Reviewerly/exHarmony huggingface dataset:
Contrastive Predictive Coding for Human Activity Recognition

Feature extraction is crucial for human activity recognition (HAR) using body-worn movement sensors. Recently, learned representations have been used successfully, offering promising alternatives to manually engineered features. Our work focuses on effective use of small amounts of labeled data and the opportunistic exploitation of unlabeled data that are straightforward to collect in mobile and ubiquitous computing scenarios. We hypothesize and demonstrate that explicitly considering the temporality of sensor data at representation level plays an important role for effective HAR in challenging scenarios. We introduce the Contrastive Predictive Coding (CPC) framework to human activity recognition, which captures the temporal structure of sensor data streams. Through a range of experimental evaluations on real-life recognition tasks, we demonstrate its effectiveness for improved HAR. CPC-based pre-training is self-supervised, and the resulting learned representations can be integrated into standard activity chains. It leads to significantly improved recognition performance when only small amounts of labeled training data are available, thereby demonstrating the practical value of our approach. Through a series of experiments, we also develop guidelines to help practitioners adapt and modify the framework towards other mobile and ubiquitous computing scenarios.
CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild

Human pose estimation from single images is a challenging problem in computer vision that requires large amounts of labeled training data to be solved accurately. Unfortunately, for many human activities (e.g. outdoor sports) such training data does not exist and is hard or even impossible to acquire with traditional motion capture systems. We propose a self-supervised approach that learns a single image 3D pose estimator from unlabeled multi-view data. To this end, we exploit multi-view consistency constraints to disentangle the observed 2D pose into the underlying 3D pose and camera rotation. In contrast to most existing methods, we do not require calibrated cameras and can therefore learn from moving cameras. Nevertheless, in the case of a static camera setup, we present an optional extension to include constant relative camera rotations over multiple views into our framework. Key to the success are new, unbiased reconstruction objectives that mix information across views and training samples. The proposed approach is evaluated on two benchmark datasets (Human3.6M and MPII-INF-3DHP) and on the in-the-wild SkiPose dataset.
VoxelContext-Net: An Octree based Framework for Point Cloud Compression

In this paper, we propose a two-stage deep learning framework called VoxelContext-Net for both static and dynamic point cloud compression. Taking advantages of both octree based methods and voxel based schemes, our approach employs the voxel context to compress the octree structured data. Specifically, we first extract the local voxel representation that encodes the spatial neighbouring context information for each node in the constructed octree. Then, in the entropy coding stage, we propose a voxel context based deep entropy model to compress the symbols of non-leaf nodes in a lossless way. Furthermore, for dynamic point cloud compression, we additionally introduce the local voxel representations from the temporal neighbouring point clouds to exploit temporal dependency. More importantly, to alleviate the distortion from the octree construction procedure, we propose a voxel context based 3D coordinate refinement method to produce more accurate reconstructed point cloud at the decoder side, which is applicable to both static and dynamic point cloud compression. The comprehensive experiments on both static and dynamic point cloud benchmark datasets(e.g., ScanNet and Semantic KITTI) clearly demonstrate the effectiveness of our newly proposed method VoxelContext-Net for 3D point cloud geometry compression.
TransPose

Motion capture is facing some new possibilities brought by the inertial sensing technologies which do not suffer from occlusion or wide-range recordings as vision-based solutions do. However, as the recorded signals are sparse and quite noisy, online performance and global translation estimation turn out to be two key difficulties. In this paper, we present TransPose, a DNN-based approach to perform full motion capture (with both global translations and body poses) from only 6 Inertial Measurement Units (IMUs) at over 90 fps. For body pose estimation, we propose a multi-stage network that estimates leaf-to-full joint positions as intermediate results. This design makes the pose estimation much easier, and thus achieves both better accuracy and lower computation cost. For global translation estimation, we propose a supporting-foot-based method and an RNN-based method to robustly solve for the global translations with a confidence-based fusion technique. Quantitative and qualitative comparisons show that our method outperforms the state-of-the-art learning- and optimization-based methods with a large margin in both accuracy and efficiency. As a purely inertial sensor-based approach, our method is not limited by environmental settings (e.g., fixed cameras), making the capture free from common difficulties such as wide-range motion space and strong occlusion.
Quasi Oppositional Dragonfly Algorithm for Load Balancing in Cloud Computing Environment

