Reviewerly/exHarmony huggingface dataset:
Cyber Warfare Threat Categorization on CPS by Dark Web Terrorist

The Industrial Internet of Things (IIoT) also referred as Cyber Physical Systems (CPS) as critical elements, expected to play a key role in Industry 4.0 and always been vulnerable to cyber-attacks and vulnerabilities. Terrorists use cyber vulnerability as weapons for mass destruction. The dark web's strong transparency and hard-to-track systems offer a safe haven for criminal activity. On the dark web (DW), there is a wide variety of illicit material that is posted regularly. For supervised training, large-scale web pages are used in traditional DW categorization. However, new study is being hampered by the impossibility of gathering sufficiently illicit DW material and the time spent manually tagging web pages. We suggest a system for accurately classifying criminal activity on the DW in this article. Rather than depending on the vast DW training package, we used authorized regulatory to various types of illicit activity for training Machine Learning (ML) classifiers and get appreciable categorization results. Espionage, Sabotage, Electrical power grid, Propaganda and Economic disruption are the cyber warfare motivations and We choose appropriate data from the open source links for supervised Learning and run a categorization experiment on the illicit material obtained from the actual DW. The results shows that in the experimental setting, using TF-IDF function extraction and a AdaBoost classifier, we were able to achieve an accuracy of 0.942. Our method enables the researchers and System authoritarian agency to verify if their DW corpus includes such illicit activity depending on the applicable rules of the illicit categories they are interested in, allowing them to identify and track possible illicit websites in real time. Because broad training set and expert-supplied seed keywords are not required, this categorization approach offers another option for defining illicit activities on the DW.
A survey on vision-based driver distraction analysis

Motor vehicle crashes are great threats to our life, which may result in numerous fatalities, as well as tremendous economic and societal costs. Driver inattention, either distraction or fatigue, is the major cause among human factors responsible for the crashes. Distracted driving has been getting increasingly severe, and has caused many more crashes than drowsy driving, while the latter has been more extensively studied. Therefore, we are motivated to present a comprehensive survey on vision-based approaches for driver distraction analysis. In the paper, we firstly provide an overview of driver distraction, then introduce the available datasets and explore the various cues for driver behavior distraction analysis. After that, two forms of driver behavior distraction (visual distraction and manual distraction) are analyzed separately. Lastly, we conclude the evolvement and future directions of driver distraction analysis for safe driving. To the best of our knowledge, we are the first to propose driver behavior distraction and analyze it in a hierarchical way.
Locker: Locally Constrained Self-Attentive Sequential Recommendation

Recently, self-attentive models have shown promise in sequential recommendation, given their potential to capture user long-term preferences and short-term dynamics simultaneously. Despite their success, we argue that self-attention modules, as a non-local operator, often fail to capture short-term user dynamics accurately due to a lack of inductive local bias. To examine our hypothesis, we conduct an analytical experiment on controlled 'short-term' scenarios. We observe a significant performance gap between self-attentive recommenders with and without local constraints, which implies that short-term user dynamics are not sufficiently learned by existing self-attentive recommenders. Motivated by this observation, we propose a simple framework, (Locker) for self-attentive recommenders in a plug-and-play fashion. By combining the proposed local encoders with existing global attention heads, Locker enhances short-term user dynamics modeling, while retaining the long-term semantics captured by standard self-attentive encoders. We investigate Locker with five different local methods, outperforming state-of-the-art self-attentive recom- menders on three datasets by 17.19% ([email protected]) on average.
Experiencing power over AI: The fit effect of perceived power and desire for power on consumers' choice for voice shopping

The question of how artificial intelligence (AI) systems shape consumer behavior has attracted strong interest from HCI scholars and business practitioners. The current study focuses on AI assistants (e.g., Amazon Alexa) and a novel behavior – shopping via AI assistants – called voice shopping. The voice interaction between users and AI assistants features an unbalanced mode wherein users command their AI assistants to perform various tasks. We examine how the power experience in human-AI interaction impacts voice shopping. Our two-wave longitudinal data reveal that perceived power over AI assistants can reduce risk perception regarding voice shopping. The results of polynomial modeling and response surface analysis show that when perceived power fits desire for power, users perceive less risk and are more willing to shop via AI assistants. Our findings suggest that developers should be attentive to the match between power experience and desire for power when developing voice shopping functionalities. Our study also indicates that users could be vulnerable to the manipulation of the power experience by AI companies, which is of critical importance for policy-makers.
A multicriteria decision making methodology based on two-dimensional uncertainty by hesitant Z-fuzzy linguistic terms with an application for blockchain risk evaluation

