Reviewerly/exHarmony huggingface dataset:
Energy-Based Temporal Summarized Attentive Network for Zero-Shot Action Recognition

Recently, Action Recognition (AR) is facing the scalability problem, since collecting and annotating data for the ever-growing action categories is exhausting and inappropriate. As an alternative to AR, Zero-Shot Action Recognition (ZSAR) is getting more and more attention in the community, as they could utilize a shared semantic/attribute space to recognize novel categories without annotated data. Different from the AR focuses on learning the correlation between actions, ZSAR needs to consider the correlation of action-action, label-label and action-label at the same time. However, as far as we know, there is no work to provide structural guidance for the framework design of ZSAR according to its task characteristics. In this paper, we demonstrate the rationality of using the Energy-Based Model (EBM) to guide the framework design of ZSAR based on their inference mechanism. Furthermore, under the guidance of EBM, we propose an Energy-based Temporal Summarized Attentive Network (ETSAN) to achieve ZSAR. Specifically, to ensure the effectiveness of cross-modal matching, EBM needs to capture the correlations of input-input, output-output and input-output, based on discriminative and focused input and output space. To this end, we first design the Temporal Summarized Attentive Mechanism (TSAM) to capture the correlation of action-action by constructing discriminative and focused input space. Then, a Label Semantic Adaptive Mechanism (LSAM) is proposed to learn the correlation of label-label by adjusting the semantic structure according to the target task. Finally, we devise an Energy Score Estimation Mechanism (ESEM) to measure the compatibility (i.e. energy score) between video representation and label semantic embedding. With end-to-end training, our framework can capture all three of the correlations mentioned above simultaneously by minimizing the energy score of the correct action-label pair. Experiments on the HMDB51 and UCF101 datasets show that the proposed architecture achieves comparable results among methods based on the spatial-temporal visual feature of sequence-level, which demonstrates the efficiency of the EBM in guiding the framework design of ZSAR. In addition, our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/oOHCIOo/ETSAN</uri> .
Attention Guided Policy Optimization for 3D Medical Image Registration

Learning-based image registration approaches typically learn to map from input images to a transformation matrix. Regarding the current deep-learning-based image rigid registration approaches learn a transformation matrix in a one-shot way. Our purpose is to present a deep reinforcement learning (DRL) based method for image registration to explicitly model the step-wise nature of the human registration process. We cast an image registration process as a Markov Decision Process (MDP) where actions are defined as global image adjustment operations. Then we train our proxy to learn the optimal action sequences to achieve a good registration. More specifically, we propose a DRL proxy incorporating an attention mechanism to address the challenge of large differences in appearance between images from different modalities. Registration experiments on 3D CT-MR image pairs of patients with nasopharyngeal carcinoma and on publicly available 3D PET-MR image pairs show that our approach significantly outperforms other methods, and achieves state-of-the-art performance in multi-m-modal medical image registration.
Diversity Multi-View Clustering With Subspace and NMF-Based Manifold Learning

Since the complementarity information among multiple views has been exploited to improve the clustering effect significantly, multi-view clustering has become a hot topic, and many multi-view clustering methods have emerged. Most of them only consider local features in each view, ignoring the differences in the manifold structure of the same class samples among different views. In addition, they need to balance the importance of respective views effectively, thus ignoring the diversity among views in the clustering process. To address these problems, we propose a new diversity multi- view clustering method with subspace and NMF-based manifold learning. Firstly, non-negative matrix factorization is utilized to obtain the samples’ features and local geometric structure. After that, manifold learning and latent representation facilitate the learning of standard geometric structures in different views. Moreover, the Hilbert-Schmidt independence criterion is introduced to learn diversity for mutual learning and information fusion among views. Finally, experiments on seven datasets demonstrate the superiority of the proposed method compared to ten state-of-the-art methods.
The Influence of Digital Currency on the Banking Sector in Europe

Abstract Historically, financial and monetary innovations have triggered positive and negative changes in the everyday operations of the financial sector. Similarly, digital currencies can significantly reshape the future of banking and financial intermediaries. Whether the aspect of digital currencies is through the public sector such as in central banks, digital currencies, or private means such as in cryptocurrencies, the eventual rollout of such innovative instruments has a critical influence on the retail use of digital assets. At the same time, digital currencies may create new risks and vulnerabilities in the financial or banking sector whose implications should be thoroughly assessed. However, the magnitude of the risks depends on the designs and policies attached to the digital currencies in use. These features of digital currencies trigger the subject of discussion in this paper. By outlining the benefits and risks associated with digital currencies in the banking sector, the paper aims to highlight the overall impact of digital currencies in the banking sector and available remedies to control the risks. Therefore, the paper provides information that is critical to the banking sector policymakers interested in digital currencies.
PRISC: Privacy-Preserved Pandemic Infection Risk Computation Through Cellular-Enabled IoT Devices