In cloud computing (CC), load balancing tasks remain a critical problem in distributing resources from a data center. Ensure that every virtual machine (VM) has a balanced load to maximize capacity utilization. In the CC world, load balancing is a Non-Polynomial (NP) problem resolved with metaheuristic algorithms. A new Quasi-Oppositional Dragonfly Algorithm for Load Balancing (QODA-LB) has been developed to obtain optimum resource scheduling in a CC configuration. The proposed QODA-LB algorithm uses three variables to calculate an objective function: execution time, execution cost, and charge. The QODA-LB algorithm assigns tasks to VM according to its potential and the resulting objective function. Also, the QODA-LB algorithm employs the Quasi-Oppositional Based Learning principle to increase the standard convergence rate of the Dragonfly (DA) algorithm. A complete series of experiments were conducted, and the results were analyzed in various ways to ensure the increased efficiency of the QODA-LB algorithm. Simulation results demonstrated an optimal load balancing efficiency and outperformed key approaches.
Empty Streets, Busy Internet: A Time-Series Analysis of Cybercrime and Fraud Trends During COVID-19

The unprecedented changes in routine activities brought about by COVID-19 and the associated lockdown measures contributed to a reduction in opportunities for predatory crimes in outdoor physical spaces, while people spent more time connected to the internet, and opportunities for cybercrime and fraud increased. This article applies time-series analysis to historical data on cybercrime and fraud reported to Action Fraud in the United Kingdom to examine whether any potential increases are beyond normal crime variability. Furthermore, the discrepancies between fraud types and individual and organizational victims are also analyzed. The results show that while both total cybercrime and total fraud increased beyond predicted levels, the changes in victimization were not homogeneous across fraud types and victims. The implications of these findings on how changes in routine activities during COVID-19 have influenced cybercrime and fraud opportunities are discussed in relation to policy, practice, and academic debate.
Utilizing blockchain technology for supply chain transparency: A resource orchestration perspective

Abstract Driven by pressures from multiple stakeholders, supply chain transparency (SCT) has emerged as a phenomenon of increased interest. To address concerns about practices and processes at point of origin locations for raw materials in global supply chains, blockchain technology (BCT) has the potential to enhance SCT. Supply chain research has started to advance the field's understanding of SCT, but many questions remain, including how SCT should be conceptualized, how firms can effectively facilitate it, and the benefits of providing it, especially when BCT is utilized. The gaps suggest the need for fundamental theoretical development about the resources and capabilities underlying the development, application, and derived value of SCT. This research designed a case study around a BCT implementation project between a small artisan coffee producer and a startup BCT service provider. Using the resource orchestration perspective, the findings result in theoretical insights about how the mechanisms in structuring, bundling, and leveraging processes operate to offer SCT to stakeholders, and the value creation derived as a result.
Looking back to look forward: Lessons learnt from cyber-attacks on Industrial Control Systems

Since the 1980s, we have observed a range of cyberattacks targeting Industrial Control Systems (ICS), some of which have impacted elements of critical national infrastructure (CNI). While there are access limitations on information surrounding ICS focused cyberattacks, particularly within a CNI context, this paper provides an extensive summary of those publicly reported. By identifying and analysing previous ICS focused cyberattacks, we document their evolution, affording cyber-security practitioners with a greater understanding of attack vectors, threat actors, impact, and targeted sectors and locations, critical to the continued development of holistic risk management strategies.
Factors related to students' satisfaction with holding e-learning during the Covid-19 pandemic based on the dimensions of e-learning

E-learners' satisfaction has a significant impact on the success of the e-learning process and leads to improving the quality of the e-learning system. Many factors affect e-learning satisfaction. This study aimed to determine the factors related to students' satisfaction with e-learning during the Covid-19 pandemic based on the dimensions of e-learning.The present study was a cross-sectional study, which was conducted in 2020 among students studying in different fields of Qazvin University of Medical Sciences using stratified random sampling. To collect data three parts of questionnaires were used included the demographic information, the measuring the effectiveness of e-learning, and measuring the level of satisfaction with holding e-learning during the Covid-19 period. Data were entered into spss23 and analyzed by descriptive method, chi-square, and t-test.The results showed that the mean (standard deviation) score of satisfaction with e-learning in the students was 20.75 (2.13) and 59 % of them had undesirable satisfaction. There was a significant relationship between satisfaction with e-learning and variables of gender and history of attending online classes before Covid-19. Regarding the four aspects of e-learning, there was a statistically significant difference between the two groups of students with desirable satisfaction and undesirable satisfaction. The results revealed that the mean scores of dimensions of teaching and learning; feedback and evaluation; flexibility and appropriateness; and workload among students with desirable satisfaction were higher than students with undesirable satisfaction.Considering the results, efforts should be made to improve the quality of e-learning and the factors affecting it, because due to the prevalence of Covid-19, distance education may be held for a long time. Lack of attention to these cases can reduce the quality of education and students' level of knowledge.
Jointly Optimizing Client Selection and Resource Management in Wireless Federated Learning for Internet of Things

