Reviewerly/exHarmony huggingface dataset:
Exploration of Mined Block Temporarily Holding and Enforce Fork Attacks by Selfish Mining Pool in Proof-of-Work Blockchain Systems

We explored mined block temporarily holding (MBTH) with enforce fork (MBTH-EF) attacks to understand the effect of selfish mining on winning rate and fairness. The temporary holding time provides a pool additional time to begin mining the next block early. In this situation, the pool may risk losing the mined block. One method to maintain the winning probability of the held block is to ensure the holding pool sends out the mined hash values once it receives the mined block message from the other pools. We analyzed MBTH-EF attacks and evaluated the effects of the win rate on when and how long a block is held. We propose a mining competition solution that does not involve actual hash calculation. It entails using one stochastic target hash value for batch racing simulation to evaluate the holding threshold, holding periods, mining time, mining difficulty, pool sizes, and rate of fork occurrences according to the operation data of the Bitcoin system. Because the periodic adjustment of mining difficulty reduces the holding effect, we explored an intermittent MBTH-EF (iMBTH-EF) attack model and examined the effect of intermittent holding on the mining game win rate for a long-term competition. We also identified suitable attack detection methods for the future work according to the simulation results.
DsDTW: Local Representation Learning With Deep soft-DTW for Dynamic Signature Verification

Dynamic time warping (DTW) is a popular technique for sequence alignment, and is the de facto standard for dynamic signature verification. In this paper, we go a significant step further to enhance DTW with the capability of deep representation learning, and propose an end-to-end trainable Deep soft-DTW (DsDTW) model for dynamic signature verification. Specifically, we design a convolutional recurrent adaptive network (CRAN) to process dynamic signatures, and utilize it to provide robust and discriminative local representations as inputs for DTW. As DTW is not fully differentiable with regard to its inputs, we introduce its smoothed formulation, soft-DTW, and incorporate the soft-DTW distances of signature pairs into the loss function for optimization. Because soft-DTW is differentiable, the proposed DsDTW is end-to-end trainable, and achieves an elegant integration of CRAN deep learning model and traditional DTW mechanism. Our method achieves state-of-the-art performance on several public benchmarks, and has won first place in the ICDAR 2021 competition for online signature verification. Source codes of DsDTW is available at https://github.com/KAKAFEI123/DsDTW.
<i>AutoIoT</i>: Automatically Updated IoT Device Identification With Semi-Supervised Learning

IoT devices bring great convenience to a person's life and industrial production. However, their rapid proliferation also troubles device management and network security. Network administrators usually need to know how many IoT devices are in the network and whether they behave normally. IoT device identification is the first step to achieving these goals. Previous IoT device identification methods reach high accuracy in a closed environment. But they are not applicable in the continuously changing environment. When new types of devices are plugged in, they cannot update themselves automatically. Besides, they usually rely on supervised learning and need lots of labeled data, which is costly. To solve these problems, we propose a novel IoT device identification model named <i>AutoIoT</i> , updating itself automatically when new types of devices are plugged in. Besides, it only needs a few labeled data and identifies IoT devices with high accuracy. The evaluation on two public datasets shows that <i>AutoIoT</i> can identify new device types only using 1.5 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 2.5 hours’ traffic and still have high accuracy after updating. Moreover, it has a better performance than other works when there are only a few labeled data, especially in an environment with scanning traffic.
An Intelligent Framework for Recognizing Social Human-Object Interactions

Human object interaction (HOI) recognition plays an important role in the designing of surveillance and monitoring systems for healthcare, sports, education, and public areas. It involves localizing the human and object targets and then identifying the interactions between them. However, it is a challenging task that highly depends on the extraction of robust and distinctive features from the targets and the use of fast and efficient classifiers. Hence, the proposed system offers an automated body-parts-based solution for HOI recognition. This system uses RGB (red, green, blue) images as input and segments the desired parts of the images through a segmentation technique based on the watershed algorithm. Furthermore, a convex hull-based approach for extracting key body parts has also been introduced. After identifying the key body parts, two types of features are extracted. Moreover, the entire feature vector is reduced using a dimensionality reduction technique called t-SNE (t-distributed stochastic neighbor embedding). Finally, a multinomial logistic regression classifier is utilized for identifying class labels. A large publicly available dataset, MPII (Max Planck Institute Informatics) Human Pose, has been used for system evaluation. The results prove the validity of the proposed system as it achieved 87.5% class recognition accuracy.
BIAS: A Toolbox for Benchmarking Structural Bias in the Continuous Domain