Blockchain technology (BT), which provides to record transactions among many computers as a distributed ledger, is commonly utilized by companies to develop and manage business processes. Since it is a multi-process and multi-agent complex system, the implementation process of BT contains many risk factors. Identifying and analyzing these risks in order to minimize their adverse effects during the implementation process is also completely critical for companies. Since evaluating these risks must be handled by considering many different perspectives, the evaluation of risk factors for BT can be considered as a multi-criteria decision-making (MCDM) problem. To cope with uncertainty in this process, the fuzzy set theory (FST) can be effectively utilized with MCDM methods to increase their ability to obtain more concrete and realistic results. Therefore, this paper proposes an MCDM methodology consisting of Decision-Making Trail and Evaluating Laboratory (DEMATEL) and cognitive mapping methods based on FST. These methods have been reconstructed with hesitant fuzzy Z-numbers that enable not only to state impreciseness of the assessments but also to consider indeterminacy and hesitancy of the experts to evaluate risk factors with respect to BT implementation. While the dependencies of risk factors have been determined via the hesitant fuzzy Z-DEMATEL method, their weights have been obtained through the hesitant fuzzy Z-cognitive mapping method. As a result, this study aims to prioritize risk factors related to the BT implementation process with a novel fuzzy decision-making methodology and provide a road map for companies. With this study, which is the first paper in the literature in terms of the subject of the study and the method adopted, an integrated, comprehensive fuzzy approach is introduced for the first time in evaluating BT risks. As a result of detailed research, risk factors that companies may encounter in BT use and the degrees of their importance have been determined. Through that, essential decision analysis has been put forward that companies can apply for their strategic decisions. A sensitivity analysis based on changes in the initial vector values has also been adapted to analyze the effects of the risk factors.
Joint Model Pruning and Device Selection for Communication-Efficient Federated Edge Learning

In recent years, wireless <i>federated learning</i> (FL) has been proposed to support the mobile intelligent applications over the wireless network, which protects the data privacy and security by exchanging the parameter between mobile devices and the <i>base station</i> (BS). However, the learning latency increases with the neural network scale due to the limited local computing power and communication bandwidth. To tackle this issue, we introduce model pruning for wireless FL to reduce the neural network scale. Device selection is also considered to further improve the learning performance. By removing the stragglers with low computing power or bad channel condition, the model aggregation loss caused by model pruning can be alleviated and the communication overhead can be effectively reduced. We analyze the convergence rate and learning latency of the proposed model pruning method and formulate an optimization problem to maximize the convergence rate under the given learning latency budget via jointly optimizing the pruning ratio, device selection, and wireless resource allocation. By solving the problem, the closed-form solutions of pruning ratio and wireless resource allocation are derived and the threshold-based device selection strategy is developed. Finally, extensive experiments are carried out to demonstrate that the proposed model pruning algorithm outperforms other existing schemes.
Understanding, predicting and scheduling serverless workloads under partial interference

Interference among distributed cloud applications can be classified into three types: full, partial and zero. While prior research merely focused on full interference, the partial interference that occurs at parts of applications is far more common yet still lacks in-depth study. Serverless computing that structures applications into small-sized, short-lived functions further exacerbate partial interference. We characterize the features of partial interference in serverless as exhibiting high volatility, spatial-temporal variation, and propagation. Given these observations, we propose an incremental learning predictor, named Gsight, which can achieve high precision by harnessing the spatial-temporal overlap codes and profiles of functions via an end-to-end call path. Experimental results show that Gsight can achieve an average error of 1.71%. Its convergence speed is at least 3X faster than that in a serverful system. A scheduling case study shows that the proposed method can improve function density by ≥ 18.79% while guaranteeing the quality of service (QoS).
DeepRPN-BIQA: Deep architectures with region proposal network for natural-scene and screen-content blind image quality assessment

With the emerging use of technology and screen-oriented applications in our daily life, screen content images have gained the same importance as natural scene images. This results in many natural-scene and screen-content blind image quality assessment (BIQA) models to evaluate the perceptual quality without any prior information regarding the reference image. Recently, patch-based techniques for image quality assessment (IQA) have shown promising results. As per our knowledge, no IQA technique in literature is available that can be equally effective for both natural-scene and screen-content images. In this work, we have proposed a deep architecture with a region proposal network (RPN) for blind natural-scene and screen-content image quality assessment, named DeepRPN-BIQA. The proposed architecture computes visual saliency using RPN to extract important regions having a high contribution towards the image quality. Important regions are extracted by utilizing the texture and edges of images by sliding the network over the extracted feature map from deep architectures i.e., VGGNet and ResNet. The regions proposed (RP) that overlap more than 60% are merged into one proposal and are called the region of interest (ROI). The overlap between RPs is computed using anchors having 3 different scales and aspect ratios. A local quality score is computed over each ROI and the total quality score is computed by taking the average of all the local quality scores. Experimental results show that the DeepRPN-BIQA shows a high correlation between mean observer score and predicted quality score and performs better than other models for screen content images, synthetically distorted images, images taken in real-life conditions using mobile phone cameras, and large scale image quality assessment database.
On the Privacy Risks of Algorithmic Fairness