The pandemics, such as COVID-19 are worldwide health risks and result in catastrophic impacts on the global economy. To prevent the spread of pandemics, it is critical to trace the contacts between people to identify the infection chain. Nevertheless, the privacy concern is a great challenge to contact tracing. Moreover, existing contact tracing apps cannot obtain the macro-level infection risk information, e.g., the hotspots where the infection occurs, which, however, is critical to optimize healthcare planning to better control and prevent the outbreak of pandemics. In this article, we develop a novel privacy-preserved pandemic tracing system, privacy-preserved pandemic infection risk computation (PRISC), to compute the infection risk through cellular-enabled IoT devices. In the PRISC system, there are three parties: 1) a mobile network operator (MNO); 2) a social network provider; and 3) the department of health. The physical contact records between users are obtained by the MNO from the users’ cellular-enabled IoT devices. The social contacts are obtained by the social network provider, while the health department has the records of pandemic patients. The three parties work together to compute a heatmap of pandemic infection risk in a region, while fully protecting the data privacy of each other. The heatmap provides both macro and micro-level infection risk information to help control pandemics. The experiment results indicate that PRISC can compute an infection risk score within a couple of seconds and a few mega-bytes (MBs) communication cost, for data sets with 100000 users.
Edge-Cloud-Assisted Certificate Revocation Checking: An Efficient Solution Against Irresponsible Service Providers

Certificate revocation checking (CRC) is a fundamental requirement in certificate-based public-key cryptographic systems. Most existing CRC schemes are not tailored for edge-cloud computing systems, and directly applying these schemes would cause security and efficiency problems. In this article, we first propose a two-layer edge-cloud-assisted CRC framework, dubbed ECA-CRC, where edge nodes utilizing a probabilistic checking algorithm serve as a first layer, and the cloud server utilizing a deterministic checking algorithm serves as a second layer. Both the edge nodes and the cloud server collaboratively provide verifiable CRC services for devices. The most prominent manifestations of ECA-CRC are that: 1) most CRC requests can be processed with the probabilistic checking layer, which reduces the checking delay significantly while providing an accurate CRC service and 2) devices can detect the irresponsible behavior of the service provider, including using an incorrect revoked certificate set (RCS) to compute checking results or procrastinating on updating the RCS, as soon as possible. We then propose an efficient instantiation of ECA-CRC, dubbed eECA-CRC, by utilizing a Merkle hash tree (MHT)-based homomorphic signature, Cuckoo filter, and Othello. We formally prove the security of eECA-CRC against the irresponsible service provider under the random oracle model. We implement an eECA-CRC prototype and conduct a comprehensive performance evaluation based on a public certificate database. Our results show that 95% of CRC requests are completed on the edge nodes, and only 5% of CRC requests need to be handled by the cloud server.
A Privacy-Preserved and User Self-Governance Blockchain-Based Framework to Combat COVID-19 Depression in Social Media

Faced with the impact of the coronavirus disease (COVID-19) pandemic, governments must protect the well-being of the population. Aside from considerations, such as keeping the virus from spreading and treating patients, the government should also be concerned about the mental health of its citizens during the epidemic. This study aimed to help users who develop depression due to COVID-19 on social media, reduce the cost of counselling, and reduce the need for users to visit the hospital for counselling. This study investigated the opportunities for blockchain technology to provide psychological help to social media users suffering from depression caused by the pandemic. Blockchain-based technology has been used to develop a new model that enables a user autonomy system to allow users to control their own data fully. The model utilizes a delegated proof of stake consensus blockchain to manage depression data to enable low cost and information security while discussing aspects related to trust, privacy, interoperability, and integration with other information communication technologies. The blockchain framework has been proven to provide secure and reliable information management that meets the user requirements for information autonomy. The frame-work can be combined with various other information technologies to extend its functionality further.
VARF: An Incentive Mechanism of Cross-Silo Federated Learning in MEC

