Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Access Control Techniques for IoT Applications

The Internet of Things is gaining more importance in the present era of Internet technology. It is considered as one of the most important technologies of everyday life. Moreover, IoT systems are ceaselessly growing with more and more devices. They are scalable, dynamic, and distributed, hence the origin of the crucial security requirements in IoT. One of the most challenging issues that the IoT community must handle recently is how to ensure an access control approach that manages the security requirements of such a system. Traditional access control technologies are not suitable for a large-scale and distributed network structure. Most of them are based on a centralized approach, where the use of a trusted third party (TTP) is obligatory. Furthermore, the emergence of blockchain technology has allowed researchers to come up with a solution for these security issues. This technology is highly used to record access control data. Additionally, it has great potential for managing access control requests. This paper proposed a blockchain-based access control taxonomy according to the access control nature: partially decentralized and fully decentralized. Furthermore, it presents an overview of blockchain-based access control solutions proposed in different IoT applications. Finally, the article analyzes the proposed works according to certain criteria that the authors deem important.
KuaiRand: An Unbiased Sequential Recommendation Dataset with Randomly Exposed Videos

Recommender systems deployed in real-world applications can have inherent exposure bias, which leads to the biased logged data plaguing the researchers. A fundamental way to address this thorny problem is to collect users' interactions on randomly expose items, i.e., the missing-at-random data. A few works have asked certain users to rate or select randomly recommended items, e.g., Yahoo!, Coat, and OpenBandit. However, these datasets are either too small in size or lack key information, such as unique user ID or the features of users/items. In this work, we present KuaiRand, an unbiased sequential recommendation dataset containing millions of intervened interactions on randomly exposed videos, collected from the video-sharing mobile App, Kuaishou. Different from existing datasets, KuaiRand records 12 kinds of user feedback signals (e.g., click, like, and view time) on randomly exposed videos inserted in the recommendation feeds in two weeks. To facilitate model learning, we further collect rich features of users and items as well as users' behavior history. By releasing this dataset, we enable the research of advanced debiasing large-scale recommendation scenarios for the first time. Also, with its distinctive features, KuaiRand can support various other research directions such as interactive recommendation, long sequential behavior modeling, and multi-task learning. The dataset is available at https://kuairand.com.
Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding, called RSA-SSA. The proposed method introduces a better search space to find the optimal solution at each iteration. However, we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level thresholds. The obtained solutions by the proposed method are represented using the image histogram. The proposed RSA-SSA employed Otsu's variance class function to get the best threshold values at each level. The performance measure for the proposed method is valid by detecting fitness function, structural similarity index, peak signal-to-noise ratio, and Friedman ranking test. Several benchmark images of COVID-19 validate the performance of the proposed RSA-SSA. The results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration

Compression plays an important role on the efficient transmission and storage of images and videos through band-limited systems such as streaming services, virtual reality or videogames. However, compression unavoidably leads to artifacts and the loss of the original information, which may severely degrade the visual quality. For these reasons, quality enhancement of compressed images has become a popular research topic. While most state-of-the-art image restoration methods are based on convolutional neural networks, other transformers-based methods such as SwinIR, show impressive performance on these tasks. In this paper, we explore the novel Swin Transformer V2, to improve SwinIR for image super-resolution, and in particular, the compressed input scenario. Using this method we can tackle the major issues in training transformer vision models, such as training instability, resolution gaps between pre-training and fine-tuning, and hunger on data. We conduct experiments on three representative tasks: JPEG compression artifacts removal, image super-resolution (classical and lightweight), and compressed image super-resolution. Experimental results demonstrate that our method, Swin2SR, can improve the training convergence and performance of SwinIR, and is a top-5 solution at the “AIM 2022 Challenge on Super-Resolution of Compressed Image and Video". Our code can be found at https://github.com/mv-lab/swin2sr .
Uniform interpolation and the existence of sequent calculi

This paper presents a uniform and modular method to prove uniform interpolation for several intermediate and intuitionistic modal logics. The proof-theoretic method uses sequent calculi that are extensions of the terminating sequent calculus G4ip for intuitionistic propositional logic. It is shown that whenever the rules in a calculus satisfy certain structural properties, the corresponding logic has uniform interpolation. It follows that the intuitionistic versions of K and KD (without the diamond operator) have uniform interpolation. It also follows that no intermediate or intuitionistic modal logic without uniform interpolation has a sequent calculus satisfying those structural properties, thereby establishing that except for the seven intermediate logics that have uniform interpolation, no intermediate logic has such a sequent calculus.
Multi-View Vehicle Type Recognition With Feedback-Enhancement Multi-Branch CNNs