Algorithmic fairness and privacy are essential pillars of trustworthy machine learning. Fair machine learning aims at minimizing discrimination against protected groups by, for example, imposing a constraint on models to equalize their behavior across different groups. This can subsequently change the influence of training data points on the fair model, in a disproportionate way. We study how this can change the information leakage of the model about its training data. We analyze the privacy risks of group fairness (e.g., equalized odds) through the lens of membership inference attacks: inferring whether a data point is used for training a model. We show that fairness comes at the cost of privacy, and this cost is not distributed equally: the information leakage of fair models increases significantly on the unprivileged subgroups, which are the ones for whom we need fair learning. We show that the more biased the training data is, the higher the privacy cost of achieving fairness for the unprivileged subgroups will be. We provide comprehensive empirical analysis for general machine learning algorithms.
LIMU-BERT

Deep learning greatly empowers Inertial Measurement Unit (IMU) sensors for various mobile sensing applications, including human activity recognition, human-computer interaction, localization and tracking, and many more. Most existing works require substantial amounts of well-curated labeled data to train IMU-based sensing models, which incurs high annotation and training costs. Compared with labeled data, unlabeled IMU data are abundant and easily accessible. In this work, we present LIMU-BERT, a novel representation learning model that can make use of unlabeled IMU data and extract generalized rather than task-specific features. LIMU-BERT adopts the principle of self-supervised training of the natural language model BERT to effectively capture temporal relations and feature distributions in IMU sensor measurements. However, the original BERT is not adaptive to mobile IMU data. By meticulously observing the characteristics of IMU sensors, we propose a series of techniques and accordingly adapt LIMU-BERT to IMU sensing tasks. The designed models are lightweight and easily deployable on mobile devices. With the representations learned via LIMU-BERT, task-specific models trained with limited labeled samples can achieve superior performances. We extensively evaluate LIMU-BERT with four open datasets. The results show that the LIMU-BERT enhanced models significantly outperform existing approaches in two typical IMU sensing applications.
Membership Inference Attacks Against Recommender Systems

Recently, recommender systems have achieved promising performances and become one of the most widely used web applications. However, recommender systems are often trained on highly sensitive user data, thus potential data leakage from recommender systems may lead to severe privacy problems.
A deep learning approach in predicting products’ sentiment ratings: a comparative analysis

We present a benchmark comparison of several deep learning models including Convolutional Neural Networks, Recurrent Neural Network and Bi-directional Long Short Term Memory, assessed based on various word embedding approaches, including the Bi-directional Encoder Representations from Transformers (BERT) and its variants, FastText and Word2Vec. Data augmentation was administered using the Easy Data Augmentation approach resulting in two datasets (original versus augmented). All the models were assessed in two setups, namely 5-class versus 3-class (i.e., compressed version). Findings show the best prediction models were Neural Network-based using Word2Vec, with CNN-RNN-Bi-LSTM producing the highest accuracy (96%) and F-score (91.1%). Individually, RNN was the best model with an accuracy of 87.5% and F-score of 83.5%, while RoBERTa had the best F-score of 73.1%. The study shows that deep learning is better for analyzing the sentiments within the text compared to supervised machine learning and provides a direction for future work and research.
Applying deep learning technology for automatic fall detection using mobile sensors

With improved quality of life, many countries are facing a serious aging problem. Falls, one of the most common issues affecting the health of the elderly, are likely to cause irreversible damage to them. Therefore, the problem of how to accurately identify falls has great research value. Extracting features that can effectively represent a fall is the key to detecting them. Conventional machine learning (ML) methods which extract features manually are tedious and time-consuming. In contrast, deep learning (DL) can autonomously learn features from input data; however, configuring its architecture is somewhat complex, and the training time is long. In this study, a novel DL model, the Gated Recurrent Units (GRU) architecture, is proposed to obtain high-level features for classification. We evaluate its relative performance against six popular ML-based classifiers and three DL architectures using two popular open-source datasets, collected using mobile sensors. Our results show that the proposed method outperformed other algorithms in nearly all of the five performance metrics we examined, for the datasets we tested. The accuracy of prediction reached 99.56% and 90.69%, and the F1 score reached 96.83% and 87.29%, respectively, showing good performance.
Signal Processing-Based Deep Learning for Blind Symbol Decoding and Modulation Classification