Federated learning (FL) has been proposed to efficiently and privacy-preserving distributed machine learning architecture for the Internet of Things (IoT). In a wireless FL system, clients in IoT devices train their local models over the local data sets. The derived local models are uploaded to an FL server to generate a global model, broadcasted to the clients in the next global iteration for further training. Owing to the heterogeneous feature of the clients, client selection is critical to determine the overall training time. Traditionally, the objective of client selection is to select the maximum number of clients who can derive and upload their local models before the deadline in each global iteration. However, selecting more clients increases the energy consumption of the clients. Moreover, selecting the maximum number of clients is unnecessary as having fewer clients in early global iterations and more clients in later global iterations have been proved to achieve higher model accuracy. Hence, this article proposes to dynamically adjust and optimize the tradeoff between maximizing the number of selected clients and minimizing the total energy consumption of the clients by selecting suitable clients and allocating appropriate resources in terms of CPU frequency and transmission power. We formulate the joint client selection and resource management problem and design the energy and latency-aware resource management and client selection (ELASTIC) algorithm to efficiently solve the problem. Extensive simulations are conducted to demonstrate the performance of ELASTIC.
The impact of blockchain technology on the tea supply chain and its sustainable performance

Blockchain technology (BCT) has recently attracted interest from academics and practitioners. However, little is known about the benefits and impact of BCT on the tea supply chain and its sustainable performance. To bridge this gap, this study extends the resource-based view (RBV) and network theory (NT) by integrating BCT into the tea supply chain. We develop a conceptual model of a BCT-driven tea supply chain, which we analyse using a partial least squares regression-based structural equation modelling method with data collected from 305 experts in India. The findings show that the use of BCT has a significant positive effect on the tea supply chain; in particular, transparency and reliability are shown as the sustainable performance parameters. The implementation of BCT is a progressive paradigm shift that encourages actors to change their attitudes and become more competent in the tea sector. This study is the first report on integrating BCT into supply chains, contributing to the scant literature on this subject. Furthermore, our conceptual framework could help develop a more sustainable supply chain for the global tea industry.
Blockchain governance in the public sector: A conceptual framework for public management

A key challenge behind the adoption of blockchain in the public sector is understanding the dynamics of blockchain governance. Based on a systematic literature review, this article analyzes different approaches to blockchain governance across disciplines and develops a comprehensive conceptual framework for the study of blockchain governance decisions in the public sector. The framework clusters nine types of governance decisions (infrastructure architecture, application architecture, interoperability, decision-making mechanism, incentive mechanism, consensus mechanism, organization of governance, accountability of governance, and control of governance) into three levels of analysis (micro, meso, and macro-levels). Drawing on public management theories and concepts, the article elucidates the implications of various governance choices in each level of governance and provides a primer for researchers and policy practitioners on the design of blockchain-based systems in the public sector.
Contextual ensemble network for semantic segmentation

Recently, exploring features from different layers in fully convolutional networks (FCNs) has gained substantial attention to capture context information for semantic segmentation. This paper presents a novel encoder-decoder architecture, called contextual ensemble network (CENet), for semantic segmentation, where the contextual cues are aggregated via densely usampling the convolutional features of deep layer to the shallow deconvolutional layers. The proposed CENet is trained in terms of end-to-end segmentation to match the resolution of input image, and allows us to fully explore contextual features through ensemble of dense deconvolutions. We evaluate our CENet on two widely-used semantic segmentation datasets: PASCAL VOC 2012 and CityScapes. The experimental results demonstrate our CENet achieves superior performance with respect to recent state-of-the-art results. Furthermore, we also evaluate CENet on MS COCO dataset and ISBI 2012 dataset for the task of instance segmentation and biological segmentation, respectively. The experimental results show that CENet obtains promising results on these two datasets.
Deep Ensemble of Slime Mold Algorithm and Arithmetic Optimization Algorithm for Global Optimization

