Reviewerly/exHarmony huggingface dataset:
An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video (ADSV). At the input of the ADSV, a shots boundary detection technique is used to segment prominent frames. Next, The Light-weight Convolution Neural Network (LWCNN) model receives the segmented frames to extract spatial and temporal information from the intermediate layer. Following that, spatial and temporal features are learned using Long Short-Term Memory (LSTM) cells and Attention Network from a series of frames for each anomalous activity in a sample. To detect motion and action, the LWCNN received chronologically sorted frames. Finally, the anomaly activity in the video is identified using the proposed trained ADSV model. Extensive experiments are conducted on complex and challenging benchmark datasets. In addition, the experimental results have been compared to state-of-the-art methodologies, and a significant improvement is attained, demonstrating the efficiency of our ADSV method.
Cybersecurity and Cyber Forensics for Smart Cities: A Comprehensive Literature Review and Survey

Smart technologies, such as the Internet of Things (IoT), cloud computing, and artificial intelligence (AI), are being adopted in cities and transforming them into smart cities. In smart cities, various network technologies, such as the Internet and IoT, are combined to exchange real-time information, making the everyday lives of their residents more convenient. However, there is a lack of systematic research on cybersecurity and cyber forensics in smart cities. This paper presents a comprehensive review and survey of cybersecurity and cyber forensics for smart cities. We analysed 154 papers that were published from 2015 to 2022 and proposed a new framework based on a decade of related research papers. We identified four major areas and eleven sub-areas for smart cities. We found that smart homes and the IoT were the most active research areas within the cybersecurity field. Additionally, we found that research on cyber forensics for smart cities was relatively limited compared to that on cybersecurity. Since 2020, there have been many studies on the IoT (which is a technological component of smart cities) that have utilized machine learning and deep learning. Due to the transmission of large-scale data through IoT devices in smart cities, ML and DL are expected to continue playing critical roles in smart city research.
Connecting simple and precise <i>P</i>‐values to complex and ambiguous realities (includes rejoinder to comments on “Divergence vs. decision <i>P‐</i>values”)

Abstract Mathematics is a limited component of solutions to real‐world problems, as it expresses only what is expected to be true if all our assumptions are correct, including implicit assumptions that are omnipresent and often incorrect. Statistical methods are rife with implicit assumptions whose violation can be life‐threatening when results from them are used to set policy. Among them are that there is human equipoise or unbiasedness in data generation, management, analysis, and reporting. These assumptions correspond to levels of cooperation, competence, neutrality, and integrity that are absent more often than we would like to believe. Given this harsh reality, we should ask what meaning, if any, we can assign to the P ‐values, “statistical significance” declarations, “confidence” intervals, and posterior probabilities that are used to decide what and how to present (or spin) discussions of analyzed data. By themselves, P ‐values and CI do not test any hypothesis, nor do they measure the significance of results or the confidence we should have in them. The sense otherwise is an ongoing cultural error perpetuated by large segments of the statistical and research community via misleading terminology. So‐called inferential statistics can only become contextually interpretable when derived explicitly from causal stories about the real data generator (such as randomization), and can only become reliable when those stories are based on valid and public documentation of the physical mechanisms that generated the data. Absent these assurances, traditional interpretations of statistical results become pernicious fictions that need to be replaced by far more circumspect descriptions of data and model relations.
AutoML with Bayesian Optimizations for Big Data Management

The field of automated machine learning (AutoML) has gained significant attention in recent years due to its ability to automate the process of building and optimizing machine learning models. However, the increasing amount of big data being generated has presented new challenges for AutoML systems in terms of big data management. In this paper, we introduce Fabolas and learning curve extrapolation as two methods for accelerating hyperparameter optimization. Four methods for quickening training were presented including Bag of Little Bootstraps, k-means clustering for Support Vector Machines, subsample size selection for gradient descent, and subsampling for logistic regression. Additionally, we also discuss the use of Markov Chain Monte Carlo (MCMC) methods and other stochastic optimization techniques to improve the efficiency of AutoML systems in managing big data. These methods enhance various facets of the training process, making it feasible to combine them in diverse ways to gain further speedups. We review several combinations that have potential and provide a comprehensive understanding of the current state of AutoML and its potential for managing big data in various industries. Furthermore, we also mention the importance of parallel computing and distributed systems to improve the scalability of the AutoML systems while working with big data.
Big Data Security through Privacy – Preserving Data Mining (PPDM): A Decentralization Approach