Cross-silo federated learning (FL) is a privacy-preserving distributed machine learning where organizations acting as clients cooperatively train a global model without uploading their raw local data. Recently, the cross-silo FL in multiaccess edge computing (MEC) is used in increasing industrial applications. Most existing research on cross-silo FL pays attention to the performance aspect, ignoring the incentive mechanism for high-quality client selection and long participation in model training for efficient and stable FL, which has prevented the widespread adoption of cross-silo FL in MEC. In this article, we propose an incentive mechanism with quality-Aware and reputation-Aware based on the infinitely repeated game for cross-silo FL named VARF. VARF selects high-quality and high-reputation edge nodes (ENs) as candidates for model training in the cross-silo FL by a heuristic algorithm and then motivates the selected ENs to actively contribute their resources. VARF also models the long-term behavior of ENs in cross-silo FL as an infinitely repeated game and derives a stable and long-term cooperative strategy for clients while maximizing the amount of local data for model learning in cross-silo FL. Extensive simulations with real-world data sets demonstrate that the performance of VARF is more beneficial than other benchmarks. Meanwhile, experimental results show that cloud platforms (CPs) and ENs eventually form a long and stable cooperative relationship under the trigger strategy.
D<sup>2</sup>IFLN: Disentangled Domain-Invariant Feature Learning Networks for Domain Generalization

Domain generalization (DG) aims to learn a model that generalizes well to an unseen test distribution. Mainstream methods follow the domain-invariant representational learning philosophy to achieve this goal. However, due to the lack of priori knowledge to determine which features are domain specific and task-independent, and which features are domain invariant and task relevant, existing methods typically learn entangled representations, limiting their capacity to generalize to the distribution-shifted target domain. To address this issue, in this article, we propose novel disentangled domain-invariant feature learning networks (D2IFLN) to realize feature disentanglement and facilitate domain-invariant feature learning. Specifically, we introduce a semantic disentanglement network and a domain disentanglement network, disentangling the learned domain-invariant features from both domain-specific class-irrelevant features and domain-discriminative features. To avoid the semantic confusion in adversarial learning for domain-invariant feature learning, we further introduce a graph neural network to aggregate different domain semantic features during model training. Extensive experiments on three DG benchmarks show that the proposed D2IFLN performs better than the state of the art.
Cascaded Denoising Transformer for UAV Nighttime Tracking

The automation of unmanned aerial vehicles (UAVs) has been greatly promoted by visual object tracking methods with onboard cameras. However, the random and complicated real noise produced by the cameras seriously hinders the performance of state-of-the-art (SOTA) UAV trackers, especially in low-illumination environments. To address this issue, this work proposes an efficient plug-and-play cascaded denoising Transformer (CDT) to suppress cluttered and complex real noise, thereby boosting UAV tracking performance. Specifically, the novel U-shaped cascaded denoising network is designed with a streamlined structure for efficient computation. Additionally, shallow feature deepening (SFD) encoder and multi-feature collaboration (MFC) decoder are constructed based on multi-head transposed self-attention (MTSA) and multi-head transposed cross-attention (MTCA), respectively. A nested residual feed-forward network (NRFN) is developed to focus more on high-frequency information represented by noise. Extensive evaluation and test experiments demonstrate that the proposed CDT has a remarkable denoising effect and improves UAV nighttime tracking performance.
L₂,₁-Norm Regularized Robust and Sparse Linear Discriminant Analysis via an Alternating Direction Method of Multipliers

Recently, an absolute value inequalities discriminant analysis criterion with robustness and sparseness for supervised dimensionality reduction was studied. However, it obtains discriminant directions one by one through greedy search, which makes the sparseness of multiple discriminant directions unexplainable. In addition, it relaxes the original problem into a series of linear programming problems which makes it time consuming. In this paper, we construct a novel linear discriminant analysis with robustness and sparseness jointly through the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm. The proposed approach obtains all the discriminant directions simultaneously, and rather than solving linear programming problems, it is solved by a more effective alternating direction method of multipliers. The effectiveness of the proposed method is supported by preliminary experimental results on two artificial datasets, some benchmark datasests and two face image datasets.
Enhanced Feature Model Based Hybrid Neural Network for Text Detection on Signboard, Billboard and News Tickers

Recognizing text from the nature scene images and videos has been the challenging task of computer vision and machine learning research community in recent years. These texts are difficult to recognize because of their shapes, complex backgrounds, color, shape and size variations. However, text recognition is very much useful in indexing, keyword-based image and video search, and information retrieval. In this research paper, a model is proposed to detect the isolated text characters in the photographic images of natural scenes. The proposed model uses the combination of Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) for recognizing the text in natural images. The model uses two networks, where the first network combines the low-level and middle-level features to increase the feature size and passes the enriched information to the second network. Here, features are again widened by combining with high-level features, resulting in powerful and robust features. To evaluate the proposed model, ICDAR2003 (IC03), ICDAR2013 (IC13), SVT (Street View Text) datasets have been used. And an extensive Tamil news tickers image dataset has been developed to evaluate the model. The experimental results show that the combined feature fusion technique outperforms the other methods on the ICDAR2003, ICDAR2013, SVT and Tamil news tickers datasets.
Leveled Fully Homomorphic Signcryption From Lattices

