Reviewerly/exHarmony huggingface dataset:
Blockchain Applications, Challenges, and Opportunities: A Survey of a Decade of Research and Future Outlook

Ongoing advancements in Block chain Innovation's development have made an imaginative gathering for the two industrialists and analysts. Block chain has various points of interest, for example, disseminated systems, security y, and trustless structures. There are different block chain applications extending from riches the board, cross border protection cryptographic money moves, budgetary administrations, chance management, government, and social government assistance Internet of Things (loT); There are a few articles dependent on the use of block chain innovation in various fields and features of utilization, there is no orderly investigation of block chain innovation in both specialized and business viewpoint. To address this void, an itemized review of the block chain innovation was done. This paper would, specifically, give various specialists the usage of block chain, its executions and talk about innovative difficulties just as ongoing improvements in conquering the difficulties. Likewise, this paper additionally investigates possibilities for concentrate soon.
Human Parsing With Part-Aware Relation Modeling

In this paper, a Part-aware Relation Modeling (PRM) is developed to handle the task of human parsing. For pixel-level recognition, it is essential to generate features with adaptive context for various sizes and shapes of human parts. To address the issue, we adaptively capture contexts based on the part-aware relation mechanism. PRM mainly consists of three modules, including a part class module, a part-relation aggregation module, and a part-relation dispersion module. The part class module selectively enhances spatial details of the high-level features to obtain enhanced original features, and then extracts the high-level representations of every human part from a categorical perspective. A part-relation aggregation module is developed to extract the representative global context by exploring associated semantics of human parts, adaptively augmenting the context for human parts. Finally, a part-relation dispersion module is designed to generate the discriminative and effective local context and neglect distracting one by making the affinity of human parts disperse. It ensures that features of the same class will be close to each other and away from those of different classes. By fusing the outputs of the two part-relation modules and the first outputs of the part class module, our PRM produces adaptive contextual features for diverse sizes of human parts, boosting the parsing accuracy. Extensive experiments are conducted to validate the effectiveness of our network, and a new state-of-the-art segmentation performance is achieved on three challenging human parsing datasets, \emph{i.e}., PASCAL-Person-Part, LIP, and CIHP. PRM is also extended to other tasks like animal parsing, and exhibits its generality.
Extensions for Wireless Technology Emulation to Wire-based Network Testbed

Services premised on wireless communication are diversifying, and their reliability of the communication is required. On the other hand, many factors are involved in the parameter change of the wireless section, and it is difficult to clearly determine the cause and effect when the wireless section is actually incorporated into the evaluation environment. By emulating the wireless section and fixing the quality of the wireless section, the behavior of the implementation of a specific wireless service can be efficiently evaluated. We have been developing StarBED, which is a large-scale network testbed built with wired networks, and NETorium, which enables application evaluation by imitating a wireless section on a wired network on a large scale. We are designing and prototyping supporting software that can flexibly introduce new functions as an alternative to the supporting software SpringOS that has been operated on StarBED. This paper describes the design of architecture and implementation of the supporting software when the wireless emulation function is realized as an additional function.
Animal Detection for Road safety using Deep Learning

Over the years, Accidents due to animals crossing the road at unexpected moments have still been a significant cause of road death. Roads near the forest are dark and dense; hence drivers cannot spot the animals clear. Truck drivers face issues due to blindspot regions. This paper proposes a model that can efficiently detect the animals and alarm the driver. Using Machine learning - A deep learning algorithm, we are segregating the animals with the help of a vast open-source dataset. Using convolution neural networks, the model will predict the object for every image frame received from the Live Camera. If the machine marks an object as an animal, the system gives an alert of 3 seconds to make the driver conscious about the approaching animal. This model doesn't stop with few animals as the dataset is open-sourced the variety of animals detection keep increasing. The model gives 91% accuracy.
Interconnections between governance and socioeconomic conditions: Understanding the challenges in sub-Saharan Africa

Given that challenges on the issue of socioeconomic development faced by countries in sub-Saharan Africa (SSA) have been identified as critical to strengthening the inherent link between governance and socioeconomic conditions, this study examines the interconnections between governance and socioeconomic conditions in SSA. With a focus on 25 countries in SSA between 2005 and 2019, we conduct the analysis based on the Panel-Corrected Standard Error and System Generalized Method of Moments estimations and panel causality tests. The results show that SSA does not seem to have the means of effective governance to spur improved socioeconomic conditions. Moreover, the pervasiveness of institutional problems in many countries of SSA has been responsible for the poor socioeconomic conditions in the region. Likewise, governance quality and socioeconomic conditions are found to influence each other. An improvement in socioeconomic conditions could result in better governance quality. On the other hand, governance quality is viewed as a vital ingredient in achieving needed socioeconomic development outcomes. Thus, it is suggested that there is a need for countries in SSA to streamline governing systems toward engendering improved well-being. The introduction and implementation of transformative policies through effective governance are also necessary for ensuring critical structural changes and increasing social service provision. Overall, there should be a proactive identification of ineffective policies and procedures by policymakers to enhance meaningful impacts in the region.
Image Generation Using Continuous Conditional Generative Adversarial Networks

