Reviewerly/exHarmony huggingface dataset:
Hopping Spider Monkey Optimization

The area of swarm intelligence has been increasing extensively and new algorithms are getting formulated inspiring from the collective behavior of the social animal societies. One such algorithm is the spider monkey optimization (SMO) algorithm evolved by Bansal et al. (Memetic Comput. 6(1):31–47, 2014) in 2014. It is based on Fission-Fusion socially structured animals. It has been considered as a well-balanced algorithm and has outperformed many of the other competitive algorithms. In this paper, to advance its diversification and intensification capability, a new alternative of SMO algorithm namely Hopping Spider Monkey Optimization (HSMO) has been proposed on being inspired by the hopping mechanism of a grasshopper. To testify the efficacy and to derive how accurate this newly proposed variant is, it is proved over 15 standard benchmark functions. The so obtained numerical results are analyzed and contrasted with numerous state-of-art algorithms accessible in the research and hence validate the newly proposed approach.
Anti-Drone Capabilities: Using a Quality Assurance Technology to Identify Exploitable UAV Weaknesses

This paper considers the challenge of cybersecurity attacks against unmanned aerial vehicles (UAVs) using a quality assurance (QA) paradigm. Anti-quality assurance (AQA), in essence turns quality assurance, which is designed to identify defects to prevent incorrectly manufactured or damaged products from being sold or deployed, on its head. Using the same QA technologies, AQA uses remote-sensed imagery and fault tree analysis to identify exploitable weaknesses in UAVs that represent a prospective pathway to achieving drone impairment or destruction goals. This paper presents the AQA concept and its application to drones.
Snoopy Cache and Shared Memory Model for Commodity Computing using Cloud Management System

Now a day’s famous buzzword trolling around globe is cloud computing. Cloud computing solves lot challenges related to businesses. It is successful used in various businesses like amazon, Microsoft, google, IBM and so on with successful stories. Cloud is suitable for small scale to larger scale business. This type of system obviously requires more computers or PCs for working purposes. This group of systems are located geographically somewhere corner in the globe and they are spelled as commodity computers and they are used for computing purpose. Group of people working on one different commodity computers and zones want to work commonly, they require common memory for that purpose. Common memory or temporary memory which is allocated to group of computers is shared memory. Shared memory can be part of resource pool and after completing the task they can be released for better cloud management system. By using Shared memory and cache, performance can be increased. SCSM model for Cloud Management System is introduced with snoopy cache and shared memory for commodity Computing. SCSM model purpose is to increase the performance between the commodity systems.
Towards Grammatical Evolution-Based Automated Design of Differential Evolution Algorithm

Differential evolution (DE) is a robust evolutionary algorithm that has been applied for various real world optimization problems. However, the performance of DE depends on the optimal choice of variation operators and control parameters. The dizzying choice of heuristics for choosing mutation strategies, crossover operator and control parameters makes DE design a challenging task for a practitioner. We present a meta-evolutionary approach with grammatical evolution (GE) to evolve effective parameter configurations for classical differential evolution algorithm. It has been observed that the GE evolved DE configurations performed competitively on the chosen ten standard benchmark functions. This work is a preliminary step towards automating DE algorithm designs, which has the potential to relieve a user from the painful task of trial-and-error manual designs.
You Can't Always Get What You Want: Towards User-Controlled Privacy on Android

Mobile applications (hereafter, apps) collect a plethora of information regarding the user behavior and his device through third-party analytics libraries. However, the collection and usage of such data raised several privacy concerns, mainly because the end-user - i.e., the actual owner of the data - is out of the loop in this collection process. Also, the existing privacy-enhanced solutions that emerged in the last years follow an "all or nothing" approach, leaving the user the sole option to accept or completely deny the access to privacy-related data. This work has the two-fold objective of assessing the privacy implications on the usage of analytics libraries in mobile apps and proposing a data anonymization methodology that enables a trade-off between the utility and privacy of the collected data and gives the user complete control over the sharing process. To achieve that, we present an empirical privacy assessment on the analytics libraries contained in the 4500 most-used Android apps of the Google Play Store between November 2020 and January 2021. Then, we propose an empowered anonymization methodology, based on MobHide, that gives the end-user complete control over the collection and anonymization process. Finally, we empirically demonstrate the applicability and effectiveness of such anonymization methodology thanks to HideDroid, a fully-fledged anonymization app for the Android ecosystem.
A Large-Scale Analysis of Attacker Activity in Compromised Enterprise Accounts

