Reviewerly/exHarmony huggingface dataset:
The Future of Blockchain Technology and the Internet of Things in Healthcare

Using body sensor networks and other wearable sensors to practise for emergencies is becoming more and more common. If privacy and safety of patients are protected in healthcare systems, then using technology may be helpful. Block chain technology has quickly become a popular research topic on websites like Wikipedia and Google. We look at developments and ideas in the Internet of Things that can be used in the vast majority of situations. The healthcare business has a lot of good reasons to use the Block chain. It has everything that people want: decentralisation, immutability, security, privacy, and being open to everyone. The vast majority of the research is focused on these parts. As part of a plan to improve the efficiency of health care services, the Internet of Things (IoT) and other cutting-edge computer technologies were used. In the next section, the application is explained in great detail. It's no secret that medical professionals are looking into the potential of the Internet of Things (IoT) and Block Chain technology in the areas of medication tracking, remote patient monitoring, storing and retrieving medical data, and managing patient medical records. Also, it's important to note that the challenges of using IoT and blockchain technologies in healthcare systems have been well researched and are now being talked about.
AB-Mapper: Attention and BicNet based Multi-agent Path Planning for Dynamic Environment

Multi-agent path finding in dynamic environments is of great academic and practical value for multi-robot systems in the real world. To improve the effectiveness and efficiency of the learning process during path planning in dynamic environments, we introduce an algorithm called Attention and BicNet based Multi-agent path planning with effective reinforcement (AB-Mapper) under the actor-critic reinforcement learning framework. In this framework, on one hand, we design an actor-network that can utilize the BicNet with communication function to achieve the intra-team coordination. On the other hand, we propose a critic network that can selectively allocate attention weights to surrounding agents. This attention mechanism allows an individual agent to automatically learn a better evaluation of actions by considering the behaviours of its surrounding agents. Compared with the SOTA method Mapper in crowded environments with dynamic obstacles, our AB-Mapper is more effective (90.27±0.06% vs. 61.65±13.90% in terms of mean success rate) in solving the general multi-agent path finding problem.
A Virtual Structure Approach to Formation Control of Multi Robots with Collision Avoidance in Space Station

We present an algorithm framework for formation control of multi robots with obstacle avoidance in the space station environment. First, the 2-dimensional complex environment is discretized using hierarchical rectangular partitions in the form of quad-trees; second, the formation in the space station is regarded as a virtual structure, and an optimal path is generated among the obstacle-free rectangles for the virtual structure by the A* algorithm; lastly, vector fields are derived not only for the virtual structure to track the optimal path, but also for each robot to maintain its required pose in the formation. Given the space station environment, the formation requirements and control constraints of the robot, the feedback control law of each robot can be generated independently. Simulations in MATLAB are developed to show the effectiveness and applicability of our method.
Propagation Regularizer for Semi-supervised Learning with Extremely Scarce Labeled Samples

Semi-supervised learning (SSL) is a method to make better models using a large number of easily accessible unlabeled data along with a small number of labeled data obtained at a high cost. Most of existing SSL studies focus on the cases where sufficient amount of labeled samples are available, tens to hundreds labeled samples for each class, which still requires a lot of labeling cost. In this paper, we focus on SSL environment with extremely scarce labeled samples, only 1 or 2 labeled samples per class, where most of existing methods fail to learn. We propose a propagation regularizer which can achieve efficient and effective learning with extremely scarce labeled samples by suppressing confirmation bias. In addition, for the realistic model selection in the absence of the validation dataset, we also propose a model selection method based on our propagation regularizer. The proposed methods show 70.9%, 30.3%, and 78.9% accuracy on CIFAR-10, CIFAR-100, SVHN dataset with just one labeled sample per class, which are improved by 8.9% to 120.2% compared to the existing approaches. And our proposed methods also show good performance on a higher resolution dataset, STL-10.
SPIDE-Net: Spectral Prior-Based Image Dehazing and Enhancement Network

