Reviewerly/exHarmony huggingface dataset:
Enable Fair Proof-of-Work (PoW) Consensus for Blockchains in IoT by Miner Twins (MinT)

Blockchain technology has been recognized as a promising solution to enhance the security and privacy of Internet of Things (IoT) and Edge Computing scenarios. Taking advantage of the Proof-of-Work (PoW) consensus protocol, which solves a computation intensive hashing puzzle, Blockchain ensures the security of the system by establishing a digital ledger. However, the computation intensive PoW favors members possessing more computing power. In the IoT paradigm, fairness in the highly heterogeneous network edge environments must consider devices with various constraints on computation power. Inspired by the advanced features of Digital Twins (DT), an emerging concept that mirrors the lifespan and operational characteristics of physical objects, we propose a novel Miner Twins (MinT) architecture to enable a fair PoW consensus mechanism for blockchains in IoT environments. MinT adopts an edge-fog-cloud hierarchy. All physical miners of the blockchain are deployed as microservices on distributed edge devices, while fog/cloud servers maintain digital twins that periodically update miners’ running status. By timely monitoring of a miner’s footprint that is mirrored by twins, a lightweight Singular Spectrum Analysis (SSA)-based detection achieves the identification of individual misbehaved miners that violate fair mining. Moreover, we also design a novel Proof-of-Behavior (PoB) consensus algorithm to detect dishonest miners that collude to control a fair mining network. A preliminary study is conducted on a proof-of-concept prototype implementation, and experimental evaluation shows the feasibility and effectiveness of the proposed MinT scheme under a distributed byzantine network environment.
Intelligent sports prediction analysis system based on improved Gaussian fuzzy algorithm

In order to improve the effect of intelligent sports prediction and analysis, this paper analyzes the traditional high-speed model and proposes a reliable sports action recognition algorithm on this basis. With this algorithm as the core algorithm, sports action recognition can be performed. The paper mainly constructs a standard database structure, inputs a reliable standardized movement model into the database structure, and compares the identified sports movement model with the standard movement model to judge the accuracy of the sports movement. Moreover, this paper uses statistical analysis to study sports training and sports competitions, and obtains prediction results through multi-factor analysis and comparison. In addition, this paper uses statistical methods to process experimental data to draw graphs and tables that can visually observe the results of sports predictive analysis. From the experimental analysis, it can be seen that the algorithm model constructed in this paper has a significant effect.
A Socio-Technical Perspective

Chapter 7 uses a philosophical approach to discuss the frailty of the human psyche with regards to the implementation and use of systems through our engagement with cyberspace. Our constant exposure to newsworthy cyber security events can desensitize people to the warnings that are either apparent or subliminal. A number of key topical subject areas are discussed exploring human psychology: why people are susceptible to psychological vulnerabilities, characteristics of the human psyche that facilitate errors, how these traits can be exhibited through flawed actions causing mistakes and preventative measures to stop deliberate and accidental actions. This analysis is of vital importance and relevance in order to combat the risks, which to the computer end-user may appear distant and intangible.
A Study of Athlete Pose Estimation Techniques in Sports Game Videos Combining Multiresidual Module Convolutional Neural Networks

In this paper, we propose a multiresidual module convolutional neural network-based method for athlete pose estimation in sports game videos. The network firstly designs an improved residual module based on the traditional residual module. Firstly, a large perceptual field residual module is designed to learn the correlation between the athlete components in the sports game video within a large perceptual field. A multiscale residual module is designed in the paper to better solve the inaccuracy of the pose estimation due to the problem of scale change of the athlete components in the sports game video. Secondly, these three residual modules are used as the building blocks of the convolutional neural network. When the resolution is high, the large perceptual field residual module and the multiscale residual module are used to capture information in a larger range as well as at each scale, and when the resolution is low, only the improved residual module is used. Finally, four multiresidual module convolutional neural networks are used to form the final multiresidual module stacked convolutional neural network. The neural network model proposed in this paper achieves high accuracy of 89.5% and 88.2% on the upper arm and lower arm, respectively, so the method in this paper reduces the influence of occlusion on the athlete's posture estimation to a certain extent. Through the experiments, it can be seen that the proposed multiresidual module stacked convolutional neural network-based method for athlete pose estimation in sports game videos further improves the accuracy of athlete pose estimation in sports game videos.
Frequency-hopping frequency reconnaissance and prediction for non-cooperative communication network