Vehicle type recognition (VTR) is a quite common requirement and one of the key challenges in real surveillance scenarios, such as intelligent traffic and unmanned driving. Usually coarse-grained and fine-grained VTRs are applied in different applications, and the challenge from multiple viewpoints is critical for both cases. In this paper, we propose a feedback-enhancement multi-branch CNN (FM-CNN) to solve the challenge in these two cases. The proposed FM-CNN takes three derivatives of an image as input and leverages the advantages of hierarchical details, feedback enhancement, model average, and stronger robustness to translation and mirroring. A single global cross-entropy loss is insufficient to train such a complex CNN and so we add extra branch losses to enhance feedbacks to each branch. Though reusing pre-trained parameters, we propose a novel parameter update method to adapt FM-CNN to task-specific local visual patterns and global information in new datasets. To test the effectiveness of FM-CNN, we create our own multi-view VTR (MVVTR) data set since there are no such data sets available. And, for fine-grained VTR, we use the CompCars data set. Compared with state-of-the-art classification solutions without special preprocessing, the proposed FM-CNN demonstrates better performance in both coarse-grained and fine-grained scenarios. For coarse-grained VTR, it achieves 94.9% Top-1 accuracy on the MVVTR data set. For fine-grained VTR, it achieves 91.0% Top-1 and 97.8% Top-5 accuracies on the CompCars data set.
Heterogeneous Architectures for Big Data Batch Processing in MapReduce Paradigm

The amount of digital data produced worldwide is exponentially growing. While the source of this data, collectively known as Big Data, varies from among mobile services to cyber physical systems and beyond, the invariant is their increasingly rapid growth for the foreseeable future. Immense incentives exist, from marketing campaigns to forensics and to research in social sciences, that motivate processing increasingly bigger data so as to extract information and knowledge for the betterment of processes and benefits. Consequently, the need for more efficient computing systems tailored to such big data applications is increasingly intensified. Such custom architectures would expectedly embrace heterogeneity to better match each phase of the computation. In this paper we review state of the art as well as envisioned future large-scale computing architectures customized for batch processing of big data applications in the MapReduce paradigm. We also provide our view of current important trends relevant to such systems, and their impacts on future architectures and architectural features expected to address the needs of tomorrow big data processing in this paradigm.
What the hack: Systematic risk contagion from cyber events

This paper examines the impact of cybercrime and hacking events on equity market volatility across publicly traded corporations. The volatility influence of these cybercrime events is shown to be dependent on the number of clients exposed across all sectors and the type of the cyber security breach event, with significantly large volatility effects presented for companies who find themselves exposed to cybercrime in the form of hacking. Evidence is presented to suggest that corporations with large data breaches are punished substantially in the form of stock market volatility and significantly reduced abnormal stock returns. Companies with lower levels of market capitalisation are found to be most susceptible. In an environment where corporate data protection should be paramount, minor breaches appear to be relatively unpunished by the stock market. We also show that there is a growing importance in the contagion channel from cybersecurity breaches to markets volatility. Systematic weakness in the existent mechanisms for cybersecurity oversight and enforcement could be improved through the ring-fenced incentivisation of both current and ex-hackers. Their expertise is central to the identification of weak corporate cybersecurity practices.
Energy efficiency comparison of hypervisors

Current cloud data centers are fully virtualized for service consolidation and power/energy reduction. Although virtualization could reduce the real-time power consumption and overall energy consumption, the energy characteristics of hypervisors hosting different workloads have not been well profiled or understood thus far. In this study, we investigate the power and energy characteristics of four mainstream hypervisors and a container engine, namely VMware ESXi, Microsoft Hyper-V, KVM, XenServer, and Docker, on six different platforms (three mainstream 2U rack servers, one emerging ARM64 server, one desktop server, and one laptop) with power measurements made over prolonged periods. We use computation-intensive, memory-intensive, and mixed Web server-database workloads to explore the power and energy characteristics of different hypervisors in order to emulate realistic multi-tenant cloud environments. The results of extensive experiments conducted with four workload levels (very light, light, fair, and very heavy) indicate that the hypervisors exhibit different power and energy characteristics. Our findings are as follows. (1) Hypervisors exhibit different power and energy consumptions on the same hardware running the same workload. (2) Although mainstream hypervisors have different energy efficiencies aligned with different workload types and workload levels, no single hypervisor outperforms the other hypervisors on all platforms in terms of power or energy consumption. (3) Although container virtualization is considered as lightweight virtualization in terms of implementation and maintenance, it is essentially not more power-efficient than conventional virtualization technology. (4) Although the ARM64 server has low power consumption, it completes computation tasks with a long execution time and, sometimes, high energy consumption. Further, ARM64 servers have medium energy consumption per database operation for mixed workloads. The results presented in this paper can provide system designers and data center operators with useful insights for power-aware workload placement and virtual machine scheduling.
Intensional Learning to Efficiently Build Up Automatically Annotated Emotion Corpora

