Reviewerly/exHarmony huggingface dataset:
Blackmarket-Driven Collusion on Online Media: A Survey

Online media platforms have enabled users to connect with individuals and organizations, and share their thoughts. Other than connectivity, these platforms also serve multiple purposes, such as education, promotion, updates, and awareness. Increasing, the reputation of individuals in online media (aka social reputation ) is thus essential these days, particularly for business owners and event managers who are looking to improve their publicity and sales. The natural way of gaining social reputation is a tedious task, which leads to the creation of unfair ways to boost the reputation of individuals artificially. Several online blackmarket services have developed a thriving ecosystem with lucrative offers to attract content promoters for publicizing their content online. These services are operated in such a way that most of their inorganic activities are going unnoticed by the media authorities, and the customers of the blackmarket services are less likely to be spotted. We refer to such unfair ways of bolstering social reputation in online media as collusion . This survey is the first attempt to provide readers a comprehensive outline of the latest studies dealing with the identification and analysis of blackmarket-driven collusion in online media. We present a broad overview of the problem, definitions of the related problems and concepts, the taxonomy of the proposed approaches, and a description of the publicly available datasets and online tools, and we discuss the outstanding issues. We believe that collusive entity detection is a newly emerging topic in anomaly detection and cyber-security research in general, and the current survey will provide readers with an easy-to-access and comprehensive list of methods, tools, and resources proposed so far for detecting and analyzing collusive entities on online media.
A Crow Particle Swarm Optimization Algorithm with Deep Neural Network (CPSO-DNN) for High Dimensional Data Analysis

Diagnosis of any disease at its early stage correct treatment of the patients is necessary to save productive lives. Many techniques are adopted by different researchers to diagnose the disease at an early stage, but none of them are suitable due to the course of dimension dilemma. To differentiate the gap between logical and biological variation between the samples of the considered datasets, feature selection plays an important role. From different research, it is clear that while considering the local optima, PSO algorithm converges prematurely and decreases the diversity of the population. To avoid the limitations Crow Particle Optimization (CPSO) is implemented to identify the featured genes from a high dimensional dataset. The main concepts behind this CPO are related to the bird crow which hides a large number of foods in different places securely and collects it as per the need. To understand performance of the (CPSO-DNN) we have used three different evolutionary searchings like firefly search, elephant search and squirrel search. Gradient descent based Deep neural network with soft-max activation function has been used to maximize the no of feature genes by reducing the low impact features. Simulation results demonstrate that the (CPSO-DNN) exhibits an outstandingly higher accomplishment in terms of accuracy and can be considered as a better classification model as compared to other algorithms.
Communication-Efficient Proactive Secret Sharing for Dynamic Groups with Dishonest Majorities

In Secret Sharing (SS), a dealer shares a secret s among n parties such that an adversary corrupting no more than t parties does not learn s, while any $$t+1$$ parties can efficiently recover s. Proactive Secret Sharing (PSS) retains confidentiality of s even when a mobile adversary corrupts all parties over the secret's lifetime, but no more than a threshold t in each epoch (called a refresh period). Withstanding such adversaries is becoming increasingly important with the emergence of settings where private keys are secret shared and used to sign cryptocurrency transactions, among other applications. Feasibility of (single-secret) PSS for static groups with dishonest majorities was recently demonstrated, but with a protocol that requires inefficient communication of $$O(n^4)$$ . In this work, using new techniques, we improve over prior work in two directions: batching without incurring a linear loss in corruption threshold and communication efficiency. While each of properties we improve upon appeared independently in the context of PSS and in other previous work, handling them simultaneously (and efficiently) in a single scheme faces non-trivial challenges. SomePSS protocols can handle batching of $$\ell \sim n$$ secrets, but all of them are for the honest majority setting. The techniques typically used to accomplish such batching decrease the tolerated corruption threshold bound by a linear factor in $$\ell $$ , effectively limiting the number of elements that can be batched with dishonest majority. We solve this problem by finding a way to reduce the decrease to $$\sqrt{\ell }$$ instead, allowing to reach the dishonest majority setting when $$\ell \sim n$$ . Specifically, this work introduces new bivariate-polynomials-based sharing techniques allowing to batch up to $$n-2$$ secrets in our PSS. Next, we tackle the efficiency bottleneck and construct a PSS protocol with $$O(n^3/\ell )$$ communication complexity for $$\ell $$ secrets, i.e., an amortized communication complexity of $$O(n^2)$$ when the maximum batch size is used.
Users' mental models for<scp>computer‐mediated</scp>communication: Theorizing emerging technology and behavior in<scp>eHealth</scp>applications

