Reviewerly/exHarmony huggingface dataset:
Audio-Guided Attention Network for Weakly Supervised Violence Detection

Detecting violence in video is a challenging task due to its complex scenarios and great intra-class variability. Most previous works specialize in the analysis of appearance or motion information, ignoring the co-occurrence of some audio and visual events. Physical conflicts such as abuse and fighting are usually accompanied by screaming, while crowd violence such as riots and wars are generally related to gunshots and explosions. Therefore, we propose a novel audio-guided multimodal violence detection framework. First, deep neural networks are used to extract visual and audio features, respectively. Then, a Cross-Modal Awareness Local-Arousal (CMA-LA) network is proposed for cross-modal interaction, which implements audio-to-visual feature enhancement over temporal dimension. The enhanced features are then fed into a multilayer perceptron (MLP) to capture high-level semantics, followed by a temporal convolution layer to obtain high-confidence violence scores. To verify the effectiveness of the proposed method, we conduct experiments on a large public violent video dataset, i.e., XD-Violence. Experimental results demonstrate that our model outperforms several methods and achieves new state-of-the-art performance.
FH-CFI: Fine-grained hardware-assisted control flow integrity for ARM-based IoT devices

Code reuse attacks (CRAs), such as return-oriented programming (ROP) and jump-oriented programming (JOP) attacks, have become a great threat to the runtime security of ARM-based Internet of Things (IoT) devices. Attackers can utilize CRAs to hijack the control flow of programs in ARM-based IoT devices to make them perform malicious actions without injecting any codes. Control flow integrity (CFI) is an important cornerstone for the security of ARM-based IoT devices, as it enforces the correct control flow of devices and defends against CRAs. However, coarse-grained CFI schemes suffer from security issues, like key leakage and coarse-grained protection, which allows attackers to bypass their defenses. Meanwhile, fine-grained CFI schemes bring high overhead and have the multi-to-one problem. In this paper, we propose FH-CFI, a fine-grained hardware-assisted CFI scheme to help ARM-based IoT devices resist refined CRAs without leaking their encryption/decryption keys. We utilize hash-based message authentication codes to protect the return addresses from being changed, thus resisting ROP attacks without key leakage. Moreover, we encrypt instructions at target sites with the call sites' information to defeat JOP attacks in fine-grained. Additionally, we have designed a diverter to solve the multi-to-one problem that exists in fine-grained CFI schemes. Theoretical analyses demonstrate the security of our FH-CFI. Experimental evaluations on ARM-based IoT devices show that FH-CFI has greater effectiveness and stronger security than existing state-of-the-art CFI schemes, with few additional overheads.
CBFF: A cloud–blockchain fusion framework ensuring data accountability for multi-cloud environments

In order to develop emerging industries such as smart healthcare and intelligent transportation, the government has established various organizations and platforms to manage the growth of industries and encourage the cooperation of companies. However, as different companies’ data are scattered on different cloud platforms, it is difficult to have a unified control and reliable accountability for the operation of cloud data, which obstructs the establishment of platforms and cooperation. In this paper, to address the problems above, we propose a Cloud–Blockchain Fusion Framework (CBFF) to achieve data accountability among multiple clouds. CBFF improves the trustworthiness of operation to cloud data by designing secure mechanisms between clouds and blockchain. It designs a unified data Naming and Addressing Mechanism to publish and locate cloud data globally in a multi-cloud environment. Also, it proposes the Operation Tracing Mechanism to achieve reliable operation logging and tracing. With CBFF, we present a prototype implementation system using Hyperledger Fabric blockchain and Alibaba Cloud Computing. Our system could provide secure data uploading, sharing, and updating among multiple clouds. Finally, we also provide experiments and analysis to demonstrate that our framework has significant efficiency and security improvements.
Sensitive Parameter Analysis for Solar Irradiance Short-Term Forecasting: Application to LoRa-Based Monitoring Technology

