Reviewerly/exHarmony huggingface dataset:
A Critical Examination of Virtual Reality Technology in the Context of the Minority Body

Virtual Reality (VR) holds the promise of immersing people in virtual worlds. However, initial work on the relationship between VR and disability suggests that VR is a body-centric technology that poses barriers for disabled users. We supplement this work with a theoretical analysis of immersive VR through the lens of Surrogate Body theory, a concept from media theory for the structured examination of interactive media in use. Leveraging Critical Disability Studies, particularly the theory of the Minority Body, we explore the assumptions about bodies inherent in VR, and we reflect on implications of these assumptions when disabled people engage with the technology. Our findings show that VR is an inherently ableist technology that assumes a ‘corporeal standard’ (i.e., an ‘ideal’, non-disabled human body), and fails to adequately accommodate disabled people. We conclude with implications for HCI research on VR, and discuss design approaches that foster inclusive technology development.
Temporal Query Networks for Fine-grained Video Understanding

Our objective in this work is fine-grained classification of actions in untrimmed videos, where the actions may be temporally extended or may span only a few frames of the video. We cast this into a query-response mechanism, where each query addresses a particular question, and has its own response label set.We make the following four contributions: (i) We propose a new model—a Temporal Query Network—which enables the query-response functionality, and a structural understanding of fine-grained actions. It attends to relevant segments for each query with a temporal attention mechanism, and can be trained using only the labels for each query. (ii) We propose a new way—stochastic feature bank update—to train a network on videos of various lengths with the dense sampling required to respond to fine-grained queries. (iii) we compare the TQN to other architectures and text supervision methods, and analyze their pros and cons. Finally, (iv) we evaluate the method extensively on the FineGym and Diving48 bench-marks for fine-grained action classification and surpass the state-of-the-art using only RGB features. Project page: https://www.robots.ox.ac.uk/~vgg/research/tqn/.
Applications of quantum computing for investigations of electronic transitions in phenylsulfonyl-carbazole TADF emitters

Abstract A quantum chemistry study of the first singlet ( S 1 ) and triplet ( T 1 ) excited states of phenylsulfonyl-carbazole compounds, proposed as useful thermally activated delayed fluorescence (TADF) emitters for organic light emitting diode (OLED) applications, was performed with the quantum Equation-Of-Motion Variational Quantum Eigensolver (qEOM-VQE) and Variational Quantum Deflation (VQD) algorithms on quantum simulators and devices. These quantum simulations were performed with double zeta quality basis sets on an active space comprising the highest occupied and lowest unoccupied molecular orbitals (HOMO, LUMO) of the TADF molecules. The differences in energy separations between S 1 and T 1 (Δ E S T ) predicted by calculations on quantum simulators were found to be in excellent agreement with experimental data. Differences of 17 and 88 mHa with respect to exact energies were found for excited states by using the qEOM-VQE and VQD algorithms, respectively, to perform simulations on quantum devices without error mitigation. By utilizing state tomography to purify the quantum states and correct energy values, the large errors found for unmitigated results could be improved to differences of, at most, 4 mHa with respect to exact values. Consequently, excellent agreement could be found between values of Δ E S T predicted by quantum simulations and those found in experiments.
Deepfakes Detection Techniques Using Deep Learning: A Survey

Deep learning is an effective and useful technique that has been widely applied in a variety of fields, including computer vision, machine vision, and natural language processing.Deepfakes uses deep learning technology to manipulate images and videos of a person that humans cannot differentiate them from the real one.In recent years, many studies have been conducted to understand how deepfakes work and many approaches based on deep learning have been introduced to detect deepfakes videos or images.In this paper, we conduct a comprehensive review of deepfakes creation and detection technologies using deep learning approaches.In addition, we give a thorough analysis of various technologies and their application in deepfakes detection.Our study will be beneficial for researchers in this field as it will cover the recent state-of-art methods that discover deepfakes videos or images in social contents.In addition, it will help comparison with the existing works because of the detailed description of the latest methods and dataset used in this domain.
Efficient closed high-utility pattern fusion model in large-scale databases

