Reviewerly/exHarmony huggingface dataset:
Towards sustainable smart cities: A secure and scalable trading system for residential homes using blockchain and artificial intelligence

This paper proposes a secure blockchain based energy trading system for residential homes. In the system, a new proof-of-computational closeness (PoCC) consensus protocol is proposed for the selection of miners and the creation of blocks. Moreover, an analytical energy pricing policy is designed to solve the problem of existing energy pricing policies in a distributed trading environment. A dynamic multi-pseudonym mechanism is developed for the prosumers to preserve their transactional privacy during energy trading. Since it requires extra storage and computing resources for the blockchain miners to simultaneously execute both mining process and application intensive tasks, therefore, an improved sparse neural network (ISNN) is proposed for computation offloading to the cloud servers. In ISNN, a Jaya optimization algorithm is used to accelerate the error convergence rate while reducing the number of connections between different layers of neurons. Besides, ISNN optimizes the overall computational cost of the system. Furthermore, the security of the prosumers is ensured using blockchain technology while security analysis shows that the system is robust against the Sybil attack. The proposed blockchain based peer-to-peer secure energy trading system is extremely important for sustainable cities and society. Simulations are conducted to evaluate the effectiveness of the proposed system. The proposed pricing policy is compared with time-of-use pricing, critical peak pricing and real-time pricing policies. From the results, it is proved that the prosumers achieve a higher degree of satisfaction and utility when using the proposed pricing policy. Moreover, the probability of a successful Sybil attack is zero as the number of attackers’ identities and computational capacities increases. Under different sizes of data to be uploaded, the proposed ISNN scheme has the least average computational cost and data transmission time as compared to deep reinforcement learning combined with genetic algorithm (DRGO) and sparse evolutionary training and multi-layer perceptron (SET-MLP) schemes in the literature. Moreover, the proposed system is tested for scalability by increasing the number of prosumers. Extensive simulations are performed and the results depict the satisfactory performance of the proposed system.
Digging into Uncertainty in Self-supervised Multi-view Stereo

Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions, lacking comprehensive explanations about the effectiveness of the pretext task in self-supervised MVS. To this end, we propose to estimate epistemic uncertainty in self-supervised MVS, accounting for what the model ignores. Specially, the limitations can be categorized into two types: ambiguious supervision in foreground and invalid supervision in background. To address these issues, we propose a novel Uncertainty reduction Multi-view Stereo (U-MVS) framework for self-supervised learning. To alleviate ambiguous supervision in foreground, we involve extra correspondence prior with a flow-depth consistency loss. The dense 2D correspondence of optical flows is used to regularize the 3D stereo correspondence in MVS. To handle the invalid supervision in background, we use Monte-Carlo Dropout to acquire the uncertainty map and further filter the unreliable supervision signals on invalid regions. Extensive experiments on DTU and Tank&Temples benchmark show that our U-MVS framework <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> achieves the best performance among unsupervised MVS methods, with competitive performance with its supervised opponents.
Greedy Gradient Ensemble for Robust Visual Question Answering

Language bias is a critical issue in Visual Question Answering (VQA), where models often exploit dataset biases for the final decision without considering the image information. As a result, they suffer from performance drop on out-of-distribution data and inadequate visual explanation. Based on experimental analysis for existing robust VQA methods, we stress the language bias in VQA that comes from two aspects, i.e., distribution bias and shortcut bias. We further propose a new de-bias framework, Greedy Gradient Ensemble (GGE), which combines multiple biased models for unbiased base model learning. With the greedy strategy, GGE forces the biased models to over-fit the biased data distribution in priority, thus makes the base model pay more attention to examples that are hard to solve by biased models. The experiments demonstrate that our method makes better use of visual information and achieves state-of-the-art performance on diagnosing dataset VQACP without using extra annotations.
SeBS

Function-as-a-Service (FaaS) is one of the most promising directions for the future of cloud services, and serverless functions have immediately become a new middleware for building scalable and cost-efficient microservices and appli cations. However, the quickly moving technology hinders reproducibility, and the lack of a standardized benchmarking suite leads to ad-hoc solutions and microbenchmarks being used in serverless research, further complicating meta-analysis and comparison of research solutions. To address this challenge, we propose the Serverless Benchmark Suite: the first benchmark for FaaS computing that systematically covers a wide spectrum of cloud resources and applications. Our benchmark consists of the specification of representative workloads, the accompanying implementation and evaluation infrastructure, and the evaluation methodology that facilitates reproducibility and enables interpretability. We demonstrate that the abstract model of a FaaS execution environment ensures the applicability of our benchmark to multiple commercial providers such as AWS, Azure, and Google Cloud. Our work facilities experimental evaluation of serverless systems, and delivers a standardized, reliable and evolving evaluation methodology of performance, efficiency, scalability and reliability of middleware FaaS platforms.
Watermarking schemes for digital images: Robustness overview