Benchmarking heuristic algorithms is vital to understand under which conditions and on what kind of problems certain algorithms perform well. Most benchmarks are performance based, to test algorithm performance under a wide set of conditions. There is also resource- and behavior-based benchmarks to test the resource consumption and the behavior of algorithms. In this article, we propose a novel behavior-based benchmark toolbox: BIAS (Bias in algorithms, structural). This toolbox can detect structural bias (SB) per dimension and across dimension-based on 39 statistical tests. Moreover, it predicts the type of SB using a random forest model. BIAS can be used to better understand and improve existing algorithms (removing bias) as well as to test novel algorithms for SB in an early phase of development. Experiments with a large set of generated SB scenarios show that BIAS was successful in identifying bias. In addition, we also provide the results of BIAS on 432 existing state-of-the-art optimization algorithms showing that different kinds of SB are present in these algorithms, mostly toward the center of the objective space or showing discretization behavior. The proposed toolbox is made available open-source and recommendations are provided for the sample size and hyper-parameters to be used when applying the toolbox on other algorithms.
SafeAPT: Safe Simulation-to-Real Robot Learning Using Diverse Policies Learned in Simulation

The framework of sim-to-real learning, i.e., training policies in simulation and transferring them to real-world systems, is one of the most promising approaches towards data-efficient learning in robotics. However, due to the inevitable reality gap between the simulation and the real world, a policy learned in the simulation may not always generate a safe behaviour on the real robot. As a result, during policy adaptation in the real world, the robot may damage itself or cause harm to its surroundings. In this work, we introduce SafeAPT, a multi-goal robot learning algorithm that leverages a diverse repertoire of policies evolved in simulation and transfers the most promising safe policy to the real robot through episodic interaction. To achieve this, SafeAPT iteratively learns probabilistic reward and safety models from real-world observations using simulated experiences as priors. Then, it performs Bayesian optimization to select the best policy from the repertoire with the reward model, while maintaining the specified safety constraint using the safety model. SafeAPT allows a robot to adapt to a wide range of goals safely with the same repertoire of policies evolved in the simulation. We compare SafeAPT with several baselines, both in simulated and real robotic experiments, and show that SafeAPT finds high-performing policies within a few minutes of real-world operation while minimizing safety violations during the interactions.
An Identity-Based Data Integrity Auditing Scheme for Cloud-Based Maritime Transportation Systems

With the development of Internet of Things (IoT)-enabled Maritime Transportation Systems (MTS), massive data generated in the system not only requires to be stored reliably and cheaply, but also needs to be analyzed timely. The Cloud-based Maritime Transportation Systems (CMTS) allow users to upload the data without worrying about the price, capacity, location and so on. However, CMTS also brings some security issues, where the integrity protection of outsourced data is one of the most important issues since it is crucial for the safety, reliability and efficiency of sea lanes. To solve this problem, we propose an identity-based dynamic data integrity auditing scheme for CMTS. Our scheme decreases the burden of key management and improves the auditing efficiency by batch auditing. Besides, our scheme also supports dynamic operations on the outsourced data for CMTS. The security analysis shows that our scheme can ensure the feature of storage correctness and resist common attacks. In addition, the performance comparison results with other related schemes show that our scheme not only has the lowest computational cost on all entities, but also greatly reduces the communication overhead of the auditing phase. Therefore, our scheme is very suitable for data integrity verification in CMTS.
Interpretable AI in Healthcare: Enhancing Fairness, Safety, and Trust

The value and future potentials of AI in healthcare are becoming self-evident, presenting an escalating body of evidence. However, the adoption into clinical practice is still significantly impacted by the lack of transparency, stemming from inadequate focus on human-comprehensive information from AI, i.e. Interpretable AI. AI interpretations of uncertainty, significance, and causality translate to a more fair, safe, and reliable AI. This is especially pertinent to safer clinical decision making and for minimising any risk to the patient. In this chapter we aim to elucidate what interpretability means and why most machine learning (i.e. AI) models fail to satisfy these definitions. We lay this phenomenon out through what we believe to be its canonical components: predictions, uncertainty, significance, and causality; explaining how these different types of interpretations can support various explanations and how overcoming this barrier can permit the adoption of AI into healthcare.
U-WeAr: User Recognition on Wearable Devices through Arm Gesture

The use of wearable devices equipped with inertial sensors has become increasingly pervasive. It has been widely demonstrated in the literature that inertial signals acquired by these sensors can be used by machine learning algorithms to predict actions performed and/or to recognize the identities of the person wearing the sensors. In this article, we present a hardware/software system for arm gesture recognition, identity recognition, and verification of a person based on inertial sensors. The hardware part is a custom wristband that consists of a computing unit, a wireless communication unit, and an inertial sensor. The software part is an algorithm based on recurrent neural networks that is able to process the signals coming from the sensor and to return a prediction. To validate the system, a dataset consisting of 25 symbols drawn with the arm is collected. These symbols are performed by 33 subjects. We conduct two evaluations: 1) performance evaluation for arm gesture recognition, user recognition and verification; and 2) usability assessment of the system. The performance of the three recognition tasks indicate that this system can be reliably applied in real environments with an accuracy above 96% for gesture recognition, an accuracy of about 85% for user identification, and an equal error rate of about 13% for user verification. The outcome of the usability test proves a great satisfaction from the users in terms of high simplicity in the use of the wristband and goodness of the machine learning predictions.
Multispectral Fusion Transformer Network for RGB-Thermal Urban Scene Semantic Segmentation

