Reviewerly/exHarmony huggingface dataset:
A Literature Survey of Face Recognition Under Different Occlusion Conditions

Face recognition is a very old and popular biometric problem. In the last two decades, several challenges occur in face recognition such as expression, occlusion, illumination, pose variation, and low-quality image. Also, several techniques exist, such as Principle Component Analysis (PCA), Linear Discriminate Analysis (LDA), Local Non-Negative Matrix Factorization (LNME), Independent Component Analysis (ICA), and other variants, which finds good results under constrained scenarios. But, they give poor accuracy in an unconstrained scenarios such as illumination, pose variation, and occlusion. As Face recognition biometrics has gained great importance in occlusion condition, for example, a terrorists cover his face using facial accessories like sunglasses, scarves, or masks. This occlusion face recognition is categorized into two parts. 1) Feature extraction without occlusion detection face recognition. 2) Feature extraction with occlusion detection face recognition 3) occlusion recovery face recognition. Feature-based and fractal-based methods consider features around the eyes, nose, or mouth region to be used in the recognition face of the algorithm. This paper describes the different techniques and databases to handle the partial occlusion problem.
Configuring Logic Operations from New Reversible Toffoli Gate using Pockel's Effect of Ti:LiNbO<sub>3</sub>

A reversible logic device (RLD) is capable of performing forward and backward computation following the same data path. This idea steered a breakthrough framework to design any logical and arithmetic process that not only provides a distinct output for a certain input pattern, but also offers a cushion for designers to notice a way lesser heat dissipation as per Landauer's theory. This has been implemented by various research communities in applications like nanotechnology, quantum computing, low power design and so on. To further boost the performance of RLDs, the best-known practice is to implement them using the verve of photons due to its integral property of parallelism and ultrafast speed. With this motive, this paper focuses on implementing AND, NAND, XOR and NOT logic using a new configuration of Ti:LiNbO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf> based MZI Toffoli gate, which is supported by Beam Propagation Method (BPM) in OptiBPM tool. The power modelling is carried out to validate the BPM simulations through MATLAB.
Recognition of Human Activity from ECG and IMU Signals Using Deep Learning Networks

Human activity recognition (HAR) is becoming more significant in various industrial applications, including medical consideration and rehabilitation monitoring. With the rapid development of information and communication technologies, wearable technologies have inspired a new variety of human-computer interaction. Wearable inertial sensors are typically operated in the area of HAR because they deliver the most critical data on motion signals. Researchers in HAR continuously investigate alternative methodologies and signal sources to enhance HAR approaches. This study aims to determine the effect of combining biosignals with a publicly available dataset derived from wearable sensors on detecting everyday human activities. We utilized the MHEALTH dataset, which contains electrocardiogram (ECG), accelerometer, gyro-scope, and magnetometer data gathered from ten individuals engaged in twelve everyday actions. We proposed a unique deep learning technique for automatically extracting features and constructing a recognition model in several circumstances, including integrated sensor data. Our findings indicate that combining the ECG and IMU signals increases the F1-score of the classifier by 11.53%, from 86.83% to 98.36%.
Development of Object Tracking System Utilizing Camera Movement and Deep Neural Network

Object tracking is an essential field of study in the field of computer science with its applications ranging from video surveillance to autonomous driving. Object tracking also has been a challenging task in the field of computer vision, the main difficulties stemming from issues in the image such as background distractions, multiple tracking scales, occlusion etc. There are numerous approaches to keep track of objects moving in the camera frame. The new objects appearing in the camera frame are registered for tracking and the ones that are lost or disappeared from the frame are deregistered. This method does not certainly help if the user wants to track one specific object throughout, even if the object is moving out of the camera frame. This paper presents a novel approach to track a specific user defined object, utilizing camera movement and custom-trained deep neural network so that even if the object is about to move out of the camera frame, the camera orientation is changed in order to keep that object well centered in the frame. In this approach the camera is mounted on a 2-axis motorized gimbal and video stream is fed to a computer which runs YOLOv4, a one-stage object detection model. The user defined object, if detected, its position relative to overall frame is calculated and necessary change to the camera orientation is made to keep the object at the center of frame. The proposed object tracking algorithm works flawlessly as long as object detection algorithm performs accurately.
Sign Language Translation using a Chrome extension for Google Meet