This chapter is a tutorial for the continuous conditional generative adversarial network (CcGAN) [8], the first generative model for image generation with continuous, scalar conditions (termed regression labels). Existing conditional GANs (cGANs) are mainly designed for categorical conditions (e.g., class labels); conditioning on regression labels is mathematically distinct and raises two fundamental problems: (P1) Since there may be very few (even zero) real images for some regression labels, minimizing existing empirical versions of cGAN losses (a.k.a. empirical cGAN losses) often fails in practice; (P2) Since regression labels are scalar and infinitely many, conventional label input methods are not applicable. CcGAN solves the above problems, respectively, by (S1) reformulating existing empirical cGAN losses to be appropriate for the continuous scenario; and (S2) proposing a novel method to incorporate regression labels into the generator and the discriminator. The reformulation in (S1) leads to two novel empirical discriminator losses, termed the hard vicinal discriminator loss (HVDL) and the soft vicinal discriminator loss (SVDL) respectively, and a novel empirical generator loss. The error bounds of a discriminator trained with HVDL and SVDL are derived under mild assumptions. As case studies, experiments show that CcGAN can outperform cGAN in the continuous image generation scenario.
Research on the Dynamic Monitoring System of Intelligent Digital Teaching

Education is one of the areas with a higher impact on digitalization, which takes various forms such as education through digital devices and technology to improve the learning process. Online and tangible computing platforms have become more interested in pursuing active teaching through educational technologies within the curriculum. Some common problems and considerations can be addressed, such as access, capacity, financing, periodic progress measurements, and evaluation results. The productive learning activity can be immediately and constantly monitored by proposing Digital Tangible Intelligent Monitoring systems (DTIMS). The decision tree method’s teaching methodology can efficiently perform this proposed system, which monitors the former challenges. At the same time, the next is resolved by a dynamic evaluation process based on the Internet of Things (IoT). The research is evaluated using the education systems currently adopted. The results highlighted the potential of the proposed model and helped to gain information in digital teaching. The simulation analysis is performed based on accuracy 97.69%, vulnerability 91.09%, and efficiency, proving the proposed framework’s reliability of 85.10%.
CZU-MHAD: A Multimodal Dataset for Human Action Recognition Utilizing a Depth Camera and 10 Wearable Inertial Sensors

Human action recognition has been widely used in many fields of life, and many human action datasets have been published at the same time. However, most of the multi-modal databases have some shortcomings in the layout and number of sensors, which cannot fully represent the action features. Regarding the problems, this paper proposes a freely available dataset, named CZU-MHAD (Changzhou University: a comprehensive multi-modal human action dataset). It consists of 22 actions and three modals temporal synchronized data. These modals include depth videos and skeleton positions from a kinect v2 camera, and inertial signals from 10 wearable sensors. Compared with single modal sensors, multi-modal sensors can collect different modal data, so the use of multi-modal sensors can describe actions more accurately. Moreover, CZU-MHAD obtains the 3-axis acceleration and 3-axis angular velocity of 10 main motion joints by binding inertial sensors to them, and these data were captured at the same time. Experimental results are provided to show that this dataset can be used to study structural relationships between different parts of the human body when performing actions and fusion approaches that involve multi-modal sensor data.
Multicriteria Classifier Ensemble Learning for Imbalanced Data

One of the vital problems with the imbalanced data classifier training is the definition of an optimization criterion.Typically, since the exact cost of misclassification of the individual classes is unknown, combined metrics and loss functions that roughly balance the cost for each class are used.However, this approach can lead to a loss of information, since different trade-offs between class misclassification rates can produce similar combined metric values.To address this issue, this paper discusses a multi-criteria ensemble training method for the imbalanced data.The proposed method jointly optimizes precision and recall, and provides the end-user with a set of Pareto optimal solutions, from which the final one can be chosen according to the user's preference.The proposed approach was evaluated on a number of benchmark datasets and compared with the single-criterion approach (where the selected criterion was one of the chosen metrics).The results of the experiments confirmed the usefulness of the obtained method, which on the one hand guarantees good quality, i.e., not worse than the one obtained with the use of single-criterion optimization, and on the other hand, offers the user the opportunity to choose the solution that best meets their expectations regarding the trade-off between errors on the minority and the majority class.
Exponential Mixture Density Based Approximation to Posterior Cramér-Rao Lower Bound for Distributed Target Tracking