The increasing familiarity and improvements in data mining technologies bring serious security threats to the data of individuals. An emerging technology that provides better security and data security is PPDM: Privacy-Preserving Data Mining. The fundamental tenet of PPDM is to alter the data in such a manner that data mining techniques may be carried out successfully without affecting the privacy of sensitive data included in the data. The research article is focused on reducing the security and privacy risk that is created by data mining operations. During the process of data collection, Information processing, and publication of data there may be much security breached in the processing of the data in the data mining process. This paper focuses on the wide problems and is concerned with data mining, and finding a solution to ensure security and access through the data by diving the centralized data into various segments to ensure security by privacy-preserving methods. Through the process of finding various user interfaces related to data processing and issues related to the security of private information, this article will also show the vital role of PPDM in data mining and security.
Robust fall detection in video surveillance based on weakly supervised learning

Fall event detection has been a research hotspot in recent years in the fields of medicine and health. Currently, vision-based fall detection methods have been considered the most promising methods due to their advantages of a non-contact characteristic and easy deployment. However, the existing vision-based fall detection methods mainly use supervised learning in model training and require much time and energy for data annotations. To address these limitations, this work proposes a detection method that uses a weakly supervised learning-based dual-modal network. The proposed method adopts a deep multiple instance learning framework to learn the fall events using weak labels. As a result, the proposed method does not require time-consuming fine-grained annotations. The final detection result of each video is obtained by integrating the information obtained from two streams of the dual-modal network using the proposed dual-modal fusion strategy. Experimental results on two public benchmark datasets and a proposed dataset demonstrate the superiority of the proposed method over the current state-of-the-art methods.
Prioritizing tasks in software development: A systematic literature review

Task prioritization is one of the most researched areas in software development. Given the huge number of papers written on the topic, it might be challenging for IT practitioners-software developers, and IT project managers-to find the most appropriate tools or methods developed to date to deal with this important issue. The main goal of this work is therefore to review the current state of research and practice on task prioritization in the Software Engineering domain and to individuate the most effective ranking tools and techniques used in the industry. For this purpose, we conducted a systematic literature review guided and inspired by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, otherwise known as the PRISMA statement. Based on our analysis, we can make a number of important observations for the field. Firstly, we found that most of the task prioritization approaches developed to date involve a specific type of prioritization strategy-bug prioritization. Secondly, the most recent works we review investigate task prioritization in terms of "pull request prioritization" and "issue prioritization," (and we speculate that the number of such works will significantly increase due to the explosion of version control and issue management software systems). Thirdly, we remark that the most frequently used metrics for measuring the quality of a prioritization model are f-score, precision, recall, and accuracy.
Summarization of Videos with the Signature Transform

This manuscript presents a new benchmark for assessing the quality of visual summaries without the need for human annotators. It is based on the Signature Transform, specifically focusing on the RMSE and the MAE Signature and Log-Signature metrics, and builds upon the assumption that uniform random sampling can offer accurate summarization capabilities. We provide a new dataset comprising videos from Youtube and their corresponding automatic audio transcriptions. Firstly, we introduce a preliminary baseline for automatic video summarization, which has at its core a Vision Transformer, an image–text model pre-trained with Contrastive Language–Image Pre-training (CLIP), as well as a module of object detection. Following that, we propose an accurate technique grounded in the harmonic components captured by the Signature Transform, which delivers compelling accuracy. The analytical measures are extensively evaluated, and we conclude that they strongly correlate with the notion of a good summary.
A new horizon for possibility thinking: A conceptual case study of Human × AI collaboration

Possibility thinking (PT) can be described as an imaginative and action-oriented process that drives movement from what is to what could be. The promise of PT is most fully realized in collaboration with others. With recent advances in AI, it can be argued that natural language models (e.g. OpenAI’s GPT models) represent a possible “other” for such collaborations. This article aims to illustrate, through a conceptual case study, how Human × AI collaboration can support PT. The article opens with a brief overview of the basic principles of PT and how those principles can be applied in Human × AI collaboration. Next, a conceptual case study will be presented, which reports on six examples of Human × AI collaborations used for PT. The article closes with a brief discussion of implications and future directions, including the importance of developing a principled approach when using AI for PT. This article likely will be of interest to broad audiences within and across domains, including readers focused on human-centered AI collaborations and anyone interested in learning more about new and emerging ways to generate possibilities in their learning, work, and lives.
TABI: Trust-Based ABAC Mechanism for Edge-IoT Using Blockchain Technology

