Reviewerly/exHarmony huggingface dataset:
Reinforcement learning approaches for efficient and secure blockchain-powered smart health systems

Emerging technological innovation toward e-Health transition is a worldwide priority for ensuring people’s quality of life. Hence, secure exchange and analysis of medical data amongst diverse organizations would increase the efficiency of e-Health systems toward elevating medical phenomena such as outbreaks and acute patients’ disorders. However, medical data exchange is challenging since issues, such as privacy, security, and latency may arise. Thus, this paper introduces Healthchain-RL, an adaptive, intelligent, consortium, and secure Blockchain-powered health system employing artificial intelligence, especially Deep Reinforcement Learning (DRL). Blockchain and DRL technologies show their robust performance in different fields, including healthcare systems. The proposed Healthchain-RL framework aggregates heterogeneous healthcare organizations with different requirements using the power of Blockchain while maintaining an optimized framework via an online intelligent decision-making RL algorithm. Hence, an intelligent Blockchain Manager (BM) was proposed based on the DRL, mainly Deep Q-Learning and it is variations, to optimizes the Blockchain network’s behavior in real-time while considering medical data requirements, such as urgency and security levels. The proposed BM works toward intelligently changing the blockchain configuration while optimizing the trade-off between security, latency, and cost. The optimization model is formulated as a Markov Decision Process (MDP) and solved effectively using three RL-based techniques. These three techniques are Deep Q-Networks (DQN), Double Deep Q-Networks (DDQN), and Dueling Double Deep Q-Networks (D3QN). Finally, a comprehensive comparison is conducted between the proposed techniques and two heuristic approaches. The proposed strategies converge in real-time adaptivity to the system status while maintaining maximum security and minimum latency and cost.
2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition

3D convolutional networks are prevalent for video recognition. While achieving excellent recognition performance on standard benchmarks, they operate on a sequence of frames with 3D convolutions and thus are computationally demanding. Exploiting large variations among different videos, we introduce Ada3D, a conditional computation framework that learns instance-specific 3D usage policies to determine frames and convolution layers to be used in a 3D network. These policies are derived with a two-head lightweight selection network conditioned on each input video clip. Then, only frames and convolutions that are selected by the selection network are used in the 3D model to generate predictions. The selection network is optimized with policy gradient methods to maximize a reward that encourages making correct predictions with limited computation. We conduct experiments on three video recognition benchmarks and demonstrate that our method achieves similar accuracies to state-of-the-art 3D models while requiring 20% − 50% less computation across different datasets. We also show that learned policies are transferable and Ada3D is compatible to different backbones and modern clip selection approaches. Our qualitative analysis indicates that our method allocates fewer 3D convolutions and frames for "static" inputs, yet uses more for motion-intensive clips.
No frame left behind: Full Video Action Recognition

Not all video frames are equally informative for recognizing an action. It is computationally infeasible to train deep networks on all video frames when actions develop over hundreds of frames. A common heuristic is uniformly sampling a small number of video frames and using these to recognize the action. Instead, here we propose full video action recognition and consider all video frames. To make this computational tractable, we first cluster all frame activations along the temporal dimension based on their similarity with respect to the classification task, and then temporally aggregate the frames in the clusters into a smaller number of representations. Our method is end-to-end trainable and computationally efficient as it relies on temporally localized clustering in combination with fast Hamming distances in feature space. We evaluate on UCF101, HMDB51, Breakfast, and Something-Something V1 and V2, where we compare favorably to existing heuristic frame sampling methods.
Challenges, Techniques, and Trends of Simple Knowledge Graph Question Answering: A Survey

Simple questions are the most common type of questions used for evaluating a knowledge graph question answering (KGQA). A simple question is a question whose answer can be captured by a factoid statement with one relation or predicate. Knowledge graph question answering (KGQA) systems are systems whose aim is to automatically answer natural language questions (NLQs) over knowledge graphs (KGs). There are varieties of researches with different approaches in this area. However, the lack of a comprehensive study to focus on addressing simple questions from all aspects is tangible. In this paper, we present a comprehensive survey of answering simple questions to classify available techniques and compare their advantages and drawbacks in order to have better insights of existing issues and recommendations to direct future works.
Communication-Aware Multi-Agent Metareasoning for Decentralized Task Allocation

