Reviewerly/exHarmony huggingface dataset:
Hand Gesture Recognition Based on Point Cloud Sequences and Inverse Kinematics Model

Hand gesture recognition is an important topic in human-computer interaction. With the rapid development of 3D sensors, gesture recognition methods using 3D input images have become mainstream. However, most of the current methods are based on depth maps and do not take full advantage of 3D information. In addition, static gesture recognition usually uses only one frame for recognition and cannot make use of redundant gesture-forming frames. This paper proposes a static gesture recognition method based on point cloud sequences and an inverse kinematics model. In the initial posture with five fingers open, the input point cloud sequences are divided into boundary points for fingertips marking and inner points for deformed joints marking, which is based on the K-curvature algorithm and curvature difference respectively. And those joints that cannot be marked by curvature differences, their positions are estimated by inverse kinematics. Then the bending angles and fingertips position are selected as features, and the recognition is completed by KNN. The performance of the proposed method has been experimentally evaluated using self-sampled data, and the average recognition accuracy is about 96%. Besides, because of the use of higher-order geometric features, the proposed method is highly abstract, easy to construct and adjust, and highly adaptable to different application scenarios.
Exploiting the Distortion-Semantic Interaction in Fisheye Data

In this work, we present a methodology to shape a fisheye-specific representation space that reflects the interaction between distortion and semantic context present in this data modality. Fisheye data has the wider field of view advantage over other types of cameras, but this comes at the expense of high radial distortion. As a result, objects further from the center exhibit deformations that make it difficult for a model to identify their semantic context. While previous work has attempted architectural and training augmentation changes to alleviate this effect, no work has attempted to guide the model towards learning a representation space that reflects this interaction between distortion and semantic context inherent to fisheye data. We introduce an approach to exploit this relationship by first extracting distortion class labels based on an object's distance from the center of the image. We then shape a backbone's representation space with a weighted contrastive loss that constrains objects of the same semantic class and distortion class to be close to each other within a lower dimensional embedding space. This backbone trained with both semantic and distortion information is then fine-tuned within an object detection setting to empirically evaluate the quality of the learnt representation. We show this method leads to performance improvements by as much as 1.1% mean average precision over standard object detection strategies and .6% improvement over other state of the art representation learning approaches.
A Traffic Model Based Approach to Parameter Server Design in Federated Learning Processes

This letter proposes a model to describe the data traffic generated by a Federated Learning (FL) process in a wireless network with asynchronous Parameter Server (PS) orchestration and heterogeneous clients. The model accounts for the local update processes implemented by individual clients and it is used to enforce requirements on the PS design, namely to regulate the interval among consecutive global model updates. PS requirements are validated on realistic pools of resource-constrained wireless edge devices, typically found in Internet-of-Things (IoT) setups. Numerical results show that the proposed policy is effective when devices have unbalanced resources, namely, different sample distributions and computational capabilities. It permits an accuracy gain of up to 15-17% on average with respect to typical asynchronous PS designs.
Cyber Risk Assessment and Optimization: A Small Business Case Study

Assessing and controlling cyber risk is the cornerstone of information security management, but also a formidable challenge for organisations due to the uncertainties associated with attacks, the resulting risk exposure, and the availability of scarce resources for investment in mitigation measures. In this paper, we propose a cybersecurity decision-support framework, called CENSOR, for optimal cyber security investment. CENSOR accounts for the serial nature of a cyber attack, the uncertainty in the time required to exploit a vulnerability, and the optimisation of mitigation measures in the presence of a limited budget. First, we evaluate the cost that an organisation incurs due to a cyber security breach that progresses in stages and derive an analytical expression for the distribution of the present value of the cost. Second, we adopt a Set Covering and a Knapsack formulation to derive and compare optimal strategies for investment in mitigation measures. Third, we validate CENSOR via a case study of a small business (SB) based on: (i) the 2020 Common Weakness Enumeration (CWE) top 25 most dangerous software weaknesses; and (ii) the Center for Internet Security (CIS) Controls. Specifically, we demonstrate how the Knapsack formulation provides solutions that are both more affordable and entail lower risk compared to those of the Set Covering formulation. Interestingly, our results confirm that investing more in cybersecurity does not necessarily lead to an analogous cyber risk reduction, which indicates that the latter decelerates beyond a certain point of security investment intensity.
Agents Based Cyber-Physical Diffused Museums Over Web Interoperability Standards