Semantic segmentation plays a vital role in autonomous vehicles. Fusing the rich details of RGB image and the illumination robustness of thermal image has great potential to improve the performance of RGB-T semantic segmentation. In multispectral feature fusion, the current main methods are less effective in the characterization of correlations and complementarities of RGB-T. In order to generate robust cross-spectral fusion features, we propose a multispectral fusion transformer network (MFTNet). Specifically, we first design an MFT module to handle the intraspectra correlation and the interspectra complementarity of RGB-T in the multispectral fusion encoder. MFT effectively enhances the RGB-T feature representation under various challenges. Then, an optimization strategy with progressive deep supervision (PDS) loss is proposed to directly supervise the upper and lower layers of the decoder. This strategy can guide the decoder to achieve precise segmentation in a coarse-to-fine manner. Finally, plenty of experimental results prove the effectiveness of our method. On the MFNet dataset, MFNet achieved 74.7 mAcc and 57.3 mIoU, outperforming the state-of-the-art methods.
A Secured Healthcare Medical System Using Blockchain Technology

In our current healthcare system, the most inevitable requirement is to give the protection against the patient’s information that has to be preserved confidential. There are several other approaches to address this issue, but the usual effective solution chosen in this paper is by Blockchain technology. The reason behind in choosing the Blockchain technology is mainly due to its unremarkable features like peer-peer, decentralized, distributed, scalability, extensibility, immutability, interoperability, and public ledger that ensures trustworthiness, protection, and security in supporting the healthcare system. The main basic idea behind this paper is whenever the patient’s information needed to be accessed that has to be authorized and protected using encryption algorithms. So, in this study, the idea in designing framework is to achieve maximum security against in accessing the patient’s medical record. To accomplish this task, entire framework is partitioned into three modules and they are Authentication, Encryption, and Data retrieval.
Development of cyber security and privacy by precision decentralized actionable threat and risk management for mobile communication using Internet of Things (IOT)

In the recent past, with the tremendous growth of Internet of Things (IoT) and mobile communication the security and privacy are needed for the information transmitted from transmitter to receiver. IoT refers to connecting wireless sensor network, various mobile communication applications, machine, people living in different location and equipment collectively by utilizing local or broad are wireless communication and cloud communication, with the internet as the adhesive, during original path to construct a precision wireless sensor network. In this paper, a novel cyber security and privacy methodology is proposed based on precision decentralized actionable threat and risk management for mobile communication using IoT. The threat and risk can be managed by providing efficient and accurate action and the methodology is the decentralized amount the wireless sensor network to carry mobile communication precision. The huge- scale operation of the IoT want to face few problems, particularly in security and privacy problems for precision, linking between wireless sensor network, and mobile communication with IoT devices because of the truth that IoT has various individuality through conventional wireless communication networks, linked to its particular risk and threats. The security and privacy solutions for IoT should give IoT wireless nodes (users, servers, things and objects) with information validity, privacy, integrity and originality certification, and approval. The risk and threat management should be properly decentralized protection must be considered.
Multimodal Classification of Onion Services for Proactive Cyber Threat Intelligence Using Explainable Deep Learning

The dark web has been confronted with a significant increase in the number and variety of onion services of illegitimate and criminal intent.Anonymity, encryption, and the technical complexity of the Tor network are key challenges in detecting, disabling, and regulating such services.Instead of tracking an operational location, cyber threat intelligence can become more proactive by utilizing recent advances in Artificial Intelligence (AI) to detect and classify onion services based on the content, as well as provide an interpretation of the classification outcome.In this paper, we propose a novel multimodal classification approach based on explainable deep learning that classifies onion services based on the image and text content of each site.A Convolutional Neural Network with Gradient-weighted Class Activation Mapping (Grad-CAM) and a pre-trained word embedding with Bahdanau additive attention are the core capabilities of this approach that classify and contextualize the representative features of an onion service.We demonstrate the superior classification accuracy of this approach as well as the role of explainability in decision-making that collectively enables proactive cyber threat intelligence in the dark web.
Guided by Meta-Set: A Data-Driven Method for Fine-Grained Visual Recognition

