Reviewerly/exHarmony huggingface dataset:
Colour Image Encryption Based on Fisher-Yates Algorithm and Chaotic Maps

Due to its highly sensitive initial values and parameters and complicated features such as ergodicity and pseudorandomness, chaos-based systems are extensively used in digital image encryption. On the other hand, the authors looked at the impact of these maps on the encryption of colour images. The current research presents an image encryption using Fisher-Yates shuffling (FYS) and a 2D-LTM. FYS is a program that creates random permutations of finite sequences. At first, this FYS is used for randomly permuting the plain mage in rows and columns wise. Second, at the diffusion step, the 2D-LTM is utilized to confuse the values of pixel in the shuffled image. The suggested method is used to process a variety of images. Many tests are carried out to ensure the proposed algorithm’s security and performance. Furthermore, numerical simulations and experimental findings have ensured that the suggested method can withstand various attacks.
RMGN: A Regional Mask Guided Network for Parser-free Virtual Try-on

Virtual try-on (VTON) aims at fitting target clothes to reference person images, which is widely adopted in e-commerce. Existing VTON approaches can be narrowly categorized into Parser-Based (PB) and Parser-Free (PF) by whether relying on the parser information to mask the persons’clothes and synthesize try-on images. Although abandoning parser information has improved the applicability of PF methods, the ability of detail synthesizing has also been sacrificed. As a result, the distraction from original cloth may persist in synthesized images, especially in complicated postures and high resolution applications. To address the aforementioned issue, we propose a novel PF method named Regional Mask Guided Network (RMGN). More specifically, a regional mask is proposed to explicitly fuse the features of target clothes and reference persons so that the persisted distraction can be eliminated. A posture awareness loss and a multi-level feature extractor are further proposed to handle the complicated postures and synthesize high resolution images. Extensive experiments demonstrate that our proposed RMGN outperforms both state-of-the-art PB and PF methods. Ablation studies further verify the effectiveness of modules in RMGN. Code is available at https://github.com/jokerlc/RMGN-VITON.
Stackelberg Strategic Guidance for Heterogeneous Robots Collaboration

In this study, we explore the application of game theory, in particular Stackelberg games, to address the issue of effective coordination strategy generation for heterogeneous robots with one-way communication. To that end, focusing on the task of multi-object rearrangement, we develop a theoretical and algorithmic framework that provides strategic guidance for a pair of robot arms, a leader and a follower where the leader has a model of the follower's decision-making process, through the computation of a feedback Stackelberg equilibrium. With built-in tolerance of model uncertainty, the strategic guidance generated by our planning algorithm not only improves the overall efficiency in solving the rearrangement tasks, but is also robust to common pitfalls in collaboration, e.g., chattering.
Momentum Space Controlled Flexible Spatial Light Modulator for Optical Wireless Communication

Traditional spatial light modulators (SLMs) shape an incident wavefront pixel-by-pixel. This talk explores a new class of SLMs named momentum space controlled spatial light modulators (MSC-SLMs). An optical-wireless-communication link enabled by MSC-SLMs is demonstrated.
Text Detergent: The Systematic Combination of Text Pre-processing Techniques for Social Media Sentiment Analysis

During catastrophes such as natural or man-made disasters, social media services have evolved into a crucial tool utilised by communities to disseminate information. Because a vast number of social media data is being used for many applications, including sentiment analysis, sentiment analysis has become a very useful and demanding problem. Social media data cannot be applied directly because it is raw and unstructured or semi-structured data. Consequently, text pre-processing becomes one of the most important tasks because the process is strongly constrained by its dependable workflow. This reason creates a complex pattern in pre-processing workflows. For this purpose, different text pre-processing techniques have been used on Twitter, Facebook, and YouTube datasets to study the impact of different pre-processing techniques on the accuracy of machine learning algorithms. This paper applied different text pre-processing techniques in a specific sequence based on significance testing. This study examines their influence on sentiment classification accuracy using a machine learning classifier, Support Vector Machines (SVM). Results proved that applying all 14 techniques systematically can achieve up to 82.57% of the accuracy of the SVM classifier with unigram representations. By using Text Detergent, the YouTube dataset achieve the highest accuracy compared to Facebook and Twitter datasets. This will potentially improve the quality of the text and leads to better feature extraction, which in turn helps the sentiment analyst produce a better classifier.
Logic and Pragmatics in AI Explanation

