Reviewerly/exHarmony huggingface dataset:
A New Framework for Secure Biometric Data Transmission using Block-wise Reversible Data Hiding Through Encryption

Reversible data hiding (RDH) is an emerging area in the field of information security. The RDH schemes are widely explored in the field of cloud computing for data authentication and in medical image transmission for clinical data transmission along with medical images. The RDH schemes allow the data hider to embed sensitive information in digital content in such a way that later it can be extracted while recovering the original image. In this research, we explored the use of the RDH through the encryption scheme in a biometric authentication system. The internet of things (IoT) enabled biometric authentication systems are very common nowadays. In general, in biometric authentication, computationally complex tasks such as feature extraction and feature matching will be performed in a cloud server. The user-side devices will capture biometric data such as the face, fingerprint, or iris and it will be directly communicated to the cloud server for further processing. Since the confidentiality of biometric data needs to be maintained during the transmission, the original biometric data will be encrypted using any one of the data encryption techniques. In this manuscript, we propose the use of RDH through encryption approach to transmit two different biometric data as a single file without compromising confidentiality. The proposed scheme will ensure the integrity of the biometric data during transmission. For data hiding purposes, we have used a block-wise RDH through encryption scheme. The experimental study of the proposed scheme is carried out by embedding fingerprint data in the face images. The validation of the proposed scheme is carried out by extracting the fingerprint details from the face images during image decryption. The scheme ensures the exact recovery of face image images and fingerprint data at the receiver site.
Robust control of robot manipulators with an adaptive fuzzy unmodelled parameter estimation law

Summary For robot manipulators, there are two types of disturbances. One is model parametric uncertainty; the other is unmodelled parameters such as joint friction forces and external disturbances. Unmodelled joint frictions and external disturbances reduce performance in terms of positioning accuracy and repeatability. In order to compensate for unmodelled parameters, the design of a new controller is considered. First, the modelled and unmodelled parameters are included in a dynamic model. Then, based on the dynamic model, a new Lyapunov function is developed. After that, new nonlinear joint friction and external disturbance estimation laws are derived as an analytic solution from the Lyapunov function; thus, the stability of the closed system is guaranteed. Better values of the adaptive dynamic compensators can be extracted by fuzzy rules according to the tracking error. Limitations and knowledge about friction and external disturbances are not required for the design of the controller. The controller compensates for all possible model parameter uncertainties, all possible unknown joint frictions and external disturbances.
Action recognition system for security monitoring

In terms of security detection, security personnel often need to find potential dangerous events in time, although there are many monitoring equipment that can monitor behavior at any time. However, the real-time performance of monitoring equipment is not strong enough. Therefore, this paper proposes a novel action detection method suitable for violent action detection. In this method, the common camera is used as the data acquisition equipment, and the human skeleton structure is obtained by using the algorithm of object detection and pose estimation. Finally, it is recognized by graph convolution neural network. In addition, TensorRT acceleration technology is used in object detection and pose estimation algorithm, which further improves the real-time performance of action recognition and detection algorithm. Through the detection of the actual environment, it shows that the algorithm has good real-time performance and can be widely used in security monitoring.
Person Re-Identification (Pre-id) in Blur, Low Light, Low Resolution Surveillance Videos: Object Detection and Characterization of Sequences

Person re-identification (Pre-id) aims at matching an individual across different cameras, time and place. Pre-id has moved from an image-based system to a video-based system and datasets are moving from closed datasets to real-world surveillance videos. Collection of datasets and annotating them is one of the challenges. Deep Learning (DL) outperforms conventional feature extraction and matching methods used for Pre-id. Cloud platforms such as Google Colab ease out the burden of huge resource requirements of DL algorithms. Problem with pedestrian detection, classical object detection is at the root of Pre-id. This paper focuses on Pre-id methods trends in datasets and annotation methods and providing information at-a-glance. It further discusses pedestrian detection using Deep CNN architecture for differently characterized sequences and tries to correlate the results with model and sequence. The results outperform the detection task. It achieves detection of all, except maximally occluded persons with an average probability of more than 90% in varied situations. This paper contributes to establish a foundation for the next task of Pre-id. It compiles datasets and annotation information at a glance along with the information on methods and trends in Pre-id.
Alleviating Feature Confusion in Cross-Subject Human Activity Recognition via Adversarial Domain Adaptation Strategy

