Reviewerly/exHarmony huggingface dataset:
Prohibited Items Detection in X-ray Images in YOLO Network

In order to safeguard public spaces from security issues, such as terrorism, security mechanisms have long played a crucial role. With the increase of population and crowd density in public transportation hubs of big cities, rapid, automatic, and accurate detection of prohibited items in X-ray scanning images becomes increasingly significant. Therefore, a one-stage detection algorithm, namely an improved You Only Look Once (YOLO) algorithm, is proposed. Firstly, the datasets are put to the the third version of YOLO(YOLOv3) network for iterative training by using a loss function named Distance Intersection over Union (DIoU). Secondly, the Spatial Pyramid Pooling (SPP)[15] model is utilized in the YOLOv3 network, can help to obtain feature maps from images of any size. Finally, the training and test results are visualized through the Tensorboard toolkit for performance evaluation. The experiment is also trained in two datasets named COCO and PASCAL VOC. The experimental findings demonstrate that the approach employed in this paper has better Frame Per Second (FPS) than other one-stage object algorithms such as Single Shot Multibox Detector (SSD), Resnet50-SSD and YOLOv3. The mean Average Precision (mAP) improves 2% than the original YOLOv3 network. The SIXRay datasets, derived from real images acquired of security checks in several subway stations, is used for testing under real-world conditions. Overall, the new method has been proven highly effective and holding promising potentials for large-scale implementation.
Performance Comparison of EKF and UKF for Estimation of COE using Kozai Mechanism

The paper presents performance comparison of Extended Kalman Filter and Unscented Kalman Filter for estimation of Classical Orbital Elements (COE) using RADAR measurements. SGP4 algorithm has been used for relating the Two Line Elements (TLE) information with orbital parameters and subsequently is used for generating the RADAR measurements of position and velocity in the orbit. Zero mean, white Gaussian additive noise has been considered for RADAR measurements. The nonlinearity in the state and measurement models have been dealt with EKF and UKF for estimation of TLE information at every epoch and the results of both the estimators have been compared.
Improving Learnability Capabilities in Desktop VR Medical Applications

The main objective of this study was to evaluate the implicit and explicit learning experiences of two distinct training segments, a tutorial and a Free Play Mode (FPM), of a desktop-based virtual reality (VR) medical operations simulator to assess aspects of learnability for a first-time user. Our goal was to evaluate the tutorial simulator and User Interface (UI) design by interpreting results through the lens of Mayer's principles of multimedia learning. The experiment was conducted remotely and the study sample comprised of ten upper-year medical students. The video recording from the participant's desktop camera was retrieved to determine their affective responses by analyzing facial micro-expressions and infer valence pain points (VPPs). Participants performed the simulation's tutorial followed by the FPM tasks, partitioned into two types: twelve retention tasks designed to verify how well users learned UI elements through the tutorial, and an exploration task to observe how the user explored the interface when few instructions were given. Results showed that the explicit user experience did not differ between the tutorial and the retention tasks. In contrast, users reported significantly higher cognitive load and lower system usability during the exploration task than during the tutorial. A negative correlation was found between perceived self-efficacy and perceived cognitive load. Results pertaining to VPPs indicated that FPM tasks were associated with more negative affective responses when compared to the tutorial. The manuscript concludes with methodological guidelines to assess the learnability of complex, ecologically valid simulations while reinforcing the need to use complementary methods to assess the users' experience.
Shortest Path to Secured Hardware: Domain Oriented Masking with High-Level-Synthesis

Implementing hardware secure against side-channel attacks (SCA) demands significant time and expertise in hardware design. In this paper, we propose a simple and fast approach for synthesizing masked block cipher hardware from a C-code exploiting High-Level-Synthesis (HLS), which allows a very short design time. Compared to previous approaches, our proposal provides a systematic and general flow based on state-of-the-art Domain-Oriented Masking (DOM). We also present a fast security-validation flow for the synthesized circuits at the early design stages using commercial-off-the-shelf CAD tools. Efficacy of the proposed design-flow has been established over a set of representative benchmarks including a masked S-Box and a lightweight block-cipher.
MATLAB Wavelet analysis of electron energy spectrum in one-dimensional quantum well with infinitely high walls for Al-Ga-As system (0 &lt; x &lt; 1)

Abstract This paper presents calculations of electronic states in Al x Ga 1-x As semiconductor nanostructures and simulates the envelope wave functions of quantum energy levels in a one-dimensional quantum well with infinitely high walls of a given width at various values of x . For the analysis of results the authors choose the function wtmm from the Matlab library that fixes the extremums and which is a characteristic of the fractality of the envelope wave functions of quantum energy levels.
Onboard Real-time Object Detection for UAV with Embedded NPU