During hazy or foggy conditions, the acquired images are degraded and resulting in reduced visibility, contrast and color fidelity. This image degradation occurs due to atmospheric particles that attenuates and scatters the source radiations. The degradation intensity depends on diverse scenarios having variable densities of atmospheric particles, their wavelength and distance from acquisition device. Existing image dehazing methods for visible-band images are either based on prior assumptions to reconstruct the transmission map or used some learning mechanism to directly estimate the dehazed image. Recently, performance comparison of existing popular image dehazing methods using spectral hazy images are performed in which selected wavelength bands from different fog density levels are used for comparisons. The comparison results showed performance degradation of existing methods with wavelength bands selection and fog density levels. In this study, we design an effective spectral and prior based image dehazing and enhancement network (SPIDE-Net) showing better performance as compared to existing methods when using spectral hazy images from variable wavelength bands and fog density levels. Our SPIDE-Net consists of two networks:1) Spectral Image Dehazing Network (SID-Net), which is trained on multi-spectral hazy images between 450 nm and 720 nm, and takes advantage of varying attenuations in different wavelength bands. 2) Multi-scale Prior based image Dehazing Network (MPD-Net) uses multi-scale dark-channel and color attenuation priors on image triplets selected from a multi-spectral hazy image database. The proposed method is an encoder-decoder style CNN network that combines information from both SID-Net and MPD-Net by sharing a common decoder stage. The proposed network was trained on the SHIA dataset and evaluated at different fog density levels. Compared with popular prior and learning-based methods evaluated at SHIA dataset, the proposed method achieves superior performance both qualitatively and quantitatively.
Digital Crimes in Cloud Environment and the Analysis via Blockchain

In the internet era cloud via blockchain performs a vital role in the domain of Information Technology. Cyber forensics faces several challenges in the cloud such as AWS, IBM, Google, Microsoft, etc. in terms of investigations to resolve various cybercrimes. In current trends, cloud computing is ubiquitous and popular for availing inexpensive services with good speed. The systems of this computing are distinct from the conventional internet technology by employing advanced computations. The availability of the information and its ascertainment case of need is an important factor to monitor the counterfeit attack by the cyber forensic study is one of the investigations processes to prove the authentication. To overcome such situations the forensics method via blockchain is proposed with the hands-on experimental output.
Sentiment Analysis using BLSTM-ResNet on Textual Images

Sentiment Analysis is a popular classification problem where the degree of emotion is analysed based on text. Convolution Neural Network (CNN) are part of various sentiment classification models, but their use has been limited to one-dimensional (1D) convolution or shallow two-dimensional (2D) convolution. The primary focus of this research is to use deep 2D-CNN architectures, inspired by the popular computer vision model ResNet, to replace a shallow 2D-CNN in an existing BLSTM (Bidirectional Long Short Term Memory)-2DCNN model. This research investigates a new method for sentiment analysis which is an amalgam of the practices popular in Natural Language Processing (NLP) as well as Computer Vision, striving to extract opinions from a text by transforming text into images. The text images are formed by transforming the word embeddings matrix into a greyscale image with the help of BLSTM cell. Intensive experiments have been conducted on the Sentiment140 dataset with our novel BLSTM-ResNet model, which contains residual blocks with skip connections. Several BLSTM-ResNet variants were tested to investigate the impact of network depth and dataset size on sentiment detection. Moreover, two sets of residual blocks are designed to form our shallow and deep BLSTM-ResNet models. Our best shallow BLSTM-ResNet models have achieved 4.06% and 3.43% increases in accuracy for dataset sizes 80,000 and 200,000 respectively, compared with the baseline BLSTM-2DCNN model. In addition, an overall improvement is observed on accuracy with every additional residual block in our shallow BLSTM-ResNet model until accuracy saturates, and the same trend has been seen on the impact of dataset size on the performance. Our deep BLSTM-ResNet models show the same positive impact of network depth impact and dataset size on sentiment analysis. Further investigation on the shallow and deep BLSTM-ResNet models shows that deep BLSTM-ResNet outperforms shallow BLSTM-ResNet, in general.
Triplet Ratio Loss for Robust Person Re-identification

Triplet loss has been proven to be useful in the task of person re-identification (ReID). However, it has limitations due to the influence of large intra-pair variations and unreasonable gradients. In this paper, we propose a novel loss to reduce the influence of large intra-pair variations and improve optimization gradients via optimizing the ratio of intra-identity distance to inter-identity distance. As it also requires a triplet of pedestrian images, we call this new loss as triplet ratio loss. Experimental results on four widely used ReID benchmarks, i.e., Market-1501, DukeMTMC-ReID, CUHK03, and MSMT17, demonstrate that the triplet ratio loss outperforms the previous triplet loss.
Learning Reward Functions from a Combination of Demonstration and Evaluative Feedback

As robots become more prevalent in society, they will need to learn to act appropriately under diverse human teaching styles. We present a human-centered approach for teaching robots reward functions by using a mixture of teaching strategies when communicating action appropriateness and goal success. Our method incorporates two teaching strategies for learning: explicit action instruction and evaluative, scalar-based feedback. We demonstrate that a robot instantiating our method can learn from humans who use both kinds of strategies to train the robot in a complex navigation task that includes norm-like constraints.
Continuous Verification of Open Source Components in a World of Weak Links