Sensor-based Human Activity Recognition (HAR) plays an important role in health care. However, great individual differences limit its application scenarios and affect its performance. Although general domain adaptation methods can alleviate individual differences to a certain extent, the performance of these methods is still not satisfactory, since the feature confusion caused by individual differences tends to be underestimated. In this paper, for the first time, we analyze the feature confusion problem in cross-subject HAR and summarize it into two aspects: Confusion at Decision Boundaries (CDB) and Confusion at Overlapping (COL). The CDB represents the misclassification caused by the feature located near the decision boundary, while the COL represents the misclassification caused by the feature aliasing of different classes. In order to alleviate CDB and COL to improve the stability of trained model when processing the data from new subjects, we propose a novel Adversarial Cross-Subject (ACS) method. Specifically, we design a parallel network that can extract features from both image space and time series simultaneously. Then we train two classifiers adversarially, and consider both features and decision boundaries to optimize the distribution to alleviate CDB. In addition, we introduce Minimum Class Confusion loss to reduce the confusion between classes to alleviate COL. The experiment results on USC-HAD dataset show that our method outperforms other generally used cross-subject methods.
Blockchain, AI &amp; IoT Based COVID-19 Contact Tracing and Distancing Framework

COVID-19 contact tracing, maintaining social distance from an infected person, and spotting the hotspots is a difficult and time taking process. Many traditional methods can be used for this purpose but these methods have limited capabilities. Using these methods the process becomes more complex, slower, and difficult. This process involves the storage and sharing of personal data that requires security and privacy. To address these issues, we have proposed a blockchain, AI, and IoT-based framework for COVID-19 contact tracing and distancing. In this framework, Every user registers on the blockchain and gets public and private key pairs, the access level of every user is defined in the smart contract. The activity data of the user is collected using the smartwatch, which ensures real-time and accurate data collection. The data is encrypted with the keys of the data owner and stored on the cloud. So in case of data leakage, no one can decrypt the data without keys, so in this way, the secure data storage issue is resolved. Every transaction is recorded on the blockchain, it makes auditing easy in case of any malicious data transaction. Every user can access data according to its access level defined in the smart contract, it ensures the security of data. By the analysis of data, the ML model predicts the infected person and spot the COVID-19 hotspot areas.
Developing Core Technologies for Resource-Scarce Nguni Languages

The creation of linguistic resources is crucial to the continued growth of research and development efforts in the field of natural language processing, especially for resource-scarce languages. In this paper, we describe the curation and annotation of corpora and the development of multiple linguistic technologies for four official South African languages, namely isiNdebele, Siswati, isiXhosa, and isiZulu. Development efforts included sourcing parallel data for these languages and annotating each on token, orthographic, morphological, and morphosyntactic levels. These sets were in turn used to create and evaluate three core technologies, viz. a lemmatizer, part-of-speech tagger, morphological analyzer for each of the languages. We report on the quality of these technologies which improve on previously developed rule-based technologies as part of a similar initiative in 2013. These resources are made publicly accessible through a local resource agency with the intention of fostering further development of both resources and technologies that may benefit the NLP industry in South Africa.
Vehicular Edge Offloading based on Anticipated Value of Computational Tasks

Vehicular edge computing is enabling a variety of new services that better assist safety and comfort of driving. However, vehicles cannot offload an unlimited amount of computational tasks and input sensor data to a remote edge server because of the limitations in network bandwidth. In this paper, we design a learning-based task of floading mechanism that selects a small subset of input sensor data, which are expected to improve the application performance if processed by a rich and resource-intensive algorithm, hosted by the edge server. As a case study, we apply this framework to a vision-based object tracking application. The simulation results show that the proposed solution significantly improves object tracking accuracy with the same amount of resource consumption.
Energy-aware Retinaface: A Power Efficient Edge-Computing SOC for Face Detector in 40nm

In this work, an energy-awaring face detector is implemented in 40nm technology SoC. Based on the art-of-state face detector, a highest accuracy retinaface detector (91.4% average precision) on the WIDER FACE dataset is quantized in the int8 domain. For this neural network, an 8-bit CNN accelerator in a hybrid SOC architecture is designed to achieve an end-to-end face detector. The entire detector runs at 15fps with 66.67mw power per frame. Furthermore, redundant layers in this CNN are analyzed based on this performance. For different sizes of face, some calculations can be reduced with no loss brought to results. To address this improvement, this network is divided into three branches according to different sizes of faces in a single input image. Besides, a simple two-layer classifier is trained to determine the calculation graph in the current run and implemented on SOC. Finally, the face detector increases to 36fps, and energy power decreases to 27.78mw power per frame. This is the highest accuracy(85.8%) face detector hardware implementation on the WILDER FACE dataset.
CLARITY AND PRONUNCIATION OF AB-INITIO AIR TRAFFIC CONTROLLER

