Reviewerly/exHarmony huggingface dataset:
Video Detection of Helmet Wearing Based on Improved YOLOv4

In recent years, the real-time video monitoring of helmet wearing based on deep learning has attracted extensive attention. However, due to the high computational complexity of deep learning model, it leads to serious consumption of computer memory and poor real-time detection. In this work, a method of combing MOG2 and YOLOv4 was developed to improve the detection efficiency of helmet wearing in video stream. MOG2 is helpful in removing the interference of static redundant frames and save computing resources. YOLOv4 was used to identify whether workers wear safety helmets. The experimental results have shown that the proposed method has good practicability in moving target detection.
Adaptive Replication Strategy in Highly Distributed Data Management Systems

The performance of the execution of an analytical workload critically impacts the speed at which companies are able to react to market changes. In the era of Big Data, it is imperative that large, complex analytics are executed in a timely manner. In this paper, we propose a method to analyze the data access pattern of analytical workloads on large datasets to identify optimal data partitioning and replication strategies. This, in turn, helps the already existing query optimization components of modern data management systems.
Self-testing quantum random number generator with easily testable assumptions

We propose a new paradigm for security of quantum protocols. Instead of making one, powerful, difficult to check assumption about the system, we make a few, which are easy to verify or otherwise justify. This enables us to combine very high security levels with relatively low hardware complexity. We present a self-testing quantum random number generator that demonstrates the usefulness of our paradigm. We describe this device, prove its security against active attacks, backdoors and malfunctions and analyze its efficiency.
DAST: Depth-Aware Assessment and Synthesis Transformer for RGB-D Salient Object Detection

The introduction and popularity of depth maps have brought new vitality and growth into salient object detection (SOD), and plentiful RGB-D SOD methods have been proposed, which mainly focus on how to utilize and integrate the depth map. Although existing methods have achieved promising performance, the negative effects of low-quality depth maps have not been effectively addressed. In this paper, we solve the problem with a strategy of judging low-quality depth maps and assigning low factors to low-quality depth maps. To this end, we proposed a novel Transformer-based SOD framework, namely Depth-aware Assessment and Synthesis Transformer (DAST), to further improve the performance of RGB-D SOD. The proposed DAST involves two primary designs: 1) a Swin Transformer-based encoder is employed instead of a convolutional neural network for more effective feature extraction and long-range dependencies capture; 2) a Depth Assessment and Synthesis (DAS) module is proposed to judge the quality of depth maps and fuse the multi-modality salient features by computing the difference of saliency maps from RGB and depth streams in a coarse-to-fine manner. Extensive experiments on five benchmark datasets demonstrate that the proposed DAST achieves favorable performance as compared with other state-of-the-art (SOTA) methods.
Twitter Bot Detection using Social Network Analysis

Twitter is an online platform that provides social networking services for hundreds of millions of active accounts. However, an estimation of 48 million accounts on Twitter are not human. These accounts are bots, which are automated software that control accounts. Identifying Twitter bots is desirable as human can view Twitter bots as a credible source of information. Existing methods to detect Twitter bots are mainly feature-based and text-based. In this paper, we propose a graph-based approach to detect Twitter bots as an alternative to the traditional bot detection methods. The paper outlines the data collection process, behaviors to observe in an account, and a proposal for a machine learning classifier utilizing these behaviors.
Predicting Physical Object Properties from Video

We present a novel approach to estimating physical properties of objects from video. Our approach consists of a physics engine and a correction estimator. Starting from the initial observed state, object behavior is simulated forward in time. Based on the simulated and observed behavior, the correction estimator then determines refined physical parameters for each object. The method can be iterated for increased precision. Our approach is generic, as it allows for the use of an arbitrary-not necessarily differentiable-physics engine and correction estimator. For the latter, we evaluate both gradient-free hyperparameter optimization and a deep convolutional neural network. We demonstrate faster and more robust convergence of the learned method in several simulated 2D scenarios focusing on bin situations.
Enhanced chatbot security framework using MAC address authentication to improve customer service quality

