where the image usability is determined by the weighted quality of image's blocks. It is difficult to extract the handcraft features from remote-sensing images with complex mixture distortion. Recently, convolutional neural network (CNN) has been introduced into blind quality assessment for images with uniform distortion, which includes feature learning and regression in one processing. In this paper, we first describe and systematically analyze the usability of remote-sensing images in detail. Then, we propose a remote-sensing image usability assessment (RSIUA) method based on a residual network by combining edge and texture maps. The score of remote-sensing image usability was obtained with the weighted averaging of the quality scores of all image blocks, and the weight of each image block was determined by its quality score. We compared the proposed method with three traditional image quality assessment methods, one CNN-based method for images with simulated distortion, and one scale-invariant feature transform-based RSIUA method. The linear correlation coefficient, Spearman's rank ordered correlation coefficient, and root-mean-squared error of experiments demonstrate that our method outperforms all five competitors. The experiments also reveal that the edge and texture maps can improve the performance.
Deterministic oversampling methods based on SMOTE

In supervised classification if one of the classes has fewer objects than the other, we have a class imbalance problem. One of the most common solutions to address class imbalance problems is oversampling, and SMOTE is the most referenced and well-known oversampling method. However, SMOTE creates s ynthetic objects in a random way, therefore it produces a different result each time it is applied, and in practice the user has to apply SMOTE several times for choosing the best of all the generated balanced datasets. For this reason, in this paper, we present SMOTE-D, a deterministic version of SMOTE, and propose new deterministic SMOTE-D-based versions of some of the most recent and successful SMOTE-based methods. In our experiments, we show that all proposed deterministic methods produce as good results as random methods but our proposals need to be applied just once. This is very important from a practical point of view since our proposals save time by avoiding multiple applications of them as SMOTE does and they provide one unique result.
Improving Open Set Domain Adaptation Using Image-to-Image Translation

The open set domain adaptation problem was rarely studied and its existing solutions are mostly based on learning a joint latent space which may encounter issues when the domains differ significantly from each other. This work is driven by the question whether or not it is beneficial to operate the source images to another image domain as close to the target as possible. We propose to address the open set domain adaptation problem by aligning sample at both feature space and pixel space. Our approach, called Open Set Translation and Adaptation Network (Ostan), consists of two main components: translation and adaptation. The translation model is a cycle-consistent generative adversarial network, which translates any source sample to the "style" of a target domain. The adaptation network is built upon OpenBP, an open set domain adaptation framework, and trained using both (labeled) translated source images and (unlabeled) target images. The proposed Ostan model significantly outperforms the state-of-the-art open set domain adaptation methods on multiple public datasets. Our experiment also demonstrates that an image-to-image translation component can further improve the decision boundaries for both known and unknown classes.
A public key cryptosystem using Affine Hill Cipher under modulation of prime number

M.K. Viswanath in [1] proposed the concepts a public key cryptosystem using Hill’s Cipher. This paper develops a public key cryptosystem using Affine Hill Cipher. The proposed method increases the security of the system because it involves two or more digital signatures under modulation of prime number.
Search Engine Optimization (SEO) for Websites

With the growth of the Internet in the recent years, search engines such as Google, Bing, and Yahoo are becoming more and more crucial and reliable. The role of search engines is to index billions of web pages and display only the most relevant results for a given search query. When creating the website, many webmasters forget to take into consideration an essential factor, which is making the world aware of their website. Most of the times the main focus is set on making the website as user-friendly as possible, stable, fast, and secure. However, all of these techniques can be useless if the website does not have any visitor or people just cannot find it. To solve this problem, and to enhance the structure of the website to become more search-engine-friendly, a web application is developed to analyze any given webpage and to provide information on how to enhance and improve its structure to make it more search-engine-friendly and to improve its ranking on search engines. This process is also known as Search Engine Optimization (SEO).
Detecting Anomalous Trajectories via Recurrent Neural Networks

Detecting anomalies from trajectory data is an important task in video surveillance. However, it is difficult to give a precise definition of this term since trajectory data obtained from different camera views may vary in shape, direction, and spatial distribution. In this paper, we propose trajectory distance metrics based on a recurrent neural network to measure similarities and detect anomalies from trajectory data. First, we use an autoencoder to capture the dynamic features of a trajectory. The distance between two trajectories is defined by the reconstruction errors based on the learned models. We then detect anomalies based on the nearest neighbors using the proposed metric. As such, we can deal with various kinds of anomalies in different scenes and detect anomalous trajectories in either a supervised or unsupervised manner. Experiments show that the proposed algorithm performs favorably against the state-of-the-art anomaly detections on the benchmark datasets.
Comparative Analysis of Lightweight Block Ciphers in IoT-Enabled Smart Environment

With the rapid technological growth in the present context, Internet of Things (IoT) has attracted the worldwide attention and has become pivotal technology in the smart computing environment of 21 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> century. IoT provides a virtual view of real-life things in resource-constrained environment where security and privacy are of prime concern. Lightweight cryptography provides security solutions in resource-constrained environment of IoT. Several software and hardware implementation of lightweight ciphers have been presented by different researchers in this area. This paper presents a comparative analysis of several lightweight cryptographic solutions along with their pros and cons, and their future scope. The comparative analysis may further help in proposing a 32-bit ultra-lightweight block cipher security model for IoT enabled applications in the smart environment.
Visual Analysis of Missing Values in Longitudinal Cohort Study Data