The posterior Cramr-Rao lower bound (PCRLB) and a number of its extensions including the unconditional PCRLB (U-PCRLB) and the conditional PCRLB (C-PCRLB) have been widely studied in multi-sensor target tracking (MSTT). Previous MSTT works assume that the measurements are conditionally independent, which is often not the case in practice. When the correlations of the measurements are unknown, the standard Bayes update and PCRLB computations cannot be performed. Inspired by geometric average (GA) fusion that implements data fusion with unknown data correlations by employing exponential mixture density (EMD) to compute the global posterior, EMD-based approximations to U-PCRLB and C-PCRLB are derived in this paper for two classic distributed fusion architectures, namely the hierarchical and consensus architectures. The PCRLB can be decomposed into two parts, one coming from the prior information and the other from the information contained in the data. The data information part of PCRLB is approximated via EMD-based posterior, leading to the approximation of the PCRLB. We present a sequential Monte Carlo solution to recursively compute the proposed approximation to the PCRLB for nonlinear non-Gaussian estimation problems. Numerical simulations are provided to show that the proposed approximation to the bound on the estimation mean square error (MSE) is tighter compared to the existing bounds based on likelihood fusion obtained under the measurement independence assumption.
Binocular Feature Fusion and Spatial Attention Mechanism Based Gaze Tracking

Gaze tracking is widely used in driver safety driving, visual impairment detection, virtual reality, human robot interaction, and reading process tracking. However, varying illumination, various head poses, different distances between human and cameras, occlusion of hair or glasses, and low-quality images pose huge challenges to accurate gaze tracking. In this article, based on binocular feature fusion and convolution neural network, a novel method of gaze tracking is proposed, in which local binocular spatial attention mechanism (LBSAM) and global binocular spatial attention mechanism (GBSAM) are integrated into the network model to improve the accuracy. Furthermore, the proposed method is validated on the GazeCapture database. In addition, four groups of comparative experiments have been conducted: between binocular feature fusion model and binocular data fusion model; among the local binocular spatial attention model, the local binocular channel attention model, and the model without local binocular attention mechanism; between the model with GBSAM and that without GBSAM; and between the proposed method and other state-of-the-art approaches. The experimental results verify the advantages of binocular feature fusion, LBSAM and GBSAM, and the effectiveness of the proposed method.
Fault-Tolerant Distributed Transactions on Blockchain

Since the introduction of Bitcoin-the first widespread application driven by blockchainthe interest of the public and private sectors in blockchain has skyrocketed.In recent years, blockchain-based fabrics have been used to address challenges in diverse fields such as trade, food production, property rights, identity-management, aid delivery, health care, and fraud prevention.This widespread interest follows from fundamental concepts on which blockchains are built that together embed the notion of trust, upon which blockchains are built.1. Blockchains provide data transparancy.Data in a blockchain is stored in the form of a ledger, which contains an ordered history of all the transactions.This facilitates oversight and auditing.
WiFi-based IoT Devices Profiling Attack based on Eavesdropping of Encrypted WiFi Traffic

Recent research has shown that in-network observers of WiFi communication (i.e., observers who have joined the WiFi network) can obtain much information regarding the types, user identities, and activities of Internet-of-Things (IoT) devices in the network. What has not been explored is the question of how much information can be inferred by an out-of-network observer who does not have access to the WiFi network. This attack scenario is more realistic and much harder to defend against, thus imposes a real threat to user privacy. In this paper, we investigate privacy leakage derived from an out-of-network traffic eavesdropper on the encrypted WiFi traffic of popular IoT devices. We instrumented a testbed of 12 popular IoT devices and evaluated multiple machine learning methods for fingerprinting and inferring what IoT devices exist in a WiFi network. By only exploiting the WiFi frame header information, we have achieved 95% accuracy in identifying the devices and often their working status. This study demonstrates that information leakage and privacy attack is a real threat for WiFi networks and IoT applications.
LiDAR-Based Dense Pedestrian Detection and Tracking