The increasing number of aircraft flying around the world has led to the requirement for air traffic controllers to improve their communication skills to face high demand traffic in the future. The paper examines the communication errors in the pilot-controller communication of six ab-initio air traffic controllers during simulation training. More than three hours of conversation were collected and analyzed qualitatively using conversational analysis. The transcribed data yielded a total of 62 instances of communication errors. The data revealed that clarity and pronunciation of ab-initio controllers contributed to problematic communication and reduced the efficiency of the air traffic controllers in communicating. In contrast, pronunciation errors rarely diminished comprehension amongst the controllers and pilots who share a similar first language and are familiar with the use of English in a lingua franca setting. The study also describes other instances of communication errors in pilot-controller communication. The results indicate that ab-initio air traffic controllers need to be proficient in three main areas in pilot controller communication to improve their performance: aviation phraseology, aviation English, and aviation knowledge. The findings suggest that pilots and air traffic controllers should achieve level 4 (operational) in aviation language proficiency test, before proceeding to aviation training that requires them to be proficient in their language skills.
Project-Oriented Game-Based Learning

In this chapter, the authors investigate the potential of project-oriented game-based learning in making students of educational institutes more engaged and gain a deep understanding of the curriculum content. The literature review presents the main definitions and benefits of project-oriented game-based learning, followed by its contribution to improving the performance of students' training. The results of the research are based on testing the main statements of project-oriented game-based learning empirically – if it really can provide additional value for learners in higher education. The empirical data have been collected based on Ukrainian case study and allow the authors to prove the influence of project-oriented game-based learning on increasing students' engagement, satisfaction, performance, and improving learning outcomes. The main idea of the teaching project was to take as an object of the research a character from a fairytale and analyze its managerial activities. The chapter also analyzes the e-learning instruments which can be used in remote teaching.
An Interval Type-2 Possibilistic C-Means Clustering Algorithm and Its Application

Abstract In this paper, we aim at the fuzzy uncertainty caused by noise in pattern data. The advantages of PCM algorithm to deal with noise and interval type-2 fuzzy sets to deal with high-order uncertainties are used, respectively. An interval type-2 probability C-means clustering (IT2-PCM) based on penalty factor is proposed. The performance of the algorithm is evaluated by two sets of data sets and two groups of images segmentation experiments. The results show that IT2-PCM algorithm can assign proper membership degrees to clustering samples with noise, and it can detect noise points effectively, and it has good performance in image segmentation.
A novel method to ensure the security of the shared medical data using smart contracts: Organ transplantation sample

Abstract In recent years, the rapid development in information technologies appears in the form of digitalization, all of the processes in the health domain. Among the state‐of‐art, virtual reality, artificial intelligence, and blockchain technologies are among the most mentioned. In addition, the exponential increase in data kept in the electronic environment causes traditional central applications to face new challenges. The most important of these are accountability, transparency, security, cost, and time efficiency. In this study, a model based on blockchain technologies has been proposed to increase the transparency, accountability, and security of multi‐stakeholder shared medical data by using smart contracts (SCs). In the first part, there are briefly introduced the current situation of the health domain, blockchain technology, hyperledger fabric (HLF) and SCs. Afterward, in line with the latest technological developments, the studies carried out using the blockchain framework related to organ/tissue transplantation were examined. Lastly, the proposed framework was designed based on blockchain technologies for organ/tissue transplantation by using the HLF environment. The blockchain technology not only provides more visibility, more security, and better outcomes but also enables to store data securely and cheaply without allocating extra resources to a trusted authority. Thus, it can be ensured that the system is more transparent and more accountable.
Extend PBFT Protocol with L-DAG

The linear structure of blockchain ensures data security and credibility. But at the same time, it has become the performance bottleneck of the entire system, limiting the growth of the transaction processing rate. The inherent concurrency of directed acyclic graph (DAG) technology solves these problems, but it also brings new problems: blocks total order and ledger consistency. In this paper, we propose a Layer-based DAG (L-DAG) blockchain, which avoids the complex total ordering algorithms by keeping blocks in order between and within layers during the generation process. And we introduce the proportional-integral-derivative (PID) controller to dynamically control the width of layers by using in-degree and out-degree of blocks to achieve the consistency of ledger. We extend Practical Byzantine Fault Tolerance (PBFT) protocol in parallel based on the L-DAG structure and successfully apply it to consortium blockchain scenarios. The L-DAG blockchain structure is implemented through Hyperledger Fabric. Experimental results show that as the number of consensus threads increases, the TPS of L-DAG-based PBFT can grow with near-linear efficiency.
Virtual Hand Illusion in younger and older adults