This theoretical review focuses on defining users' mental models in modern computer-mediated communication (CMC) where users interact with intelligent virtual agents. The proposed framework integrates relevant literature in CMC, human–computer interaction, social behavior, and mental models and illustrates the relationship between emerging technology and user behaviors. Using an eHealth context, the framework outlines the initiating (user knowledge and intentions) and mediating (virtual agent representations, context, perceived agency, interactions, and limitations) factors for the development of users' mental models and the process for calibration and behavioral modification upon prolonged use. This framework is expected to be applied to other domain applications and to complement theoretical models that focus on the technological side of human–agent interaction. Better understanding of users' mental models will lead to the timely design and evaluation of intelligent systems and the redefinition of the roles for humans as we embrace more emerging and innovative technologies.
Input-Output Hidden Markov Model for System Health Diagnosis Under Missing Data

Sensor data can be used to diagnose the system's health. A challenge comes when the data contain missing or invalid data. It is common that sensors misread for various reasons. So, data contain missing measurements and sensor saturation. The main contribution in this paper is to implement a method based on the Input-Output Hidden Markov Model that trains the model using the missing measurements and sensor saturation, then diagnoses the system health at given operating conditions. Usually, if a data set contains some sequences with missing elements then they can be excluded from the analysis. It cleans the data set but reduces its size. This strategy knows as list-wise or case-wise deletion is less suitable for real application cases. The proposed method includes the sequences with missing data into the analysis by generating the missing elements to complete the sequence. The maximum likelihood is applied to estimate IOHMM parameters that offer substantial improvements over list-wise deletion. A numerical application with simulated data sets illustrates the method.
A Fuzzy Rule-Based Control System for Fast Line-Following Robots

This paper focuses on the optimization of the line-tracking and following control technique used in high speed autonomous movable IoT devices such as line-following robotic vehicles. A fusion of a new pro-active / feed-forward control system, based on the simultaneous use of computer vision and the use of an array of analog infrared reflective phototransistors with an optimised PID based feed-back implementation is proposed. Experimental evaluations demonstrate that the proposed approach aids and improves the performance of line-following robots compared to the traditional PID technique.
Learning Joint Structure for Human Pose Estimation

Recently, tremendous progress has been achieved on human pose estimation with the development of convolutional neural networks (CNNs). However, current methods still suffer from severe occlusion, back view, and large pose variation due to the lack of consideration of the spatial relationship between different joints, which can provide strong cues for localizing the hidden keypoints. In this work, we design a Structural Pose Network (SPN) to take full advantage of joint structure for human pose estimation under unconstrained environment. Specifically, the proposed model is composed of two subnets: Structure Residual Network (SRN) and Structure Improving Network (SIN). Given an input image, SRN first captures rich joint structure as priors through a multi-branch feature extraction module, following a hourglass network with pyramid residual units to enlarge the receptive field and further obtain structural feature representations. SIN, based on coordinate regression, can optimize the spatial relationship of different joints via the attention mechanism, thus refining the initial prediction from SRN. In addition, we propose a novel structure-consistency constraint, which can maintain the structural consistency between the joints and body parts via estimating whether the joints are located in their corresponding parts. At the same time, an online hard regions mining (OHRM) strategy is introduced to drive the network to pay corresponding attention to different body parts. The experimental results on three challenging datasets show that our method outperforms other state-of-the-art algorithms.
Facial expression description and recognition based on fuzzy semantic concepts

This paper proposes a novel approach to identifying various expressions using semantic concepts. Based on the framework of the axiomatic fuzzy set, facial features are transformed into semantic concepts, which are then considered as a ruleset to differentiate expression categories. This method has two main advantages. First, it bridges the descriptors between image features and semantic concepts, according to which facial geometric features can be mirrored directly. Second, it alters the description patterns of fuzzy rulesets, which can reduce the dimensionality of expression features. We establish optimization criteria for selecting salient semantic concepts to represent expression characteristics. Experiments are conducted using the proposed method on the FEI and CK+ databases. Semantic concepts are considered as a ruleset to describe the differences between various expressions. The performances of state-of-the art classifiers and the proposed method are compared and analyzed. The results demonstrate that the proposed method provides excellent interpretability and classification performance for facial expressions.
An Enhanced Deep Learning Architecture for the Classification of Cancerous Lymph Node Images