Customer complaints are handled as part of the customer service function. The process can be used to control customer dissatisfaction. The greater the number of customers who are pleased with the benefits of a product, the better the customer service. Currently, chatbots are widely used in businesses to improve and simplify customer service. Its existence is advantageous in that it allows for faster data access, allowing it to provide services to customers more quickly. Because of this ease of access, businesses are obligated to ensure that all devices they own are secure. Based on this, the researcher will increase the Security Framework on the Chatbot by using MAC address authentication, which can only be accessed by the registered customer's hardware machine address, so that the chatbot menu will not be able to run if it is accessed on a different hardware device (smartphone, laptop, or tablet). Prior to software authentication such as hybrids such as matching name and password, unique code, and email verification, MAC address authentication on hardware is expected to be the first stage to be authenticated by the system.
Effective Reinforcement Learning using Transfer Learning

Using visual observation of environments to identify an ideal action is the problem Reinforcement Learning attempts to solve. Even though several algorithms have used convolutional neural networks, they are not very efficient at learning the representations quickly and generally require large periods of time to converge. Transfer learning has been used as a means to minimize training time and resources in machine learning as it removes the need for a large dataset. This paper describes an approach to implementing transfer learning in actor-critic methods by integrating a pre-trained ResNet50 in the approach to Asynchronous Advantage Actor Critic (A3C). The proposed method is known as ResNet Transfer Learning in Reinforcement Learning (ResTLRL) and it demonstrates that transfer learning can be applied to working with different environments with an improvement of over 68% in terms of maximum rewards when compared to the original implementation on OpenAI Atari benchmarks.
Generating Attack–Defense Tree by Automatically Retrieving Domain-Specific Security Attack Patterns

With the rapid integration of different technologies such as IoT, cloud computing, and big data in complex and safety-critical systems, security attacks have become a common concern in today’s world. Identification of anticipated security attacks at the early phases of development is a difficult task due to uncertainty and lack of knowledge. However, elicitation and mitigation of security threats well in advance may be instrumental in assuring the security of the modern-day systems. Mostly, the threat elicitation techniques available in the literature are reported to be manual and unstructured. They usually depend upon the knowledge and expertise of the security expert. This paper proposes a methodology for recommending relevant attack patterns and generating an attack–defense tree (ADTree) against the functional requirements (FRs). Word embedding technique and word mover’s distance (WMD) are used to map FRs with the relevant attack patterns in the CAPEC database. Finally, the ADTree tool is used to generate the corresponding ADTree for each functional requirements. The technique was applied on the functional requirements of an electronic health record (EHR) system. The results seem promising, indicating that the model may be adequately used to retrieve pertinent attack patterns automatically. Further, the attack patterns can be used to generate relevant ADTree for each of the functional requirements. The work may significantly help requirements engineers and security analysts in effectively analyzing the potential attacks on the system. Furthermore, it may substantially reduce manual identification of security threats, which is a cumbersome and error-prone task.
Unpaired and Self-supervised Optical Coherence Tomography Angiography Super-Resolution

Optical coherence tomography angiography (OCTA) is usually used to observe the blood flow information of retina and choroid. It is meaningful for clinicians to observe more microvascular details by enhancing the resolution of OCTA images, which is conducive to the diagnosis of diseases. However, due to the limitation of imaging equipment, when the resolution of OCTA is improved, the field of view (FOV) will be reduced. In the existing methods to enhance the resolution of OCTA, paired training data from the same eye are generally required, but paired data are usually difficult to be obtained, and the resolution of enhanced images is difficult to exceed that of original high resolution (3 × 3 mm2) OCTA images. Therefore, to improve the resolution of low resolution (6 × 6 mm2) OCTA images, this paper proposes an unpaired and self-supervised OCTA super-resolution (USOSR) method by down sampling and enhancing the original 3 × 3 mm2 OCTA images. Experimental results demonstrate that the enhanced 6 × 6 mm2 OCTA images have significantly stronger contrast, sharper edges and higher information entropy than the original images.
2-D latent space models: Layer-wise perceptual training and spatial grounding

Deep autoencoder models work on an information bottleneck principle with a lower-dimensional latent space of typically tens or hundreds of dimensions. Two and three-dimensional spaces are key for representing information that can be mapped in physical space, or that can be intuitively navigated and interpreted by a human without extensive background knowledge. Nevertheless, training autoencoder models with extremely low dimensional latent spaces is challenging for multiple reasons: (i) the dimensionality of the latent space is lower than the intrinsic dimensionality of the data manifold, and (ii) optimization of a complicated non-convex objective can lead to convergence to a non-global optimum. In this work, we demonstrate that layer-wise training strategies lead to improved convergence, as well as better perceptual properties when applied to models with extremely low dimensional latent spaces. Experiments on the CelebA dataset show improved performance on the Fréchet Inception Distance over standard autoencoder training. Additionally, we demonstrate the utility of low dimensional, physical latent representations. We map satellite image patches to a low dimensional latent space where we align the representation with the physical attributes of each patch, such as the geographic coordinates. We show that such representations are inherently interpretable and allow for an interactive and physically intuitive approach to generating new images.
Research on feature matching algorithm of remote sensing scenes

