Reviewerly/exHarmony huggingface dataset:
Pittsburgh learning classifier systems for explainable reinforcement learning

Interest in reinforcement learning (RL) has recently surged due to the application of deep learning techniques, but these connectionist approaches are opaque compared with symbolic systems. Learning Classifier Systems (LCSs) are evolutionary machine learning systems that can be categorised as eXplainable AI (XAI) due to their rule-based nature. Michigan LCSs are commonly used in RL domains as the alternative Pittsburgh systems (e.g. SAMUEL) suffer from complex algorithmic design and high computational requirements; however they can produce more compact/interpretable solutions than Michigan systems. We aim to develop two novel Pittsburgh LCSs to address RL domains: PPL-DL and PPL-ST. The former acts as a "zeroth-level" system, and the latter revisits SAMUEL's core Monte Carlo learning mechanism for estimating rule strength. We compare our two Pittsburgh systems to the Michigan system XCS across deterministic and stochastic FrozenLake environments. Results show that PPL-ST performs on-par or better than PPL-DL and outperforms XCS in the presence of high levels of environmental uncertainty. Rulesets evolved by PPL-ST can achieve higher performance than those evolved by XCS, but in a more parsimonious and therefore more interpretable fashion, albeit with higher computational cost. This indicates that PPL-ST is an LCS well-suited to producing explainable policies in RL domains.
Noninvasive Biomarkers: Emerging Trends in Early Detection of Breast Cancer

Breast cancer (BC) is one of the leading mortality among women throughout the globe. Screening of breast cancer and early detection aids better prognosis and successful therapeutic upshots. Currently, the mammography is the only method used for the diagnosis as the gold standard offers effective revenue, limits to its effectiveness, accuracy, overdiagnosis and its inability to detect small-scaled cancers, mainly in female with high density breast tissues, remains a foremost problem in breast oncology. Hence, it is an unmet clinical need to recognize convenient noninvasive biomarkers from an easily approachable source that could overcome the shortcomings of mammography. Promising biomarkers using epigenetic approach just like miRNAs, circulating tumor cells (CTCs), circulating cell-free DNAs or RNAs, lipids, proteins, volatile organic compounds as well as biomarkers from tears, nipple aspirate fluid (NAF), sweat, and urine have shown great potential to detect BC at pre-invasive stages. In addition, there is a superior requirement of mutational signature for individualized treatments; the high-penetrance molecular biomarkers including TP53, BRCA1, BRCA2 are risk alleles for the breast cancer progression. The current chapter would focus on the recent discoveries aimed to harnessing novel molecular biomarkers for the early detection of BC biomarkers as well as the therapeutic implications that need to be overcome.
SeeFar: Vehicle Speed Estimation and Flow Analysis from a Moving UAV

Visual perception from drones has been largely investigated for Intelligent Traffic Monitoring System (ITMS) recently. In this paper, we introduce SeeFar to achieve vehicle speed estimation and traffic flow analysis based on YOLOv5 and DeepSORT from a moving drone. SeeFar differs from previous works in three key ways: the speed estimation and flow analysis components are integrated into a unified framework; our method of predicting car speed has the least constraints while maintaining a high accuracy; our flow analysor is direction-aware and outlier-aware. Specifically, we design the speed estimator only using the camera imaging geometry, where the transformation between world space and image space is completed by the variable Ground Sampling Distance. Besides, previous papers do not evaluate their speed estimators at scale due to the difficulty of obtaining the ground truth, we therefore propose a simple yet efficient approach to estimate the true speeds of vehicles via the prior size of the road signs. We evaluate SeeFar on our ten videos that contain 929 vehicle samples. Experiments on these sequences demonstrate the effectiveness of SeeFar by achieving 98.0% accuracy of speed estimation and 99.1% accuracy of traffic volume prediction, respectively.
Product weighted taxonomy extraction using Twitter

Social networks and microblogging websites such as Twitter have changed the face of the consumer product market. Nowadays, even before a new product comes to the market, or is to be announced, like at an Apple or Google event, users start the rumours/talk about the product design or specifications, and their expectations of it. With all this discussion, it is not only the product which affects the future success of the product, but various other factors which can directly or indirectly influence the user's perspective. In this work, we present an approach to extract the most important concepts related to a product, being discussed on Twitter. We believe that this weighted taxonomy can be used to predict the success of the product even before the official release, align marketing resources, etc. in a beneficial manner.
Performance Investigation of a Proposed CBIR Search Engine Using Deep Convolutional Neural Networks