High-Utility Itemset Mining (HUIM) is considered a major issue in recent decades since it reveals profit strategies for use in industry for decision-making. Most existing works have focused on mining high-utility itemsets from databases showing large amount of patterns; however exact decisions are still challenging to make from that large amounts of discovered knowledge. Closed High-utility itemset mining (CHUIM) provides a smart way to present concise high-utility itemsets that can be more effective for making correct decisions. However, none of the existing works have focused on handling large-scale databases to integrate discovered knowledge from several distributed databases. In this paper, we first present a large-scale information fusion architecture to integrate discovered closed high-utility patterns from several distributed databases. The generic composite model is used to cluster transactions regarding their relevant correlation that can ensure correctness and completeness of the fusion model. The well-known MapReduce framework is then deployed in the developed DFM-Miner algorithm to handle big datasets for information fusion and integration. Experiments are then compared to the state-of-the-art CHUI-Miner and CLS-Miner algorithms for mining closed high-utility patterns and the results indicated that the designed model is well designed for handling large-scale databases with less memory usage. Moreover, the designed MapReduce framework can speed up the mining performance of closed high-utility patterns in the developed fusion system.
A revocable and outsourced multi-authority attribute-based encryption scheme in fog computing

Fog computing is a revolutionary technology for the next generation to bridge the gap between cloud data centers and end-users. Fog computing is not a counterfeit for cloud computing but a persuasive counterpart. It also accredits by utilizing the network edge while still rendering the possibility to interact with the cloud. Nevertheless, the features of fog computing are encountering several security challenges. The security of end-users and/or fog servers brings a significant dilemma in implementing fog computing. Moreover, in conventional cloud computing, the attribute-based encryption (ABE) technology is not appropriate for end-users due to restricted computing resources, i.e., limited resources, high end-to-end delay, and transmission capability. Hence, the revocation and outsourcing mechanisms become inappropriate between end-users and cloud servers. In this regard, this paper recommends a multi-authority attribute-based encryption (MA-ABE) technique to support revocation and outsource the attributes to fog computation. We present an attribute revocation scheme based on cipher-text attribute-based encryption by introducing the attribute group keys. In this process, the secret keys are dynamically altered and realized the requirement of immediate attribute revocations. Hence, we provide the complete encryption and decryption process for end-users and fog servers based on multi-authority, attribute revocation, and outsourcing computation, while most of the existing scheme lack to incorporate all these parameters. Our scheme also outsources the complicated encryption and decryption tasks to the fog server that significantly improves the overall computation efficiency compared to the state-of-the-art work.
Human-Centric Spatio-Temporal Video Grounding With Visual Transformers

In this work, we introduce a novel task – Human-centric Spatio-Temporal Video Grounding (HC-STVG). Unlike the existing referring expression tasks in images or videos, by focusing on humans, HC-STVG aims to localize a spatio-temporal tube of the target person from an untrimmed video based on a given textural description. This task is useful, especially for healthcare and security related applications, where the surveillance videos can be extremely long but only a specific person during a specific period is concerned. HC-STVG is a video grounding task that requires both spatial (where) and temporal (when) localization. Unfortunately, the existing grounding methods cannot handle this task well. We tackle this task by proposing an effective baseline method named Spatio-Temporal Grounding with Visual Transformers (STGVT), which utilizes Visual Transformers to extract cross-modal representations for video-sentence matching and temporal localization. To facilitate this task, we also contribute an HC-STVG datasetThe new dataset is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/tzhhhh123/HC-STVG</uri> . consisting of 5,660 video-sentence pairs on complex multi-person scenes. Specifically, each video lasts for 20 seconds, pairing with a natural query sentence with an average of 17.25 words. Extensive experiments are conducted on this dataset, demonstrating that the newly-proposed method outperforms the existing baseline methods.
Dual CNN-Based Channel Estimation for MIMO-OFDM Systems

Recently, convolutional neural network (CNN)-based channel estimation (CE) for massive multiple-input multiple-output communication systems has achieved remarkable success. However, complexity even needs to be reduced, and robustness can even be improved. Meanwhile, existing methods do not accurately explain which channel features help the denoising of CNNs. In this paper, we first compare the strengths and weaknesses of CNN-based CE in different domains. When complexity is limited, the channel sparsity in the angle-delay domain improves denoising and robustness whereas large noise power and pilot contamination are handled well in the spatial-frequency domain. Thus, we develop a novel network, called dual CNN, to exploit the advantages in the two domains. Furthermore, we introduce an extra neural network, called HyperNet, which learns to detect scenario changes from the same input as the dual CNN. HyperNet updates several parameters adaptively and combines the existing dual CNNs to improve robustness. Experimental results show improved estimation performance for the time-varying scenarios. To further exploit the correlation in the time domain, a recurrent neural network framework is developed, and training strategies are provided to ensure robustness to the changing of temporal correlation. This design improves channel estimation performance but its complexity is still low.
Long and short-term impacts of regulation in the cryptocurrency market