We present a large-scale characterization of attacker activity across 111 real-world enterprise organizations. We develop a novel forensic technique for distinguishing between attacker activity and benign activity in compromised enterprise accounts that yields few false positives and enables us to perform fine-grained analysis of attacker behavior. Applying our methods to a set of 159 compromised enterprise accounts, we quantify the duration of time attackers are active in accounts and examine thematic patterns in how attackers access and leverage these hijacked accounts. We find that attackers frequently dwell in accounts for multiple days to weeks, suggesting that delayed (non-real-time) detection can still provide significant value. Based on an analysis of the attackers’ timing patterns, we observe two distinct modalities in how attackers access compromised accounts, which could be explained by the existence of a specialized market for hijacked enterprise accounts: where one class of attackers focuses on compromising and selling account access to another class of attackers who exploit the access such hijacked accounts provide. Ultimately, our analysis sheds light on the state of enterprise account hijacking and highlights fruitful directions for a broader space of detection methods, ranging from new features that hone in on malicious account behavior to the development of non-real-time detection methods that leverage malicious activity after an attack’s initial point of compromise to more accurately identify attacks.
All‐Resonance Controlled‐Phase Gate on Two Nonlocal Nitrogen‐Vacancy Center Ensembles Assisted by a Superconducting Quantum Circuit

Abstract The realization of universal quantum logical gates on nonlocal nitrogen‐vacancy center ensembles (NVEs) is a basic task for universal quantum computing with NVEs. In contrast to previous works on the construction of gates, a one‐step all‐resonance scheme is proposed to construct a controlled‐phase gate on two nonlocal NVEs that are coupled to different superconducting transmission line resonators connected by a superconducting transmon qubit. The gate can be easily realized using only one step. Moreover, the all‐resonance operation allows the gate to be achieved within a short time. By considering the feasible parameters obtained in the experiments, this gate can be completed within 101 ns with a high fidelity of approximately 97.8%, which has reached the threshold of fault‐tolerance quantum computing.
Region-aware POI Recommendation with Semantic Spatial Graph

The development of Location-Based Social Networks (LBSNs) offers an opportunity for understanding user preferences and promoting Point-of-Interest (POI) recommendation. The user preferences usually change when the environment changes, which will affect the performance of POI recommendation. Many existing methods extract the environmental features from static regions, but they cannot capture user preferences in real time with the fine-grained changes in user locations. Meanwhile, the similarity of POI categories, which is significant to capture user preferences, is usually ignored. To address these issues, we propose RegDM, a region-aware POI recommendation model that employs a semantic spatial graph to model the relations among POIs. With the semantic spatial graph, RegDM uses a Graph Neural Network (GNN) to extract fine-grained region features and user preferences for personalized recommendation. We evaluate RegDM on two datasets, and the experiment results demonstrate the effectiveness of our model.
Deep Learning Technique Based Visually Impaired People Using YOLO V3 Framework Mechanism

Near or far vision impairment affects at least 2.2 billion people worldwide. Vision deficiency may have been avoided in at least 1 billion, or almost half of these cases. The leading causes of vision impaired and blindness are uncorrected refractive errors and falls. The majority of people with vision impaired and blindness are over the age of 50 years. Still, vision loss can distress persons of all ages. Vision impaired positions a giant overall financial burden with the yearly worldwide charges of yield losses associated with vision impaired from uncorrected shortsightedness and presbyopia alone estimated to be US $ 244 billion and US $ 25.4 billion. So these problems are overcome by the assistance of Yolo V3, we proposed a scheme. The multi-view object tracking (MVOT) system is used in this proposed system to address multiple cameras monitoring a neighborhood from various angles and recording videos. They contain complementary material, and by combining the knowledge contained in the videos, a powerful and accurate framework can be developed. This is the role of cameras with various settings that correspond to each other. Each segmented group of objects in one view is mapped to the corresponding group in another view using the Yolo V3 algorithm. These agreeing sets corresponded to blob gatherings, which allow data to be exchanged between cameras. These images are transformed into voice output after they are captured by the camera. As a result, visually impaired people gain more and can more readily identify which object is present within the images. As a result, for multi-view artefacts, we present a two-pass regression method.
Semi-supervised Convolutional Triplet Neural Networks for Assessing Paper Texture Similarity

In the context of papers used in the graphic arts, including silver gelatin, inkjet, and wove papers, prior work has studied measures of texture similarity for purposes of classifying such papers. The majority of prior work has been based on classical image processing approaches such as Fourier, wavelet, and fractal analysis. In this work, recent advances in deep learning are used to develop a texture similarity approach for measuring paper texture similarity. Since the available datasets generally lack labels, the convolutional neural network is trained using triplet loss to minimize the feature distance of tiles from the same image while simultaneously maximizing the feature distance of tiles drawn from different images. The approach is tested on three paper texture image databases considered in prior works and the results suggest the proposed approach achieves state-of-the-art performance.
A Real-time Multi-target tracking method based on Deep Learning