During the last decade, the volume of image databases and collections has significantly increased. Further, most current image retrieval systems are moving from traditional methods, which are mainly metadata-based approaches, to Content-Based Image Retrieval (CBIR) approaches. These latter use an image as an input query without considering any metadata associated with the image. Generally, a CBIR-based system extracts visual features such as color, image edge, and texture to search and retrieve similar images. Our goal is to develop a fast and accurate CBIR-based search engine. In this paper, we investigated the performance of two Convolutional Neural Networks (CNN) models, namely VGG16 and MobileNet, in a real-life project environment. Further, we used both Faiss and Annoy libraries for indexing and similarity search, while the Principal Component Analysis (PCA) technique is applied for reducing the dimensionality of the feature vector space. From the experiments, we found that the VGG16 and MobileNet models differ in terms of precision, recall, and response time recorded. However, they both have succeeded in supporting the CBIR-based search engine.
Efficient Email Classification Algorithm for Better Customer Support

Email has become one of the most important daily applications. Due to the continuing rise in email users, the number of unsolicited emails, also known as spam messages, has increased enormously. It is a big challenge to handle and classify this huge number of emails, while in spam message separating, traditional technologies are quite effective. Various steps must be taken to make spam filtering more accurate. We worked on this effort to identify and filter spam messages while delivering them. The high dimensionality of email through syntactic feature selection was addressed in most methods presented for resolving this issue. This article deals with an efficient approach to email filtering based on semantic techniques and similarity measures for dropping the huge number of extracted textual features, and therefore, the space and time complexities are reduced. This study is intended to enhance the manually defined rules. The algorithms currently used for the use of machine learning in a telecommunications company's business. The suggested model with long short-term memory (LSTM) retains data in the memory for a longer duration of time and has a better F1-score and accuracy. It integrates or migrates the administrative and maintenance tasks from a manually designed rule-based model into a machine learning model. It should also increase the flexibility of the model.
Augment Small Training Sets Using Matching-Graphs

Both data access and data acquisition have become increasingly easy over the past decade, leading to rapid developments in many areas of intelligent information processing. In many cases, the underlying data is complex, making vectorial structures rather inappropriate for data representation. In these cases graphs provide a versatile alternative to purely numerical approaches. Regardless the representation formalism actually used, it is inevitable for supervised pattern recognition algorithms to have access to large sets of labeled training samples. In some cases, however, this requirement cannot be met because the set of labeled samples is inherently limited. In a recent research project a novel encoding of pairwise graph matchings is introduced. The basic idea of this encoding is to formalize the stable cores of pairs of patterns by means of graphs, termed matching-graphs. In the present paper we propose a novel scenario for the use of these matching-graphs. That is, we employ them to enlarge small training sets of graphs in order to stabilize the training of a classifier. In an experimental evaluation on four graph data sets we show that this novel augmentation technique improves the classification accuracy of an SVM classifier with statistical significance.
A Deep-Learning Based Automated COVID-19 Physical Distance Measurement System Using Surveillance Video

The contagious Corona Virus (COVID-19) transmission can be reduced by following and maintaining physical distancing (also known as COVID-19 social distance). The World Health Organisation (WHO) recommends it to prevent COVID-19 from spreading in public areas. On the other hand, people may not be maintaining the required 2-m physical distance as a mandated safety precaution in shopping malls and public places. The spread of the fatal disease may be slowed by an active monitoring system suitable for identifying distances between people and alerting them. This paper introduced a deep learning-based system for automatically detecting physical distance using video from security cameras. The proposed system employed the fine-tuning YOLO v4 for object detection and classification and Deepsort for tracking the detected people using bounding boxes from the video. Pairwise L2 vectorized normalization was utilized to generate a three-dimensional feature space for tracking physical distances and the violation index, determining the number of individuals who follow the distance rules. For training and testing, we use the MS COCO and Oxford Town Centre (OTC) datasets. We compared the proposed system to two well-known object detection models, YOLO v3 and Faster RCNN. Our method obtained a weighted mAP score of 87.8% and an FPS score of 28; both are computationally comparable.
Violence Detection in Video Footages Using I3D ConvNet

With increasing surveillance systems through the help of drones and wide deployment of closed-circuit television (CCTV) cameras in public spaces, the need for monitoring these videos is more than ever before. Drones and CCTV footages have helped in solving crimes that had already been committed but have not been able to prevent them from happening. To monitor these huge live video feeds manually to prevent or identify, a crime is a labor-intensive and expensive task. Thus, automatic monitoring of these videos for any violent behavior is the optimal solution for this problem. In this paper, generation of optical flow images using FlowNet2 (Ilg et al., FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks, pp. 1647–1655, 2017) and training these on the Inflated 3D (I3D) (Carreira and Zisserman, Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset, pp. 4724–4733, 2017) deep neural network, drastically improves the state-of-the-art performance upon the surveillance camera fight dataset.
A Framework for Improving the Accuracy with Different Sampling Techniques for Detection of Malicious Insider Threat in Cloud