With the continuous and rapid development of Cloud Computing, Big Data and Internet of Things, it is extremely critical to protect data with homomorphism, privacy and integrity. For this, Rezaeibagha et al. proposed a new cryptographic primitive, called homomorphic signcryption. However, the current homomorphic signcryption schemes either only support linear computation or are built on non-standard assumption. Therefore, it is interesting to design a leveled fully homomorphic signcryption (FHSC) scheme from the standard assumption. In this work, we present a leveled FHSC scheme from lattices. For this, we exert classical sign-then-encrypt method and surmount the difficulty of homomorphic multiplicative evaluation in the way of encrypting every elements. Moreover, we prove its indistinguishability against chosen plaintext attacks (IND-CPA) and strong unforgeability (SUF) under hard problems of standard lattices.
The effectiveness of a VR-based mobile learning system for university students to learn geological knowledge

This study developed a VR-based mobile learning system to simulate the natural environment of a geopark for university students to learn geological knowledge. This learning method used a Google cardboard equipped with smartphones, where students can watch stereoscopic scenes and experience virtual environments as if visiting the geopark. In order to evaluate the learning effectiveness of the proposed system, we collected the learning outcomes, learning motivation, and cognitive load of students before and after the learning activity. The results indicated that students had significantly better learning achievement in their comprehension ability and learning motivation after using the proposed system. Therefore, it can be inferred that the VR-based mobile learning system not only helps students restructure knowledge and improves their comprehension skills but also enhances their learning motivation tendencies. In addition, the results showed that the students did not have high intrinsic and extraneous cognitive loads but had a high germane cognitive load. This finding implies that the proposed system can promote students’ learning without overloading their working memories.
Exploration of Unknown Environment using Deep Reinforcement Learning

Exploring the unknown environment is a very crucial task where human life is at risks like search and rescue operations, abandoned nuclear plants, covert operations and more. Autonomous robots could serve this task efficiently. The existing methods use uncertainty models for localization and map building to explore the unknown areas requiring high onboard computation and time. We propose to use Deep Reinforcement Learning (DRL) for the autonomous exploration of unknown environments. In DRL, the agent interacts with the environment and learns based on experiences (feedback/reward). We propose extrinsic and curiosity-driven reward functions to explore the environment. The curiosity-based reward function motivates the agent to explore unseen areas by predicting future states, while the extrinsic reward function avoids collisions. We train the differential drive robot in one environment and evaluate its performance in another unknown environment. We observe curiosity-driven reward function outperformed the extrinsic reward by exploring more areas in the unknown environment. The test results show the generalization capability to explore unknown environments with the proposed methods.
Deep Learning Robustness Verification for Few-Pixel Attacks

While successful, neural networks have been shown to be vulnerable to adversarial example attacks. In L 0 adversarial attacks, also known as few-pixel attacks, the attacker picks t pixels from the image and arbitrarily perturbs them. To understand the robustness level of a network to these attacks, it is required to check the robustness of the network to perturbations of every set of t pixels. Since the number of sets is exponentially large, existing robustness verifiers, which can reason about a single set of pixels at a time, are impractical for L 0 robustness verification. We introduce Calzone, an L 0 robustness verifier for neural networks. To the best of our knowledge, Calzone is the first to provide a sound and complete analysis for L 0 adversarial attacks. Calzone builds on the following observation: if a classifier is robust to any perturbation of a set of k pixels, for k &gt; t , then it is robust to any perturbation of its subsets of size t . Thus, to reduce the verification time, Calzone predicts the largest k that can be proven robust, via dynamic programming and sampling. It then relies on covering designs to compute a covering of the image with sets of size k . For each set in the covering, Calzone submits its corresponding box neighborhood to an existing L ∞ robustness verifier. If a set’s neighborhood is not robust, Calzone repeats this process and covers this set with sets of size k ′&lt; k . We evaluate Calzone on several datasets and networks, for t ≤ 5. Typically, Calzone verifies L 0 robustness within few minutes. On our most challenging instances (e.g., t =5), Calzone completes within few hours. We compare to a MILP baseline and show that it does not scale already for t =3.
South Korea in 2022

At the presidential and local levels, progressives were replaced by conservatives in 2022. But the National Assembly is still dominated by the opposition Democratic Party, so the government is more divided than ever. As a conservative government came into power in South Korea, North Korea fired the most missiles ever, and inter-Korean relations were very strained. The economy is in serious condition due to inflation, interest rate hikes, and slowing growth.