Reviewerly/exHarmony huggingface dataset:
Towards data mining in IoT cloud computing networks : Collaborative filtering based recommended system

The ever-increasing data due to gaining popularity of Internet of Things (IoT) needs to be adequate storage to compute complex task with accuracy and low latency and finally recommend the user’s preference in the near future. The cloud computing and data mining technique having ability to provide open platform for communication and generate precise recommendation. In this regard, this paper mainly carried out two aspects: firstly, we built four layers IoT cloud computing architecture that provides an open platform for communication with various heterogeneous multi-source things. Secondly, we present a recommended system model based on collaborative filtering algorithm to enhance the accuracy rate of the items in the top priority recommended list. The proposed model inherently utilizes the user-item’s scoring matrix, asymmetrical influence degree on the similar items between users and time weight decay function for the user’s preferences. Finally, extensive simulations are done to show the accuracy rate, loss rate and recall rate of recommendation for the proposed model. Further, comparative analysis of results proved that our proposed model outperforms the other state-of-art model in terms of accuracy rate of the recommendation on the item with respect to data sample set size.
Comparative Review of the Regulatory Framework of Cryptocurrency in Selected Jurisdictions

This chapter compares the current legal and regulatory landscape of cryptocurrency regulations of selected countries. Countries have adopted distinct and disparate regulatory approaches in regulating cryptocurrency. Countries such as Gibraltar, Malta, Switzerland, Singapore, and certain states in the United States have enacted proactive, enabling, and industry-specific laws to regulate cryptocurrency. The Philippines and Denmark are relatively forward-looking in their endeavour to regulate cryptocurrency by allowing its utilization and/or trade but with a restrictive and cautious approach. Certain countries have imposed rigorous restrictions or banned the usage or trade of cryptocurrency. With the rapid evolution and emergence of cryptocurrency markets, policymakers are adopting different trajectories to develop a suitable regulatory framework to regulate cryptocurrency. Countries around the world should harness the capabilities of cryptocurrency by devising favourable regulations rather than inhibit the application of cryptocurrency.
Lightweight Threshold Private Set Intersection via Oblivious Transfer

As a significant basis for privacy-preserving applications of Secure Multiparty Computation (MPC), Private Set Intersection (PSI) has long been a question of great interest in a wide range of field, such as ad conversion rates and private contact tracing. Threshold PSI (\(t\)-\(\mathsf {PSI}\)), a variant of PSI, allows two parties to learn the intersection of two sets only if the cardinality of intersection is larger (or lesser) than a threshold t. In this paper, we give a generic \(t\)-\(\mathsf {PSI}\) construction that relies heavily on Oblivious Transfer (OT). Without resorting to the relatively expensive homomorphic calculation approaches from public-key mechanism, two kinds of \(t\)-\(\mathsf {PSI}\) protocols could be efficiently implemented based on our proposed construction, i.e., \(t^{\scriptscriptstyle {\le }}\mathsf {PSI}\) and \(t^{\scriptscriptstyle {>}}\text {-}\mathsf {PSI}\). Specially, we construct two efficient protocols named secret-sharing private equality test Open image in new window and membership text Open image in new window , which enable PSI to scale to a wide range of practical applications. The experimental simulation results show that our protocols are efficient and computation friendly.
Attack Traffic Detection Based on LetNet-5 and GRU Hierarchical Deep Neural Network

The paper converts the network traffic information about a single-channel grayscale image as input data. In addition, a deep hierarchical network model is designed, which combines LetNet-5 and GRU neural networks to analyze traffic data from both time and space dimensions. At the same time, two networks can be trained simultaneously to achieve better classification results because of the reasonable network association method. This paper uses the CICID2017 dataset, which contains multiple types of attacks and is time-sensitive. The experimental results show that, through the combination of deep neural networks, the model can classify attack traffic with extremely high accuracy.
Disrupting adversarial transferability in deep neural networks

Adversarial attack transferability is well recognized in deep learning. Previous work has partially explained transferability by recognizing common adversarial subspaces and correlations between decision boundaries, but little is known beyond that. We propose that transferability between seemingly different models is due to a high linear correlation between the feature sets that different networks extract. In other words, two models trained on the same task that are distant in the parameter space likely extract features in the same fashion, linked by trivial affine transformations between the latent spaces. Furthermore, we show how applying a feature correlation loss, which decorrelates the extracted features in corresponding latent spaces, can reduce the transferability of adversarial attacks between models, suggesting that the models complete tasks in semantically different ways. Finally, we propose a dual-neck autoencoder (DNA), which leverages this feature correlation loss to create two meaningfully different encodings of input information with reduced transferability.
What is my Problem Identifying Formal Tasks and Metrics in Data Mining on the Basis of Measurement Theory