In this paper, a new hybrid algorithm based on two meta-heuristic algorithms is presented to improve the optimization capability of original algorithms. This hybrid algorithm is realized by the deep ensemble of two new proposed meta-heuristic methods, i.e., slime mold algorithm (SMA) and arithmetic optimization algorithm (AOA), called DESMAOA. To be specific, a preliminary hybrid method was applied to obtain the improved SMA, called SMAOA. Then, two strategies that were extracted from the SMA and AOA, respectively, were embedded into SMAOA to boost the optimizing speed and accuracy of the solution. The optimization performance of the proposed DESMAOA was analyzed by using 23 classical benchmark functions. Firstly, the impacts of different components are discussed. Then, the exploitation and exploration capabilities, convergence behaviors, and performances are evaluated in detail. Cases at different dimensions also were investigated. Compared with the SMA, AOA, and another five well-known optimization algorithms, the results showed that the proposed method can outperform other optimization algorithms with high superiority. Finally, three classical engineering design problems were employed to illustrate the capability of the proposed algorithm for solving the practical problems. The results also indicate that the DESMAOA has very promising performance when solving these problems.
Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

Blockchain technology is becoming increasingly popular because of its applications in various fields.It gives an edge over the traditional centralized methods as it provides decentralization, immutability, integrity, and anonymity.The most popular application of this technology is cryptocurrencies, which showed a massive rise in their popularity and market capitalization in recent years.Individual investors, big institutions, and corporate firms are investing heavily in it.However, the crypto market is less stable than traditional commodity markets.It can be affected by many technical, sentimental, and legal factors, so it is highly volatile, uncertain, and unpredictable.Plenty of research has been done on various cryptocurrencies to forecast accurate prices, but the majority of these approaches can not be applied in real-time.Motivated from the aforementioned discussion, in this paper, we propose a deep-learning-based hybrid model (includes Gated Recurrent Units (GRU) and Long Short Term Memory (LSTM)) to predict the price of Litecoin and Zcash with inter-dependency of the parent coin.The proposed model can be used in real-time scenarios and it is well trained and evaluated using standard data sets.Results illustrate that the proposed model forecasts the prices with high accuracy compared to existing models.
Ensuring Improved Security in Medical Data Using ECC and Blockchain Technology with Edge Devices

Hospital data management is one of the functional parts of operations to store and access healthcare data. Nowadays, protecting these from hacking is one of the most difficult tasks in the healthcare system. As the user’s data collected in the field of healthcare is very sensitive, adequate security measures have to be taken in this field to protect the networks. To maintain security, an effective encryption technology must be utilised. This paper focuses on implementing the elliptic curve cryptography (ECC) technique, a lightweight authentication approach to share the data effectively. Many researches are in place to share the data wirelessly, among which this work uses Electronic Medical Card (EMC) to store the healthcare data. The work discusses two important data security issues: data authentication and data confidentiality. To ensure data authentication, the proposed system employs a secure mechanism to encrypt and decrypt the data with a 512-bit key. Data confidentiality is ensured by using the Blockchain ledger technique which allows ethical users to access the data. Finally, the encrypted data is stored on the edge device. The edge computing technology is used to store the medical reports within the edge network to access the data in a very fast manner. An authenticated user can decrypt the data and process the data at optimum speed. After processing, the updated data is stored in the Blockchain and in the cloud server. This proposed method ensures secure maintenance and efficient retrieval of medical data and reports.
Diverse Image Inpainting with Bidirectional and Autoregressive Transformers

Image inpainting is an underdetermined inverse problem, which naturally allows diverse contents to fill up the missing or corrupted regions realistically. Prevalent approaches using convolutional neural networks (CNNs) can synthesize visually pleasant contents, but CNNs suffer from limited perception fields for capturing global features. With image-level attention, transformers enable to model long-range dependencies and generate diverse contents with autoregressive modeling of pixel-sequence distributions. However, the unidirectional attention in autoregressive transformers is suboptimal as corrupted image regions may have arbitrary shapes with contexts from any direction. We propose BAT-Fill, an innovative image inpainting framework that introduces a novel bidirectional autoregressive transformer (BAT) for image inpainting. BAT utilizes the transformers to learn autoregressive distributions, which naturally allows the diverse generation of missing contents. In addition, it incorporates the masked language model like BERT, which enables bidirectionally modeling of contextual information of missing regions for better image completion. Extensive experiments over multiple datasets show that BAT-Fill achieves superior diversity and fidelity in image inpainting qualitatively and quantitatively.