Digital watermarking is an important scientific direction located at the intersection of cybersecurity and multimedia processing. Digital watermarking is used for digital objects copyright protection and protection against forgery. The importance of those tasks is highlighted by the ongoing COVID-19 pandemic that lasts for more than a year and forced multiple industries to transit to online. The main digital watermarking application include active attackers whose goal is to destroy or damage watermarks. Thus, digital watermarking schemes must be robust to attacks. Despite of the large number of existing digital watermarking schemes, a comprehensive analysis of their robustness towards various attacks does not exist. Our study aims to fill this gap. In this article, latest years findings in digital watermarking are systematized. First of all, we review the most prominent distinctive features of watermarking schemes, attack types, and performance measures to facilitate navigation in digital image watermarking. Then, we introduce classification of digital watermarking schemes by their robustness towards various classes of attacks. We believe this classification could be useful for researchers who aim to choose a digital watermarking scheme for their application with a known set of attacks. Finally, we highlight the most promising solutions that further development could lead to schemes with better robustness compared to existing schemes.
All-Around Real Label Supervision: Cyclic Prototype Consistency Learning for Semi-Supervised Medical Image Segmentation

Semi-supervised learning has substantially advanced medical image segmentation since it alleviates the heavy burden of acquiring the costly expert-examined annotations. Especially, the consistency-based approaches have attracted more attention for their superior performance, wherein the real labels are only utilized to supervise their paired images via supervised loss while the unlabeled images are exploited by enforcing the perturbation-based "unsupervised" consistency without explicit guidance from those real labels. However, intuitively, the expert-examined real labels contain more reliable supervision signals. Observing this, we ask an unexplored but interesting question: can we exploit the unlabeled data via explicit real label supervision for semi-supervised training? To this end, we discard the previous perturbation-based consistency but absorb the essence of non-parametric prototype learning. Based on the prototypical networks, we then propose a novel cyclic prototype consistency learning (CPCL) framework, which is constructed by a labeled-to-unlabeled (L2U) prototypical forward process and an unlabeled-to-labeled (U2L) backward process. Such two processes synergistically enhance the segmentation network by encouraging morediscriminative and compact features. In this way, our framework turns previous "unsupervised" consistency into new "supervised" consistency, obtaining the "all-around real label supervision" property of our method. Extensive experiments on brain tumor segmentation from MRI and kidney segmentation from CT images show that our CPCL can effectively exploit the unlabeled data and outperform other state-of-the-art semi-supervised medical image segmentation methods.
Positional Contrastive Learning for Volumetric Medical Image Segmentation

The success of deep learning heavily depends on the availability of large labeled training sets. However, it is hard to get large labeled datasets in medical image domain because of the strict privacy concern and costly labeling efforts. Contrastive learning, an unsupervised learning technique, has been proved powerful in learning image-level representations from unlabeled data. The learned encoder can then be transferred or fine-tuned to improve the performance of downstream tasks with limited labels. A critical step in contrastive learning is the generation of contrastive data pairs, which is relatively simple for natural image classification but quite challenging for medical image segmentation due to the existence of the same tissue or organ across the dataset. As a result, when applied to medical image segmentation, most state-of-the-art contrastive learning frameworks inevitably introduce a lot of false negative pairs and result in degraded segmentation quality. To address this issue, we propose a novel positional contrastive learning (PCL) framework to generate contrastive data pairs by leveraging the position information in volumetric medical images. Experimental results on CT and MRI datasets demonstrate that the proposed PCL method can substantially improve the segmentation performance compared to existing methods in both semi-supervised setting and transfer learning setting. (Code available at github.com/dewenzeng/positional_cl).
Adversarial XAI Methods in Cybersecurity