The design and analysis of experimental research in Data Mining (DM) is anchored in a correct choice of the type of task addressed (clustering, classification, regression, etc.). However, although DM is a relatively mature discipline, there is no consensus yet about what is the taxonomy of DM tasks, which are their formal characteristics, and their corresponding metrics. In this paper, we formalize DM tasks in terms of Measurement Theory, which is a cornerstone of quantitative research in many disciplines, but has not yet been incorporated (in a consensual way) into some areas of Computer Science, including DM. The proposed formal framework provides a methodology to precisely define DM tasks for any given scenario and identify appropriate metrics. We validate this framework via (i) its coverage of existing DM tasks, (ii) its capability to group existing metrics into families, and (iii) its coverage of actual DM research problems, using about 250 papers from ACM KDD 2019 and IEEE ICDM 2019 conferences as reference sample.
An Evaluation of YOLO-Based Algorithms for Hand Detection in the Kitchen

Convolutional Neural Networks have offered an accurate method with which to run object detection on images. Specifically, the YOLO family of object detection algorithms have proven to be relatively fast and accurate. Since its inception, the different variants of this algorithm have been tested on different datasets. In this paper, we evaluate the performances of these algorithms on the recent Epic Kitchens-100 dataset. This dataset provides egocentric footage of people interacting with various objects in the kitchen. Most prominently shown in the footage is an egocentric view of the participants' hands. We aim to use the YOLOv3 algorithm to detect these hands within the footage provided in this dataset. In particular, we examine the YOLOv3 algorithm using two different backbones: MobileNet-lite and VGG16. We trained them on a mixture of samples from the Egohands and Epic Kitchens-100 datasets. In a separate experiment, average precision was measured on an unseen Epic Kitchens-100 subset. We found that the models are relatively simple and lead to lower scores on the Epic Kitchens 100 dataset. This is attributed to the high background noise on the Epic Kitchens 100 dataset. Nonetheless, the VGG16 architecture was found to have a higher Average Precision (AP) and is, therefore, more suited for retrospective analysis. None of the models was suitable for real-time analysis due to complex egocentric data.
A novel hierarchical light field coding scheme based on hybrid stacked multiplicative layers and Fourier disparity layers for glasses-free 3D displays

We present a novel hierarchical coding scheme for light fields based on transmittance patterns of low-rank multiplicative layers and Fourier disparity layers. The proposed scheme identifies multiplicative layers of light field view subsets optimized using convolutional neural networks for different scanning orders. Our approach exploits the hidden low-rank structure in the multiplicative layers obtained from the subsets of different scanning patterns. The spatial redundancies in the multiplicative layers can be efficiently removed by performing low-rank approximation at different ranks on the Krylov subspace. The intra-view and inter-view redundancies between approximated layers are further removed by HEVC encoding. Next, a Fourier disparity layer representation is constructed from the first subset of the approximated light field based on the chosen hierarchical order. Subsequent view subsets are synthesized by modeling the Fourier disparity layers that iteratively refine the representation with improved accuracy. The critical advantage of the proposed hybrid layered representation and coding scheme is that it utilizes not just spatial and temporal redundancies in light fields, but also efficiently exploits intrinsic similarities among neighboring sub-aperture images in both horizontal and vertical directions as specified by different predication orders. In addition, the scheme is flexible to realize a range of multiple bitrates at the decoder within a single integrated system. Comparison with state-of-the-art light field coders exhibits superior compression performance of the proposed scheme for real-world light fields. We achieve substantial bitrate savings and also maintain good light field reconstruction quality.
Blockchain-Based User Authentication in Cloud Governance Model

Evolution of digital communication has facilitated human beings to avail electronic services at their doorsteps. User can overcome the compulsion of their physical presence for accessing any type of service and hence can perform multiple tasks while availing electronic services within the same time frame. Furthermore, the present situation of global pandemic of corona virus (COVID19) has shown the necessity of electronic communication, so as to maintain the rules of social distancing to prevent transmission of virus within our society. In this entire process, utmost focus should be given over security of sensitive information which are transmitted through open digital medium (like Cloud). Advanced security schemes like Blockchains may be used to ensure digital identity of users. This task becomes more compact if an integrated environment can be designed for deployment, monitoring, and maintenance of multifaceted electronic services in a secured and Citizen centric manner. In this case, a multivariate smart card will act as a single window interface between the Citizen and Government for transmission of SERVICE REQUEST and SERVICE RESPONSE, respectively. The application of Blockchain within this integrated electronic service delivery model will build trust among the end users. To achieve this objective, authors have proposed Blockchain-based user authentication scheme during Citizen to Government (C2G) type of Cloud Governance transaction.
A Survey of Image Clustering: Taxonomy and Recent Methods