The availability of interoperability standards allows using a uniform model for integrating technologies and designing collaborating applications with a shared understanding of the exchanged information.In this perspective, we propose here the design and development of an agent-based platform that uses an interoperability framework to build a diffused museum in a cyber-physical system, where intelligent agents deliver cultural content through innovative interaction models.In particular, agents' awareness, natural language understanding capability, and interaction ability are extended through the monitoring of user's interaction with the IIIF (International Image Interoperability Framework) standard and the integration with IIIF compliant technologies.The scalability of the proposed solution is evaluated stressing the platform with simulated workloads.
Efficient and Secure Quantile Aggregation of Private Data Streams

Computing the quantile of a massive data stream has been a crucial task in networking and data management. However, existing solutions assume a centralized model where one data owner has access to all data. In this paper, we put forward a study of secure quantile aggregation between <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">private</i> data streams, where data streams owned by different parties would like to obtain a quantile of the union of their data without revealing anything else about their inputs. To this end, we designed efficient cryptographic protocols that are secure in the semi-honest setting as well as the malicious setting. By incorporating differential privacy, we further improve the efficiency by 1.1× to 73.1×. We implemented our protocol, which shows practical efficiency to aggregate real-world data streams efficiently.
A Fuzzy Adaptive Zeroing Neural Network Model With Event-Triggered Control for Time-Varying Matrix Inversion

Time-varying matrix inversion (TVMI) is a basic mathematical problem, which is widely involved in many scientific fields. In this article, an event-triggered control fuzzy adaptive zeroing neural network (ETC-FAZNN) model is proposed for solving the TVMI problem, where the fuzzy adaptive convergence parameter (FACP) is got by the redesigned fuzzy logic system, which makes the ETC-FAZNN model adaptive. Meanwhile, the event-triggered control is introduced to control the update of the FACP, which improves the calculation speed of the ETC-FAZNN model. Moreover, a novel activation function called segmented predefined-time activation function is put forward in this article to improve the convergence and robustness of the ETC-FAZNN model. Theoretical analysis and simulation experiments reveal that the ETC-FAZNN model can realize stability, predefined-time convergence, robustness, and adaptability performances in solving the TVMI problem.
Learning Spatio-Temporal Semantics and Cluster Relation for Zero-Shot Action Recognition

Zero-shot Action Recognition (ZSAR) aims at bridging the video <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\rightarrow $ </tex-math></inline-formula> class relation with only labeled training data of seen classes while generalizing the model to alleviate the heterogeneity of unseen actions. Most existing methods have comprehensively represented videos and action classes, however, the semantic gap and the hubness problem between them remain crucial challenges that are under-explored. In this paper, we propose an effective method to tackle the above issues. Specifically, to narrow the semantic gap, we end-to-end generate a spatio-temporal semantics for each video, which provides essential textual information to refine the video representation. Furthermore, we propose a compactness-separability loss that optimizes the intra- and inter-class relations in a unified formula and quantitatively constrains cluster distribution, thus effectively diminishing the impact of the hubness problem. Extensive experiments on UCF101, HMDB51, and Olympic Sports datasets prove the effectiveness of the proposed approach and demonstrate our approach outperforms the state-of-the-art methods.
Self-Information Domain-Based Neural CSI Compression With Feature Coupling

Deep learning (DL)-based channel state information (CSI) feedback methods compressed the CSI matrix by exploiting its delay and angle features straightforwardly, while the measure in terms of information contained in the CSI matrix has rarely been considered. Based on this observation, we introduce self-information as an informative CSI representation from the perspective of information theory, which reflects the amount of information of the original CSI matrix in an explicit way. Then, a novel DL-based network is proposed for temporal CSI compression in the self-information domain, namely SD-CsiNet. The proposed SD-CsiNet projects the raw CSI onto a self-information matrix in the newly-defined self-information domain, extracts both temporal and spatial features of the self-information matrix, and then couples these two features for effective compression. Experimental results verify the effectiveness of the proposed SD-CsiNet by exploiting the self-information of CSI. Particularly for compression ratios <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1/8$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1/16$</tex-math></inline-formula> , the SD-CsiNet respectively achieves 7.17 dB and 3.68 dB performance gains compared to state-of-the-art methods.
MAD-EN: Microarchitectural Attack Detection Through System-Wide Energy Consumption