Abstract This paper reviews logical approaches and challenges raised for explaining AI. We discuss the issues of presenting explanations as accurate computational models that users cannot understand or use. Then, we introduce pragmatic approaches that consider explanation a sort of speech act that commits to felicity conditions, including intelligibility, trustworthiness, and usefulness to the users. We argue Explainable AI (XAI) is more than a matter of accurate and complete computational explanation, that it requires pragmatics to address the issues it seeks to address. At the end of this paper, we draw a historical analogy to usability. This term was understood logically and pragmatically, but that has evolved empirically through time to become more prosperous and more functional.
Giftedness: A Three Way Approach and the role of ICTs

This article presents a group of students who are found in the general classroom. This group consists of gifted children, whose way of learning has not been extensively studied. High achievement students have more developed skills than their peers and this often results in them not engaging in school lessons as they seem too simplistic. This article first introduces the different levels of intelligence that have been formulated from time to time. Secondly, studies showing the relationship between working memory and giftedness, are presented. Finally, ways of teaching giftedness students are suggested with the use of ICTs.
Securing personal identity using blockchain

Sharing of identity information is common and is used to avail different kinds of benefits from the organisations. But this process is becoming trustless and complicated because of the involvement of physical documents or sharing through third-party online service providers. To overcome this, the paper proposes a decentralised way of sharing users' identity information with organisations by using blockchain technology. The information stored in blockchain becomes tamper-less and credible. The identity information of the user is tagged with their public key. So, whenever the user wishes to share identity information, they simply share their public key to the organisation and provide access to them. Also, by making use of smart contracts only required information is shared with the organisation for a limited period. This avoids the leakage of identity information.
MARMOT

Political activity on social media presents a data-rich window into political behavior, but the vast amount of data means that almost all content analyses of social media require a data labeling step. However, most automated machine classification methods ignore the multimodality of posted content, focusing either on text or images. State-of-the-art vision-and-language models are unusable for most political science research: they require all observations to have both image and text and require computationally expensive pretraining. This paper proposes a novel vision-and-language framework called multimodal representations using modality translation (MARMOT). MARMOT presents two methodological contributions: it can construct representations for observations missing image or text, and it replaces the computationally expensive pretraining with modality translation. MARMOT outperforms an ensemble text-only classifier in 19 of 20 categories in multilabel classifications of tweets reporting election incidents during the 2016 U.S. general election. Moreover, MARMOT shows significant improvements over the results of benchmark multimodal models on the Hateful Memes dataset, improving the best result set by VisualBERT in terms of accuracy from 0.6473 to 0.6760 and area under the receiver operating characteristic curve (AUC) from 0.7141 to 0.7530. The GitHub repository for MARMOT can be found at github.com/patrickywu/MARMOT.
A method for detecting objects in dense scenes

Abstract Recent object detectors have achieved excellent performance in accuracy and speed. Even with such impressive results, the most advanced detectors are challenging in dense scenes. In this article, we analyze and find the reasons for the decrease in detection accuracy in dense scenes. We started our work in terms of region proposal and location loss. We found that low-quality proposal regions during the training process are the main factors affecting detection accuracy. To prove our research, we established and trained a dense detection model based on Cascade R-CNN. The model achieves an accuracy of mAP 0.413 on the SKU-110K sub-dataset. Our results show that improving the quality of recommended regions can effectively improve the detection accuracy in dense scenes.
The effect of social media marketing, compatibility and perceived ease of use on marketing performance: Evidence from hotel industry

This research effort addresses the role of social media marketing activities, the compatibility with latest technology availability and perceived ease of use of social media and technological applications and systems to influence the attitude towards adoption that further enhances the marketing performance. The hotel industry of UAE is required to adopt the social networking sites and online marketing approaches instead of traditional marketing pattern to attract the customers on large-scale worldwide specifically. In addition, Due to Covid19 pandemic era, the need for online applications has increased dramatically to avoid the physical contact. The study contributes to the body of knowledge by explaining the role of social media marketing, its compatibility and ease of use to predict the attitude towards adoption that further influences the marketing performance. The study reported that social media marketing efforts, the compatibility of online applications and perceived ease of use influence the attitude towards the adoption of social networking sites significantly, moreover the marketing performance is influenced by inclined attitude of adoption network related applications.
Cooperative Location-Sensing Network Based on Vehicular Communication Security Against Attacks