Due to the relevant penetration of solar PV power plants, an accurate power generation forecasting of these installations is crucial to provide both reliability and stability of current grids. At the same time, PV monitoring requirements are more and more demanded by different agents to provide reliable information regarding performances, efficiencies, and possible predictive maintenance tasks. Under this framework, this paper proposes a methodology to evaluate different LoRa-based PV monitoring architectures and node layouts in terms of short-term solar power generation forecasting. A random forest model is proposed as forecasting method, simplifying the forecasting problem especially when the time series exhibits heteroscedasticity, nonstationarity, and multiple seasonal cycles. This approach provides a sensitive analysis of LoRa parameters in terms of node layout, loss of data, spreading factor and short time intervals to evaluate their influence on PV forecasting accuracy. A case example located in the southeast of Spain is included in the paper to evaluate the proposed analysis. This methodology is applicable to other locations, as well as different LoRa configurations, parameters, and networks structures; providing detailed analysis regarding PV monitoring performances and short-term PV generation forecasting discrepancies.
An ensemble extended belief rule base decision model for imbalanced classification problems

Class imbalance is a common problem in real-world applications and usually poses a major challenge to artificial intelligent (AI)-based decision models. The present work introduces a novel ensemble decision model which utilizes an explainable and fast-growing rule-based system, called extended belief rule base (EBRB) decision model, to alleviate the impact of class imbalance, where the proposed ensemble EBRB model includes two core components: a diversity-based base EBRB construction scheme and a consistency-based ensemble EBRB inference scheme. Specifically, for the purpose of enhancing diversity in the construction scheme, various kinds of oversampling techniques are applied to construct diverse base EBRBs firstly, followed by the calculation of attribute weights based on information gain. As for the inference scheme, the proposed ensemble EBRB model aims to produce inferential outputs not only integrating the rules activated from all base EBRBs, but also taking into consideration the consistency of the activated rules. In experimental study, twenty-six imbalanced classification datasets are used to demonstrate the effectiveness of the proposed ensemble EBRB decision model. Results demonstrate that the proposed model outperforms conventional EBRB systems and other typical imbalanced classifiers.
Improving Session Search by Modeling Multi-Granularity Historical Query Change

In session search, it's important to utilize historical interactions between users and the search engines to improve document retrieval. However, not all historical information contributes to document ranking. Users often express their preferences in the process of modifying the previous query, which can help us catch useful information in the historical interactions. Inspired by it, we propose to model historical query change to improve document ranking performance. Especially, we characterize multi-granularity query change between each pair of adjacent queries at both term level and semantic level. For term level query change, we calculate three types of term weights, including the retained term weights, added term weights and removed term weights. Then we perform term-based interaction between the candidate document and historical queries based on the term weights. For semantic level query change, we calculate an overall representation of user intent by integrating the representations of each historical query obtained by different types of term weights. Then we adopt representation-based matching between this representation and the candidate document. To improve the effect of query change modeling, we introduce query change classification as an auxiliary task. Experimental results on AOL and TianGong-ST search logs show that our model outperforms most existing models for session search.
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This paper is devoted to the study of intermittent H∞ control problem for discrete-time switched interval type-2 fuzzy systems (DSIT2FSs) under minimum dwell time switching. A novel virtual-clock-dependent H∞ intermittent interval type-2 (IT2) fuzzy controller design scheme is proposed. Firstly, we transform the N-mode DSIT2FS with intermittent control inputs into a new one with N controller-busy modes and N controller-free modes. Then, by constructing a class of non-increasing virtual-clock-dependent multiple quadratic Lyapunov functions (VMQLFs), sufficient conditions on the existence of the H∞ intermittent IT2 fuzzy controllers are obtained for DSIT2FSs with dwell time constraints. The controller gains can be gotten by solving a set of strict linear matrix inequalities (LMIs). The obtained results are also applicable to non-switched IT2 fuzzy systems. It is shown that the designed IT2 fuzzy controllers work intermittently but still can guarantee stability and H∞ performance of the close-loop systems. Finally, a tunnel diode circuit system model and a single link robot arm model are provided to illustrate the effectiveness and advantages of the results.
Security in Social-Media: Awareness of Phishing Attacks Techniques and Countermeasures

As time progresses, the social media security is one of the basic aspects to consider when browsing the internet. Nowadays all people using social media approximately, using social media is widely spread among different age groups nowadays to meet several purposes including education, entertainment, or even pursue a research. With the increase of using smartphones and social media, the cyber security threats also increased. The most widely-perpetrated form of social media attacks is phishing; it is used to gather sensitive information such as social security numbers, credit card numbers, bank account numbers, user logins and passwords, as well as other information entered through a web site. Furthermore, many users have been exposed to social media security violations most times due to a lack of awareness about phishing attacks and how to deal with them. Our paper focuses primarily to make people aware of exposure to this type of attack while using social media networks.
The oppositional affordances of data activism