The use of deep learning techniques to diagnose medical disorders has gained increasing popularity in recent times. The unbeatable accuracy of deep learning algorithms often surpasses the performance of human doctors in the diagnosis of diseases like cancer. In line with this trend, this paper proposes a novel architecture aimed at classifying sections of lymph node scans. Analysis of methods to enhance the existing architecture and produce an algorithm that can be used to identify tumor cells is discussed. An enhancing combination of kernel initialization and image preprocessing such as stain channel extraction, color inversion, and hybridization of these has been obtained by contextual and empirical analysis. Incorporating the successful enhancement features with a ResNet-50 leads to a new strategy to identify the presence of metastatic cancer in lymph node patch images. This system can be used to aid human pathologists or independently (with human supervision) for the diagnosis of metastatic cancer from lymph node images.
Cloud based Acute Lymphoblastic Leukemia Detection Using Deep Convolutional Neural Networks

Acute lymphoblastic leukemia is a condition that occurs in the bone marrow. Most cases of Acute Lymphoblastic Leukemia (ALL) develop from cells that would turn into white blood cells (other than lymphocytes), but some cases of ALL develop in blood-forming cells of various kinds. Anything begins in the bone marrow (the soft inner part of some bones, where new blood cells are made), yet it moves rapidly into the blood by and large. It may spread to various parts of the body once in a while, including the lymph nodes, liver, spleen, central nervous system (cerebrum and spinal cord), and testicles. The traditional method used by doctors to detect ALL is tedious and time consuming. It consists of several methods such as blood testing, aspiration and biopsy to the bone marrow, image testing using CT scanning and MRI. These methods take time and are expensive. The proposed approach uses imaging processing techniques to detect irregular blood cells in the microscopic images of the blood. Using the training data collection, the program is trained using DCNN and is uploaded into HDFS Hadoop framework. The learned method classifies when a new image is inserted into the program whether the collected picture includes cancerous leukemia cells or irregular cells. This way the process of detection of leukemia is automated and can be accessed by any of the hospitals with connected to the cloud system.
Study on Risk Assessment System of Power Failure in Distribution Network of Large Cities

The distribution network of the city is connected to the main network and the power users, The stable operation of the distribution network is related to the stable operation of the whole power system, It is of great practical significance to evaluate the urban distribution network and identify the weak link of the power grid. From the actual situation of the intelligent distribution network, this paper focuses on the intelligent distribution network risk assessment and safety early warning mechanism. From the structural characteristics of intelligent distribution network, equipment running status and environmental factors characteristics, this paper makes use of probability analysis, data mining, information fusion technology and other methods, the risk evaluation index system and the risk index quantification calculation model are established to analyze the potential risks and the extent of damage to the intelligent distribution network.
SRS Automator - An Attempt to Simplify Software Development Lifecycle

A Software Requirements Specification (SRS) document showcases the details of the functions along with the capabilities that a software system should provide and all the constraints it should respect. This document serves as a base document for all the succeeding steps for the project which involves planning, designing, coding, testing maintenance. Everyone from being a developer to a tester to the project managers who are involved in the project rely on the SRS. The design team, the development team, the quality assurance and maintenance staff, the app or product testers, the support staff, and even the sales and marketing department people require the SRS. This makes this document one of the most important documents. The lack of an entirely free and open source tool to mechanize construction of a well-laid out requirements document proves to be a major hindrance to carry out the process of Requirements Engineering efficiently. Over the years, several new algorithms and tools have been proposed using a diverse kind of techniques. In this paper, we propose and develop a framework to solve the conversion problem of informal ideas to well-structured requirements specifications document by automating the SRS development process.
Human Activity Recognition Using Wearable Sensors

The advancement of the internet coined a new era for inventions. Internet of Things (IoT) is one such example. IoT is being applied in all sectors such as healthcare, automobile, retail industry etc. Out of these, Human Activity Recognition (HAR) has taken much attention in IoT applications. The prediction of human activity efficiently adds multiple advantages in many fields. This research paper proposes a HAR system using the wearable sensor. The performance of this system is analyzed using four publicly available datasets that are collected in a real-time environment. Five machine learning algorithms namely Decision tree (DT), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (kNN), and Support Vector Machine (SVM) are compared in terms of recognition of human activities. Out of this SVM responded well on all four datasets with the accuracy of 77%, 99%, 98%, and 99% respectively. With the support of four datasets, the obtained results proved that the performance of the proposed method is better for human activity recognition.
Datasets and Performance Metrics for Greek Named Entity Recognition

The quality of linguistic resources determines the robustness and efficiency of language processing models to a great extent. This is especially evident with less represented languages, where dataset availability is scarce, preventing the development of state-of-the-art natural language processing algorithms. In this paper, we attempt to partially remedy the situation for the Greek language by introducing a dataset focused on named entity recognition (NER), coined as elNER. Motivated by CoNLL-2003 and OntoNotes 5 datasets in English, two versions of elNER are described, namely elNER-4 and elNER-18, respectively. Current state-of-the-art NER models are trained on these datasets. The performance metrics disclosed here are comparable to those achieved by the respective approaches in English.
Efficient Regularization Framework for Histopathological Image Classification Using Convolutional Neural Networks.