Metareasoning refers to reasoning about one's own decision making process.This paper considers metareasoning about the decision making process in multi-agent settings.We present a multiagent metareasoning approach that enables a multi-agent team to select which task allocation algorithm to use as a function of changing communication quality level.Given a set of multi-agent task allocation algorithms, we synthesize a policy that prescribes the best algorithm to use among a predefined set of algorithms for a given communication level.Since each agent in the team runs the same policy, the team (or a part of the team) will collectively switch between task allocation algorithms as a function of the observed level of communication.We apply reactive synthesis to generate the policy from high-level specifications written in Linear Temporal Logic encoding the agents' switching behavior with respect to the state of the environment.We perform experiments in simulation to identify the best performing algorithms under different communication levels.The communication environment is modeled using the Rayleigh fading model and communication estimation is done through the exchange of heartbeat messages among agents.We test our metareasoning policy in three types of scenarios: search & rescue, fire monitoring, and ship protection scenarios.For each scenario, we demonstrate that our policy achieved better performance with respect to either max distance traveled, max number of transmitted messages or both compared to running any single algorithm.
Data-Aware Device Scheduling for Federated Edge Learning

Federated Edge Learning (FEEL) involves the collaborative training of machine learning models among edge devices, with the orchestration of a server in a wireless edge network. Due to frequent model updates, FEEL needs to be adapted to the limited communication bandwidth, scarce energy of edge devices, and the statistical heterogeneity of edge devices’ data distributions. Therefore, a careful scheduling of a subset of devices for training and uploading models is necessary. In contrast to previous work in FEEL where the data aspects are under-explored, we consider data properties at the heart of the proposed scheduling algorithm. To this end, we propose a new scheduling scheme for non-independent and-identically-distributed (non-IID) and unbalanced datasets in FEEL. As the data is the key component of the learning, we propose a new set of considerations for data characteristics in wireless scheduling algorithms in FEEL. In fact, the data collected by the devices depends on the local environment and usage pattern. Thus, the datasets vary in size and distributions among the devices. In the proposed algorithm, we consider both data and resource perspectives. In addition to minimizing the completion time of FEEL as well as the transmission energy of the participating devices, the algorithm prioritizes devices with rich and diverse datasets. We first define a general framework for the data-aware scheduling and the main axes and requirements for diversity evaluation. Then, we discuss diversity aspects and some exploitable techniques and metrics. Next, we formulate the problem and present our data-aware scheduling (DAS) algorithm for FEEL. Evaluations in different scenarios show that DAS algorithm can help achieve high accuracy in few rounds with a reduced cost.
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network With Attention and Cyclic Loss

Magnetic resonance imaging (MRI) is an important medical imaging modality, but its acquisition speed is quite slow due to the physiological limitations. Recently, super-resolution methods have shown excellent performance in accelerating MRI. In some circumstances, it is difficult to obtain high-resolution images even with prolonged scan time. Therefore, we proposed a novel super-resolution method that uses a generative adversarial network with cyclic loss and attention mechanism to generate high-resolution MR images from low-resolution MR images by upsampling factors of 2× and 4×. We implemented our model on pelvic images from healthy subjects as training and validation data, while those data from patients were used for testing. The MR dataset was obtained using different imaging sequences, including T2, T2W SPAIR, and mDIXON-W. Four methods, i.e., BICUBIC, SRCNN, SRGAN, and EDSR were used for comparison. Structural similarity, peak signal to noise ratio, root mean square error, and variance inflation factor were used as calculation indicators to evaluate the performances of the proposed method. Various experimental results showed that our method can better restore the details of the high-resolution MR image as compared to the other methods. In addition, the reconstructed high-resolution MR image can provide better lesion textures in the tumor patients, which is promising to be used in clinical diagnosis.
Joint Learning of Latent Similarity and Local Embedding for Multi-View Clustering

Spectral clustering has been an attractive topic in the field of computer vision due to the extensive growth of applications, such as image segmentation, clustering and representation. In this problem, the construction of the similarity matrix is a vital element affecting clustering performance. In this paper, we propose a multi-view joint learning (MVJL) framework to achieve both a reliable similarity matrix and a latent low-dimensional embedding. Specifically, the similarity matrix to be learned is represented as a convex hull of similarity matrices from different views, where the nuclear norm is imposed to capture the principal information of multiple views and improve robustness against noise/outliers. Moreover, an effective low-dimensional representation is obtained by applying local embedding on the similarity matrix, which preserves the local intrinsic structure of data through dimensionality reduction. With these techniques, we formulate the MVJL as a joint optimization problem and derive its mathematical solution with the alternating direction method of multipliers strategy and the proximal gradient descent method. The solution, which consists of a similarity matrix and a low-dimensional representation, is ultimately integrated with spectral clustering or K-means for multi-view clustering. Extensive experimental results on real-world datasets demonstrate that MVJL achieves superior clustering performance over other state-of-the-art methods.
Lithological mapping of Waiotapu Geothermal Field (New Zealand) using hyperspectral and thermal remote sensing and ground exploration techniques