Various attacks in communication network threaten the security of vehicular system. For cooperative location-sensing system, man-in-the-middle attack and eavesdropping attack would lead to catastrophic collapse of vehicular network localization. In this paper, we propose a federated cryptosystem localization based on optimized constraints. In the localization phase, a message passing algorithm combining belief propagation (BP) and variational message passing (VMP) is derived by defining penalty function, which can detect the distance outliers. The messages generated by each vehicular node or base station are encrypted with Paillier cryptosystem. Because Paillier cryptosystem is featured by homomorphic addition, message aggregation reduces large amount of decryption. The localization messages finally work in a federated transmission scheme for privacy preserving against man-in-the-middle attack and eavesdropping attack. In terms of localization accuracy, algorithm parameters, convergence analysis and efficiency, simulation results show that cybersecurity of cooperative localization is proved to be effective in various scenarios of vehicular network.
Target Detection method for Based on Deep Learning

Target detection is an important method in the field of deep learning. In view of the low efficiency and low accuracy of traditional target detection methods, this paper mainly focuses on the analysis of two-stage detection algorithm Faster R-CNN and single-stage detection algorithm Yolo. Based on the Paddle Detection model library, the above algorithms are replicated. Detecting the same image, the advantages and disadvantages of these algorithms will be compared in accuracy and efficiency. Based on the analysis of anchor frame and real frame, it is found that network mismatch and overfitting lead to low detection accuracy and low efficiency. In order to solve the above problems, this paper proposes the method of connecting multiple mixed detectors in the existing detection network, which can not only improve the quality of samples, but also keep the diversity of samples. Experimental results show that compared with traditional target detection methods, the accuracy of the proposed method is about 10% higher. The proposed algorithm can solve the problem of network mismatch as well as reduce the overfitting problem, effectively improve the efficiency and accuracy of network detection, and better meet the requirements of high precision and fast identification.
Perspective transformation based-initial value estimation for the speckle control points matching in an out-of-focus camera calibration using a synthetic speckle pattern

Despite camera calibration methods using regular planar chessboard or circular marker array calibration targets having been widely used, the control point extraction accuracy is low if the image is defocused or if the noise level is high. Due to the noise robustness of digital image correlation (DIC) in speckle image matching, random speckle pattern is a better choice for camera calibration than chessboard or circular markers, if the imaging quality is low. The foremost process of this method is to conduct speckle control points matching DIC, where the initial value must be estimated close to the true value. It is challenging to provide accurate initial values for DIC if the difference of physical pixel scale is large between the reference image and the target image or if the target image is out-of-focus. To solve this problem, this work presents an efficient initial value estimation method for speckle control points matching using DIC, based on perspective transformation. Firstly, the four pairs of corners of the speckle regions in the reference image and target image are detected. Secondly, the target image is transformed to a new image that has the considerable size of pixel scale with the reference image, then four neighborhood points of the control point in the reference image and the corresponding points in the transformed new image are matched coarsely by fixed subset searching. Lastly, the matched points in the transformed target image are transformed back to the origin target image by the inverse perspective transformation matrix, then the initial value for DIC can be estimated by the matched four pairs of neighborhood points. Experiment results confirm the higher calibration accuracy delivered by the proposed method, rather than that of the chessboard or the circular marker array. Measurement precision is higher than the speckle pattern calibration method that uses SIFT-based initial value estimation.
A scalable algorithm for network reachability analysis with cyclic attack graphs

Today, it is indispensable for us to know our IT infrastructure’s security level to make rational decisions to protect it from malicious attacks. However, the ever-growing scale and complexity of our infrastructures make it difficult. One available approach is attack-graph analysis, a model-based algorithm that generates a directed graph called an attack graph composed of possible attack actions and their AND/OR combinations. An attack graph allows us to identify the logical possibility of cyber-attacks. Unfortunately, attack graphs lack quantitative information, such as the likelihood of a successful attack – the reachability. Meanwhile, some advanced approaches, such as the Bayesian attack-graph, compute reachabilities with an attack graph. Still, those methods have problems in the scalability and the handling of cycles contained in attack graphs. This study proposes and examines another approach named reachability-graph analysis. A reachability graph is a directed graph obtained from an attack graph by which we can compute the reachabilities. It is extremely fast to generate, thus applicable to large-scale network analysis. Furthermore, this method allows cycles in attack graphs. Such a scalable algorithm has been awaited for over a decade; it generates probabilistic security metrics and is guaranteed applicable to any cyclic AND/OR graphs. We compare our approach’s computational efficiency with the Bayesian attack-graph. Also, we apply our approach to large-scale, cyclic attack graphs. Several theoretical properties with proofs and the computational complexity of our approach are examined too.
LL(O)D and NLP perspectives on semantic change for humanities research