At present, satellite remote sensing technology has become a research hotspot for establishing space sensing systems at home and abroad. In remote sensing image processing, detecting and matching feature points in remote sensing images play a key role. In this article, three mainstream image feature extraction and matching methods are introduced, namely the SIFT method, SURF method, and ORB method. Experiments are designed to test and compare the operating efficiency and matching accuracy of these three feature point extraction and matching algorithms in remote sensing image scenes. The conclusion is as follows: ORB method is the fastest, SURF is second, and SIFT is the slowest. In terms of correct matching rate, the SURF algorithm and ORB algorithm have the advantage and show a high correct matching rate in remote sensing image scenes.
Feasibility Study of Machine Learning &amp; AI Algorithms for Classifying Software Requirements

Software requirements[15] description and classification is the fundamental and most important activity in the software engineering process. Requirements are obtained through an elicitation process which generally involves interaction with stakeholders such as; exchange of information in person, on notes, by email, on phone, through meetings, etc., which involves a communication language such as English. The description of requirements (ex: functional, non-functional, related others) encompasses few properties such as; understandability, completeness, accuracy, clarity, unambiguousness, testability and related others. Classifying requirements into functional and non-functional category using Machine learning approaches have proved to be successful in the past. The goodness of software requirement properties impact’s the quality levels during the development of a software product and on the resulting product quality. The classification should address semantic details and implicit information during classification to completely satisfy a requirement. This paper presents results of applying different ML algorithms using a simple problem (and data set) for classifying software requirements. The requirements have been described in English following semantic language rules adopted to ease the writing process. The requirement may be obtained from a use case tool (for example rational unified software) or alternate sources. The purpose of this research work is for understanding the application and use of Machine Learning algorithms for the problem of requirements classification, while providing inputs for developing a “software requirements definition and description framework” using English language.
Development of the software to improve the accuracy of emotion recognition

The theory of pattern recognition is one of the directions of scientific and technical development in the field of image analysis and machine vision, etc. The authors consider the possibility of using information systems to improve the quality and accuracy of recognizing emotions on faces from images. The purpose of the research is to develop an application that uses the most effective algorithm for detecting faces and recognizing emotions. The methodology is based on the principles of deep learning, includes the use of convolutional neural networks, their software embodiments. The functional scheme is implemented in the application using the methods of the library of computer vision. Result of the research, a module was developed with the ability to determine the user’s emotions from an image from a webcam in real time. At the testing stage a data set for training was formed. A convolutional neural network was trained. The functional for generating graphs describing the emotional portrait of the user for the entire time of using the module was implemented. In the future, it is planned to improve the quality of neural network recognition, and prepare a web service for integration into mobile applications.
Segmentation Of Biological Cells In Microscopic Images

Image analysis of cells is an important aspect for research in biomedical applications. Image segmentation is the important step in image analysis. The task is not so easy as to identify the cells as there a lot of complexities like the removal of background noise, overlapping of cells, and change in the position of the cell. This paper examines and analyses various methods for pre-processing and segmentation using metrics, which are used to study the shape, size and behavior of the cells. First step is the pre-processing to reduce the noise present in the image. Then the pre-processed image is taken as the input and the cells in the image are segmented. The compared results of various techniques by measuring the accuracy, Jaccard index and number of cells detected in the image.
Physical Fitness Inspection System using Deep Learning

Object detection is an important application in deep learning technology, which is used to identify and locate objects in an image or video. It has improvement of accuracy and performance. It is various popular application such as pedestrian detection, medical images, robotics, face detection, self-driving cars, etc. The paper mainly focuses on object detection methods by including one stage object detector. In this paper, described the detection of human pose, localization, and classification of multi-person activity. We propose object detector-based human detection to detect human localization and pose classification of multi-person activity. By using the proposed detection method, obtained good performance of physical fitness inspection in real-time. The accuracy of proposed system is 99.7% and the Intersection over Union (IOU) score is 97% which were tested in real time condition.
Intelligent Interpretation of High-resolution Remote Sensing Images based on Deep Learning