Machine Learning methods are playing a vital role in combating ever-evolving threats in the cybersecurity domain.Explanation methods that shed light on the decision process of black-box classifiers are one of the biggest drivers in the successful adoption of these models.Explaining predictions that address 'Why?/Why Not?' questions help users/stakeholders/analysts understand and accept the predicted outputs with confidence and build trust.Counterfactual explanations are gaining popularity as an alternative method to help users to not only understand the decisions of black-box models (why?) but also to provide a mechanism to highlight mutually exclusive data instances that would change the outcomes (why not?).Recent Explainable Artificial Intelligence literature has focused on three main areas : (a) creating and improving explainability methods that help users better understand how the internal of ML models work as well as their outputs; (b) attacks on interpreters with a white-box setting; (c) defining the relevant properties, metrics of explanations generated by models.Nevertheless, there is no thorough study of how the model explanations can introduce new attack surfaces to the underlying systems.A motivated adversary can leverage the information provided by explanations to launch membership inference, and model extraction attacks to compromise the overall privacy of the system.Similarly, explanations can also facilitate powerful evasion attacks such as poisoning and back door attacks.In this paper, we cover this gap by tackling various cybersecurity properties and threat models related to counterfactual explanations.We propose a new black-box attack that leverages Explainable Artificial Intelligence (XAI) methods to compromise the confidentiality and privacy properties of underlying classifiers.We validate our approach with datasets and models used in the cyber security domain to demonstrate that our method achieves the attacker's goal under threat models which reflect the real-world settings.
FedProc: Prototypical contrastive federated learning on non-IID data

Federated learning (FL) enables multiple clients to jointly train high-performance deep learning models while maintaining the training data locally. However, it is challenging to accomplish this form of efficient collaborative learning when all the clients’ local data are not independent and identically distributed (i.e., non-IID). Despite extensive efforts to address this challenge, the results for image classification tasks remain inadequate. In this paper, we propose FedProc: prototypical contrastive federated learning. The core idea of this approach is to utilize the prototypes as global knowledge to correct the drift of each client’s local training. Specifically, we designed a local network structure and a global prototype contrast loss to regulate the training of the local model. These efforts make the direction of local optimization consistent with the global optimum such that the global model achieves good performance on non-IID data. Evaluative studies supported by theoretical significance demonstrate that FedProc improves accuracy by 1.6% to 7.9% with an acceptable computational cost compared to state-of-the-art federated learning methods.
YolactEdge: Real-time Instance Segmentation on the Edge

We propose YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7 FPS on an RTX 2080 Ti) with a ResNet-101 backbone on 550x550 resolution images. To achieve this, we make two improvements to the state-of-the-art image-based real-time method YOLACT [1]: (1) applying TensorRT optimization while carefully trading off speed and accuracy, and (2) a novel feature warping module to exploit temporal redundancy in videos. Experiments on the YouTube VIS and MS COCO datasets demonstrate that YolactEdge produces a 3-5x speed up over existing real-time methods while producing competitive mask and box detection accuracy. We also conduct ablation studies to dissect our design choices and modules. Code and models are available at https://github.com/haotian-liu/yolact_edge.
Single Underwater Image Restoration by Contrastive Learning

Underwater image restoration attracts significant attention due to its importance in unveiling the underwater world. This paper elaborates on a novel method that achieves state-of-the-art results for underwater image restoration based on the unsupervised image-to-image translation framework. We design our method by leveraging from contrastive learning and generative adversarial networks to maximize mutual information between raw and restored images. Additionally, we release a large-scale real underwater image dataset to support both paired and unpaired training modules. Extensive experiments with comparisons to recent approaches further demonstrate the superiority of our proposed method.
Improving social sustainability and reducing supply chain risks through blockchain implementation: role of outcome and behavioural mechanisms

The implementation of blockchain technology holds promise for improving social sustainability and minimising risks across the supply chain. A theory-driven analysis of how blockchain implementation affects social sustainability and minimises risks (outcomes) is missing in supply chain management literature. In particular, the role of technology service providers in meeting these outcomes is unknown. This research addresses these gaps by identifying the outcome-based and behavioural mechanisms needed to generate social sustainability and reduce risks through blockchain projects using agency theory as a theoretical lens. We conduct in-depth interviews with key stakeholders for four blockchain implementation projects to answer these questions. We identify that developing user-friendly applications, developing secure digital payment systems, providing support for suppliers and farmers and adapting to local conditions as the key outcome-based mechanisms. Educating and engaging with customers and building local relationships are found to be the key behavioural mechanisms needed to improve social sustainability and minimise risks using blockchain. Finally, we compare the cases and develop propositions.
YOLOStereo3D: A Step Back to 2D for Efficient Stereo 3D Detection