Image clustering is a fundamental problem in computer vision domains. In this survey, we provide a comprehensive overview of image clustering. Specifically, we first discuss the applications of image clustering across various domains. Then, we summarize the common algorithms and propose a classification of image clustering. The existing methods are classified from four aspects: autoencoder based methods, subspace clustering, graph convolution network (GCN) based methods and some other clustering methods. We introduce the main research contents and existing problems of various image clustering methods. We also introduce some recent methods and summarize the experimental results. Based on our taxonomy and analysis, creating and verifying new methods is more straightforward. Finally, we propose the future opportunities in this fast developing field.
Inaccurate-Supervised Learning With Generative Adversarial Nets

Inaccurate-supervised learning (ISL) is a weakly supervised learning framework for imprecise annotation, which is derived from some specific popular learning frameworks, mainly including partial label learning (PLL), partial multilabel learning (PML), and multiview PML (MVPML). While PLL, PML, and MVPML are each solved as independent models through different methods and no general framework can currently be applied to these frameworks, most existing methods for solving them were designed based on traditional machine-learning techniques, such as logistic regression, KNN, SVM, decision tree. Prior to this study, there was no single general framework that used adversarial networks to solve ISL problems. To narrow this gap, this study proposed an adversarial network structure to solve ISL problems, called ISL with generative adversarial nets (ISL-GANs). In ISL-GAN, fake samples, which are quite similar to real samples, gradually promote the Discriminator to disambiguate the noise labels of real samples. We also provide theoretical analyses for ISL-GAN in effectively handling ISL data. In this article, we propose a general framework to solve PLL, PML, and MVPML, while in the published conference version, we adopt the specific framework, which is a special case of the general one, to solve the PLL problem. Finally, the effectiveness is demonstrated through extensive experiments on various imprecise annotation learning tasks, including PLL, PML, and MVPML.
Parallel Cloud Movement Forecasting based on a Modified Boids Flocking Algorithm

Nowcasting is vital for PV farms in order to match supply and demand on energy markets. The accuracy of existing forecasting methods relies on short term modelling of cloud movement and dynamics by combining satellite images with ground based allsky observations. Cloud movement is modelled based on complex nonlinear numerical models which on the short term are outperformed by image analysis techniques. In this research we propose a simplified nature inspired model for nowcasting which scales with the data. The model is based on forecasting wind direction extracted from motion vector fields by modelling wind using a modified Boids Flocking algorithm. We show that the model is accurate in predicting the cloud coverage up to several hours ahead and that it scales with the number of particles on shared memory systems suited for commodity machines available to PV farm operators.
Bridging the gap from academia to application: rapid algorithm design and deployment for artificial intelligence

Machine Learning (ML) and Artificial intelligence (AI) have led to an increase in automation potential within defense applications such as border protection, compound security, and surveillance applications. Recent academic advances in deep learning aided computer vision have yielded impressive results on object detection, and recognition, necessary capabilities to increase automation in defense applications. These advances are often open-sourced, enabling the opportunistic integration of state-of-the-art (SOTA) algorithms into real systems. However, these academic achievements do not translate easily to engineered systems. Academics often are looking at a single capability with metrics such as accuracy or F1 score without consideration of system-level performance and how these algorithms must integrate or what level of computational performance is required. An engineered system is developed as a system of algorithms that must work in conjunction with each other with deployment constraints. This paper describes a system, called Rapid Algorithm Design and Deployment for Artificial Intelligence (RADD-AITM), developed to enable the rapid development of systems of algorithms incorporating these advances in a modular fashion using networked Application Programming Interfaces (APIs). The inherent modularity mitigates the assumption of monolithic integration within a single ecosystem that creates vendor lock. This monolith assumption does not account for the reality that frameworks are usually targeted toward different types of problems and learning vs inference capabilities. RADD-AI makes no such assumption. If a different framework solves subsets of the system more eloquently, they can be integrated into the larger pipeline. RADD-AI enables the integration of state-of-the-art ML into deployed systems while also supporting the necessary ML engineering tasks, such as transfer learning, to operationalize academic achievements. To motivate how RADD-AI enables applications of ML/AI, we detail how this system is used to implement a defense application, a border surveillance capability, via the integration of detection, recognition, and tracking algorithms. This system, implemented and developed within RADD-AI, utilizes several SOTA models and traditional algorithms within multiple frameworks bridging the gap from academic achievement to fielded system.
Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture

We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision-making, as well as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld.
A novel fault-tolerant privacy-preserving cloud-based data aggregation scheme for lightweight health data

Mobile health networks (MHNWs) have facilitated instant medical health care and remote health monitoring for patients. Currently, a vast amount of health data needs to be quickly collected, processed and analyzed. The main barrier to doing so is the limited amount of the computational storage resources that are required for MHNWs. Therefore, health data must be outsourced to the cloud. Although the cloud has the benefits of powerful computation capabilities and intensive storage resources, security and privacy concerns exist. Therefore, our study examines how to collect and aggregate these health data securely and efficiently, with a focus on the theoretical importance and application potential of the aggregated data. In this work, we propose a novel design for a private and fault-tolerant cloud-based data aggregation scheme. Our design is based on a future ciphertext mechanism for improving the fault tolerance capabilities of MHNWs. Our scheme is privatized via differential privacy, which is achieved by encrypting noisy health data and enabling the cloud to obtain the results of only the noisy sum. Our scheme is efficient, reliable and secure and combines different approaches and algorithms to improve the security and efficiency of the system. Our proposed scheme is evaluated with an extensive simulation study, and the simulation results show that it is efficient and reliable. The computational cost of our scheme is significantly less than that of the related scheme. The aggregation error is minimized from ${\rm{O}}\left( {\sqrt {{\bf{w + 1}}} } \right)$ in the related scheme to O(1) in our scheme.
Exploring System Performance of Continual Learning for Mobile and Embedded Sensing Applications

Continual learning approaches help deep neural network models adapt and learn incrementally by trying to solve catastrophic forgetting. However, whether these existing approaches, applied traditionally to image-based tasks, work with the same efficacy to the sequential time series data generated by mobile or embedded sensing systems remains an unanswered question. To address this void, we conduct the first comprehensive empirical study that quantifies the performance of three predominant continual learning schemes (i.e., regularization, replay, and replay with examples) on six datasets from three mobile and embedded sensing applications in a range of scenarios having different learning complexities. More specifically, we implement an end-to-end continual learning framework on edge devices. Then we investigate the generalizability, trade-offs between performance, storage, computational costs, and memory footprint of different continual learning methods. Our findings suggest that replay with exemplars-based schemes such as iCaRL has the best performance trade-offs, even in complex scenarios, at the expense of some storage space (few MBs) for training examples (1% to 5%). We also demonstrate for the first time that it is feasible and practical to run continual learning on-device with a limited memory budget. In particular, the latency on two types of mobile and embedded devices suggests that both incremental learning time (few seconds - 4 minutes) and training time (1 - 75 minutes) across datasets are acceptable, as training could happen on the device when the embedded device is charging thereby ensuring complete data privacy. Finally, we present some guidelines for practitioners who want to apply a continual learning paradigm for mobile sensing tasks.
Concept and Algorithm of Quantum Computing During Pandemic Situation of COVID-19

We are observing in this pandemic situation of COVID-19 the world in very challenging and to solve this complex problem in quick time. Today, we are facing a difficult complex problem such as Coronavirus. This Coronavirus affects human life. Quantum computing is the only support that can give us quick results by processing the Coronavirus compound at high computation speed. Whatever present circuits in VLSI domain, we cannot perform the high-speed computation and not tackle the complex case as present COVID-19. In this article, we have been discussed about quantum computing era during the pandemic situation of COVID-19. Further, this paper presents fundamental about quantum properties such as superposition, entanglement, and quantum programming tools such as Qiskit (IBM), pyQuil (Google), ProjectQ (ETH), Revkit, and RCvewier + . We have presented quantum circuit and its decomposed circuit of such gates as Toffoli, Fredkin, Peres, and new fault tolerance. In addition, we proposed algorithm as transforming cascade to the quantum circuit which is extended for verification based. All these concepts presented here will be very useful to researcher, academician, and industry person to tackle this pandemic situation of COVID-19.
Private Data Access in Blockchain Systems Employing Coded Sharding

In present blockchain systems, privacy of transactions is maintained by keeping the identity of accounts anonymous. The associated pseudonyms are ephemeral in nature, and can not be easily traced back to the real identity. An alternate infallible approach is to make use of private information retrieval (PIR) protocols that enable users to fetch details of transactions without revealing which transactions they seek. In this paper, we formalize this approach for blockchain systems that employ coded sharding. We present a PIR protocol for private data access, in particular private balance-checking, in blockchain systems when data is stored using generalized Reed-Solomon codes. Our protocol can be readily applied to the PolyShard scheme that has been recently proposed as a method to build truly scalable blockchain system.