This study draws on several data activism projects and applies discursive interface analysis to understand the material means by which activist software strives to empower users vis-à-vis data power. The analysis uncovers four types of oppositional affordances: (i) enabling the use of hidden affordances (ii) imagining new affordances (iii) creating meta-affordances (resignifying perceptible affordances of corporate platforms and reconstructing their meaning), and (iv) creating anti-affordances (hindering or distorting corporate platforms’ affordances to the extent that they do not perform their intended function). Although not without limitations, oppositional affordances reveal the actual agentic possibilities of data activism for users other than activists to affect the very algorithms that produce them as datafied subjects. The proposed typology provides a means for further empirical analysis of critical software and its subversive potential for users. The article concludes with a critical discussion of data activism as a means of vernacular critical praxis.
CNN for User Activity Detection Using Encrypted In-App Mobile Data

In this study, a simple yet effective framework is proposed to characterize fine-grained in-app user activities performed on mobile applications using a convolutional neural network (CNN). The proposed framework uses a time window-based approach to split the activity’s encrypted traffic flow into segments, so that in-app activities can be identified just by observing only a part of the activity-related encrypted traffic. In this study, matrices were constructed for each encrypted traffic flow segment. These matrices acted as input into the CNN model, allowing it to learn to differentiate previously trained (known) and previously untrained (unknown) in-app activities as well as the known in-app activity type. The proposed method extracts and selects salient features for encrypted traffic classification. This is the first-known approach proposing to filter unknown traffic with an average accuracy of 88%. Once the unknown traffic is filtered, the classification accuracy of our model would be 92%.
edge–SR: Super–Resolution For The Masses

Classic image scaling (e.g. bicubic) can be seen as one convolutional layer and a single upscaling filter. Its implementation is ubiquitous in all display devices and image processing software. In the last decade deep learning systems have been introduced for the task of image super-resolution (SR), using several convolutional layers and numerous filters. These methods have taken over the benchmarks of image quality for upscaling tasks. Would it be possible to replace classic upscalers with deep learning architectures on edge devices such as display panels, tablets, laptop computers, etc.? On one hand, the current trend in Edge–AI chips shows a promising future in this direction, with rapid development of hardware that can run deep–learning tasks efficiently. On the other hand, in image SR only few architectures have pushed the limit to extreme small sizes that can actually run on edge devices at real-time. We explore possible solutions to this problem with the aim to fill the gap between classic upscalers and small deep learning configurations. As a transition from classic to deep–learning upscaling we propose edge–SR (eSR), a set of one–layer architectures that use interpretable mechanisms to upscale images. Certainly, a one–layer architecture cannot reach the quality of deep learning systems. Nevertheless, we find that for high speed requirements, eSR becomes better at trading–off image quality and runtime performance. Filling the gap between classic and deeplearning architectures for image upscaling is critical for massive adoption of this technology. It is equally important to have an interpretable system that can reveal the inner strategies to solve this problem and guide us to future improvements and better understanding of larger networks.
Memory-harvesting VMs in cloud platforms

loud platforms monetize their spare capacity by renting “Spot” virtual machines (VMs) that can be evicted in favor of higher-priority VMs. Recent work has shown that resource-harvesting VMs are more effective at exploiting spare capacity than Spot VMs, while also reducing the number of evictions. However, the prior work focused on harvesting CPU cores while keeping memory size fixed. This wastes a substantial monetization opportunity and may even limit the ability of harvesting VMs to leverage spare cores. Thus, in this paper, we explore memory harvesting and its challenges in real cloud platforms, namely its impact on VM creation time, NUMA spanning, and page fragmentation. We start by characterizing the amount and dynamics of the spare memory in Azure. We then design and implement memory-harvesting VMs (MHVMs), introducing new techniques for memory buffering, batching, and pre-reclamation. To demonstrate the use of MHVMs, we also extend a popular cluster scheduling framework (Hadoop) and a FaaS platform to adapt to them. Our main results show that (1) there is plenty of scope for memory harvesting in real platforms; (2) MHVMs are effective at mitigating the negative impacts of harvesting; and (3) our extensions of Hadoop and FaaS successfully hide the MHVMs’ varying memory size from the users’ data-processing jobs and functions. We conclude that memory harvesting has great potential for practical deployment and users can save up to 93% of their costs when running workloads on MHVMs.
A Siamese Neural Network-Based Face Recognition from Masked Faces

