Reviewerly/exHarmony huggingface dataset:
Cross-view action recognition with small-scale datasets

• Methodology for cross-view action recognition in videos, targeting small-scale datasets. • A pipeline addressing widely diverse viewpoints, including egocentric view of actions. • A domain adaptation procedure to transfer pre-trained features to different target views. • A thorough experimental analysis for 2 scenarios: single view and multiple-view learning. Cross-view action recognition refers to the task of recognizing actions observed from view-points that are unfamiliar to the system. To address the complexity of the problem, state of the art methods often rely on large-scale datasets, where the variability of viewpoints is appropriately represented. However, this comes to a significant price, in terms of computational power, time, costs, energy for both gathering data annotation and training the model. We propose a methodological pipeline that tackles the same challenges with specific focus on small-scale datasets and attention to the amount of resources required. The core idea of our method is to transfer knowledge from an intermediate, pre-trained representation, under the hypothesis that it already may implicitly incorporate relevant cues for the task. We rely on an effective domain adaptation strategy coupled with the design of a robust classifier that promotes view-invariant properties and allows us to efficiently generalise to action recognition to unseen viewpoints. In contrast to other state-of-art methods employing also alternative data modalities, our approach is purely video-based and thus has a wider field of applications. We present a thorough experimental analysis justifying the choices on the design of the pipeline, and providing a comparison with existing approaches in the two main scenarios of one-one learning and multiple view learning, where our approach provides superior performance.
Overcoming information-sharing challenges in cyber defence exercises

Abstract Active usage of threat intelligence information supports effective prevention, mitigation and defence against cyberattacks by threat actors ranging from individual amateurs to state organizations. However, threat intelligence highly depends on security specialists’ ability to share incident data on threat information-sharing platforms. Unfortunately, business managers and educational institutions undervalue the information-sharing aspect when planning the professional development of cybersecurity-related specialists. Consequently, cybersecurity specialists are insufficiently motivated to correctly communicate and propagate relevant information with team members, superiors, relevant institutions and the global community about the impact of the incident. Literature review reveals many technological, legal and psychological obstacles hindering successful information exchange. This research aims to improve threat information sharing by focusing on the educational aspect of the problem and analysing the attitude of cybersecurity specialists during cyber defence exercises (CDX). Our case study disclosed nine factors obstructing both proper reporting to relevant authorities and adequate communication among teams. By addressing these factors, CDX organizers could substantially improve the development of highly beneficial soft skills of the technical specialists.
Semi-supervised clustering under a compact-cluster assumption

Semi-supervised clustering (SSC) aims to improve clustering performance with the support of prior knowledge (i.e., side information). Compared with pairwise constraints, the partial labeling information is more natural to characterize the data distribution in a high level. However, the natural gap between the class information and the clustering is not adequately taken into account in exiting SSC methods when utilizing partial labeling information to guide the clustering procedure. In order to address this problem, we present a compact-cluster assumption for SSC to utilize the partial labeling information via a cluster-splitting technique. Based on this assumption, a general framework, CSSC, is proposed to supervise the traditional clustering with an objective function which is defined by incorporating an item to measure the compact degree of clusters. Furthermore, we provide two effective solutions for Kmeans and spectral clustering within the CSSC framework and derive the corresponding algorithms to seek the optimum number of clusters and their centroids. Corresponding theoretical analyses demonstrate the feasibility and effectivity of the proposed method. Finally, the extensive experiments on eight real-world datasets demonstrate the superiority of our method over other state-of-the-art SSC methods.
A Dual Incentive Value-Based Paradigm for Improving the Business Market Profitability in Blockchain Token Economy

Blockchain solves the problem of mutual trust and consensus in the business market of the token economy. In the existing paradigm of blockchain token economy, there are disadvantages of lacking the incentive mechanism, business applications and virtual token value. These shortcomings reduce consumers’ willingness to consume and the profits of the merchants. In this paper, we propose a novel “Dual incentive value-based” paradigm to improve the business market profitability in blockchain token economy. To evaluate our proposed paradigm, we propose a business study case for improving merchants’ environment state. In this case, we set up two economic models and make simulations to validate the profitability. The result shows that merchants with the novel paradigm have 32% more profit compared with those without the paradigm and at most 10% more profitable than those in existing paradigms.
Phonological parsing via an integrated I-language