Microarchitectural attacks have become increasingly threatening the society with diverse set of attacks such as Spectre and Meltdown. Vendor patches cannot keep up with the pace of the new threats, which makes the dynamic anomaly detection tools more evident than before. Unfortunately, hardware performance counters (HPCs) utilized in previous works can detect a few microarchitectural attacks due to the small number of counters that can be profiled concurrently while introducing high performance overhead. These challenges consequently yield to inefficient detection tools in real-world security-critical systems. In this study, we introduce MAD-EN dynamic detection tool that leverages system-wide energy consumption traces collected from a generic Intel RAPL tool to detect ongoing anomalies in two different microarchitectures, namely Intel Comet Lake and Intel Tiger Lake. In the first phase of MAD-EN, we can distinguish 16 variants from 11 different micro-architectural attacks from benign applications by utilizing a binary-class CNN-based model with an F1 score of 0.998, which makes our tool the most generic attack detection tool so far. In the second phase, MAD-EN can recognize the respective attack types with a 95% accuracy by utilizing a multi-class CNN-based classification technique after the anomaly is detected. We demonstrate that MAD-EN introduces 69.3% less performance overhead compared to performance counter-based detection mechanisms, allowing more feasible real-time detection tool for generic purpose systems.
Probabilistic Approach for Road-Users Detection

Object detection in autonomous driving applications implies the detection and tracking of semantic objects that are commonly native to urban driving environments, as pedestrians and vehicles. One of the major challenges in state-of-the-art deep-learning based object detection are false positives which occur with overconfident scores. This is highly undesirable in autonomous driving and other critical robotic-perception domains because of safety concerns. This paper proposes an approach to alleviate the problem of overconfident predictions by introducing a novel probabilistic layer to deep object detection networks in testing. The suggested approach avoids the traditional Sigmoid or Softmax prediction layer which often produces overconfident predictions. It is demonstrated that the proposed technique reduces overconfidence in the false positives without degrading the performance on the true positives. The approach is validated on the 2D-KITTI objection detection through the YOLOV4 and SECOND (Lidar-based detector). The proposed approach enables interpretable probabilistic predictions without the requirement of re-training the network and therefore is very practical.
Towards Data-Independent Knowledge Transfer in Model-Heterogeneous Federated Learning

Federated Distillation (FD) extends classic Federated Learning (FL) to a more general training framework that enables model-heterogeneous collaborative learning by Knowledge Distillation (KD) across multiple clients and the server. However, existing KD-based algorithms usually require a set of shared input samples for each client to produce soft-prediction for distillation. Worse still, such a manual selection is accompanied by careful deliberations or prior information on clients’ private data distribution, which is not in line with the privacy-preserving characteristic of classic FL. In this paper, we propose a novel training framework to achieve data-independent knowledge transfer by properly designing a distributed generative adversarial network (GAN) between the server and clients that can synthesize shared feature representations to facilitate the FD training. Specifically, we deploy a generator on the server and reuse each local model as a federated discriminator to form a lightweight efficient distributed GAN that can automatically synthesize simulated global feature representations for distillation. Moreover, since the synthesized feature representations are usually more faithful and homologous with global data distribution, faster and better training convergence can be obtained. Extensive experiments on different tasks and heterogeneous models demonstrate the effectiveness of the proposed framework on model accuracy and communication overhead.
Comparison of Public and Critics Opinion About the Taliban Government Over Afghanistan Through Sentiment Analysis

The usage of social media has increased exponentially these days. People worldwide are sharing their opinions on different platforms such as Twitter, personal blogs, Facebook, and other similar platforms. Twitter has grown in popularity as a platform for people to express their thoughts and opinions on many different topics. The data from Twitter about the Taliban has been examined in this research work, and various machine learning algorithms have been applied including SVM, LR, and random forest. Text sentiments have been captured via TextBlob. Among the machine learning models applied, SVM outperformed all other models and achieved an accuracy score of around 94% on the tweet dataset and logistic regression outperformed other models with an accuracy score of 83% on the news article dataset.
Hate Speech and Offensive Language Detection in Twitter Data Using Machine Learning Classifiers