Since cryptocurrencies were first created, the related markets have been known for their fluctuations, whether in terms of high volatility or illiquidity. Partially for those reasons, public authorities and regulators around the world have frequently attempted to regulate those markets. We aim in our study to examine whether cryptocurrency traders perceive market regulation in a beneficial way. Using an event study methodology for daily data covering the 2015–2019 period, we assess how regulatory news and events have affected returns in cryptocurrency markets. We further assess whether financial cryptocurrency characteristics and in particular their liquidity can explain cross-sectional variations in cryptocurrency return reactions. The results suggest that events that increase the probability of regulation adoption are associated with negative abnormal returns for the cryptocurrencies concerned. We also find that the magnitude of the return reactions is not the same across all the cryptocurrencies in our sample. We show that investors reacted less negatively for the most illiquid cryptocurrencies and for those that incurred more information asymmetry risk. Finally, we analyze a longer-term effect of regulatory events by studying the performance of cryptocurrencies. The risk-adjusted return in the pre-event period is positive and significant, but it appears not to be significantly different from zero in the post-event period.
RDA: Robust Domain Adaptation via Fourier Adversarial Attacking

Unsupervised domain adaptation (UDA) involves a supervised loss in a labeled source domain and an unsupervised loss in an unlabeled target domain, which often faces more severe overfitting (than classical supervised learning) as the supervised source loss has clear domain gap and the unsupervised target loss is often noisy due to the lack of annotations. This paper presents RDA, a robust domain adaptation technique that introduces adversarial attacking to mitigate overfitting in UDA. We achieve robust domain adaptation by a novel Fourier adversarial attacking (FAA) method that allows large magnitude of perturbation noises but has minimal modification of image semantics, the former is critical to the effectiveness of its generated adversarial samples due to the existence of ‘domain gaps’. Specifically, FAA decomposes images into multiple frequency components (FCs) and generates adversarial samples by just perturbating certain FCs that capture little semantic information. With FAA-generated samples, the training can continue the ‘random walk’ and drift into an area with a flat loss landscape, leading to more robust domain adaptation. Extensive experiments over multiple domain adaptation tasks show that RDA can work with different computer vision tasks with superior performance.
Learning Dynamic Spatial-Temporal Regularization for UAV Object Tracking

With the wide vision and high flexibility, unmanned aerial vehicle (UAV) has been widely used into object tracking in recent years. However, its limited computing capability poses a great challenges to tracking algorithms. On the other hand, Discriminative Correlation Filter (DCF) based trackers have attracted great attention due to their computational efficiency and superior accuracy. Many studies introduce spatial and temporal regularization into the DCF framework to achieve a more robust appearance model and further enhance the tracking performance. However, such algorithms generally set fixed spatial or temporal regularization parameters, which lack flexibility and adaptability under cluttered and challenging scenarios. To tackle such issue, in this letter, we propose a novel DCF tracking model by introducing dynamic spatial regularization weight, which encourage the filter focuses on more reliable region during training stage. Furthermore, our method could optimize the spatial and temporal regularization weight simultaneously using Alternative Direction Method of Multiplies (ADMM) technique method, where each sub-problem has closed-form solution. Through the joint optimization, our tracker could not only suppress the potential distractors but also construct robust target appearance on the basis of reliable historical information. Experiments on two UAV benchmarks have demonstrated that our tracker performs favorably against other state-of-the-art algorithms.
Rotating behind Privacy: An Improved Lightweight Authentication Scheme for Cloud-based IoT Environment

The advancements in the internet of things (IoT) require specialized security protocols to provide unbreakable security along with computation and communication efficiencies. Moreover, user privacy and anonymity has emerged as an integral part, along with other security requirements. Unfortunately, many recent authentication schemes to secure IoT-based systems were either proved as vulnerable to different attacks or prey of inefficiencies. Some of these schemes suffer from a faulty design that happened mainly owing to undue emphasis on privacy and anonymity alongside performance efficiency. This article aims to show the design faults by analyzing a very recent hash functions-based authentication scheme for cloud-based IoT systems with misunderstood privacy cum efficiency tradeoff owing to an unadorned design flaw, which is also present in many other such schemes. Precisely, it is proved in this article that the scheme of Wazid et al. cannot provide mutual authentication and key agreement between a user and a sensor node when there exists more than one registered user. We then proposed an improved scheme and proved its security through formal and informal methods. The proposed scheme completes the authentication cycle with a minor increase in computation cost but provides all security goals along with privacy.
Phishing websites detection via CNN and multi-head self-attention on imbalanced datasets