Object detection in 3D with stereo cameras is an important problem in computer vision, and is particularly crucial in low-cost autonomous mobile robots without LiDARs. Nowadays, most of the best-performing frameworks for stereo 3D object detection are based on dense depth reconstruction from disparity estimation, making them extremely computationally expensive. To enable real-world deployments of vision detection with binocular images, we take a step back to gain insights from 2D image-based detection frameworks and enhance them with stereo features. We incorporate knowledge and the inference structure from real-time one-stage 2D/3D object detector and introduce a light-weight stereo matching module. Our proposed framework, YOLOStereo3D, is trained on one single GPU and runs at more than ten fps. It demonstrates performance comparable to state-of-the-art stereo 3D detection frameworks without usage of LiDAR data. The code will be published in https://github.com/Owen-Liuyuxuan/visualDet3D.
ReLMoGen: Integrating Motion Generation in Reinforcement Learning for Mobile Manipulation

Many Reinforcement Learning (RL) approaches use joint control signals (positions, velocities, torques) as action space for continuous control tasks. We propose to lift the action space to a higher level in the form of subgoals for a motion generator (a combination of motion planner and trajectory executor). We argue that, by lifting the action space and by leveraging sampling-based motion planners, we can efficiently use RL to solve complex, long-horizon tasks that could not be solved with existing RL methods in the original action space. We propose ReLMoGen – a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. To validate our method, we apply ReLMoGen to two types of tasks: 1) Interactive Navigation tasks, navigation problems where interactions with the environment are required to reach the destination, and 2) Mobile Manipulation tasks, manipulation tasks that require moving the robot base. These problems are challenging because they are usually long-horizon, hard to explore during training, and comprise alternating phases of navigation and interaction. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. In all settings, ReLMoGen outperforms state-of-the-art RL and Hierarchical RL baselines. ReLMoGen also shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots. For more information, please visit project website: http://svl.stanford.edu/projects/relmogen.
Unique Action Identifier by Using Magnetometer, Accelerometer and Gyroscope: KNN Approach

In today's world, where technology is advancing every single day, new methodologies are being developed, and are brought in everyday use making our lives simpler, faster, safer, and powerful. Similarly, Human Activity Recognition (HAR) is getting more popular with all the revolutions made in the technologies. Sensor Network Technology is used in industrial applications, smart homes and system. A massive amount of data can be obtained from these sensors which are linked to the human body. Recognition of Human Activities using these sensors, and wearable technologies has been actively studied. Behavior Recognition seeks to distinguish one or more people's activities and goals through a collection of observations on the actions and environmental conditions of the person. Health surveillance, aged treatment, and plenty of other domains can be used to automatically understand the behavioral context. An existing dataset consisting of 10 subjects (5 females, 5 males) is being used in the paper, which incorporates both young and old volunteers between 19 and 60 years of old with weights ranging from 55 to 85 kg. The dataset reflects motion data collected when subjects are engaged in 11 separate (static and dynamic) smart home activities: computer usage (1 min), telephone conversation (1 min), vacuum cleaning (1 min), book reading (1 min), TV watching (1 min), ironing (1 min), walking (1 min), exercise (1 min), cooking (1 min), drinking (20 times), hair brushing (1 min) (20 times). Most of the activities are similar because of the multi sensor environment which makes it more difficult. Using three tri axial IMU (inertial measurement unit), Magnetometer, Accelerometer, Gyroscope sensors attached to the subject area of the hand, chest, and thigh, using Machine Learning we introduced a better model to prognosticate the human activity. We have applied various machine learning classification algorithms like Random Forest Classifier, K-Nearest Neighbors, Decision Tree, Multi-layer Perceptron Classifier, Extra Tree Classifier, Ensemble Extra Trees Classifier, Label Propagation and Label Spreading. The experimental results are tabulated and analyzed, and might be effectively accustomed to recognize human activities in terms of efficiency and accuracy.
New challenges in supply chain management: cybersecurity across the supply chain

Recent cybersecurity breaches have highlighted the resulting economic, political, and social effects. These incidents highlight that cybersecurity is now a supply chain issue. This should not be surprising given the interconnected digital world that defines organisational ecosystems. Due to their relative ‘newness’, coupled with the complexity of both supply chains and cybersecurity, there is lack of clarity and gaps in the current knowledge base regarding cybersecurity across the supply chain. At the same time, this issue is of critical importance to both practitioners and researchers, and thus needs to be structured in such a way to facilitate a common understanding of what cybersecurity across the supply chain cybersecurity entails and the subsequent research opportunities. Thus, the primary objective of this paper is to develop a research framework for cybersecurity across the supply chain to guide future research. An exploratory research methodology was applied, which relied on multiple sources to develop the research framework, including structured literature reviews, anecdotal evidence, interviews with subject matter experts, and external validation by both practitioners and researchers. The paper concludes by identifying areas that demand further research and with a call for more research on cybersecurity across the supply chain.