This paper presents an overview of the LL(O)D and NLP methods, tools and data for detecting and representing semantic change, with its main application in humanities research. The paper’s aim is to provide the starting point for the construction of a workflow and set of multilingual diachronic ontologies within the humanities use case of the COST Action Nexus Linguarum, European network for Web-centred linguistic data science, CA18209. The survey focuses on the essential aspects needed to understand the current trends and to build applications in this area of study.
A Physics-based Metaheuristic Algorithm Based on Doppler Effect Phenomenon and Mean Euclidian Distance Threshold

Doppler Effect (DE) is a physical phenomenon observed by Doppler, an Austrian mathematician, in 1842. In recent years, the mathematical formulation of this phenomenon has been used to improve the frequency equation of the standard Bat Algorithm (BA) developed by Yang in 2010. In this paper, we use the mathematical formulation of DE with some idealized rules to update the observer velocity existing in the Doppler equation. Thus, a new physics-based Metaheuristic (MH) optimizer is developed. In the proposed algorithm, the observers’ velocities as the algorithm’s search agents are updated based on the DE equation. A new mechanism named Mean Euclidian Distance Threshold (MEDT) is introduced to enhance the quality of the observers. The proposed MEDT mechanism is also employed to avoid the locally optimum solutions and increase the convergence rate of the presented optimizer. Since the proposed algorithm simultaneously utilizes the DE equation and MEDT mechanism, it is called the Doppler Effect-Mean Euclidian Distance Threshold (DE-MEDT) metaheuristic algorithm. The proposed DE-MEDT algorithm’s efficiency is evaluated by solving well-known unconstrained and constrained optimization problems. In the unconstrained optimization problems, 23 well-known optimization functions are used to assess the exploratory, exploitative, and convergence behaviors of the DE-MEDT algorithm.
Accelerated Service Oriented Architecture Implementation of Deduplication Principle

The phrase “Web Services (WSs)” are emerging as a creative scheme for furnishing the services to various immanent devices over the World Wide Web. The hasty intensification of the WSs applications and the availability of the vast count of the Service Providers create the certainty of selecting the “efficient” Service Provider by the consumers. The scenario Deduplication and Quality-of-Service (QoS) swears out as an objective to distinguish various Service Providers (SPs). The process of selecting proficient WSs / SPs, positioning and optimization of WSs Compositions are exigent dimensions of research with momentous entailments for the fruition of the “Web of Services” revelation. The term “Semantic WSs” follows appropriate semantic descriptions of WS functionality and a medium to facilitate programmed cogitating over WS Compositions (WSCs). The persisting model of the Semantic Web Services (SWSs) deals with the intriguing emerges like wretched forecast of best WSs and gemination of services with effective SPs, which heads to Quality level degradation on the Semantic Web. To deal the above identified issues, the anticipated research is planned to construct a model to manipulative the content similarities (semantic), consumption of a mixture of WSs and its corresponding SPs. After assessing these params, all the WSs are stratified on the basis of its consumption. Ultimately, the nominated scheme, selects the best and non duplicated copy of the WSs on the basis of its rating and placed it in the WSC. The process of detecting the duplicate copy would be performed by the Cryptographic Hash value of the Services. From the experimental annotations, it is recognized that our anticipated design amends the functionality of the SWSs in terms of Processor Utilization, Accessing Time, and its Space optimizations.
Recommendation Systems

Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the MapReduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream-processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets, and clustering. This third edition includes new and extended coverage on decision trees, deep learning, and mining social-network graphs.
Development of An Intelligent NLP-Based Audit Plan Knowledge Discovery System

High level of knowledge and expertise are required in auditing, which makes it a knowledge-intensive professional service. Auditors' brainstorming meetings involve various topics on risks, which provide numerous valuable knowledge on how auditors identify and assess risks and achieve decisions. However, it is very difficult to retrieve useful knowledge from these meeting conversations. With the help of Natural Language Processing (NLP) techniques, this paper proposes an intelligent NLP-based audit plan knowledge discovery system (APKDS) that will continuously and automatically extract important knowledge from audit brainstorming discussions and provide decision support to auditors in future engagement cases.
Cybereason: Ransomware – the true cost to business

Ransomware is the most prevalent and immediate cyberthreat facing organisations today. It has evolved from a nuisance encountered by individuals to large-scale ‘cybercrime as a service’ operations targeting major businesses and critical national infrastructure (CNI).