Sign language is a source of communication used primarily by the community of speech and hearing-impaired people. The history of sign language goes as far behind as the Seventeenth century wherein the basic tool of communication for the aforementioned community was not universal but geography-specific. In this paper, we discuss our findings on the implementation of a Google Chrome extension for the Google meet (Videoconferencing) software which translates sign language into captions with the use of Machine Learning. Additionally, there is also a provision for text to speech for the translated texts. By making this extension, we lay the foundation for a bridge between users of ASL signs and the people who cannot understand them without translation for seamless communication.
Comparing the Impact of Low-Cost 360° Cultural Heritage Videos Displayed in 2D Screens Versus Virtual Reality Headsets

The continuous price reduction of head mounted displays (HMDs) raises the following question in the field of cultural heritage: Is it possible to adapt desktop passive virtual reality (VR) 360° experiences for HMDs? This work presents a comparison of low-cost 360° videos of cultural heritage displayed across two devices: a desktop display and an HMD. The study case is the virtual reconstruction of Burgos (Spain) in 1921. The key factors of these videos are short duration, virtual reconstruction based on 3D modelling and photo editing and the inclusion of real actors performing out looping micro stories. The comparison of both displays devices has been carried out by a group of 32 students from the University of Burgos. The validation includes user satisfaction, knowledge acquisition and visual identification. The results are the following: 1) better knowledge acquisition and immersion in the HMD group, 2) better user satisfaction for the desktop group and 3) more fault identification related to the characters for the HMD group. Looking at these results, the most important elements to improve are the integration of characters and increase the length of the videos.
Real-time identification of eye fixations and saccades using radial basis function networks and Markov chains

Analysis of eye-movements is crucial to many applications, from medical diagnosis to gaming. A critical step in this process lies in segmenting raw gaze coordinates provided by the eye-tracker into eye saccade and fixation events. This detection is generally executed offline, as most methods require a complete dataset or large temporal windows. Many of these algorithms also rely on heuristics such as fixed velocity thresholds, yielding variations in the results depending on the user’s choice of parameters. To overcome such limitations, we designed a new approach, named RBFNMC, which combines Radial Basis Function Network (RBFN) and Markov Chains (MC) in a Concept Drift framework. Our approach is capable of automatically categorizing saccades and fixations in an online scenario. Comparisons with previous detection techniques revealed accurate predictions, while not requiring fixed threshold parameters. Our results were estimated from real eye-movement datasets collected in experiments with: (i) monkeys in a free-viewing paradigm; (ii) human subjects looking at different types of stimuli. Comparing RBFNMC with several other methods widely cited in the literature, our contribution constitutes a new computational approach to process spatial data streams in an online and unsupervised fashion. As a consequence, we provide an efficient mechanism to detect saccade and fixations which support eye-tracking research in neuroscience and other areas. Finally, it is worth emphasizing that our work additionally provides the possibility of interpreting the decision process by inspecting graph-based visualizations of RBFN and of the transition probabilities in MC.
A conditional one-output likelihood formulation for multitask Gaussian processes

Multitask Gaussian processes (MTGP) are the Gaussian process (GP) framework’s solution for multioutput regression problems in which the T elements of the regressors cannot be considered conditionally independent given the observations. Standard MTGP models assume that there exist both a multitask covariance matrix as a function of an intertask matrix, and a noise covariance matrix. These matrices need to be approximated by a low rank simplification of order P in order to reduce the number of parameters to be learnt from T2 to TP. Here we introduce a novel approach that simplifies the multitask learning process by reducing it to a set of conditioned univariate GPs without the need for any low rank approximations, therefore completely eliminating the need to select an adequate value for hyperparameter P. At the same time, by extending this approach with both a hierarchical and an approximate model, the proposed extensions are capable of recovering the multitask covariance and noise matrices after learning only 2T parameters, avoiding the validation of any model hyperparameter and reducing the overall complexity of the model as well as the risk of overfitting. Experimental results over synthetic and real problems confirm the advantages of this inference approach in its ability to accurately recover the original noise and signal matrices, as well as the achieved performance improvement in comparison to other state of art MTGP approaches. We have also integrated the model with standard GP toolboxes, showing that it is computationally competitive with state of the art options.
Hitting times of quantum and classical random walks in potential spaces