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, implementing blockchain technology directly on IoT networks is prone to high overheads and energy-expensive operations. Therefore, in this paper, we use edge computing technology to avoid these problems. We also propose a novel Trust-based Access Control Mechanism for Edge-IoT Networks using Blockchain technology (named TABI) to implement end-to-end security in resource-constrained IoT networks. The TABI mechanism utilizes both access control and trust evaluation mechanisms to mitigate the impact of malicious IoT users and devices. Additionally, it incorporates permissioned Hyperledger blockchain technology to provide an added layer of security through authentication. The trust evaluation mechanism is implemented as a trust calculation contract (TCC) on the edge devices using Hyperledger Composer. The access control mechanism employs an Attribute-based Access Control (ABAC) mechanism, which is implemented on the Hyperledger blockchain using two smart contracts: the attribute contract (AC) and the access control contract (ACC). We implement a proof-of-concept (PoC) implementation using Hyperledger Caliper (a benchmark testing tool) and Docker images. Our evaluation includes five analyses: Trust Evaluation Mechanism, Access Control Mechanism, Security, Blockchain, and IoT Applications. Through this evaluation, we highlight the effectiveness of TABI in terms of throughput, latency, detection of malicious IoT devices, and resource consumption of the IoT devices. Our analyses demonstrate that TABI is particularly useful in IoT applications that require low latency and resource efficiency.
Improving the Generalization of Visual Classification Models Across IoT Cameras via Cross-Modal Inference and Fusion

The performance of visual classification models across Internet of Things devices is usually limited by the changes in local environments, resulted from the diverse appearances of the target objects and differences in light conditions and background scenes. To alleviate these problems, existing studies usually introduce the multimodal information to guide the learning process of the visual classification models, making the models extract the visual features from the discriminative image regions. Especially, cross-modal alignment between visual and textual features has been considered as an effective way for this task by learning a domain-consistent latent feature space for the visual and semantic features. However, this approach may suffer from the heterogeneity between multiple modalities, such as the multimodal features and the differences in the learned feature values. To alleviate this problem, this article first presents a comparative analysis of the functionality of various alignment strategies and their impacts on improving visual classification. Subsequently, a cross-modal inference and fusion framework (termed as CRIF) is proposed to align the heterogeneous features in both the feature distributions and values. More importantly, CRIF includes a cross-modal information enrichment module to improve the final classification and learn the mappings from the visual to the semantic space. We conduct experiments on four benchmarking data sets, i.e., the Vireo-Food172, NUS-WIDE, MSR-VTT, and ActivityNet Captions data sets. We report state-of-the-art results for basic classification tasks on the four data sets and conduct subsequent experiments on feature alignment and fusion. The experimental results verify that CRIF can effectively improve the learning ability of the visual classification models, and it is a model-agnostic framework that consistently improves the performance of state-of-the-art visual classification models.
Towards a thematic dimensional framework of online fraud: An exploration of fraudulent email attack tactics and intentions

Despite anti-phishing filters, social engineering-based cyber-attacks still result in billions of dollars lost annually, significant personal identity theft, loss of corporate secrets, and espionage. We review the phishing literature on psychological attacks and design tactics employed for deception by attackers. The result reveals the need to continuously identify tactics embedded in fraudulent email content to understand why users still fall prey to phishing attacks. Using the literature as a backdrop, we identify and conceptualize a thematic dimensional framework of fraudulent phishing attacks. This study uses benchmark datasets of fraudulent email to empirically validate the proposed dimensional framework. Specifically, a combination of machine learning-based content analysis and topic modeling found a majority of the discovered topics were labeled against the proposed dimensions. Statistical analysis was employed to provide empirical evidence of the thematic dimensions of fraudulent email attacks. This study thus, not only extends the cybersecurity literature by identifying and validating eight dimensions that enrich our understanding of phishing and attack identification but stimulates cumulative research endeavors to develop yet more comprehensive dimensional frameworks of phishing email attacks.
Making in silico predictive models for toxicology FAIR