Textual emotion detection has a high impact on business, society, politics or education with applications such as, detecting depression or personality traits, suicide prevention or identifying cases of cyber-bulling. Given this context, the objective of our research is to contribute to the improvement of emotion recognition task through an automatic technique focused on reducing both the time and cost needed to develop emotion corpora. Our proposal is to exploit a bootstrapping approach based on intensional learning for automatic annotations with two main steps: 1) an initial similarity-based categorization where a set of seed sentences is created and extended by distributional semantic similarity (word vectors or word embeddings); 2) train a supervised classifier on the initially categorized set. The technique proposed allows us an efficient annotation of a large amount of emotion data with standards of reliability according to the evaluation results.
Intelligent Agents: Multi-Agent Systems

This article provides an overview of the fundamentals related to agent-oriented paradigm and Multi Agent Systems (MASs). Specifically, in the first part, the definition of agent and its main features are reported. The second section discusses multi-agent systems and their main features by focusing on the concepts of automation, coordination, norms and emerging behavior which characterize a MAS. Finally, in the sections third and forth, two main ways to approach and use such paradigm for developing respectively agent-based simulations and agent-based software, by breaking down the leading and most renowned reference software platforms, are discussed.
Algebraic Signatures-Based Data Integrity Auditing for Efficient Data Dynamics in Cloud Computing

With the rapid development of cloud services, the resources-constrained enterprises and individuals can outsource the huge sensitive data into the Cloud Service Providers (CSPs) who fully control the data physically. Since CSPs are not fully trusted, it is essential to protect the integrity and confidentiality of users' data. Plenty of researchers have devoted considerable attention to solve this issue in the last decade such as various PDP and POR schemes. In this paper, we propose an algebraic signature-based data integrity auditing scheme that ensures the cloud data integrity and confidentiality with batch auditing. Moreover, one advantage of the scheme is that it can also support data dynamics by using only one cloud server. The security analysis shows that our construction can achieve the desired security properties. We also provide the simulation results of the dynamic operations on different numbers of data blocks and sub-blocks, which show that our scheme is efficient for real-world applications.
Exploiting Negative Evidence for Deep Latent Structured Models

The abundance of image-level labels and the lack of large scale detailed annotations (e.g. bounding boxes, segmentation masks) promotes the development of weakly supervised learning (WSL) models. In this work, we propose a novel framework for WSL of deep convolutional neural networks dedicated to learn localized features from global image-level annotations. The core of the approach is a new latent structured output model equipped with a pooling function which explicitly models negative evidence, e.g. a cow detector should strongly penalize the prediction of the bedroom class. We show that our model can be trained end-to-end for different visual recognition tasks: multi-class and multi-label classification, and also structured average precision (AP) ranking. Extensive experiments highlight the relevance of the proposed method: our model outperforms state-of-the art results on six datasets. We also show that our framework can be used to improve the performance of state-of-the-art deep models for large scale image classification on ImageNet. Finally, we evaluate our model for weakly supervised tasks: in particular, a direct adaptation for weakly supervised segmentation provides a very competitive model.
Privacy-preserving anomaly detection in the cloud for quality assured decision-making in smart cities

Rapid urbanisation places extensive demands on city services and infrastructure that mandate innovative and sustainable solutions which increasingly involve streamlined monitoring, collection, storage and analysis of massive, heterogeneous data. Analytics services, such as anomaly detection, work to both extract knowledge and support decision-making mechanisms that enable smart functionality over such contexts. However, data privacy and data quality remain significant challenges to assuring the quality of decision-making. This paper introduces a scalable, cloud-based model to provide a privacy preserving anomaly detection service for quality assured decision-making in smart cities. Homomorphic encryption is employed to preserve data privacy during the analysis and MapReduce based distribution of tasks and parallelisation is used to overcome computational overheads associated with homomorphic encryption. Experiments demonstrate that a high level of accuracy is maintained for anomaly detection performed on encrypted data with the adopted distributed data processing approach significantly reducing associated computational overheads.
Random Matching Pursuit for Image Watermarking