We are heading for a perfect storm, making open source software poisoning and next-generation supply chain attacks much easier to execute, which could have major im-plications for organizations. The widespread adoption of open source (99% of today's software utilizes open source), the ease of today's package managers, and the best practice of implementing continuous delivery for software projects provide an unprece-dented opportunity for attack. Once an adversary compromises a project, they can deploy malicious code into production under the auspicious of a software patch. Downstream projects will ingest the compromised patch, and now those projects are potentially running the malicious code. The impact could be implementing backdoors, gathering intelligence, delivering malware, or denying a service. According to Sonatype, a leading commercial software security company, these next-generation supply chain attacks have increased 430 % in the last year and there is not a good way to vet or monitor an open-source project prior to incorporating the project. In this paper, we analyzed two case studies of compromised open source components. We propose six continuous verification controls that enable organizations to make data-driven decisions and mitigate breaches, such as analyzing community metrics and project hygiene using scorecards and monitoring the boundary of the software in production. In one case study, the controls identified high levels of risk immediately even though the package is widely used and has over 7 million downloads a week. In both case studies we found that the controls could have prevented malicious actions despite the project breaches.
An Early Prediction and Label Smoothing Alignment Strategy for User Intent Classification of Medical Queries

Deep learning models such as RoBERTa and Bi-LSTM are widely utilized in user intention classification tasks. However, in the medical field, there are difficulties in recognizing user intents due to the complexity of medical query representations and medical-specific terms. In this paper, an alignment strategy based on early prediction and label smoothing named EP-LSA is proposed to classify user intents of medical text queries. The EP-LSA strategy uses a Chinese pre-training model RoBERTa to encode sentence features with rich semantic information, predicts the early features of Bi-LSTM in RCNN and aligns them with output features. The early knowledge from early prediction is processed utilizing cross-entropy loss incorporating label smoothing, which enhances random information to the early knowledge and helps the strategy to extract more fine-grained features related to intention labels. Experiment evaluation was performed based on two publicly available datasets KUAKE and CMID. The results demonstrated that the proposed EP-LSA strategy outperformed other baseline methods and demonstrated the effectiveness of the strategy.
ESBMC-solidity

Smart contracts written in Solidity are programs used in blockchain networks, such as Etherium, for performing transactions. However, as with any piece of software, they are prone to errors and may present vulnerabilities, which malicious attackers could then use. This paper proposes a solidity frontend for the efficient SMT-based context-bounded model checker (ESBMC), named ESBMC-Solidity, which provides a way of verifying such contracts with its framework. A benchmark suite with vulnerable smart contracts was also developed for evaluation and comparison with other verification tools. The experiments performed here showed that ESBMC-Solidity detected all vulnerabilities, was the fastest tool and provided a counterexample for each benchmark. A demonstration is available at https://youtu.be/3UH8_1QAVN0.
Fit the Joint Moments: How to Attack Any Masking Scheme

Side-Channel Analysis (SCA) allows extracting secret keys manipulated by cryptographic primitives through leakages of their physical implementations.Supervised attacks, known to be optimal, can theoretically defeat any countermeasure, including masking, by learning the dependency between the leakage and the secret through the profiling phase.However, defeating masking is less trivial when it comes to unsupervised attacks.While classical strategies such as correlation power analysis or linear regression analysis have been extended to masked implementations, we show that these extensions only hold for Boolean and arithmetic schemes.Therefore, we propose a new unsupervised strategy, the Joint Moments Regression (JMR), able to defeat any masking schemes (multiplicative, affine, polynomial, inner product...), which are gaining popularity in real implementations.The main idea behind JMR is to directly regress the leakage model of the shares by fitting a system based on higher-order joint moments conditions.We show that this idea can be seen as part of a more general framework known as the Generalized Method of Moments (GMM).This offers mathematical foundations on which we rely to derive optimizations of JMR.Simulations results confirm the interest of JMR over state-of-the-art attacks, even in the case of Boolean and arithmetic masking.Eventually, we apply this strategy to real traces and provide, to the best of our knowledge, the first unsupervised attack on the protected AES implementation proposed by the ANSSI for SCA research, which embeds an affine masking and shuffling counter-measures.
Contrastive Multi-Modal Knowledge Graph Representation Learning