The spatial search problem is an interesting and important problem in computer science and especially the area of algorithms. The objective is a marked site to be found in a finite physical space, that can be modeled as a finite lattice or a graph. Many approaches have been developed to address this problem. Classical random walks and quantum walks are efficient models that address the spatial search problem. Quantum walks is a universal model of quantum computation and can be mapped directly to quantum circuits and consequently executed on quantum computers. Quantum walks utilized for quantum search proved to achieve significantly lower hitting times than their classical counterpart, classical random walks. The evolution space for the quantum walks as well as the classical random walks is up until now a free space. In our approach, we introduce external electrical potentials to the evolution space. We study the evolution of discrete time quantum and classical random walks in such potential spaces and the probability — hitting time on finding marked sites. We considered the differences in applied potential among neighboring sites as weights for the lattice — graph. We introduce these weights to the evolution space as an operator for the discrete time quantum walk and as coin probabilities for the classical random walk. Our results show that quantum walks again, evolve faster in the evolution space with the applied potential. Quantum walks also achieve better probability — hitting time on finding the marked site in the potential space. With the introduction of electrical potentials, quantum walks evolving in potential spaces, can lead to the development of novel quantum algorithms, where input parameters can be introduced as external potentials. • Extended discrete time quantum walk model. • Electrical potentials as weights to the evolution space of the quantum walk. • Quantum and classical random walks spatial search in potential spaces.
Auto-Tune: Efficient Autonomous Routing for Payment Channel Networks

Payment Channel Network (PCN) is a scaling solution for Cryptocurrency networks. We advance the practicality of the PCN multi-path routing by better modeling the system to incorporate the cost of routing fee and the privacy requirement of the channel balance. We design our Auto-Tune algorithm to optimize the routing concerning both the success rate and the routing fee and utilizing the limited channel capacity information (due to the privacy of the PCN user, the channel balance information is withheld). The simulation result shows Auto-Tune outperforms the current PCN implementation based on single-path routing in the success rate. We compare Auto-Tune against the state-of-the-art Flash algorithm, utilizing the channel-balance information, violating the PCN user privacy, and diverging from current implementation practices. Auto-Tune achieves the routing fee close to the optimal fee obtained by Flash, and its success rate is also close to the success rate achieved by Flash.
Network-Efficient Pipelining-Based Secure Multiparty Computation for Machine Learning Applications

Secure multi-party computation (SMPC) allows mutually distrusted parties to evaluate a function jointly without revealing their private inputs. This technique helps organizations collaborate on a common goal without disclosing confidential or protected data. Despite its suitability for privacy-preserving computation, SMPC suffers from network-based performance limitations. Specifically, the SMPC parties perform the techniques in rounds, where they execute a local computation and then share their round output with the other parties. This network interchange creates a bottleneck as parties need to wait until the data propagates before resuming the execution. To reduce the SMPC execution time, we propose a pipelining-like approach for each round’s computation and communication by dividing the data and readjusting the execution order. Targeting deep learning applications, we propose strategies for the case of matrix multiplication, a core component of such applications. Our results on a distributed cloud deployment show a significant reduction in the SMPC execution time.
Secure Service Function Chaining in the Context of Zero Trust Security

Service Function Chaining (SFC) enables dynamic steering of traffic through a set of service functions based on classification of packets, allowing network operators fine-grained and flexible control of packet flows. New paradigms like Zero Trust (ZT) pose additional requirements to the security of network architectures. This includes client authentication, confidentiality, and integrity throughout the whole network, while also being able to perform operations on the unencrypted payload of packets. However, these requirements are only partially addressed in existing SFC literature. Therefore, we first present a comprehensive analysis of the security requirements for SFC architectures. Based on this analysis, we propose a concept towards the fulfillment of the requirements while maintaining the flexibility of SFC. In addition, we provide and evaluate a proof of concept implementation, and discuss the implications of the design choices.
AI-aided Hidden Camera Detection and Localization based on Raw IoT Network Traffic

This paper proposes a novel scheme to detect and localize the spy cameras based on AI algorithm based raw traffic analytics, named AI-aided Hidden Camera Locator (AHCL). In AHCL, the video streaming data are filtered via the SVM (support vector machine) algorithm to quickly monitor whole raw network traffic from a router to the networks first. Then, gathered traffic data are denoised by the Denoising Autoencoder (DAE) technique to improve the data quality of classification for localization, where a camera transmits video streaming. Based on the proof-of-concept implementation, the proposed scheme can achieve 99.5% positioning accuracy of camera detection with the Ensemble Neural Networks (NNs).
Simplicity conditions for binary orthogonal arrays