The continuous change of communication frequency brings difficulties to the reconnaissance and prediction of non-cooperative communication networks. Since the frequency-hopping (FH) sequence is usually generated by a certain model with certain regularity, the FH frequency is thus predictable. In this paper, we investigate the FH frequency reconnaissance and prediction of a non-cooperative communication network by effective FH signal detection, time-frequency (TF) analysis, wavelet detection and frequency estimation. With the intercepted massive FH signal data, long short-term memory (LSTM) neural network model is constructed for FH frequency prediction. Simulation results show that our parameter estimation methods could estimate frequency accurately in the presence of certain noise. Moreover, the LSTM-based scheme can effectively predict FH frequency and frequency interval.
A Case For Intra-rack Resource Disaggregation in HPC

The expected halt of traditional technology scaling is motivating increased heterogeneity in high-performance computing (HPC) systems with the emergence of numerous specialized accelerators. As heterogeneity increases, so does the risk of underutilizing expensive hardware resources if we preserve today’s rigid node configuration and reservation strategies. This has sparked interest in resource disaggregation to enable finer-grain allocation of hardware resources to applications. However, there is currently no data-driven study of what range of disaggregation is appropriate in HPC. To that end, we perform a detailed analysis of key metrics sampled in NERSC’s Cori, a production HPC system that executes a diverse open-science HPC workload. In addition, we profile a variety of deep-learning applications to represent an emerging workload. We show that for a rack (cabinet) configuration and applications similar to Cori, a central processing unit with intra-rack disaggregation has a 99.5% probability to find all resources it requires inside its rack. In addition, ideal intra-rack resource disaggregation in Cori could reduce memory and NIC resources by 5.36% to 69.01% and still satisfy the worst-case average rack utilization.
A Wrapper to Use a Machine-Learning-Based Algorithm for Earthquake Monitoring

Abstract Seismology is one of the main sciences used to monitor volcanic activity worldwide. Fast, efficient, and accurate seismicity detectors are crucial to assess the activity level of a volcano in near-real time and to issue timely warnings. Traditional real-time seismic processing software uses phase onset pickers followed by a phase association algorithm to declare an event and estimate its location. The pickers typically do not identify whether the detected phase is a P or S arrival, which can have a negative impact on hypocentral location quality and complicates phase association. We implemented the deep-neural-network-based method PhaseNet to identify in real time P and S seismic waves on data from one- and three-component seismometers. We tuned the Earthworm binder_ew associator module to use the phase identification from PhaseNet to detect and locate the events, which we archive in a SeisComP3 database. We assessed the performance of the algorithm by comparing the results with existing catalogs built to monitor seismic and volcanic activity in Mayotte and the Lesser Antilles region. Our algorithm, which we refer to as PhaseWorm, showed promising results in both contexts and clearly outperformed the previous automatic method implemented in Mayotte. This innovative real-time processing system is now operational for seismicity monitoring in Mayotte and Martinique.
Breast Cancer Prediction using Machine Learning

One of the most dreadful disease is breast cancer and it has a potential cause for death in women. Every year, death rate increases drastically due to breast cancer. An effective way to classify data is through classification or data mining. This becomes very handy, especially in the medical field where diagnosis and analysis are done through these techniques. Wisconsin Breast cancer dataset is used to perform a comparison between SVM, Logistic Regression, Naïve Bayes and Random Forest. Evaluating the correctness in classifying data based on accuracy and time consumption is used to determine the efficiency of the algorithms, which is the main objective. Based on the result of performed experiments, the Random Forest algorithm shows the highest accuracy (99.76%) with the least error rate. ANACONDA Data Science Platform is used to execute all the experiments in a simulated environment.
Content-Variant Reference Image Quality Assessment via Knowledge Distillation

Generally, humans are more skilled at perceiving differences between high-quality (HQ) and low-quality (LQ) images than directly judging the quality of a single LQ image. This situation also applies to image quality assessment (IQA). Although recent no-reference (NR-IQA) methods have made great progress to predict image quality free from the reference image, they still have the potential to achieve better performance since HQ image information is not fully exploited. In contrast, full-reference (FR-IQA) methods tend to provide more reliable quality evaluation, but its practicability is affected by the requirement for pixel-level aligned reference images. To address this, we firstly propose the content-variant reference method via knowledge distillation (CVRKD-IQA). Specifically, we use non-aligned reference (NAR) images to introduce various prior distributions of high-quality images. The comparisons of distribution differences between HQ and LQ images can help our model better assess the image quality. Further, the knowledge distillation transfers more HQ-LQ distribution difference information from the FR-teacher to the NAR-student and stabilizing CVRKD-IQA performance. Moreover, to fully mine the local-global combined information, while achieving faster inference speed, our model directly processes multiple image patches from the input with the MLP-mixer. Cross-dataset experiments verify that our model can outperform all NAR/NR-IQA SOTAs, even reach comparable performance than FR-IQA methods on some occasions. Since the content-variant and non-aligned reference HQ images are easy to obtain, our model can support more IQA applications with its robustness to content variations. Our code is available: https://github.com/guanghaoyin/CVRKD-IQA.
Universal Background Subtraction Based on Arithmetic Distribution Neural Network