Cloud computing provides more beneficial services to its users with limited cost. Cloud is prone to many threats, and one of the major threats is the malicious insider threat. Detection of malicious insider threats is more challenging, and many cloud datasets are available to detect a malicious insider. In real-time data collection, the data set is prone to a class imbalance problem. Minority class related to insider threat events has a smaller number of instances, whereas majority class related to non-insider threats has a minimum number of instances. Supervised classification techniques provide a better result for the classification of the majority class and a less accurate result for the minority class. Classification without treating the imbalanced class data results in adverse effects in prediction. In this paper, different sampling techniques are implemented to accurately handle the imbalanced class data to detect malicious insider threats in cloud computing. The performance of different sampling techniques is compared by implementing Support Vector Machine (SVM) algorithm using the performance metrics such as accuracy, f-score, precision and recall.
A GNN-Enhanced Game Bot Detection Model for MMORPGs

Game bots are automated programs that assist cheating players in obtaining huge superiority in Massively Multiplayer Online Role-Playing Games (MMORPGs), which has led to an imbalance in the gaming ecosystem and a collapse of interest among normal players. Game bot detection aims to identify cheating behaviors to ensure fair competition for MMORPGs. Due to the high practical value, there is much research on game bot detection at present. One main existing method is conventional machine learning algorithms, which require extensive feature engineering and get limited performance. The other main existing method is the recurrent neural network, but it fails to capture the complex behavioral patterns of players. To tackle the above problems, we propose a novel graph neural network-enhanced game bot detection model, namely GB-GNN. In the proposed model, we model players’ trajectories as graph-structured data to capture the player’s complex behavioral patterns that are difficult to reveal by traditional sequential methods. Extensive experiments on three real-world datasets show that GB-GNN outperforms the previous methods.
Pairing-Free Certificateless Key-Insulated Encryption with Keyword Search

Public key encryption with keyword search (PEKS) allows a user to make searches on ciphertexts without disclosing the information of encrypted messages and keywords. The certificateless public key encryption with keyword search scheme avoids the problems of public key certificate management and key escrow based on the realization of ciphertext retrieval. But in practice, key leakage is often difficult to avoid, and key leakage will threaten the security of the entire cryptographic system. Considering the key leakage problem in the scheme, we are desirable to incorporate the key insulation mechanism into the certificateless public key encryption with keyword search scheme. In this work, a new scheme named pairing-free certificateless key-insulated encryption with keyword search (KI-CLPEKS) is designed. This scheme realizes two functions of key insulation and ciphertext search at the same time. The security notions for KI-CLPEKS are formally defined and then a concrete KI-CLPEKS scheme is proposed. The security proofs demonstrate that the KI-CLPEKS scheme guarantees the keyword ciphertext indistinguishability against adaptive chosen keyword attacks under the complexity assumption of the computational Diffie-Hellman problem in the random oracle model. The experimental results and comparisons show that the proposed scheme is practicable.
Virtual Reality in Marketing: Consumer and Retail Perspectives

Although ‘extended reality’ has been conceptualized decades ago, with the current proliferation of new technologies its application in marketing practice has just recently come to light. Marketing practitioners have found numerous ways of applying new technologies in their practice. Significant advancements have been made particularly with the usage of virtual reality in the domains of consumption and retailing. Through virtual reality consumers gain both purchase and consumption experiences which are beyond the reality they live in. At the same time by using virtual reality retailers create innovative shopping experiences aimed at increasing passion for consumption. This paper presents the theoretical framework in which virtual reality is embedded, followed by discussion of virtual reality from consumer and retailer perspectives.
Application of cloud computing key technology in aerospace TT&amp;C

Abstract The basic concepts, technical implementation, and application scenarios of computer simulation, virtualization, and cloud computing are intertwined and each has its characteristics. In this article, the basic concepts, necessity, feasibility, and core difficulties of computer simulation, virtualization, and cloud computing are analyzed. Also, the application prospects of the three technologies are expounded, the technical differences are analyzed, and the application of some key technologies in the data center is introduced. The generalization of computer simulation technology, the specialization of virtualization technology, and the systematization of cloud computing technology are expounded, which represent the mainstream direction of technology development in the field of information and communication at this stage. The application examples of the three in the aerospace telemetry, tracking, and command field are taken to illustrate that information technology essentially serves the business logic of their respective fields, which also well reflects the basic characteristics of demand-oriented, safety-oriented, innovation, and long-term development. It has guiding significance for the technical orientation, technical selection, and engineering implementation of information system architecture design.
Area-Efficient Finite Field Multiplication Using Hybrid SET-MOS Technology