It is known that correlation-immune (CI) Boolean functions used in the framework of side-channel attacks need to have low Hamming weights. The supports of CI functions are (equivalently) simple orthogonal arrays when their elements are written as rows of an array. The minimum Hamming weight of a CI function is then the same as the minimum number of rows in a simple orthogonal array. In this paper, we use Rao's Bound to give a sufficient condition on the number of rows, for a binary orthogonal array (OA) to be simple. We apply this result for determining the minimum number of rows in all simple binary orthogonal arrays of strengths 2 and 3; we show that this minimum is the same in such case as for all OA, and we extend this observation to some OA of strengths $4$ and $5$. This allows us to reply positively, in the case of strengths 2 and 3, to a question raised by the first author and X. Chen on the monotonicity of the minimum Hamming weight of 2-CI Boolean functions, and to partially reply positively to the same question in the case of strengths 4 and 5.
A Resource Sharing System for Music Education Using the Entropy Technology

In order to improve the sharing level of music quality and educational resources within the background of emerging digital world, it is essential to design new methods. In this paper, we design a method for music quality education and a resource sharing system within the context of digital world which is established over the entropy technology. The designed structure includes the collection module of music quality education resources, the interactive compilation module of music education resources, the bottom data detection module, the cross bus control module, and the man-machine interface module under the background of digital technology. The information entropy feature extraction technology is adopted to schedule the bus in the process of sharing and transmitting music quality educational resources under the background of digital technology, and the transmission model of music quality education resources is designed. The modular design method of bottom database is adopted to realize the interactive and storage design of music quality education resources under the background of digital technology. Under the background of digital technology, the average mutual information entropy and feature distribution of association rules of music quality education resources are extracted. Furthermore, the method of fuzzy subspace information scheduling is combined with the entropy approach to share and schedule music quality educational resources, so as to comprehend the software development and, therefore, the design of music quality education and resource sharing system under the background of digital knowledge. The simulation outcomes confirm that under the background of digital technology, the music quality education and resource sharing system has good output stability and, therefore, strong human-computer interaction ability.
Applying Blockchain Security for Agricultural Supply Chain Management

Blockchain technology permits a highly secured record keeping and digital transaction. Blockchain technology is changing the digital world and industry by bringing a new view to security, transparency, and efficiency of systems. It provides a safe way for the exchange of products, services, or transaction. Blockchain will enable more agile value chains, faster product innovations, closer customer relationship sector. This paper provides an overview of blockchain technology and its potential in developing a secure and reliable agriculture supply chain management. Agriculture supply chain management systems are vital for getting food products delivered from farmers to the consumers. Blockchain technology can also be used to achieve better prices and payment options, land title registration and for transparent disbursement of subsidies to farmers.
Automatic Headlight Intensity Control using Light Dependent Resistor

Abstract For so many years, road accidents have been a huge problem for automobile industries since they must continuously upgrade their vehicles to withstand any type of damages. In this regard, many technologies are invented to reduce the figure of accidents that happen. One of the great reasons for road accident is due to bright headlight of vehicle coming in opposite direction. High beam from the headlight causes a dangerous situation for night driving. It causes temporary blindness for the drivers that may lead to collisions or may hit people passing through the road and lives can be lost. According to the law of “Indian Roads and Transport Council”, low intensity light has to be used always, however in the time of overtaking or other mandatory situations, high beam can be turned on. But due to the driver’s ignorance and since they cannot see the long-range road in low intensity, they always drive with the high intensity light on. Automatic dipper, a circuit which controls the intensity of the head light of vehicle has been designed here. From this automatic dipper system, the intensity of headlight of the vehicle can be automatically controlled and can provide a better visible road using optimization techniques for light condition.
Dynamic Task Scheduling and Resource Allocation for Microservices in Cloud

Abstract With the emergence of new companies and the expansion of the information technology sector, the need for Cloud Computing becomes apparent. Currently, the enterprises are rapidly transitioning from monolithic architecture to microservice-driven architecture. This research study has discovered that all task scheduling algorithms were designed for a specific (set) number of virtual machines, which resulted in the bottleneck problem, where multiple tasks were assigned to the microservice scheduler and the execution time of processing the tasks was significantly increased. Therefore, to address this issue, a novel model was designed based on the number of tasks and accordingly the number of virtual machines were dynamically generated to send the tasks to the microservice scheduler one by one, and the difficulties with execution time were also addressed. The study also discovered that due to the multiple workloads on the microservices, resource allocation becomes extremely difficult. To address this issue, containerized microservices were discovered. Here, the microservices would be distributed in containers. To implement the dynamic work scheduling technique, a cloud microservice translator would be developed, where a user may upload a text file and quickly get it dynamically translated. The main aim of this research work is to improve the task scheduling and resource allocation in microservices.