Social media is rapidly growing in popularity and has its advantages and disadvantages. Users posting their daily updates and opinions on social media may inadvertently hurt the feelings of others. Detecting hate speech and harmful information on social media is critical these days, lest it led to calamity. In this research, machine learning classifiers such as Naïve Bayes, support vector machines, logistic regression, and pre-trained models BERT and RoBERTa, developed by Google and Facebook, respectively, are used to detect hate speech and offensive content from Twitter data on a newly created dataset that included tweets and articles/blogs. The sentiments were obtained using the VADER sentiment analyzer. The results depicted that the pre-trained classifiers outperformed the machine learning classifiers utilized in this study. An accuracy score of 96% and 93% was scored by BERT and RoBERTa, respectively, on the tweet dataset, whereas on a dataset of articles/blogs, accuracy of 97% and 98%, respectively, was achieved by both the classifiers outperforming other classifiers used in this work. Further, it can also be depicted that neutral content is shared more in articles/blogs, hate content is mostly shared equally in both the tweets and article/blogs, whereas offensive content is shared higher in tweets than articles/blogs.
V-Shaped Binary Version of Whale Optimization Algorithm for Feature Selection Problem

Feature selection, often known as FS, is often considered to be a component of the larger issue of global optimization. FS is being used to optimize and improve the quality of huge datasets. This is accomplished by selecting prominent features and minimizing duplicate data in order to provide satisfactory classification performance. Feature selection is an approach that is taken with the goal of reducing the complexity of the classification process while simultaneously improving its level of precision. This approach is significant in a variety of fields, including data mining, data processing, and pattern classification. The primary objective here is to devise a more accurate subset of all of the data that takes into account the relevant sample. In order to solve this issue, the BWO-V method, which is short for the binary form of the whale optimization (WO) technique, has been introduced. In order to transform the findings into binary, the BWO-V makes use of a function called the hyperbolic tan function. Validation of the BWO-V algorithm’s performance is carried out on five datasets taken from the repository at UCI. The quantitative and qualitative results both show that using BWO-V helps limit the number of features picked while also optimizing the classification accuracy with significantly less effort.
An Explainable AI Approach for Diabetes Prediction

Diabetes mellitus is one of the chronic diseases worldwide. According to World Health Organization global report 2019, diabetes was the ninth major cause of mortality. Machine learning techniques have been extensively used in healthcare applications for medical diagnosis and surgical applications. These algorithms often exhibit superior performance and could not explain the predictions because of complex behavior and black-box nature. Explainable AI (XAI) systems have become more popular for post hoc explanations and model transparency. This approach helps to understand the complex structure of ML models and make them transparent and trustworthy in the data-driven and fact-based decision-making process. The practical implementation of the XAI system, the local interpretable model-agnostic explanations (LIME) for diabetes prediction are presented in this paper. The experimental results demonstrate the explanations and factors contributing to diabetes and help the medical practitioners in decision-making to address clinical diagnosis and treatment measures.
Ensuring Data Protection Using Machine Learning Integrating with Blockchain Technology

In recent years, the emergence of blockchain technology (blockchain) has become a singular, most tumultuous, and trending technology. The decentralized info in blockchain underscores information security and confidentiality. Also, the agreement mechanism in it makes positive that information is secured and legit. Throughout this paper, we have got coated the analysis on combining blockchain and machine learning technologies and demonstrate that they are attending to collaborate with efficiency and effectiveness. Machine learning might even be a general language that comes with a spread of methods, machine learning, deep learning, and reinforcement learning. These ways square measure the core technology for big information analysis. As a distributed and append-only ledger system, a blockchain can be a natural tool for sharing and handling massive information from varied sources through the incorporation of good contracts. Additional expressly, blockchain will shield information security and promote information sharing. It also permits various countries to utilize distributed computing powers, for instance, IoT, for developing on-time prediction models with varied sources of knowledge. Blockchain systems can also generate large amounts of useful data from completely different sources, and the knowledge domain analysis on combining the two technologies is of nice potential and the combination of machine learning and blockchain will give extremely precise results.
Hybrid Binary Whale Optimization Algorithm for Feature Selection Optimization Problem

In order to address feature selection (FS) optimization problems, a binary variant of hybrid particle swarm optimization (PSO) and whale optimization algorithm (WOA) is suggested in this article. The original PSOWOA is a hybrid algorithm that takes advantage of both PSO and WOA abilities. Although the higher performance, the initial hybrid solution is ideal for ongoing search space challenges. However, the selection of the feature is a binary task. Thus, a binary variant of the hybrid PSOWOA named HBPSOWOA is suggested to obtain a more significant reduction in dimension under the presumption of the feature selection accuracy. The K-nearest neighbors (KNN) is used to find the best possible solution. A benchmark database from the UCI machine learning repository is used to evaluate the HBPSOWOA algorithm. The findings show that when using a variety of performance metrics, best features, run time, and accuracy, HBPSOWOA significantly exceeded grey wolf optimizer (GWO), PSO, sine cosine algorithm (SCA), and WOA.