Abstract Schwartz and Sprouse (2021) argue against property-by-property Transfer ( Westergaard, 2021a , b ) and for wholesale transfer ( Rothman, 2015 ) into a third language grammar by questioning the cognitive plausibility of “extracting a proper subpart from the … grammar and using that proper sub-system as the basis for a new cognitive state.” I will argue that the insights from the approaches of López (2020) ; Lightfoot (2020) ; Dresher (2018) , and Westergaard (2021a) when applied to empirical data from L3 English data from L1 Arabic/L2 French speakers, give us reason to question Schwartz and Sprouse’s defence of wholesale transfer, and its typological underpinnings. We can set the study of L3A in a larger context which can unify domains such as the acquisition of phonology and syntax via a unified approach to parsing. By invoking an underspecified, minimal UG, primary linguistic data, and domain-general third factors which act in concert to parse the E-language to select structures, we can capture the underlying similarity of first, second, and third language acquisition. Parsing proceeds in an error-driven fashion, structure by structure, drawing on the Integrated I-language and UG options found in a Repository. In essence, this approach renders the wholesale/property-by-property distinction a false dichotomy.
Blockchain-based Trustworthy Service Caching and Task Offloading for Intelligent Edge Computing

The upcoming 6G era involves an increasing level of data processing and capacity, where mobile edge computing (MEC) system is deployed in large scale to support more emerging applications. For the purpose of realizing full potential of MEC, it is necessary to allocate computing and caching resources in an intelligent way. The network need to perform decisions adaptively and organize collaboration to further improve the resource utilization, leading to a higher demand for system reliability. Introducing blockchain to the edge network is an effective way to perform resource allocation effectively under the premise of ensuring trustworthiness. To ensure trustworthiness, a credibility evaluation mechanism based on Dempster-Shafer theory is designed in the overlaid blockchain network. Since it is challenging to optimize delay and credibility comprehensively in this dynamic system, we propose a deep reinforcement learning (DRL)-based approach to make offloading decision, and carry out service caching according to both credibility and offloading decisions of multiple users. Finally, the effectiveness of the proposed offloading and caching policies are demonstrated via simulation results.
Research on Big Data-Driven Rural Revitalization Sharing Cogovernance Mechanism Based on Cloud Computing Technology

The political arrangement and strategic action to realize socialist sharing in the new era is the rural revitalization strategy, which is a development strategy to complete the historical task of farmers living a better life and solve the problem of insufficient rural development. Data sharing and driving, on the other hand, remain the weak links in China’s digital village construction. This paper proposes to develop a BD (big data)-driven rural revitalization governance, sharing, and monitoring platform based on CC (cloud computing) technology to promote the reshaping of rural governance patterns and to provide systematic support for the implementation of rural revitalization strategies. Knowledge governance rules and processes for rural community public affairs are created in a BD environment. Clustering in feature space is considered to address the low efficiency caused by clustering algorithms in high-dimensional data. The method proposed in this paper is perfectly matched in both the preprocessing and clustering steps, which not only ensures the algorithm’s accuracy but also significantly improves its efficiency.
Assessing Indices Tracking Changes in River Geochemistry and Implications for Monitoring

Abstract In geochemical data analysis, assessing the potential of new techniques to identify compositional time–space changes is of great interest for monitoring purposes. This work aims to evaluate, in the light of the compositional data analysis perspective, the performance of different statistical indices in tracing the evolution of a geochemical composition and the relationships among its parts. To reach this goal, source-to-sink chemical changes in water and stream sediment composition of the Tiber river (central Italy) are analyzed using three indices: (i) the cumulative sum of unclosed perturbation factors of each composition (row sum) with respect to a reference composition; (ii) the robust Mahalanobis distance, describing the compositional differences from the same reference and, (iii) the geometric mean of each composition as a measure able to capture the interactions among the parts. The results highlight the major compositional changes downriver, allowing to explore geochemical footprints’ propagation and their natural or anthropogenic origin. The tested indices are consistent in most cases, particularly if high-variability species are treated separately and low values are rare. Under this latter condition, the geometric mean of the composition shows a close connection with the cumulative sum of unclosed perturbation factors. This indicates that both indices inherit the complex history of the changes, well capturing the interactions among the parts under the influence of environmental drivers. With this awareness, the application of these methods in monitoring and applied geochemical studies could offer new insights into the inner workings of river systems and their resilience to environmental pressures.
Line-of-sight quantum key distribution with differential phase shift keying