We propose a universal background subtraction framework based on the Arithmetic Distribution Neural Network (ADNN) for learning the distributions of temporal pixels. In our ADNN model, the arithmetic distribution operations are utilized to introduce the arithmetic distribution layers, including the product distribution layer and the sum distribution layer. Furthermore, in order to improve the accuracy of the proposed approach, an improved Bayesian refinement model based on neighboring information, with a GPU implementation, is incorporated. In the forward pass and backpropagation of the proposed arithmetic distribution layers, histograms are considered as probability density functions rather than matrices. Thus, the proposed approach is able to utilize the probability information of the histogram and achieve promising results with a very simple architecture compared to traditional convolutional neural networks. Evaluations using standard benchmarks demonstrate the superiority of the proposed approach compared to state-of-the-art traditional and deep learning methods. To the best of our knowledge, this is the first method to propose network layers based on arithmetic distribution operations for learning distributions during background subtraction.
A neurocognitive psychometrics account of individual differences in attentional control.

Attention control processes play an important role in many substantial psychological theories but are hard to reliably and validly measure on the subject-level. Therefore, associations between individual differences in attentional control and other variables are often inconsistent. Here we propose a novel neurocognitive psychometrics account of attentional control that integrates model parameters from the dual-stage two-phase model (Hübner et al., 2010), a mathematical model of selective attention, with neural correlates of conflict processing (i.e., latencies of the stimulus-locked lateralized readiness potential) in a multilayer structural equation model framework. We analyzed data from 150 participants who completed the Eriksen Flanker task while their EEG was recorded and used the neurocognitive psychometric approach to distinguish between two sequential stages of information-processing-target selection and response selection. Model parameters and neural correlates showed convergent validity and could be meaningfully related to each other. Together, these neurocognitive process parameters jointly explained 37 % of the variance in individual differences in higher-order cognitive abilities that were assessed with a battery of intelligence tests and working memory tasks. Individuals with greater cognitive abilities were not only better at focusing their attention on the target stimulus but also at subsequent response-selection. All in all, our results support the idea that individual differences in attentional control processes are related to individual differences in cognitive abilities. Moreover, they provide hope that the measurement crisis of individual differences in attentional control can be overcome by integrating measurement approaches from related disciplines such as mathematical psychology and cognitive neuroscience. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Design of a Mixed Reality Application for STEM Distance Education Laboratories

In this work, we propose a Mixed Reality (MR) application to support laboratory lectures in STEM distance education. It was designed following a methodology extendable to diverse STEM laboratory lectures. We formulated this methodology considering the main issues found in the literature that limit MR’s use in education. Thus, the main design features of the resulting MR application are students’ and teachers’ involvement, use of not distracting graphics, integration of traditional didactic material, and easy scalability to new learning activities. In this work, we present how we applied the design methodology and used the framework for the case study of an engineering course to support students in understanding drawings of complex machines without being physically in the laboratory. We finally evaluated the usability and cognitive load of the implemented MR application through two user studies, involving, respectively, 48 and 36 students. The results reveal that the usability of our application is “excellent” (mean SUS score 84.7), and it is not influenced by familiarity with Mixed Reality and distance education tools. Furthermore, the cognitive load is medium (mean NASA TLX score below 29) for all four learning tasks that students can accomplish through the MR application.
Recommender systems: Past, present, future

Abstract The origins of modern recommender systems date back to the early 1990s when they were mainly applied experimentally to personal email and information filtering. Today, 30 years later, personalized recommendations are ubiquitous and research in this highly successful application area of AI is flourishing more than ever. Much of the research in the last decades was fueled by advances in machine learning technology. However, building a successful recommender system requires more than a clever general‐purpose algorithm. It requires an in‐depth understanding of the specifics of the application environment and the expected effects of the system on its users. Ultimately, making recommendations is a human‐computer interaction problem, where a computerized system supports users in information search or decision‐making contexts. This special issue contains a selection of papers reflecting this multi‐faceted nature of the problem and puts open research challenges in recommender systems to the forefront. It features articles on the latest learning technology, reflects on the human‐computer interaction aspects, reports on the use of recommender systems in practice, and it finally critically discusses our research methodology.
Evaluation and Evolution of Object Detection Techniques YOLO and R-CNN