Deep learning methods are characterized by their capacity to learn data representation compared to the traditional machine learning algorithms. However, these methods are prone to overfitting on small volumes of data. The objective of this research is to overcome this limitation by improving the generalization in the proposed deep learning framework based on various techniques: data augmentation, small models, optimizer selection, and ensemble learning. For ensembling, the authors used selected models from different checkpoints and both voting and unweighted average methods for combination. The experimental study on the lymphomas histopathological dataset highlights the efficiency of the MobileNet2 network combined with the stochastic gradient descent (SGD) optimizer in terms of generalization. The best results have been achieved by the combination of the best three checkpoint models (98.67% of accuracy). These findings provide important insights into the efficiency of the checkpoint ensemble learning method for histopathological image classification.
A Natural Language-based Flight Searching System

When people search for flight ticket online, they interact with a web site by selecting the ticket information from a set of drop-down menus. This paper proposes a scheme that is more natural for people to interact with the flight ticket server. The scheme integrates a natural language interface with a flight ticket searching system. It allows the users to interact with the flight searching system using natural language. The system is based on the BERT model. Compared with the existing scheme, the proposed scheme achieves good accuracy.
EagleMine: Vision-guided Micro-clusters recognition and collective anomaly detection

Given a graph with millions of nodes, what patterns exist in the distributions of node characteristics? How can we detect them and separate anomalous nodes in a way similar to human visual perception? More generally, how can we identify micro-clusters in a histogram and spot some interesting patterns? In this paper, we propose a vision-guided algorithm, EagleMine, to recognize and summarize node groups in a histogram constructed from some correlated features. EagleMine hierarchically discovers node groups, which form internally connected dense areas in the histogram, by utilizing a water-level tree with multiple resolutions according to the rule of the visual recognition. EagleMine uses the statistical hypothesis test to determine the optimal groups while exploring the tree and simultaneously performs vocabulary-based summarization. Moreover, EagleMine can identify anomalous micro-clusters, consisting of nodes that exhibit very similar and suspicious behavior, deviate away from the majority. Experiments on the real-world datasets show that our method can recognize intuitive node groups as human vision does; it achieves the best summarization performance compared to baselines. In terms of anomaly detection, EagleMine also outperforms the state-of-the-art graph-based methods with significantly improving accuracy in a micro-blog dataset. Moreover, EagleMine can be used for other applications, e.g., to detect the synchronized patterns in the temporal retweet event.
CtrlFaceNet: Framework for geometric-driven face image synthesis

In this work, we introduce a novel framework based on Generative Adversarial Networks to control the pose, expression and facial features of a given face image using another face image. It can then be used for data augmentation, pose invariant face identification, face verification, and lightweight image editing. Generating new realistic face images with controllable poses, facial features, and expressions is a challenging generative learning problem due to skin tone variations, the identity preservation problem, necessity to deal with unseen large poses, and the absence of ground truth images in the training process. We make the following contributions. First, we present a network, CtrlFaceNet that can control a source face image while preserving the identity and skin tone. Second, we introduce a method for training the framework in fully self-supervised mode using a large-scale dataset of unconstrained face images. Third, we show that the style loss function can be used to preserve the skin tone of the source image. The experimental results show that our approach outperforms all other baselines. Furthermore, to the best of our knowledge, we are the first to train such a model using large-scale dataset of unconstrained face images.
Perspectives of Blockchain in the Education Sector Pertaining to the Student’s Records

Blockchain has been enforced in many sectors, and its implementation has drastically improved those sectors. Diamond trade has been greatly helped by the employment of blockchain to digitally track the diamonds being well-mined and sold-out within the market. The potential of the blockchain is being tested by varied government agencies for distribution of the various services. Through this paper, we’ve explored the entities concerned within the blockchain network enforced in an academic institute. This paper focuses the employment of the blockchain account for each student maintaining the records of the scholare that helps to spot the talents of the scholars by their prospective employers.