Unmanned aerial vehicles (UAVs) endowed with the function of computer vision are widely applied. There is an urgent need to implement real-time object detection. However, the limited memory and computing capacity of current embedded devices prevent the deployment of deep learning on UAVs. Therefore, this paper proposes an efficient onboard object detection system based on an embedded NPU device. It can be deployed on a UAV to perform real-time scenario analysis. Firstly, a deep-learning network structure based on YOLOV3-Tiny is designed for object detection. Then it is compressed by a novel pruning strategy to obtain a “slim” object detector. Finally, a detection-reinitialization mechanism is introduced to realize the robust output of detection. The experimental results demonstrate that our model and algorithm achieve efficient performance of detection compared to the unoptimized benchmark. The proposed model achieves up to 0.591 mAP with a 6M parameter size. The complete system is tested in both simulation and real environments of the aerial manipulator grasping task. The effective detection frame ratio reaches 90%, and FPS achieves about 25.
Conditional sum-product networks: Modular probabilistic circuits via gate functions

While probabilistic graphical models are a central tool for reasoning under uncertainty in AI, they are in general not as expressive as deep neural models, and inference is notoriously hard and slow. In contrast, deep probabilistic models such as sum-product networks (SPNs) capture joint distributions and ensure tractable inference, but still lack the expressive power of intractable models based on deep neural networks. In this paper, we introduce conditional SPNs (CSPNs)—conditional density estimators for multivariate and potentially hybrid domains—and develop a structure-learning approach that derives both the structure and parameters of CSPNs from data. To harness the expressive power of deep neural networks (DNNs), we also show how to realize CSPNs by conditioning the parameters of vanilla SPNs on the input using DNNs as gate functions. In contrast to SPNs whose high-level structure can not be explicitly manipulated, CSPNs can naturally be used as tractable building blocks of deep probabilistic models whose modular structure maintains high-level interpretability. In experiments, we demonstrate that CSPNs are competitive with other probabilistic models and yield superior performance on structured prediction, conditional density estimation, auto-regressive image modeling, and multilabel image classification. In particular, we show that employing CSPNs as encoders and decoders within variational autoencoders can help to relax the commonly used mean field assumption and in turn improve performance.
Human Behavior Recognition Method based on Two-layer LSTM Network with Attention Mechanism

Abstract Aiming at the problem that the exiting human skeleton-based action recognition methods cannot fully extract the relevant information before and after the action, resulting in low utilization efficiency of skeleton points, we propose a two-layer LSTM (long short term memory) network with attention mechanism. The network has two layers, the first LSTM network is used for skeleton coding and initialization of system storage units and the second LSTM network integrates attention mechanism to further process the data of the first layer network. An algorithm is designed to assign different weights to skeleton points according to the importance of human body, which greatly increases the recognition accuracy. Action classification is accomplished by multiple support vector machines. Through training and testing, the average recognition rate of 98.5% is achieved on KTH dataset. The experimental result shows that the proposed method is effective in human behavior recognition.
Application of Deep Neural Network Modifications for Face Recognition in Attendance Systems

The conventional method of collecting attendance as evidence of student attendance is considered ineffective because it consumes a lot of time and effort. The validity of the data is questionable. There have been many models that have been applied to facial recognition-based attendance systems. However, this model needs much training data so that the model's accuracy is high. In this study, a modification of the deep neural network model for the attendance system is proposed that can work on a small amount of training data. The proposed model is a modification of the DenseNet201 model with batch normalization and average pooling layer. Even though our model's training time is quite long, this model modification can achieve the highest accuracy value of about 90% compared to other pre-trained models, namely ResNet50 and MobileNet.
Virtual Machine Allocation Policy for Load Balancing

Abstract This paper focuses on the VM allocation policies for load balancing in cloud computing environment. Intermittent nature of balancing the load scheme into the cloud computing becomes a challenging job and it also affects the load balancing of the cloud. The suggested proposed model generates and step-up the VM allocation policies but also transforms the generated cloud workload. Furthermore, to improve the workload distribution of workload and stability of the overall cloud computing environment the load balancing algorithm is most important for load balancing. The work of load balancing is equally effective in the cloud computing environment and it is most essential one for load balancing algorithms to take care of all issues at the time of the work load. The researchers studied different algorithms to solve the problems of load balancing that generate problems during the distribution of workloads. The analysis VM allocation policies are tested on CloudSim environment and the results, and discussion is about to which one VM allocation policy is superior.
Two-Level Biometric Security System For Voting