Blindly decoding a signal requires estimating its unknown transmit parameters, compensating for the wireless channel impairments, and identifying the modulation type. While deep learning can solve complex problems, digital signal processing (DSP) is interpretable and can be more computationally efficient. To combine both, we propose the dual path network (DPN). It consists of a signal path of DSP operations that recover the signal, and a feature path of neural networks that estimate the unknown transmit parameters. By interconnecting the paths over several recovery stages, later stages benefit from the recovered signals and reuse all the previously extracted features. The proposed design is demonstrated to provide 5% improvement in modulation classification compared to alternative designs lacking either feature sharing or access to recovered signals. The estimation results of DPN along with its blind decoding performance are shown to outperform a blind signal processing algorithm for BPSK and QPSK on a simulated dataset. An over-the-air software-defined-radio capture was used to verify DPN results at high SNRs. DPN design can process variable length inputs and is shown to outperform relying on fixed length inputs with prediction averaging on longer signals by up to 15% in modulation classification.
A Sandbox Approach to Regulating High-Risk Artificial Intelligence Applications

Abstract This paper argues for a sandbox approach to regulating artificial intelligence (AI) to complement a strict liability regime. The authors argue that sandbox regulation is an appropriate complement to a strict liability approach, given the need to maintain a balance between a regulatory approach that aims to protect people and society on the one hand and to foster innovation due to the constant and rapid developments in the AI field on the other. The authors analyse the benefits of sandbox regulation when used as a supplement to a strict liability regime, which by itself creates a chilling effect on AI innovation, especially for small and medium-sized enterprises. The authors propose a regulatory safe space in the AI sector through sandbox regulation, an idea already embraced by European Union regulators and where AI products and services can be tested within safeguards.
High-Fidelity 3D Digital Human Head Creation from RGB-D Selfies

We present a fully automatic system that can produce high-fidelity, photo-realistic three-dimensional (3D) digital human heads with a consumer RGB-D selfie camera. The system only needs the user to take a short selfie RGB-D video while rotating his/her head and can produce a high-quality head reconstruction in less than 30 s. Our main contribution is a new facial geometry modeling and reflectance synthesis procedure that significantly improves the state of the art. Specifically, given the input video a two-stage frame selection procedure is first employed to select a few high-quality frames for reconstruction. Then a differentiable renderer-based 3D Morphable Model (3DMM) fitting algorithm is applied to recover facial geometries from multiview RGB-D data, which takes advantages of a powerful 3DMM basis constructed with extensive data generation and perturbation. Our 3DMM has much larger expressive capacities than conventional 3DMM, allowing us to recover more accurate facial geometry using merely linear basis. For reflectance synthesis, we present a hybrid approach that combines parametric fitting and Convolutional Neural Networks (CNNs) to synthesize high-resolution albedo/normal maps with realistic hair/pore/wrinkle details. Results show that our system can produce faithful 3D digital human faces with extremely realistic details. The main code and the newly constructed 3DMM basis is publicly available.
Deep Reinforcement Learning With Communication Transformer for Adaptive Live Streaming in Wireless Edge Networks

The emerging mobile edge computing (MEC) technology has been recently applied to improve the Quality of Experience (QoE) of network services, such as live video streaming. In this paper, we study an energy-aware adaptive live streaming scheme in wireless edge networks. In particular, we aim to design a joint uplink transmission and edge transcoding algorithm maximizing the video followers’ QoE, while minimizing the energy consumption of the video streamer. We formulate the problem as a Markov decision process (MDP), and propose a deep reinforcement learning (DRL) based framework, named SACCT, to determine the streamer’s encoding bitrate, the uploading power as well as the edge transcoding bitrates and frequency. We decompose the MDP problem into inter-frame and intra-frame problems to address the key design challenges that arise from continuous-discrete hybrid action space, time-varying state and action spaces, and unknown network variation. By doing so, SACCT integrates model-based optimization and model-free DRL to determine the intra-frame continuous resource allocation decisions and the inter-frame discrete bitrate adaptation decisions, respectively. To integrate both the numerical features (e.g., channel gain) and the categorical features (e.g., bitrate), we propose a communication Transformer (CT) as a backbone of SACCT by representing network states as communication tokens and running Transformers to model multi-scale dependencies. Extensive simulations manifest that compared with state-of-the-art approaches, SACCT can provide 128.23% (on average) extra reward. As such, by leveraging joint uplink adaption and edge transcoding, the proposed scheme enables an intelligent wireless network edge with QoE-assured and energy-aware live streaming services.