Abstract Free-space optical (FSO) links offer a practical approach to realize quantum key distribution (QKD) in a global scale. However, when one wants to further extend the distance from the geostationary orbit to the ground, currently known QKD schemes cannot realize practical key rates mainly due to the diffraction losses of a laser beam. If the facts that the FSO links are highly directional and must be used in the line-of-sight (LoS) condition are taken into account, one may impose some physical restrictions on an eavesdropping model to explore longer-distance QKD. In this paper, we propose a novel FSO secret key agreement scheme, line-of-sight QKD (LoS-QKD), based on a quantum wiretap channel. In our model, an eavesdropper can tap only a limited fraction of the FSO signal beam but perform any physically allowable operations on the tapped signals. Fading effects which are significant in the FSO links are fully taken into account. We provide a security proof for the differential phase shift (DPS) keying scheme in terms of the metric which meets the composability. We investigate numerically the performances of LoS-QKD with DPS keying, including finite-length analysis, showing that our proposed scheme can realize high-speed and long-distance secret key agreement with information-theoretic security.
PWDGAN: Generating Adversarial Malicious URL Examples for Deceiving Black-Box Phishing Website Detector using GANs

In recent years, the Internet has witnessed a significant increase in phishing attacks. These attacks are not merely deceiving Internet users to get their sensitive information, but phishing attacks are developing more and more sophisticated, using many new techniques to try to bypass the traditional defense solution. With the help of machine learning and deep learning algorithms, there are researched solutions and software products to help improve the ability to detect phishing attacks. In this article, we build a model based on generative adversarial network (GAN) – a deep learning-based framework to conduct black-box attacks using Phishtank and Alexa datasets that try to evade and bypass ML-based phishing detectors. We apply PWDGAN to carry out the attacks against the ML-based black-box classifiers and then deceive successfully these detectors with the rate of detecting malicious samples value of approximately 0% after 500 training epochs. The results of the paper demonstrate the effectiveness of GAN adoption in creating new patterns that can evade and bypass phishing detectors. These newly generated patterns can serve as material for future research in phishing website detection and improve the ability to detect novel anomaly attacks.
Accurate Head Pose Estimation Using Image Rectification and a Lightweight Convolutional Neural Network

Head pose estimation is an important step for many human-computer interaction applications such as face detection, facial recognition, and facial expression classification. Accurate head pose estimation benefits these applications that require face images as the input. Most head pose estimation methods suffer from perspective distortion because the users do not always align their face perfectly with the camera. This paper presents a new approach that uses image rectification to reduce the negative effect of perspective distortion and a lightweight convolutional neural network to obtain highly accurate head pose estimations. The proposed method calculates the angle between the optical axis of the camera and the projection vector of the center of the face. The face image is rectified using this estimated angle through perspective transformation. A lightweight network that is only 0.88 MB in size is designed to take the rectified face image as the input to perform head pose estimation. The output of the network, the head pose estimation of the rectified face image, is transformed back to the camera coordinate system as the final head pose estimation. Experiments on public benchmark datasets show that the proposed image rectification method and the newly designed lightweight network improve the accuracy of head pose estimation remarkably. Compared with state-of-the-art methods, our approach achieves both higher accuracy and faster processing speed.
Overview of the DravidianCodeMix 2021 Shared Task on Sentiment Detection in Tamil, Malayalam, and Kannada

We present the results of the Dravidian-CodeMix shared task1 held at FIRE 2021, a track on sentiment analysis for Dravidian Languages in Code-Mixed Text. We describe the task, its organization, and the submitted systems. This shared task is the continuation of last year's Dravidian-CodeMix shared task2 held at FIRE 2020. This year's tasks included code-mixing at the intra-token and inter-token levels. In addition to Tamil, Malayalam and Kannada were also introduced. We received 22 systems for Tamil-English, 15 systems for Malayalam-English, and 15 for Kannada-English. The top systems for Tamil-English, Malayalam-English and Kannada-English scored weighted average F1-score of 0.711, 0.804, and 0.630, respectively. In summary, the quality and quantity of the submission show that there is great interest in Dravidian languages in code-mixed setting and state of the art in this domain still needs improvement.
Deepfake Detection Using Multiple Data Modalities

Abstract Falsified media threatens key areas of our society, ranging from politics to journalism to economics. Simple and inexpensive tools available today enable easy, credible manipulations of multimedia assets. Some even utilize advanced artificial intelligence concepts to manipulate media, resulting in videos known as deepfakes . Social media platforms and their “echo chamber” effect propagate fabricated digital content at scale, sometimes with dire consequences in real-world situations. However, ensuring semantic consistency across falsified media assets of different modalities is still very challenging for current deepfake tools. Therefore, cross-modal analysis (e.g., video-based and audio-based analysis) provides forensic analysts an opportunity to identify inconsistencies with higher accuracy. In this chapter, we introduce several approaches to detect deepfakes. These approaches leverage different data modalities, including video and audio. We show that the presented methods achieve accurate detection for various large-scale datasets.
Stretching Artifacts Identification for Quality Assessment of 3D-Synthesized Views