There is a lot of controversy in India during the election process, including multiple voting, fake voting, etc. This research is to help develop a system to eradicate such disputes. To improve the voting process’s security performance, voters’ fingerprints are used as any person can be uniquely identified by matching their fingerprints, including several levels of verification to make sure the authenticity of this process. Hence, registration, enrollment and voting scheme’s authenticity can be secured in the best way using a biometric voting system. A combination of matching algorithms is used for determining a match for the input data. The voter is supposed to go for biometric verification before entering the voting booth to eradicate any chance of multiple voting, thus ensuring the purpose of ‘One Voter- One Vote’ [3]. Subsequently, all the voters match their fingerprint samples against the database’s values during the confirmation time frame.
A Human Intruder Detection System for Restricted Sensitive Areas

The development of an automated intruder detection system for restricted sensitive areas or unattended border areas is a major concern for national security perspective to avoid illegal entries of outsiders to those areas. Due to the larger radius of those areas, it is quite difficult to keep track of the activities through a manual human tracking process at regular intervals for periodic monitoring. Also, the safety of the people involved in the manual supervision process by visiting the sites is of major concern while no prior information is available on any intruder already entered that place. This work focuses on the development of an automatic human intruder detection system that can detect illegal entries of humans in restricted areas and can report to the control room in time. The model presented uses the YOLO Model to detect human objects in videos captured by cameras with some standard resolution. A number of experiments were conducted to evaluate the performance of the model under different contexts. The results obtained show the efficiency of the model in detecting human objects over other objects in the captured videos with an accuracy of approximately 96% in the considered environmental conditions.
PRUID: Practical User Interface Distribution for Multi-surface Computing

It becomes more and more common for people to have multiple mobile devices. This opens the opportunity of multi-surface computing in which users interact with an app using multiple devices simultaneously. Recently, a system called FLUID was developed, which can distribute User Interface (UI) elements of an app to multiple devices to support multi-surface computing. FLUID enables general, flexible and transparent multi-device interaction, which cannot be achieved by previous approaches such as screen mirroring, app migration, and customized app development on multiple devices. However, the practicality of FLUID is still severely limited because it requires that (1) the app source codes must be available and (2) the same app is pre-installed on all devices. This paper presents PRUID, a UI distribution system that is free from the above-mentioned limitations of FLUID. PRUID captures and extracts relevant information about UI elements to be distributed completely at run time, without requiring the app source code. An app-independent UI agent is designed to dock and render the UI components distributed to the guest device, so pre-installation of the app on guest devices is not required. We developed representative use cases to demonstrate the usage and evaluate the performance of PRUID. The evaluation results show that the extra overhead incurred due to the UI information extraction at run time is marginal and PRUID provides a smooth user experience.
Enabling adaptable Industry 4.0 automation with a modular deep reinforcement learning framework

Industry 4.0 envisions adaptable and resilient manufacturing and logistics operations capable of handling dynamic changes or deviations in operations using intelligent sensing and computation technologies. Recent advances in artificial intelligence and collaborative robotics have created unprecedented opportunities to fully automate a variety of industrial tasks such as material handling, assembly, machine tending, and inspection, among other. With the rapidly growing interest in the vision of lot-size-of-one, a fundamental and challenging question remains open: How can robots leverage the knowledge of previously-learned tasks to expedite their learning on new tasks? We tackle this problem by developing and testing a novel deep reinforcement learning framework with task modularization to enhance adaptability of collaborative robots in performing a multitude of simulated tasks. The framework is built upon the actor-critic method and the notion of task modularity, and is compared against the Soft Actor-Critic (SAC) algorithm as a baseline. Numerical experiments on the Meta-World dataset prove the ability of the proposed framework in improving the adaptability and efficiency of collaborative robots to new tasks through task modularization and transfer of policies from previously-learned task modules.
PERSONA: A personalized model for code recommendation