As an important task of intelligent interpretation research, the object detection of remote sensing images still has many problems to be solved. In this paper, aiming at the characteristics of small-sized object and complex background, in order to solve the problem of poor effect of existing object detection algorithms when applied to remote sensing images, an object detection model of remote sensing images based on the improved Faster R-CNN model is proposed. Based on the original Faster R-CNN model, the feature extraction network VGG16 is improved by designing a feature fusion module. In order to verify the effectiveness of the model in this paper, it is used to carry out experiments on NWPU VHR-10 and DOTA datasets, and mAP has reached 0.886 and 0.810 respectively, which was 6.9% and 11.6% higher than the original Faster R-CNN. The experimental results show that our method effectively improves the object detection effect of remote sensing images, and achieves good results in remote sensing images with small-sized object and complex background.
Distance Learning Scheme with Remote Desktop Application for Mikrotik Configuration Practice in the Covid-19 Pandemic Era

The research aims to reveal the potential of online learning schemes through remote desktop applications used in practical learning of Mikrotik configuration. During the Covid-19 pandemic, almost all educational activities are carried out online. Learning is increasingly dependent on information and communication technology (ICT). Innovation in the field of education is of high urgency. This study was conducted by applying the ADDIE model. This model is divided into five stages: analysis, design, development, implementation, and evaluation. The evaluation stage is carried out at every other step. This research involves students as the test subjects. The results of the study indicate that the proposed learning scheme has the potential to be implemented and developed further. In particular, the proposed learning scheme has produced good learning outcomes, where students have achieved at least 75% of the material well. In addition, the learning responses by students also resulted in good responses.
Scene Recognition for Blind Spot via Road Safety Mirror and In-Vehicle Camera

In Advanced Driver Assistance Systems (ADAS) which has been actively researched in recent years, it’s very important to understand the situation around the vehicle. Especially, since blind spots are dangerous for the driver, it’s necessary to reduce them in some way. In Japan, many road safety mirrors, which are convex mirrors for reflecting vehicles and pedestrians in the blind spot, are installed at intersections, T-junctions and sharp curves with poor visibility. This paper proposes the method to recognize the situation of blind spots using a road safety mirror and an in-vehicle camera. There are two major problems in recognizing images in road safety mirrors. One is that it’s difficult to detect and track objects in mirror images since a mirror image taken by an in-vehicle camera has low resolution. The other is that there is no public database focusing on road safety mirrors and it’s not easy to collect their images. To deal with low resolution, our method extracts moving features from mirror image sequences using DNN instead of detecting objects in each image. To compensate for not enough real-data, we also create synthetic image datasets in which CG-data style is converted to real-data style by image-to-image translation network. Our model is trained with CG datasets of real-data style. Evaluation results using our own dataset show that our method can be adapted to low-resolution images where object detection is not possible.
Fused Pruning based Robust Deep Neural Network Watermark Embedding

Deep Neural Network (DNN) models are usually trained with tremendous data and computation resources. Thus, DNN models are now regarded as important assets, however facing a great risk of being stolen and illegal distribution. In recent years, watermark is introduced to protect the ownership of DNN models. The watermark can be extracted in a relatively simple way to declare the ownership of the model. However, watermark is vulnerable to be attacked. In this work, we propose a watermark defense method for DNN model based on pruning. Inspired by the pruning methods, we design a fused channel-wise pruning strategy which selects important filters for watermarks embedding. Specifically, we introduce a novel method to enhance the watermark robustness by selecting important filters as the watermark carrier based on multiple pruning methods, including network slimming, efficient filter and entropy. We conduct experiments on the VGG-19 model with the CIFAR-10 dataset. The experimental results show that this method is robust against fine-tuning attack, pruning attack and overwriting attack. In addition, our method does not significantly change the distribution of model weights so that the watermark is hard to be detected.
Impact of hybrid teaching methodology during COVID-19 pandemic on Operating systems course

Since 2019 the COVID-19 pandemic has caused huge changes in our lives. The government's health policies restricted everyday life, especially in schools. In the school year 2021/2022 we had to teach smaller groups face-to-face and later in the semester switch to distance learning. This article focuses on the Operating systems course teaching techniques used during the school year 2021/2022. Operating systems are one of the fundamentals needed for the development of complex UAV systems.