The lack of sufficient training data has been one obstacle to fine-grained visual classification research because labeling subcategories generally requires specialist knowledge. As one optional approach to alleviating the data-hunger problem, leveraging web images as training data is drawing increasing attention. Nevertheless, web images potentially have false labels, which can misguide the training process. Although several works have been proposed to deal with label noise, it still can be difficult for the network to tackle complex real-world noisy labels without any prior knowledge. In the literature, we propose to leverage a small and clean meta-set to provide reliable prior knowledge for tackling noisy web images. Specifically, our method trains a network with two peer predicting heads, which learn from noisy web images (web head) and meta ones (meta head), respectively. The meta head produces pseudo soft labels for web images to revise their training loss, which can overcome the high noise ratio problem. Furthermore, a selection net is trained in a meta-learning strategy to identify in- and out-of-distribution noisy images. Then in-distribution ones are reused for training with pseudo soft labels produced by the meta head as supervision, while out-of-distribution ones are discarded. In this manner, the misguidance caused by label noise is remarkably alleviated and in-distribution noisy samples are properly exploited to boost model performance. The superiority of our proposed approach is demonstrated by mathematical theory with great interpretability as well as extensive experimental results on the real-world dataset WebFG-496.
Energy-Aware Non-Preemptive Task Scheduling With Deadline Constraint in DVFS-Enabled Heterogeneous Clusters

Energy conservation of large data centers for high performance computing workloads, such as deep learning with Big Data, is of critical significance, where cutting down a few percent of electricity translates into million-dollar savings. This work studies energy conservation on emerging CPU-GPU hybrid clusters through dynamic voltage and frequency scaling (DVFS). We aim at minimizing the total energy consumption of processing a batch of offline tasks or a sequence of real-time tasks under deadline constraints. We derive a fast and accurate analytical model to compute the appropriate voltage/frequency setting for each task, and assign multiple tasks to the cluster with heuristic scheduling algorithms. In particular, our model stresses the nonlinear relationship between task execution time and processor speed for GPU-accelerated applications, for more accurately capturing real-world GPU energy consumption. In performance evaluation driven by real-world power measurement traces, our scheduling algorithm shows comparable energy savings to the theoretical upper bound. With a GPU scaling interval where analytically at most 36% of energy can be saved, we record 33-35% of energy savings. Our results are applicable to energy management on modern heterogeneous clusters.
Generative Adversarial Networks: a systematic review and applications

Since their introduction in 2014 Generative Adversarial Networks (GANs) have been employed successfully in many areas such as image processing, computer vision, medical imaging, video as well as other disciplines. A large number of review papers have been published, focusing on certain application areas and proposed methods. In this paper, we collected the most recent review papers, organized the collected information according to the application field and we presented the application areas, the GAN architectures that have been applied in each case and summarized the open issues in each area.
Knowledge-Based Visual Question Generation

Visual question generation task aims to generate meaningful questions about an image targeting an answer. Existing methods focus on the visual concepts in the image for question generation. However, humans inevitably use their knowledge related to visual objects in images to construct questions. In this paper, we propose a knowledge-based visual question generation model that can integrate visual concepts and non-visual knowledge to generate questions. To obtain visual concepts, we utilize a pre-trained object detection model to obtain object-level features of each object in the image. To obtain useful non-visual knowledge, we first retrieve the knowledge from the knowledge-base related to the visual objects in the image. Considering that not all retrieved knowledge is helpful for this task, we introduce an answer-aware module to capture the candidate knowledge related to the answer from the retrieved knowledge, which ensures that the generated content can be targeted at the answer. Finally, object-level representations containing visual concepts and non-visual knowledge are sent to a decoder module to generate questions. Extensive experiments on the FVQA and KBVQA datasets show that the proposed model outperforms the state-of-the-art models.
Single Remote Sensing Image Dehazing Using Gaussian and Physics-Guided Process

Remote Sensing (RS) dehazing is a challenging task due to various haze distribution severely degrade the image quality. Recent learning-based methods achieve dramatic performance for RS dehazing, however previous ways are limited to their generality using only fully labeled datasets and less prior-guided information. In this paper, we explore the Gaussian and Physics-guided Dehazing Network (GPD-Net) to better obtain hazy feature and improve the generalization ability in real world condition. To promote the feature extraction, a novel Global Attention Mechanism (GAM) is involved to extract feature from different haze distribution. Then, an encoder-decoder network is designed with Gaussian Process (GP) in the intermediate latent space, in order to learn the full labeled dehazing and guide to handle the unlabeled learning. For the fine-tuning, we select some physical prior knowledge to refine the dehazed results. Extensive experiments demonstrate that our method outperforms the recent comparing approaches on the synthetic and real-world datasets.