Representation learning of knowledge graphs (KGs) aims to embed both entities and relations as vectors in a continuous low-dimensional space, which has facilitated various applications such as link prediction and entity retrieval. Most existing KG embedding methods focus on modeling the structured fact triples independently and ignore the multi-type relations among triples as well as the variety of data types (e.g., texts and images) associated with entities in KGs, and thus fail to capture the complex and multi-modal information that is inherently inside the entity-relation triples. In this paper, we propose a novel approach for knowledge graph embedding named Contrastive Multi-modal Graph Neural Network (CMGNN), which can encapsulate comprehensive features from multi-modal content descriptions of entities and high-order connectivity structures. Specifically, CMGNN first learns entity embeddings from multi-modal content and then contrasts encodings from multi-relational local neighbors and high-order connectivities to obtain latent representations of entities and relations simultaneously. Experimental results demonstrate that CMGNN can effectively model the multi-modalities and multi-type structures in KGs, and significantly outperforms existing state-of-the-art methods on benchmark datasets for the tasks of link prediction and entity classification.
Exploring the limits of multifunctionality across different reservoir computers

Multifunctional neural networks are capable of performing more than one task without changing any network connections. In this paper we explore the performance of a continuous-time, leaky-integrator, and next-generation ‘reservoir computer’ (RC), when trained on tasks which test the limits of multifunctionality. In the first task we train each RC to reconstruct a coexistence of chaotic attractors from different dynamical systems. By moving the data describing these attractors closer together, we find that the extent to which each RC can reconstruct both attractors diminishes as they begin to overlap in state space. In order to provide a greater understanding of this inhibiting effect, in the second task we train each RC to reconstruct a coexistence of two circular orbits which differ only in the direction of rotation. We examine the critical effects that certain parameters can have in each RC to achieve multifunctionality in this extreme case of completely overlapping training data.
EPAR: An Efficient and Privacy-Aware Augmented Reality Framework for Indoor Location-Based Services

Augmented reality (AR) defines a new information-delivery paradigm by overlaying computer-generated information on the perception of the real world. AR-integrated robot has become an appealing concept in terms of enhanced human-robot interaction. Despite intensive research on AR, existing indoor location-based AR systems are vulnerable to attacks and can hardly meet the security and privacy requirements in practice. The problem of designing a secure AR framework to ensure the efficiency and privacy of location-based AR has not been sufficiently studied. In this paper, we holistically study this problem and propose EPAR, an efficient and privacy-aware AR framework for indoor location-based services. EPAR distinguishes itself from the existing work by being the first to address the issues of AR delivery in terms of system scalability, accuracy, privacy, and efficiency. First, an effective indoor location cloaking scheme is presented to safeguard user's privacy while improving system scalability and accuracy. Then, a novel privacy-aware localization scheme is proposed to hierarchically localize the user with privacy concerns. Finally, for the AR content delivery, a new authenticated data structure is tailored to save the data transmission cost and improve system efficiency. We implement EPAR and conduct extensive experiments in real-world scenarios. Evaluation results demonstrate the effectiveness of our EPAR system.
Cross-Modal Contrastive Learning for Code Search

Code search aims to retrieve code snippets from natural language queries, which serves as a core technology to improve development efficiency. Previous approaches have achieved promising results to learn code and query representations by using BERT-based pre-trained models which, however, leads to semantic collapse problems, i.e. native representations of code and query clustering in a high similarity interval. In this paper, we propose CrossCS, a cross-modal contrastive learning method for code search, to improve the representations of code and query by explicit fine-grained contrastive objectives. Specifically, we design a novel and effective contrastive objective that considers not only the similarity between modalities, but also the similarity within modalities. To maintain semantic consistency of code snippets with different names of functions and variables, we use data augmentation to rename functions and variables to meaningless tokens, which enables us to add comparisons between code and augmented code within modalities. Moreover, in order to further improve the effectiveness of pre-trained models, we rank candidate code snippets using similarity scores weighted by retrieval scores and classification scores. Comprehensive experiments demonstrate that our method can significantly improve the effectiveness of pre-trained models for code search.
Exception Handling as a Social Concern

Exception handling has been successfully proposed in software engineering practice as a simple but effective technology to address abnormal situations possibly occurring at runtime. In this article, we present and explain the advantages of how exception handling can be seamlessly integrated in distributed systems—which are realized as multiagent systems—at an organizational level, by relying on the notion of responsibility.
A Data Deduplication Scheme Based on DBSCAN With Tolerable Clustering Deviation

To protect data privacy, users prefer to store encrypted data in cloud servers.Cloud servers reduce the cost of storage and network bandwidth by eliminating duplicate copies.To address the potential internal data leakage problem, the concept of clustering deviation is proposed for the first time.We improve the DBSCAN algorithm to tolerate clustering deviation.A data deduplication scheme is built upon the new algorithm, which considers users as clustering samples.Instead of immediately re-clustering new users, a certain deviation is tolerated to assign the users to the existing classes.We determine the popularity of the data according to user clustering results and apply different encryption schemes to protect the security of unpopular data more effectively.The performance of the algorithm is analyzed and compared with other methods through experiments, and the results verify the feasibility and efficiency of the proposed deduplication scheme.