Abstract Attrition or dropout is the most severe missingness problem in longitudinal cohort study data where some participants do not show up for follow‐up examinations. Dropouts result in biased data and cause the reduction of 1ata set size. Moreover, they limit the power of statistical analysis and the validity of study findings. Visualization can play a strong role in analysing and displaying the missingness patterns. In this work, we present VIVID, a framework for the v isual analysis of m i ssing v alues i n cohort study d ata. VIVID is inspired by discussions with epidemiologists and adds visual components to their current statistics‐based approaches. VIVID provides functions for exploration, imputation and validity check of imputations. The main focus of this paper is multiple imputation to fix the missing data.
Construction and Supervised Learning of Long-Term Grey Cognitive Networks

Modeling a real-world system by means of a neural model involves numerous challenges that range from formulating transparent knowledge representations to obtaining reliable simulation errors. However, that knowledge is often difficult to formalize in a precise way using crisp numbers. In this paper, we present the long-term grey cognitive networks which expands the recently proposed long-term cognitive networks (LTCNs) with grey numbers. One advantage of our neural system is that it allows embedding knowledge into the network using weights and constricted neurons. In addition, we propose two procedures to construct the network in situations where only historical data are available, and a regularization method that is coupled with a nonsynaptic backpropagation algorithm. The results have shown that our proposal outperforms the LTCN model and other state-of-the-art methods in terms of accuracy.
ADeX: A Tool for Automatic Curation of Design Decision Knowledge for Architectural Decision Recommendations

Software architecture can be seen as a set of architectural design decisions. These decisions, especially their rationale, play an important role in understanding software systems and constraints that are imposed on future design decisions. Documenting and managing these design decisions takes a lot of effort and is therefore often neglected by software architects. Following our vision to avoid explicit architectural knowledge management, we present an innovative tool used in industrial practise that is able to automatically recover design decisions from natural language text. The identified design decisions are then used to automatically derive further architectural knowledge, including related quality attributes and architectural elements to inhibit architectural erosion. By automatically enriching architectural knowledge and providing recommendations, the tool offers effective support for software architects in the decision-making process. The knowledge base of the tool is automatically kept up to date with information from different systems used by architects during the software development lifecycle process.
Managing Quality Assurance Challenges of DevOps through Analytics

DevOps is an intermarriage between developmental practices and operational modalities. The methodology employs the practices of continuous integration and delivery and places the deployment pipeline as the main requirement to automate, deliver and operate software in a robust way, without compromising on the quality in the software development process. Over time, many systems and tools have been developed to implement the deployment pipeline and support the continuous delivery process. A pipeline splits the process of software delivery into various stages. Each stage is designed to verify the quality of new features from a new perspective to attest to the functionality and prevent either small or big errors from impacting the end users. The pipeline must provide a response and feedback loop to the concerned team and provide a window into the flow of changes that takes place. However, there is no clear rule to define what goes into a pipeline. This paper reviews the challenges of quality assurance of DevOps and provides tentative recommendations to deal with quality issues. Our proposed pipeline with analytic features is expected to provide accurate metrics on a real-time basis.
Tidy up my room: Multi-agent cooperation for service tasks in smart environments

Low-cost robots are usually specialized systems that cannot solve complex tasks, e.g., doing the laundry or tidying up. These tasks are usually solved by more complex and expensive general-purpose systems. The most common problems are the lack of sensors and actuators or computing power to perform multiple functions in parallel. The integration of robots into intelligent environments can help to solve more complex tasks by utilizing the components of the intelligent environment. Such an approach is used in the system to detect pointing gestures from a user and locating objects that a low-priced robot then collects and carries away. This approach is performed by cameras and supported by lights of the intelligent environment. The experimental results show that the cooperation of robots and smart environment increases the success rate of complex tasks in situations where the robot or components of the intelligent environment would underperform.
A Reliable E-commerce Business Model Using Blockchain Based Product Grading System

The first step in online shopping is, apparently, to choose a reliable supplier. However, consumers and merchants may have different perceptions of product quality, and this may result in business disputes. The dispute about buying inferior goods may be caused from simple product photos or merchants make over the top claims about their products. This dispute problem cannot be resolved completely, even though customers choose reputable e-commerce companies, e.g., Amazon.com and Alibaba.com. In the final analysis, the cause is that e-commerce companies do not adopt quality rating to evaluate products. If there is an alliance, among e-commerce companies and trusted organizations, which establishes credible product grading system (PGS), online shoppers may buy genuine product at a fair price from this e-commerce alliance. In this paper, we propose blockchain based PGS (BPGS) to deal with big data for this business model. The blockchain is a decentralized technology, and thus we can speed up the verification of product grading. In addition, for the proposed BPGS, 51% of attacks cannot be completed unless 51% of merchants and e-commerce companies in this alliance are simultaneously compromised. Therefore, our ecommerce environment based on BPGS is not only reliable but also secure.
On the use of word embedding for cross language plagiarism detection

Cross language plagiarism is the unacknowledged reuse of text across language pairs. It occurs if a passage of text is translated from source language to target language and no proper citation is provided. Although various methods have been developed for detection of cross language plagiarism, less attention has been paid to measure and compare their performance, especially when tackling with different types of paraphrasing through translation. In this paper, we investigate various approaches to cross language plagiarism detection. Moreover, we present a novel approach to cross language plagiarism detection using word embedding methods and explore its performance against other state-of-the-art plagiarism detection algorithms. In order to evaluate the methods, we have constructed an English-Persian bilingual plagiarism detection corpus (referred to as HAMTA-CL) comprised of seven types of obfuscation. The results show that the word embedding approach outperforms the other approaches with respect to recall when encountering heavily paraphrased passages. On the other hand, translation based approach performs well when the precision is the main consideration of the cross language plagiarism detection system.