Hydrothermal systems develop secondary mineral assemblages due to rock and fluid interactions. Such minerals can be identified on the surface and mapped using hyperspectral imaging and thermal remote sensing to complement geothermal exploration and monitoring. These methods are effective in detecting hydrothermal alteration and thermal anomalies of extensive areas, faster and cheaper than ground-based mapping alone. Here, we study the Waiotapu Geothermal Field, located in the Taupo Volcanic Zone, New Zealand, to demonstrate the capability of such remote sensing tools for mapping surface hydrothermal lithologies and geothermal features. Application of remote sensing techniques to study the Waiotapu Geothermal Field is an optimal alternative as the site is protected for its sinter deposition springs, geysers, vulnerable geothermal features and rare thermotolerant vegetation. Airborne hyperspectral and thermal remote sensing data were complemented with 75 soil and rock samples for ground truth. These samples have been analysed using Visible Near Infrared (VNIR) and Shortwave Infrared (SWIR) spectroscopy, Scanning Electron Microscopy (SEM) with Energy Dispersive X-ray Spectroscopy (EDS) and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to constrain alteration mineralogy and bulk rock geochemistry. Lithological maps created through Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) supervised classifications using the airborne hyperspectral data benchmarked against independent image- and ground-based validation data. Three main lithologies were mapped out, including silica sinter deposits, mixed alteration zones, and acid-sulphate alteration at 57.3% for SAM and 87.5% for SVM overall accuracy. The lithological classes have been analysed using the thermal imagery, which confirmed strong spatial heterogeneity of surface heat sources and overall higher surface temperatures for acid-sulphate alteration zones. Through Principal Component Analysis, enrichment of Ag, Au, S and Sb are found to be located around the Champagne Pool, which is where the near-neutral pH high-chloride fluids discharge at surface. The combination of airborne hyperspectral and thermal imagery with ground-based methods applied at Waiotapu Geothermal Field for the first time, can provide new data to understand the hydrothermal system and confirm that such methods are useful for geothermal initial reconnaissance surveys.
Contrastive Learning Improves Model Robustness Under Label Noise

Deep neural network-based classifiers trained with the categorical cross-entropy (CCE) loss are sensitive to label noise in the training data. One common type of method that can mitigate the impact of label noise can be viewed as supervised robust methods; one can simply replace the CCE loss with a loss that is robust to label noise, or re-weight training samples and down-weight those with higher loss values. Recently, another type of method using semi-supervised learning (SSL) has been proposed, which augments these supervised robust methods to exploit (possibly) noisy samples more effectively. Although supervised robust methods perform well across different data types, they have been shown to be inferior to the SSL methods on image classification tasks under label noise. Therefore, it remains to be seen that whether these supervised robust methods can also perform well if they can utilize the unlabeled samples more effectively. In this paper, we show that by initializing supervised robust methods using representations learned through contrastive learning leads to significantly improved performance under label noise. Surprisingly, even the simplest method (training a classifier with the CCE loss) can outperform the state-of-the-art SSL method by more than 50% under high label noise when initialized with contrastive learning. Our implementation will be publicly available at https://github.com/arghosh/noisy_label_pretrain.
The impact of digital contact tracing on the SARS-CoV-2 pandemic—a comprehensive modelling study

Contact tracing is one of several strategies employed in many countries to curb the spread of SARS-CoV-2. Digital contact tracing (DCT) uses tools such as cell-phone applications to improve tracing speed and reach. We model the impact of DCT on the spread of the virus for a large epidemiological parameter space consistent with current literature on SARS-CoV-2. We also model DCT in combination with random testing (RT) and social distancing (SD). Modelling is done with two independently developed individual-based (stochastic) models that use the Monte Carlo technique, benchmarked against each other and against two types of deterministic models. For current best estimates of the number of asymptomatic SARS-CoV-2 carriers (approximately 40%), their contagiousness (similar to that of symptomatic carriers), the reproductive number before interventions ( R0 at least 3) we find that DCT must be combined with other interventions such as SD and/or RT to push the reproductive number below one. At least 60% of the population would have to use the DCT system for its effect to become significant. On its own, DCT cannot bring the reproductive number below 1 unless nearly the entire population uses the DCT system and follows quarantining and testing protocols strictly. For lower uptake of the DCT system, DCT still reduces the number of people that become infected. When DCT is deployed in a population with an ongoing outbreak where O (0.1%) of the population have already been infected, the gains of the DCT intervention come at the cost of requiring up to 15% of the population to be quarantined (in response to being traced) on average each day for the duration of the epidemic, even when there is sufficient testing capability to test every traced person.
Extracting Key Factors of Cyber Hygiene Behaviour Among Software Engineers: A Systematic Literature Review