In modern days, face recognition is a critical aspect of security and surveillance. Face recognition techniques are widely used for mobile devices and public surveillance. Occlusion is a challenge while designing face recognition applications. In the COVID19 pandemic, we are advised to wear a face mask in public places. It helps us prevent the droplets from entering our body from a potential COVID19 positive person’s nose or mouth. However, it brings difficulty for the security personnel to identify the human face by seeing the partially exposed face. Most of the existing models are built based on the entire human face. It could either fail or perform poorly in the scenario as mentioned above. In this paper, a solution has been proposed by leveraging Siamese neural network for human face recognition from the partial human face. The prototype has been developed on the celebrity faces and validated with the state-of-the-art VGGFace2 (Resnet50) model. Our proposed model has performed well and provides very competitive results of 93% and $$84.80\pm 4.71$$ % best-of-five and mean accuracy for partial face-images, respectively.
Polarimetric SAR Despeckling With Convolutional Neural Networks

Coherent imaging systems such as synthetic aperture radar (SAR) are subject to speckle, the reduction of which is an active area of study. Methods based on deep convolutional neural networks (CNNs) have recently demonstrated state-of-the-art performance in the removal of additive noise from natural images and speckle from single-channel SAR images. The application of deep learning to multichannel SAR modalities such as polarimetric SAR (PolSAR) is complicated in part by the nature of the data as images of complex-valued covariance matrices. In this article, we propose a CNN-based PolSAR despeckling approach that uses an invertible transformation involving a matrix logarithm to facilitate CNN processing of the PolSAR data. A residual learning strategy is adopted, in which the CNN is trained to identify the speckle component which is then removed from the corrupted image. The experimental results on simulated and measured PolSAR data show the proposed approach to markedly reduce speckle and preserve scene features.
A Comparative Systematic Analysis of Stakeholder’s Identification Methods in Requirements Elicitation

[Context and Motivation] Before eliciting and gathering requirements for a software project, it is considered pivotal to know about concerned stakeholders. It becomes hard to elicit the actual system requirements without identifying relevant stakeholders, leading the software project to failure. Despite the paramount importance of stakeholder identification in requirement elicitation, it has been given less attention in the software engineering literature. [Method] For this purpose, we conducted a thorough Systematic Literature Review (SLR) on stakeholder identification (SI) and its methods in requirement elicitation. However, previously, a literature study on SI in the requirement elicitation was conducted. We found that no one has proposed any standard or baseline research method for stakeholder identification, stakeholder assessment, and stakeholder interaction up to date according to our knowledge. It provides an opportunity to update the current SLR on SI in requirements elicitation from 2011 till 2021 to search for a baseline methodology for the SI. For this purpose, we explored the existing literature research that involves the SI methods in requirements elicitation. [Principle Ideas/Results] Furthermore, we identify and capture seventeen research methodologies for SI, eight key stakeholders interaction methods, and ten stakeholders assessment methods in requirement elicitation. To further enhance the stakeholder identification process, we additionally identify pivotal information such as different potential stakeholder categories, stakeholder assessments methods, and stakeholder interaction methods. Also, based on the proposed SLR, we find out the existing gaps and new opportunities for SI methods in the requirement elicitation. [Contribution] These SI methodologies help requirements engineers and practitioners identify key stakeholders and efficiently improve the requirements quality. Moreover, this research study helps identify the effective practices used for the traditional and CrowdRE SI, recover consequences that can affect the effectiveness of SI, and recommend advisable SI practices to be employed in the future. This research study would help the software researchers and developers efficiently and accurately identify correct and concerned stakeholders to improve end-user satisfaction instead of considering it a self-evident task.
White Noise Test from Ordinal Patterns in the Entropy–Complexity Plane

Summary This article serves two purposes. Firstly, it surveys the Bandt and Pompe methodology for the statistical community, stressing topics that are open for research. Secondly, it contributes towards a better understanding of the statistical properties of that approach for time series analysis. The Bandt and Pompe methodology consists of computing information theory descriptors from the histogram of ordinal patterns. Such descriptors lie in a 2D manifold: the entropy–complexity plane. This article provides the first proposal of a test in the entropy–complexity plane for the white noise hypothesis. Our test is based on true white noise sequences obtained from physical devices. The proposed methodology provides consistent results: It assesses sequences of true random samples as random (adequate test size), rejects correlated and contaminated sequences (sound test power) and captures the randomness of generators previously analysed in the literature.