Single-electron transistors (SETs) exhibit a unique characteristic of Coulomb oscillation which can find many digital applications with area efficiency. More specifically, both MOS and SET devices can be used to implement XOR gates with almost the same area costs regardless of the number of their inputs, outperforming pure CMOS solutions. As multiple-input XOR gates are abundant in the finite field polynomial multiplication which represents the most frequent computation in elliptic curve cryptosystem, hybrid SET-MOS technology can substantially reduce the area cost for this application. This paper presents polynomial multiplication architectures with hybrid SET-MOS transistors and explores Karatsuba-algorithm based multiplication for further area optimization. Simulations show that the proposed hybrid SET-MOS implementations can typically provide around 37% savings in terms of gate count compared to their traditional CMOS counterparts.
A Toolkit for Parikh Matrices

The Parikh matrix mapping is a concept that provides information on the number of occurrences of certain (scattered) subwords in a word. Although Parikh matrices have been thoroughly studied, many of their basic properties remain open. In the present paper, we describe a toolkit that has been developed to support research in this field. Its functionality includes elementary and advanced operations related to Parikh matrices and the recently introduced variants of $$\mathbb {P}$$ -Parikh matrices and $$\mathbb {L}$$ -Parikh matrices.
Formal categorical reasoning

In this paper, we present a category theory library developed in the proof assistant Coq. We discuss the design principles of the library in comparison with those existing out there. To explicitly demonstrate the utility of the library, we conclude with a case study in which a Coq formalized soundness proof of the intuitionistic propositional logic within a category theoretical settings is examined.
Deep Learning Ensemble for Seismic First-Break Event Picking

Summary In this study, we investigate the use of an ensemble of deep learning models to improve the quality and efficiency of seismic first break event picking. In traditional workflows, we often perform first break picking iteratively and spend extensive quality control and refining efforts before arriving at the final result. If we ask more than one expert to perform the picking, we expect the picks to be consistent between these experts at most locations, whereas the discrepancy only occurs at very challenging locations. Therefore, by taking only the picks that are consistent across multiple experts can provide us the high confidence picks. Following a similar strategy, we use multiple deep learning models that act as independently experts, then take only the first break picks that are consistent among these models. We can greatly reduce the uncertainty involved in the picks and reduce the amount of quality control needed. Compared with traditional workflow, the proposed strategy provides more valid picks that benefit the near surface velocity model building process, at a fraction of the cost.
KG-CRuSE: Recurrent Walks over Knowledge Graph for Explainable Conversation Reasoning using Semantic Embeddings

Knowledge-grounded dialogue systems utilise external knowledge such as knowledge graphs to generate informative and appropriate responses.A crucial challenge of such systems is to select facts from a knowledge graph pertinent to the dialogue context for response generation.This fact selection can be formulated as path traversal over a knowledge graph conditioned on the dialogue context.Such paths can originate from facts mentioned in the dialogue history and terminate at the facts to be mentioned in the response.These walks, in turn, provide an explanation of the flow of the conversation.This work proposes KG-CRUSE, a simple, yet effective LSTM based decoder that utilises the semantic information in the dialogue history and the knowledge graph elements to generate such paths for effective conversation explanation.Extensive evaluations showed that our model outperforms the stateof-the-art models on the OpenDialKG dataset on multiple metrics.
Modelling the Development and Deployment of Decentralized Applications in Ethereum Blockchain: A BPMN-Based Approach

Decentralized Applications (DApps) have emerged as a new model for building massively scalable and profitable applications. A DApp is a software application that runs on a peer-to-peer blockchain network offering censorship resistance, resilience, and transparency that overcome the challenges of typical centralized architectures. Developing and deploying a DApp in a blockchain network is highly challenging. Developers need to initially decide if developing a DApp is justified, before considering different aspects of blockchain technology (e.g., cryptography, transactions, addresses, etc.). This adversity along with the plethora of previously published works highlight the need for new tools and methods for the development of blockchain-based applications. Throughout literature there is a lack of procedures that can guide practitioners on how to develop and deploy their own applications. This paper aims to address this research gap, by standardizing and modelling such processes, through the employment of the BPMN modelling technique. Initially, a DMN decision model is presented that can facilitate developers to determine whether developing a DApp is justified. Consequently, two BPMN models are introduced, namely the DApp development and the DApp deployment process models. The models can orchestrate new DApp initiatives and facilitate the developers’ communication and implementation transparency. We expect that they can serve as a roadmap for enhancing the decision-making in the act of developing a DApp and reducing the implementation time and cost. Finally, we further discuss how the models implement a DApp for the registration and verification of academic qualifications and how BPMN can constitute a powerful tool for the development of DApps.