Object detection has boomed in areas like image processing in accordance with the unparalleled development of CNN (Convolutional Neural Networks) over the last decade. The CNN family which includes R-CNN has advanced to much faster versions like Fast-RCNN which have mean average precision(Map) of up to 76.4 but their frames per second(fps) still remain between 5 to 18 and that is comparatively moderate to problem-solving time. Therefore, there is an urgent need to increase speed in the advancements of object detection. In accordance with the broad initiation of CNN and its features, this paper discusses YOLO (You only look once), a strong representative of CNN which comes up with an entirely different method of interpreting the task of detecting the objects. YOLO has attained fast speeds with fps of 155 and map of about 78.6, thereby surpassing the performances of other CNN versions appreciably. Furthermore, in comparison with the latest advancements, YOLOv2 attains an outstanding trade-off between accuracy and speed and also as a detector possessing powerful generalization capabilities of representing an entire image
Cloud Monitoring System: Basics, Phases and Challenges

Due to many economical benefits cloud computing has quickly become popular and is widely used for delivering services over the Internet. As the number of cloud users increases day by day, data centers are continuing to grow in terms of hardware resources, virtual resources and traffic volume; thus making cloud operation and management more intricate. To manage complex infrastructure of cloud, an efficient and effective Cloud Monitoring System (CMS) is needed to improve the overall performance of cloud. Cloud monitoring is the process of reviewing, controlling and managing the operational workflow and processes within a cloud-based IT asset or infrastructure. This paper deals with detailed study of CMSs based on their architecture, monitoring phases, properties and functions. It describes various phases of cloud monitoring activities and presents the comparative study of the state-of-the-art works of theses phases. Usage of agents for monitoring of cloud activities is also described. Finally, various challenges/issues and possible future directions of cloud monitoring is discussed. This paper helps researchers, engineers and scientists to know the state-of-the-art works for monitoring cloud activities.
An amelioration of the skyline algorithm used in the cloud service research and selection system

Cloud computing is both a technological and a business model revolution that capitalised on many long proven technologies. Its ease of use resulted in its wide adoption, and users are increasingly faced with a large choice of cloud services. In this context, we have developed the cloud service research and selection system (CSRSS) that allows users to specify the requirements of the cloud services they want and select those that best meet these requirements. The CSRSS uses an algorithm based on the Skyline and gave some encouraging first results. In this paper, we explore two approaches to improve the performance of the used algorithm and decrease its time complexity by reducing the number of I/O operations necessary to compute the Skyline.
Detecting Malignant Leukemia Cells Using Microscopic Blood Smear Images: A Deep Learning Approach

Leukemia is a form of blood cancer that develops when the human body’s bone marrow contains too many white blood cells. This medical condition affects adults and is considered a prevalent form of cancer in children. Treatment for leukaemia is determined by the type and the extent to which cancer has developed across the body. It is crucial to diagnose leukaemia early in order to provide adequate care and to cure patients. Researchers have been working on advanced diagnostics systems based on Machine Learning (ML) approaches to diagnose leukaemia early. In this research, we employ deep learning (DL) based convolutional neural network (CNN) and hybridized two individual blocks of CNN named CNN-1 and CNN-2 to detect acute lymphoblastic leukaemia (ALL), acute myeloid leukaemia (AML), and multiple myeloma (MM). The proposed model detects malignant leukaemia cells using microscopic blood smear images. We construct a dataset of about 4150 images from a public directory. The main challenges were background removal, ripping out un-essential blood components of blood supplies, reduce the noise and blurriness and minimal method for image segmentation. To accomplish the pre-processing and segmentation, we transform RGB color-space into the greyscale 8-bit mode, enhancing the contrast of images using the image intensity adjustment method and adaptive histogram equalisation (AHE) method. We increase the structure and sharpness of images by multiplication of binary image with the output of enhanced images. In the next step, complement is done to get the background in black colour and nucleus of blood in white colour. Thereafter, we applied area operation and closing operation to remove background noise. Finally, we multiply the final output to source image to regenerate the images dataset in RGB colour space, and we resize dataset images to [400, 400]. After applying all methods and techniques, we have managed to get noiseless, non-blurred, sharped and segmented images of the lesion. In next step, enhanced segmented images are given as input to CNNs. Two parallel CCN models are trained, which extract deep features. The extracted features are further combined using the Canonical Correlation Analysis (CCA) fusion method to get more prominent features. We used five classification algorithms, namely, SVM, Bagging ensemble, total boosts, RUSBoost, and fine KNN, to evaluate the performance of feature extraction algorithms. Among the classification algorithms, Bagging ensemble outperformed the other algorithms by achieving the highest accuracy of 97.04%.
The State of IEEE Transactions on Intelligent Vehicles: From Passive Publication to Active and Smart Exchange and Dissemination

Presents the introductory editorial for this issue of the publication.