In silico predictive models for toxicology include quantitative structure-activity relationship (QSAR) and physiologically based kinetic (PBK) approaches to predict physico-chemical and ADME properties, toxicological effects and internal exposure. Such models are used to fill data gaps as part of chemical risk assessment. There is a growing need to ensure in silico predictive models for toxicology are available for use and that they are reproducible. This paper describes how the FAIR (Findable, Accessible, Interoperable, Reusable) principles, developed for data sharing, have been applied to in silico predictive models. In particular, this investigation has focussed on how the FAIR principles could be applied to improved regulatory acceptance of predictions from such models. Eighteen principles have been developed that cover all aspects of FAIR. It is intended that FAIRification of in silico predictive models for toxicology will increase their use and acceptance.
Improved Mask R-CNN Multi-Target Detection and Segmentation for Autonomous Driving in Complex Scenes

Vision-based target detection and segmentation has been an important research content for environment perception in autonomous driving, but the mainstream target detection and segmentation algorithms have the problems of low detection accuracy and poor mask segmentation quality for multi-target detection and segmentation in complex traffic scenes. To address this problem, this paper improved the Mask R-CNN by replacing the backbone network ResNet with the ResNeXt network with group convolution to further improve the feature extraction capability of the model. Furthermore, a bottom-up path enhancement strategy was added to the Feature Pyramid Network (FPN) to achieve feature fusion, while an efficient channel attention module (ECA) was added to the backbone feature extraction network to optimize the high-level low resolution semantic information graph. Finally, the bounding box regression loss function smooth L1 loss was replaced by CIoU loss to speed up the model convergence and minimize the error. The experimental results showed that the improved Mask R-CNN algorithm achieved 62.62% mAP for target detection and 57.58% mAP for segmentation accuracy on the publicly available CityScapes autonomous driving dataset, which were 4.73% and 3.96%% better than the original Mask R-CNN algorithm, respectively. The migration experiments showed that it has good detection and segmentation effects in each traffic scenario of the publicly available BDD autonomous driving dataset.
Enhancing Spam Message Classification and Detection Using Transformer-Based Embedding and Ensemble Learning

Over the last decade, the Short Message Service (SMS) has become a primary communication channel. Nevertheless, its popularity has also given rise to the so-called SMS spam. These messages, i.e., spam, are annoying and potentially malicious by exposing SMS users to credential theft and data loss. To mitigate this persistent threat, we propose a new model for SMS spam detection based on pre-trained Transformers and Ensemble Learning. The proposed model uses a text embedding technique that builds on the recent advancements of the GPT-3 Transformer. This technique provides a high-quality representation that can improve detection results. In addition, we used an Ensemble Learning method where four machine learning models were grouped into one model that performed significantly better than its separate constituent parts. The experimental evaluation of the model was performed using the SMS Spam Collection Dataset. The obtained results showed a state-of-the-art performance that exceeded all previous works with an accuracy that reached 99.91%.
Neighbor-Guided Consistent and Contrastive Learning for Semi-Supervised Action Recognition

Semi-supervised learning has been well established in the area of image classification but remains to be explored in video-based action recognition. FixMatch is a state-of-the-art semi-supervised method for image classification, but it does not work well when transferred directly to the video domain since it only utilizes the single RGB modality, which contains insufficient motion information. Moreover, it only leverages highly-confident pseudo-labels to explore consistency between strongly-augmented and weakly-augmented samples, resulting in limited supervised signals, long training time, and insufficient feature discriminability. To address the above issues, we propose neighbor-guided consistent and contrastive learning (NCCL), which takes both RGB and temporal gradient (TG) as input and is based on the teacher-student framework. Due to the limitation of labelled samples, we first incorporate neighbors information as a self-supervised signal to explore the consistent property, which compensates for the lack of supervised signals and the shortcoming of long training time of FixMatch. To learn more discriminative feature representations, we further propose a novel neighbor-guided category-level contrastive learning term to minimize the intra-class distance and enlarge the inter-class distance. We conduct extensive experiments on four datasets to validate the effectiveness. Compared with the state-of-the-art methods, our proposed NCCL achieves superior performance with much lower computational cost.