Embodiment involves experiencing ownership over our body and localizing it in space and is informed by multiple senses (visual, proprioceptive and tactile). Evidence suggests that embodiment and multisensory integration may change with older age. The Virtual Hand Illusion (VHI) has been used to investigate multisensory contributions to embodiment, but has never been evaluated in older adults. Spatio-temporal factors unique to virtual environments may differentially affect the embodied perceptions of older and younger adults.Twenty-one younger (18-35 years) and 19 older (65+ years) adults completed the VHI paradigm. Body localization was measured at baseline and again, with subjective ownership ratings, following synchronous and asynchronous visual-tactile interactions.Higher ownership ratings were observed in the synchronous relative to the asynchronous condition, but no effects on localization/drift were found. No age differences were observed. Localization accuracy was biased in both age groups when the virtual hand was aligned with the real hand, indicating a visual mislocalization of the virtual hand.No age-related differences in the VHI were observed. Mislocalization of the hand in VR occurred for both groups, even when congruent and aligned; however, tactile feedback reduced localization biases. Our results expand the current understanding of age-related changes in multisensory embodiment within virtual environments.
Low Complexity Gradient Computation Techniques to Accelerate Deep Neural Network Training

Deep neural network (DNN) training is an iterative process of updating network weights, called gradient computation, where (mini-batch) stochastic gradient descent (SGD) algorithm is generally used. Since SGD inherently allows gradient computations with noise, the proper approximation of computing weight gradients within SGD noise can be a promising technique to save energy/time consumptions during DNN training. This article proposes two novel techniques to reduce the computational complexity of the gradient computations for the acceleration of SGD-based DNN training. First, considering that the output predictions of a network (confidence) change with training inputs, the relation between the confidence and the magnitude of the weight gradient can be exploited to skip the gradient computations without seriously sacrificing the accuracy, especially for high confidence inputs. Second, the angle diversity-based approximations of intermediate activations for weight gradient calculation are also presented. Based on the fact that the angle diversity of gradients is small (highly uncorrelated) in the early training epoch, the bit precision of activations can be reduced to 2-/4-/8-bit depending on the resulting angle error between the original gradient and quantized gradient. The simulations show that the proposed approach can skip up to 75.83% of gradient computations with negligible accuracy degradation for CIFAR-10 dataset using ResNet-20. Hardware implementation results using 65-nm CMOS technology also show that the proposed training accelerator achieves up to 1.69× energy efficiency compared with other training accelerators.
Multimedia Data Analysis With Edge Computing

As the increasing number of sensors and mobile devices are being used and deployed, large volumes of multimedia data including image, video, audio, etc., have been acquired at the edge and need to be processed in real time for various applications. Meanwhile, deep neural network (DNN), a machine learning model that achieves state-of-the-art performance for many multimedia data analysis tasks, requires substantial amounts of computing power for model inference and training. Since the centralized computing scheme may lead to the longer latency and the higher difficulty in scalability, edge computing becomes an important component for effective and efficient real-time multimedia data analysis in many critical applications. Although many edge computing infrastructures, frameworks, and algorithms have been developed for DNNs, there remain many challenges to enable real-time multimedia data analysis at the edge, such as deploying large models on edge devices, managing the power-efficiency, and preserving data privacy.
White College Students’ Racial Prejudice and Perceptions of Racial Hate Crime

This study investigated how racial prejudice influences White college students' perceptions of hate crime. We also examined the moderating effects of the race of the victim of hate crimes and the absence of hate crime laws. Our sample included 581 White students in a predominantly White university located in a state that does not have a hate crime law. The study was set up in a 2 (race of the victim and the perpetrator) × 3 (level of assault) factorial design. Participants rated their perceptions of three scenarios (i.e., non-racially biased simple assault, racially biased simple assault, and racially biased aggravated assault). The dependent variables were perceptions of hate crime and willingness to report. The key independent variable was participants' level of racial prejudice. The moderators included race of the victim in each scenario and whether participants' state of origin has a hate crime law. Results suggest that higher levels of modern racism were associated with lower perceptions of hate crime and lower willingness to report racially biased simple and aggravated hate crime. When the victim was White, participants with higher levels of racial prejudice were more likely to perceive a hate crime and more willing to report it. The opposite was true when the victim was Black. The absence of state hate crime laws and race of victim were significant moderators. Our study suggests that racial prejudice is associated with lower perceptions of hate crime and willingness to report. Furthermore, the moderating effect of the race of victims provides insights on how racial prejudice can lead to a differential perception of hate crime, depending on whether one's racial in-group is targeted. Our findings also highlight the importance of having state-level hate crime laws to mitigate the linkage between modern racism and perceptions of hate crime.