The classical solution to an underdetermined system of linear equations mainly has two opposite directions, which lead to either a large ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm sparse solution or a non-sparse minimum ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm solution. In this paper, we systematically show that by modifying the well-known basic matching pursuit algorithm originally proposed to identify the sparse solution, an alternative solution between the two classical ones could be obtained. The modified algorithm, termed as random matching pursuit (RMP), is then used to create a novel image watermarking framework. Compared to conventional systems, the security is substantially improved by the use of random over-complete dictionaries and the order parameter of RMP. Capacity can also be increased thanks to the transform with over-complete dictionaries that could expand signal dimension. Meanwhile, imperceptibility and robustness properties of the proposed design framework are not compromised. The classical spread spectrum and improved spread spectrum techniques are applied to the proposed framework for practical implementations. The novelty and effectiveness of the proposed systems are supported by rigorous performance analysis and experimental results using an image data set. This paper reveals the potential of using over-complete dictionaries in multimedia watermarking systems, which theoretically leads to the exploration of alternative candidates among the infinite solutions to underdetermined linear systems other than minimum ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm and sparse ones.
A Filtering-Based Framework for Optical Flow Estimation

We present a novel optical flow estimation framework that reinterprets most popular algorithms (e.g., the Horn-Schunck model, PDE-based models, TV-based models, and nonlocal-based models) from an iterative filtering perspective. Different regularizers in the related optical flow estimation algorithms can be achieved by adopting different filtering operations. The key merit of the proposed framework is that a variety of filtering models and corresponding optimization strategies, which are used in image processing, can be directly utilized in the regularization term, leading to a convenient way of designing appropriate optical flow estimation algorithms. Under the proposed filtering framework, we demonstrate easily designing optical flow estimation algorithms with respect to translation consistency, rotation consistency, and divergence consistency constraints by adopting different spatial filters, respectively. We also derive a novel optical flow estimation algorithm with 3D filtering-based model for the regularization term, which makes use of nonlocal self-similarity and sparse characteristic of optical flow field. Benefited from the advantages of patch-based nonlocal sparse regularizer, the derived algorithm can remove outliers while preserving sharp flow edges and important motion details. At present, the proposed algorithm achieves a high ranking on the challenging MPI-Sintel dataset and shows good performance on the Kitti flow 2015 and Middlebury datasets.
A deep inference learning framework for healthcare

Deep learning, which is a promising end-to-end learning method for accurate health diagnosis, can extract effective representations directly from high-dimensional sensory inputs without manually feature engineering. However, it is still not enough for the end-to-end dynamic process of healthcare, which needs to derive effective representations of human body from miscellaneous observations and generalize past experience to new situations. This paper proposes an end-to-end framework that emulates the process of healthcare. There are two major modules in our framework. The first one is the deep recognition module to diagnose the health states based on the deep neural networks (DNNs). The second module is the action evaluation module based on the Bayesian inference graphs. A simulation environment is designed in order to evaluate the framework. It includes a body simulator to produce the body instance that can receive treatments, and the latent health state of the simulated patient will be changed by different interventions. It also includes a deep recognition module and an action evaluation module to nurse the body. The experiments demonstrate that the healthcare process under our framework can become more and more efficient as the time goes on with the increasing statistic data.
Approximating Dunn’s Cluster Validity Indices for Partitions of Big Data

Dunn's internal cluster validity index is used to assess partition quality and subsequently identify a "best" crisp partition of n objects. Computing Dunn's index (DI) for partitions of n p -dimensional feature vector data has quadratic time complexity O(pn2) , so its computation is impractical for very large values of n . This note presents six methods for approximating DI. Four methods are based on Maximin sampling, which identifies a skeleton of the full partition that contains some boundary points in each cluster. Two additional methods are presented that estimate boundary points associated with unsupervised training of one class support vector machines. Numerical examples compare approximations to DI based on all six methods. Four experiments on seven real and synthetic data sets support our assertion that computing approximations to DI with an incremental, neighborhood-based Maximin skeleton is both tractable and reliably accurate.