Phishing websites belong to a social engineering attack where perpetrators fake legitimate websites to lure people to access so as to illegally acquire user’s identity, password, privacy and even properties. This attack imposes a great threat to people and becomes more and more severe. In order to identify phishing websites, many proposals have shown their merits. For example, the classical proposal CNN-LSTM received a very high precision by combining Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) together. However, despite CNN achieved great success in AI area, LSTM still exists the biases issue since it always treats the later features much more important than the former ones. In the meanwhile, as the self-attention mechanism can discover the text’s inner dependency relationships, it has been widely applied to various tasks of deep learning-based Natural Language Processing (NLP). If we treat a URL as a text string, this mechanism can learn comprehensive URL representations. In order to improve the accuracy for phishing websites detection further, in this paper, we propose a novel Convolutional Neural Network (CNN) with self-attention named self-attention CNN for phishing Uniform Resource Locators (URLs) identification. Specifically, self-attention CNN first leverages Generative Adversarial Network (GAN) to generate phishing URLs so as to balance the datasets of legitimate and phishing URLs. Then it utilizes CNN and multi-head self-attention to construct our new classifier which is comprised of four blocks, namely the input block, the attention block, the feature block and the output block. Finally, the trained classifier can give a high-accuracy result for an unknown website URL. Overall thorough experiments indicate that self-attention CNN achieves 95.6% accuracy, which outperforms CNN-LSTM, single CNN and single LSTM by 1.4%, 4.6% and 2.1% respectively.
IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking

Adversarial attack arises due to the vulnerability of deep neural networks to perceive input samples injected with imperceptible perturbations. Recently, adversarial attack has been applied to visual object tracking to evaluate the robustness of deep trackers. Assuming that the model structures of deep trackers are known, a variety of white-box attack approaches to visual tracking have demonstrated promising results. However, the model knowledge about deep trackers is usually unavailable in real applications. In this paper, we propose a decision-based black-box attack method for visual object tracking. In contrast to existing black-box adversarial attack methods that deal with static images for image classification, we propose IoU attack that sequentially generates perturbations based on the predicted IoU scores from both current and historical frames. By decreasing the IoU scores, the proposed attack method degrades the accuracy of temporal coherent bounding boxes (i.e., object motions) accordingly. In addition, we transfer the learned perturbations to the next few frames to initialize temporal motion attack. We validate the proposed IoU attack on state-of-the-art deep trackers (i.e., detection based, correlation filter based, and long-term trackers). Extensive experiments on the benchmark datasets indicate the effectiveness of the proposed IoU attack method. The source code is available at https://github.com/VISION-SJTU/IoUattack.
Downlink CSI Feedback Algorithm With Deep Transfer Learning for FDD Massive MIMO Systems

In this paper, a channel state information (CSI) feedback method is proposed based on deep transfer learning (DTL). The proposed method addresses the problem of high training cost of downlink CSI feedback network in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. In particular, we obtain the models of different wireless channel environments at low training cost by fine-tuning the pre-trained model with a relatively small number of samples. In addition, the effects of different layers on training cost and model performance are discussed. Furthermore, a model-agnostic meta-learning (MAML)-based method is proposed to solve the problem associated with large number of samples of a wireless channel environment required to train a deep neural network (DNN) as a pre-trained model. Our results show that the performance of the DTL-based method is comparable with that of the DNN trained with a large number of samples, which demonstrates the effectiveness and superiority of the proposed method. At the same time, although there is a certain performance loss compared with the DTL-based method, the MAML-based method shows good performance in terms of the normalized mean square error (NMSE).
Optimizing hyperparameters of deep reinforcement learning for autonomous driving based on whale optimization algorithm

Deep Reinforcement Learning (DRL) enables agents to make decisions based on a well-designed reward function that suites a particular environment without any prior knowledge related to a given environment. The adaptation of hyperparameters has a great impact on the overall learning process and the learning processing times. Hyperparameters should be accurately estimated while training DRL algorithms, which is one of the key challenges that we attempt to address. This paper employs a swarm-based optimization algorithm, namely the Whale Optimization Algorithm (WOA), for optimizing the hyperparameters of the Deep Deterministic Policy Gradient (DDPG) algorithm to achieve the optimum control strategy in an autonomous driving control problem. DDPG is capable of handling complex environments, which contain continuous spaces for actions. To evaluate the proposed algorithm, the Open Racing Car Simulator (TORCS), a realistic autonomous driving simulation environment, was chosen to its ease of design and implementation. Using TORCS, the DDPG agent with optimized hyperparameters was compared with a DDPG agent with reference hyperparameters. The experimental results showed that the DDPG's hyperparameters optimization leads to maximizing the total rewards, along with testing episodes and maintaining a stable driving policy.