Code recommendation is an important feature of modern software development tools to improve the productivity of programmers. The current advanced techniques in code recommendation mostly focus on the crowd-based approach. The basic idea is to collect a large pool of available source code, extract the common code patterns, and utilize the patterns for recommendations. However, programmers are different in multiple aspects including coding preferences, styles, levels of experience, and knowledge about libraries and frameworks. These differences lead to various usages of code elements. When the code of multiple programmers is combined and mined, such differences are disappeared, which could limit the accuracy of the code recommendation tool for a specific programmer. In the paper, we develop a code recommendation technique that focuses on the personal coding patterns of programmers. We propose Persona, a personalized code recommendation model. It learns personalized code patterns for each programmer based on their coding history, while also combines with project-specific and common code patterns. Persona supports recommending code elements including variable names, class names, methods, and parameters. The empirical evaluation suggests that our recommendation tool based on Persona is highly effective. It recommends the next identifier with top-1 accuracy of 60-65% and outperforms the baseline approaches.
Stereoscopic facial imaging for pain assessment using rotational offset microlens arrays based structured illumination

Abstract Conventional pain assessment methods such as patients’ self-reporting restrict the possibility of easy pain monitoring while pain serves as an important role in clinical practice. Here we report a pain assessment method via 3D face reading camera assisted by dot pattern illumination. The face reading camera module (FRCM) consists of a stereo camera and a dot projector, which allow the quantitative measurement of facial expression changes without human subjective judgement. The rotational offset microlens arrays (roMLAs) in the dot projector form a uniform dense dot pattern on a human face. The dot projection facilitates evaluating three-dimensional change of facial expression by improving 3D reconstruction results of non-textured facial surfaces. In addition, the FRCM provides consistent pain rating from 3D data, regardless of head movement. This pain assessment method can provide a new guideline for precise, real-time, and continuous pain monitoring.
Addressing multiple salient object detection via dual-space long-range dependencies

Salient object detection plays an important role in many downstream tasks. However, complex real-world scenes with varying scales and numbers of salient objects still pose a challenge. In this paper, we directly address the problem of detecting multiple salient objects across complex scenes. We propose a network architecture incorporating non-local feature information in both the spatial and channel spaces, capturing the long-range dependencies between separate objects. Traditional bottom-up and non-local features are combined with edge features within a feature fusion gate that progressively refines the salient object prediction in the decoder. We show that our approach accurately locates multiple salient regions even in complex scenarios. To demonstrate the efficacy of our approach to the multiple salient objects problem, we curate a new dataset containing only multiple salient objects. Our experiments demonstrate the proposed method presents state-of-the-art results on five widely used datasets without any pre-processing and post-processing. We obtain a further performance improvement against competing techniques on our multi-objects dataset. The dataset and source code are available at: https://github.com/EricDengbowen/DSLRDNet.
Efficient Grammatical Error Correction with Hierarchical Error Detections and Correction

Noisy text is common in semantic services and can have bad effects. Grammatical Error Correction (GEC) can be used to improve text quality, but traditional neural machine translation approaches need hundreds of milliseconds to correct a single text, which is unacceptable to time-sensitive services. To improve the efficiency of GEC, we choose to detect errors first and then make corrections. We present an intuitive multitask learning approach by checking if the text contains errors, finding errors' positions, and finally generating corrections. Two classifiers are introduced to serially detect sentence-level and token-level errors as errors only take a few parts in common corpora. Different from traditional approaches, we adapt a non-autoregressive decoder and only generate needed words to correct those detected errors, making the correction stage efficient. Experiments show that our approach can be ten times faster than the traditional approach in inference, and can achieve a comparable GEC performance.
Digital Identity Proposal for Unified Medical Record using Blockchain technology

This article describes the architecture of a Blockchain subsystem to manage access through Digital Identity to the Electronic Medical Record of patients. During this work, the agile programming methodology focused on Blockchain by Marchesi et al. [1], was followed. Therefore, the development of the work was divided into a section for the programming of the Blockchain network and another section for the development of the part of user interfaces. The Hyperledger Fabric framework was used to set up the Blockchain network due to its focus on Digital Identity management. In addition, the graphical interface in React was developed to allow communication with users. The whole development had a modular approach, where three modules are worked in both sections of the project: Identity Management, Authority, and Consortium of Health Service Providers. This architecture has managed to be developed and its process and functioning are presented in this work.
Research on application of artificial intelligence in movie industry

The development of artificial intelligence has given birth to a series of innovations in the film industry. This article systematically summarizes these innovations into three parts; movie recommendation, movie distribution and audiovisual language creation. In the first part, the paper lists a series of movie recommendation systems based on AI technology. These systems can help users quickly find the movie resources they are interested in among the massive movie resources. In the second part, the article lists a series of cases of film production using AI, including analyzing film projects, estimating film box office, analyzing film reviews, and writing abstracts. In the third part, the article lists several cases about audio-visual language using AI systems,including classifying films and the aesthetic feature, analyzing image features through AI, changing the appearance and age of actors, and creating visual characters.