LiDAR-based pedestrian detection and tracking (PDT) with high-resolution sensing capability plays an important role in real-world applications such as security monitoring, human behavior analysis, and intelligent transportation. The problem of LiDAR-based PDT suffers from the complex gathering movements and the phenomenon of self- and inter-object occlusions. In this paper, the detection and tracking of dense pedestrians using three-dimensional (3D) real-measured LiDAR point clouds in surveillance applications is studied. To deal with the problem of undersegmentation of dense pedestrian point clouds, the kernel density estimation (KDE) is used for pedestrians center estimation which further leads to a pedestrian segmentation method. Three novel features are defined and used for further PDT performance improvements, which takes advantage of the pedestrians’ posture and body proportion. Finally, a new track management strategy for dense pedestrians is presented to deal with the tracking instability caused by dense pedestrians occlusion. The performance of the proposed method is validated with experiments on the KITTI dataset. The experiment shows that the proposed method can significantly increase F1 score from 0.5122 to 0.7829 compared with the STM-KDE. In addition, compared with AB3DMOT and EagerMOT, the tracking trajectories from the proposed method have the longest average survival time of 36.17 frames.
Practical revocable and multi-authority CP-ABE scheme from RLWE for Cloud Computing

Cloud computing provides enterprises and individuals with unlimited computing resources and expandable storage space. However, data leakage occurs frequently due to the lack of protection measures in the cloud storage environment. Consequently, how to protect data security in the cloud has become a critical issue. Attribute-based encryption (ABE) is an emerging encryption technique, which protects sensitive information and provides access control for data stored in the cloud. However, most of existing ABE schemes cannot resist quantum attacks and lack the capability of implementing flexible revocation on user or attribute when the attributes of a user change, which hinders the practical application of ABE. In this research, we present a Revocable and Multi-authority ciphertext-policy attribute-based encryption (RM-CP-ABE) from the ring learning with errors (RLWE) assumption, which supports multi-authority and multi-valued attributes. The scheme allows multiple authorities to involve in key distribution, and achieves the attribute revocation when the user’s access rights change. Also, the scheme enjoys reasonable computational cost and storage overhead, and is proven selectively secure under the RLWE assumption. The simulation results indicate that the performance of the proposed scheme is acceptable for practical applications.
Blockchain-based Device Identity Management with Consensus Authentication for IoT Devices

To decrease the IoT attack surface and provide protection against security threats such as introduction of fake IoT nodes and identity theft, IoT requires scalable device identity and authentication management. This work proposes a blockchain-based identity management approach with consensus authentication as a scalable solution for IoT device authentication management. The proposed approach relies on having a blockchain secure tamper proof ledger and a novel lightweight consensus-based identity authentication. The results show that the proposed decentralised authentication system is scalable as we increase number of nodes.
Analysis of the Construction of Dance Teaching System Based on Digital Media Technology

In the special performance prediction model of dance cadets, the physical form quality index is regarded as the dependent variable and the special achievement is regarded as the dependent variable. The results of association analysis and cluster analysis showthat there are 6 specific items of higher sensitivity in body shape, physical quality and special quality. Neural network is adopted to build prediction model, and reliability and error analysis are carried out for this model. The test results show that the model constructed by the neural network is more accurate than the regression model to predict the dance performance and the development level of the physical quality.
AI-based quality of service optimization for multimedia transmission on Internet of Vehicles (IoV) systems

Multimedia Communications of Internet of Vehicles (IoV) uses WLAN, NFC and Fifth Generation networks. At the same time, in multimedia communications in healthcare, IoV's essential task is optimizing the quality of experience (QoE) via regulating wireless links between cars. In addition, the artificial intelligence (AI) method has revolutionized IoV's environment in total, and portable wireless devices have become highly essential to end consumers in their various activities for transferring multimedia material into IoV systems. Most consumers face their irritated and not-so-sufficient view of the performance, QoE. When the service delivery is not pleasurable, most customers can stop, and the market can eventually devalue the overall performance of a product, organization, or system as a whole. This article initially offers two new algorithms called Energy-aware QoE Optimization Algorithm (EQOA) and Queue aware QoE Optimization Algorithm (QQOA) and contrasts their results with Baseline. This article provides an alternative approach to these problems. Secondly, it presents a system for multimodal communication. Thirdly, multimedia IoV transmission through mobile devices offers the QoE Optimization Model. The experimental findings show that the proposed methods maximize QoE by delighting end-users service of mobile devices to levels greater than Baseline reference. Therefore, the suggested algorithms surpass the Baseline such that during multimedia transmission, they can be regarded as promising contenders for IoV implementations.
Natural Language Processing for Social Media, Third Edition

In recent years, online social networking has revolutionized interpersonal communication. The newer research on language analysis in social media has been increasingly focusing on the latter's impact