The advent of new technologies and the rapid growth of internet users have given birth to the menace of cyber-crime.Unfortunately, it is increasing at an alarming pace.This situation calls for good cyber hygiene behavior to secure digital lives.Cyber hygiene behaviour holds a significant role in terms of cybersecurity across the globe.There is a dire need to understand better the user variations associated with good or bad cyber hygiene behaviour and an improved view of what users do to encourage good cyber hygiene.Cybersecurity attacks are rising due to recent advancements in ICT and the Industrial Revolution 4.0 (IR 4.0).Software development organizations are among the crucial sectors suffering from cybersecurity issues.These organizations are more vulnerable to cyber-attacks because they lack proper cybersecurity culture.Although many initiatives have been taken by academia and industry to address this rising issue, the problem still exists for Software development organizations because good cyber hygiene behaviour is not observed, which is a prerequisite to reduce cyber threats.This study performed a Systematic Literature Review (SLR) of research papers published during 2010 -2020.The key factors influencing software engineers' cyber hygiene behaviour intention are extracted from the published literature.The study examined 35 research papers out of 5,270 found from IEEE Xplore, Emerald Insight, SpringerLink, and ScienceDirect databases.The study reviewed number of factors such as the role of personal, social, sociocognitive, environmental, & technological factors that may individually or collectively influence software engineers' cyber hygiene behaviour.The positive and negative factors associated with the cyber hygiene behaviour of software engineers are also categorized.This study enriches the understanding of the potential factors related to software engineers' cyber hygiene behaviours.It provides valuable insights to researchers, software development organizations, governments, and individuals associated with the field of Software Engineering.This research will assist in changing the software engineers' behaviour towards cyber hygiene, which will ultimately lead to mitigate the issues of Cybersecurity.
A new chaotic image encryption scheme based on dynamic L-shaped scrambling and combined map diffusion

This paper proposes a new chaotic image encryption scheme based on dynamic L-shaped scrambling and combined map diffusion. First, four different chaotic sequences are generated through the 2D Hénon system, these four chaotic sequences are used in the scrambling and diffusion stages of the algorithm. Second, in the scrambling process, the image is initially confused by dynamic L-shaped scrambling, and then the image is further confused by Arnold map. Finally, a nonlinear operation is used in the diffusion stage. Based on the 1D Logistic system and the 1D Sine system and the XOR operation, two rounds of combined map diffusion are designed to change the pixel value of the image and further hide the image information. In addition, the algorithm proposed in this paper is still feasible in color image encryption. The experimental results show that the algorithm has excellent performance and high security, which can resist common attacks such as statistical attacks and differential attacks, and the algorithm runs time efficiency is also high.
Object Tracking via Spatial-Temporal Memory Network

Temporal and spatial contexts, characterizing target appearance variations and target-background differences, respectively, are crucial for improving the online adaptive ability and instance-level discriminative ability of object tracking. However, most existing trackers focus on either the temporal context or the spatial context during tracking and have not exploited these contexts simultaneously and effectively. In this paper, we propose a Spatial-TEmporal Memory (STEM) network to exploit these contexts jointly for object tracking. Specifically, we develop a key-value structured memory model equipped with a key-value index-based memory reading mechanism to model the spatial and temporal contexts simultaneously. To update the memory with new target states and ensure the diversity of the memory, we introduce a similarity-aware memory update scheme. In addition, we construct an entropy-guided ensemble strategy to fuse the prediction models based on these two contexts, such that these two contexts can be exploited to estimate the target state jointly. Extensive experimental results on eight challenging datasets, including OTB2015, TC128, UAV123, VOT2018, LaSOT, TrackingNet, GOT-10k, and OxUvA, demonstrate that the proposed method performs favorably against state-of-the-art trackers.
Legal norm retrieval with variations of the bert model combined with TF-IDF vectorization

In this work, we examine variations of the BERT model on the statute law retrieval task of the COLIEE competition. This includes approaches to leverage BERT's contextual word embeddings, fine-tuning the model, combining it with TF-IDF vectorization, adding external knowledge to the statutes and data augmentation. Our ensemble of Sentence-BERT with two different TF-IDF representations and document enrichment exhibits the best performance on this task regarding the F2 score. This is followed by a fine-tuned LEGAL-BERT with TF-IDF and data augmentation and our third approach with the BERTScore. As a result, we show that there are significant differences between the chosen BERT approaches and discuss several design decisions in the context of statute law retrieval.