Abstract In view of complex model and poor real-time performance of current multi-target tracking algorithms, a real-time online multi-target tracking method based on deep learning is proposed. Firstly, the detector is used to detect the target in video image space and obtain its detection frame. After that, the position, coordinates and motion of the next frame of the target are predicted by the Kalman filter. Then Complete Intersection over Union (CIOU) is used as the distance measure to calculate the overlap between the detection box and the prediction box, and further generate the similarity matrix. Finally, the Hungarian algorithm finds optimal matching in the bipartite graph formed by the two boxes, so as to realize the data association between multiple targets. Experimental evaluation shows that this method finishes real-time and online multi-target tracking well.
Applied Predictive Process Monitoring and Hyper Parameter Optimization in Camunda

With the growing amount of data gathered from business processes in recent years, predictive process monitoring (PPM) established as a method to provide valuable insights and make resilient forecasts. However, sophisticated machine learning algorithms and statistical techniques are always equipped with various hyper parameters, which aggravates finding the best configuration for laypeople. Tools like Nirdizati Research ( http://research.nirdizati.org/ ) or apromore ( https://apromore.org/ ) aim to assist in these tasks. Nonetheless these approaches are isolated solutions, which do not integrate into existing productive environments. In this work, a plugin for the widely used workflow and decision automation tool Camunda ( https://camunda.com/ ) is presented which allows creating classifier for the most common operations in PPM. Furthermore, the framework includes a hyper parameter optimization (HPO) and is extensible in prediction types, methods and optimization algorithms.
Periodic acoustic source tracking using propagation delayed measurements

There exist a large class of acoustic sources which have an underlying periodic phenomenon. Unlike the well-studied Bearings-Only Tracking (BOT) of an aperiodic acoustic source, this paper considers the problem of tracking a periodic acoustic source. For periodic acoustic tracking, the signal emission time is known. However, the true measurement reception time is unknown because it is corrupted by noise due to propagation delay. We augment the sensor’s signal reception time onto bearing measurements, and the information of the delay constraint is included in the original bearing measurements to compensate for the propagation delay. A Cubature Kalman Filter (CKF) is used for periodic acoustic source tracking, in which measurement prediction cannot be obtained directly because the sensor’s position at the true measurement reception time is unknown. We solve this problem by using the implicit Gauss-Helmert Sensor Model (GHSM) for estimating the sensor’s position, which consists of the sensor’s motion equation and the known measured sensor’s signal reception time equation related to the state. Then a CKF based on the GHSM (CF-GHSM) is developed for periodic acoustic tracking. Illustrative examples demonstrate that the CF-GHSM algorithm is better than other algorithms for periodic acoustic source tracking.
Leveraging External Data Sources to Enhance Secure System Design

Today's software systems are riddled with security vulnerabilities that invite attack. We envisage a secure software design process at the architectural level, in which the security requirements are adequate, thus enabling appropriate security controls to be implemented to mitigate known threats and vulnerabilities. How can we ensure that the security requirements are adequate? In this paper, we tackle this question by focusing on how external online data sources for vulnerabilities, attack patterns, threat intelligence, and other security information can be leveraged, using Natural Language Processing (NLP), to produce a report to assist designers in validating the adequacy of the security requirements. This validation is done by determining which requirements map to known threats (identified from the external data), which requirements may be extraneous, and which threats may need a closer look to identify new requirements. We first describe the availability and nature of the external data, followed by how we employ NLP to process the data and produce the report. We include an illustrative example of our approach.
Joint Discriminative Distribution Adaptation and Manifold Regularization for Unsupervised Domain Adaptation

Unsupervised Domain Adaptation (UDA) has become a basic technology for cross-domain recognition and has received extensive attention in recent years. UDA aims to obtain a classifier for the target domain by learning source instances with different data distributions. However, traditional domain adaptation algorithms cannot effectively explore the manifold structure of data while reducing the distribution differences between domains. To address this problem, this paper proposes a new UDA framework called Joint Discriminative Distribution Adaptation and Manifold Regularization (DDAMR). DDAMR makes full use of the category information and geometric structure of samples in the Grassmann manifold to learn the domain-invariant classifier. Specifically, DDAMR performs discriminative distribution adaptation during dynamic distribution calibration to enhance the discrimination ability of the feature space. In addition, DDAMR introduces manifold regularization that can maintain the proximity relationship of the samples. It can maximize effectively the consistency between the prediction structure of the domain-invariant classifier f and the inherent manifold structure of the sample. A large number of results from cross-domain experiments have demonstrated the effectiveness of our DDAMR algorithm.
A User Experience Measuring Technique to Moderate Social Media Content Through Crowdsourcing