Existing Quality Assessment (QA) algorithms consider identifying "black-holes" to assess perceptual quality of 3D-synthesized views. However, advancements in rendering and inpainting techniques have made black-hole artifacts near obsolete. Further, 3D-synthesized views frequently suffer from stretching artifacts due to occlusion that in turn affect perceptual quality. Existing QA algorithms are found to be inefficient in identifying these artifacts, as has been seen by their performance on the IETR dataset. We found, empirically, that there is a relationship between the number of blocks with stretching artifacts in view and the overall perceptual quality. Building on this observation, we propose a Convolutional Neural Network (CNN) based algorithm that identifies the blocks with stretching artifacts and incorporates the number of blocks with the stretching artifacts to predict the quality of 3D-synthesized views. To address the challenge with existing 3D-synthesized views dataset, which has few samples, we collect images from other related datasets to increase the sample size and increase generalization while training our proposed CNN-based algorithm. The proposed algorithm identifies blocks with stretching distortions and subsequently fuses them to predict perceptual quality without reference, achieving improvement in performance compared to existing no-reference QA algorithms that are not trained on the IETR dataset. The proposed algorithm can also identify the blocks with stretching artifacts efficiently, which can further be used in downstream applications to improve the quality of 3D views. Our source code is available online: https://github.com/sadbhawnathakur/3D-Image-Quality-Assessment.
The Role of Linear Discriminant Analysis for Accurate Prediction of Breast Cancer

With the recent advances in clinical technologies, a huge amount of data has been accumulated for breast cancer diagnosis. Extracting information from the data to support the clinical diagnosis of breast cancer is a tedious and time-consuming task. The use of machine learning and data mining techniques has significantly changed the whole process of a breast cancer diagnosis. In this research, a prediction model for breast cancer prediction has been developed using features extracted from individual medical screening and tests. To overcome the problem of overfitting and obtain a good prediction accuracy, a Linear Discriminant Analysis (LDA) is applied for the extraction of useful features. This is done to reduce the number of features in the experimental dataset. The proposed model can create new features from the existing features and then get rid of the original features. The newly created features were able to summarize the necessary information contained initially in the original set of features. LDA was chosen because of its usefulness in detecting whether a set of features is worthwhile in predicting breast cancer. In addition to LDA, the proposed model uses Support Vector Machine (SVM) for accurate prediction, hence the name LDA-SVM prediction model. Based on 5-fold cross-validation, the proposed model yields an accuracy of 99.2%, precision of 98.0%, and Recall of 99.0% when tested on the Wisconsin Diagnostic Breast Cancer (WDBC) dataset from the University of California- Irvine machine learning repository. Therefore, SVM shows high efficiency in handling classification problems when combined with feature extraction techniques.
Handling Imbalanced Data for Credit Card Fraud Detection

With the rising trend in online transactions, the threat of financial fraud is also rising. This makes the necessity for an effective Fraud Detection System (FDS) more than ever before. To develop such a system the financial institutes are moving towards machine learning-based approaches due to their effectiveness. Machine learning-based systems need historical data to learn. As fraud cases take place rarely, the number of positive labeled classes in financial fraud datasets are very small and the datasets remain imbalanced. For this, the possibility for machine learning-based FDS to produce misleading results is high. To counter this problem Machine Learning (ML) researchers use multiple solutions from the perspective of data-level approach, algorithm-level approach, feature engineering, ensemble models, or any combination of them. In this paper, we propose to use Generative Adversarial Network (GAN) based synthetic data generation to handle the data imbalance problem followed by an ensemble classifier for classification. We have used a standard benchmark dataset of credit card fraud data. In our experiments, we have used both traditional oversampling/undersampling and GAN-based techniques from the data-level approach and investigated their effectiveness using ML algorithms and ensemble models. We have found Generative Adversarial Network (GAN) to be more effective and stable in performance compared to traditional oversampling techniques for both ML and ensemble models. Experiments also suggest that the combination of GAN-based sampling and ensemble models provides the best results. We also have found Synthetic Minority Oversampling Technique (SMOTE) to provide more stable results compared to Adaptive Synthetic Sample (ADASYN) from the traditional oversampling technique.
An Efficient Signature Recognition System Based on Gradient Features and Neural Network Classifier

This paper proposes a novel offline signature recognition system (SRS) based on histogram of oriented gradients (HOG) and fuzzy min max classification (FMMC) methods. First of all, the signature image required a preprocessing stage, then the Histogram of Oriented Gradients features are adopted to extract features from the training images. It consists of dividing the image into adjacent cells, for each cell histogram of oriented gradients characteristics are calculated. This technique has been compared with two popular statistical methods such as Loci characteristics and profile projection (PP). The classification is performed using FMMC and it is compared with K nearest neighbors method (KNN). The presented approach achieved a recognition rate of 96% using a diverse signature database.