Crowdsourcing platforms are used to collect innovative ideas and to facilitate achieving a task by letting in participants with a wide range of demographic contributions. The responses from the crowd participants in these crowdsourcing platforms are subjective. Here in this research, we propose a new approach to gain user perspectives on the appropriateness of social media content through crowdsourcing. The proposed crowdsourcing framework would allow making good use of the subjectivity of the responses from the Sri Lankan participants' to contribute to the process of social media content moderation, and thereby to improve the user experience of social media users. Furthermore, we discuss the challenges we encountered while designing the instruments to capture user opinion. The experimental results demonstrate that we can use criteria in deciding if the content posted in social media is appropriate for their users or not. It is expected that the framework remains useful for those aiming to improve the user experience of a software product in which content plays a major role.
Machine Learning Approach to Sentiment Analysis from Movie Reviews Using Word2Vec

Nowadays, many organizations use customer reviews to improve their business. They analyze the reviews given by the customer to get a concrete decision about the quality of the products or services provided by them. Sentiment Analysis in recent times has come up in a broad way for the analysis of the customer reviews and comments to know the customer views regarding consumer products or services. Through sentiment analysis, the attitude of the customers toward the product can be easily determined. In this paper, we used movie reviews dataset to extract the sentiment of viewers using machine learning approaches. We have used Word2Vec feature extraction method to obtain features from movie reviews. An arithmetic mean of the word vectors is obtained along each dimension and, thereafter, the same mean vector is used to train the different machine learning classifiers. The same method is used on performance classifiers against the available test data. Our proposed model yields an overall good performance based on accuracy, recall, and F1 score. Finally, we made a comparative analysis, among various methods used here based on their performance.
A Quality- Time Model of Heterogeneous Agents Measure for Crowd Intelligence

With the rapid progress of artificial intelligence (AI), it becomes increasingly important to measure the crowd intelligence of both machine and human under a same benchmark. However, previous studies mainly emphasize on task-specific skill or universal ability, ignoring the hybrid property of heterogeneous agents. In this work, we present a formal definition of crowd intelligence based on quality and time. The crowd intelligence is modeled as an aggregate of agent's multiple response-abilities on environment or external stimuli. We propose the concept of crowd intelligence quotient (CIQ) to describe the size of crowd intelligence, including human and machines. Furthermore, we propose a quality-time model of heterogeneous agents measure for crowd intelligence. For the hybrid property of heterogeneous agents, we use response-quality to describe the AI's characteristic of task and use response-time to describe the human's characteristic of ability. Finally, the actual data of AI and human is used to verify our proposed model. The experimental results demonstrate proposed QTM is better than traditional methods in both AI and human. Through the experimental results, we found that the CIQ of AI is about one-quarter of that of a normal adult human.
Combinatorially XSSing Web Application Firewalls

Cross-Site scripting (XSS) is a common class of vulnerabilities in the domain of web applications. As it re-mains prevalent despite continued efforts by practitioners and researchers, site operators often seek to protect their assets using web application firewalls (WAFs). These systems employ filtering mechanisms to intercept and reject requests that may be suitable to exploit XSS flaws and related vulnerabilities such as SQL injections. However, they generally do not offer complete protection and can often be bypassed using specifically crafted exploits. In this work, we evaluate the effectiveness of WAFs to detect XSS exploits. We develop an attack grammar and use a combinatorial testing approach to generate attack vectors. We compare our vectors with conventional counterparts and their ability to bypass different WAFs. Our results show that the vectors generated with combinatorial testing perform equal or better in almost all cases. They further confirm that most of the rule sets evaluated in this work can be bypassed by at least one of these crafted inputs.
Comparison of Various Classifiers for Indian Sign Language Recognition Using State of the Art Features

Indian sign language (ISL) is used by deaf people in India for their internal communication. Sign language recognition (SLR) system recognizes gestures of sign language and converts them into text and voice, thus enabling deaf people to communicate with others. In this paper, we have presented an automatic system for ISL hand gesture recognition. Modern features like Histogram of Oriented Gradient (HOG), Speeded Up Robust Features (SURF), Fourier Descriptors, Hu Moments, and Zernike Moments have been extracted from training images of our own created dataset. Using these features, most commonly used classifiers like Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) have been trained and evaluated. Comparative performance analysis of these classifiers along with experimental results is the main topic of discussion included in this paper. Moreover, a user-friendly GUI of the system allows user to select any combination of features and classifiers to perform gesture recognition from both stored file and live camera. During experiment, it is found that the system is able to recognize gestures with a recognition rate up to 98.70%.