Reviewerly/exHarmony huggingface dataset:
Recursive Video Denoising Algorithm for Low Light Surveillance Applications

We designed a video denoising algorithm for surveillance applications under low light conditions which is targeted to run on weak CPU. State of art algorithms don't meet these requirements because of huge memory bandwidth consumption. Among them, algorithms based on neural network have generalization issues especially when there are no references for training. Besides, the complexity of such methods is unaffordable in realtime embedding application. Hence, we propose three techniques, including: 1) adaptive noise strength estimation to fit into noise profile in real applications; 2) multi-resolution background segmentation inspired by human vision system, and 3) multi-pass denoise strategy. It's recursive and of first-order Markov property. Adaptive noise strength estimation also eliminates pre-calibration steps usually required by denoising algorithm and leads to easy deployment. Experiments show that our method can achieve better subjective denoising quality compared to state of art methods in target applications, especially in extremely low light scenes. Moreover, it requires small computation loads and small storage which makes it very suitable for implementation on weak CPUs.
Effect of the covariance matrix of the Kalman filter used for optical flow estimation

In this study, we propose a method of observing the velocity of a target object using the spatio-temporal differentiation method which has lower calculation cost. Estimating and recognizing the position and velocity of the target object by motion image processing using a Kalman filter. However, while the spatio-temporal differentiation method can observe the velocity at low cost, it has a feature that when the velocity of the target object is large, the error included in the observed value becomes large. Therefore, the velocity of the target object is observed by the spatio-temporal differentiation method which introduced the compensation method. In the compensation method, how to determine the compensation amount is important, but by determining the amount based on the prediction of the Kalman filter, the error included in the observation value can be reduced. In this way, the recognition of the target object by the Kalman filter is facilitated by using the observed value of velocity, and the error of the observed value can be reduced by determining the compensation amount using the predicted value by the Kalman filter. It is expected that more stable results will be obtained than the observation by spatio-temporal differentiation method. At that time, the execution result changes by changing the variance value of the Kalman filter. Therefore, in this study, we examine how to set the covariance matrix of the Kalman filter.
Super Visual Semantic Embedding for Cross-Modal Image-Text Retrieval

Visual semantic embedding network or cross-modal cross-attention network are usually adopted for image-text retrieval. Existing works have confirmed that both visual semantic embedding network and cross-modal cross-attention network can achieve similar performance, but the former has lower computational complexity so that its retrieval speed is faster and its engineering application value is higher than the latter. In this paper, we propose a Super Visual Semantic Embedding Network (SVSEN) for cross-modal image-text retrieval, which contains two independent branch substructures including the image embedding network and the text embedding network. In the design of the image embedding network, firstly, a feature extraction network is employed to extract the fine-grained features of the image. Then, we design a graph attention mechanism module with residual link for image semantic enhancement. Finally, the Softmax pooling strategy is used to map the image fine-grained features to a common embedding space. In the design of the text embedding network, we use the pre-trained BERT-base-uncased to extract context-related word vectors, which will be fine-tuned in training. Finally, the fine-grained word vectors are mapped to a common embedding space by a maximum pooling. In the common embedding space, a soft label-based triplet loss function is adopted for cross-modal semantic alignment learning. Through experimental verification on two widely used datasets, namely MS-COCO and Flickr-30K, our proposed SVSEN achieves the best performance. For instance, on Flickr-30K, our SVSEN outperforms image retrieval by 3.91% relatively and text retrieval by 1.96% relatively ([email protected]).
Collision Avoidance based on CPA algorithm using Relative Distance and Azimuth of Radar for Unmanned Aerial Vehicles

In this paper, we propose an obstacle states estimation algorithm based on radar and a collision avoidance algorithm based on closest point of approach(CPA) for unmanned aerial vehicles(UAVs), in order to avoid collision with the obstacle that do not provide information. We estimated position of detected objects using radar and compared current position and previous position for matching objects and removing outliers. Among the detected objects, the obstacle was selected the object with the highest risk of collision. The CPA was calculated using estimated states of the UAV and the obstacle. Based on the CPA, we detected collision and generated the velocity command for collision avoidance. The performance of proposed algorithm of states estimation of obstacle, collision detection and avoidance was verified through the flight tests.
Blockchain and 6G-Based Supply Chain Management Framework for Oil and Gas Shipment

Abstract Keeping track of the oil and gas supply chain is challenging task as the route and transportation requires sophisticated security environment - both physical systems’ and IT systems’ security. Thanks to the recent advancement in IoT, specialized sensors can keep track of the required supply chain environment. With the help of blockchain, the supply chain data can be immutably saved for further sharing with stakeholders. Due to the introduction of AI as an embedded element within 6G networks, the end-to-end supply chain process can now be automated for safety, security, and efficiency purposes. By leveraging 6G, AI, blockchain, and IoT, the supply chain data during the transportation or at rest can be monitored for any changed environment during the movement of the ship through national or international routes. In this paper, we study the requirements of such intelligent and secure supply chain management system conducive to the oil and gas industry. We also show our proof-of-concept implementation and initial test results. Our obtained results show promising prospect of the current system to be deployed to safeguard the oil and gas supply chain.
MILAD: Robust Anomaly Detection for Electric Vehicles with Label Noise

Abstract One of the bottlenecks restricting the development of electric vehicle industry is the safety problem. Although numerous of anomaly detection algorithms for electric vehicles have been proposed, most of them may perform poorly due to the complexity and unpredictability of real scenes. We consider that there may be a certain degree of potential safety hazard in the battery system of electric vehicles before, during and after the process of faults in the real scenes, that is, label noise. In order to solve this problem, we propose a Multi-Instance Learning based Anomaly Detection (MILAD) framework, to perform anomaly detection for electric vehicles with label noise problem. Extensive cross validation experiments fully verify that the framework can effectively detect the existence of abnormal conditions in the presence of label noise in multivariate time series data.
Design and Implement A Low Cost Indoor Position System with Physiological Signal Monitoring

Because of the epidemic in recent years, many people have to face home isolation, which means every suspected patient must stay at home. At this time, such solitary people need to be able to monitor and control their behavior. Using a camera is a very practical method. However, smart bracelets are relatively more convenient if you consider personal privacy or you must know the blood oxygen value or heart rate. In this research, it propose a low-cost indoor positioning system that uses a Bluetooth beacon, smart bracelet and an embedded system. In addition to knowing whether a person living alone is active in a specific environment, and knowing his heart rate or blood oxygen value when there are special conditions, it can also send early warning signals to specific observation units or relatives through instant messaging software.
Conversational Recommender System Based on GRU-attention Neural Network

In recent years, the conversational recommender system (CRS) based on natural language processing technology has gained widespread attention, aiming to learn and model user preferences through interactive dialogue. Although existing research has improved the accuracy of the dialogue recommendation system to a certain extent, there are still some shortcomings that make it easy to generate more general and popular responses. This paper proposes a deep learning framework with GRU and attention mechanism on the basis of knowledge graph, namely KGGA. Through the serialized modeling of conversation content and effectively capture the user’s interest to make correct recommendations. Experiments conducted on widely baselines show that the proposed framework is superior to the state-of-the-art methods.
The Effect of ElectronixTutor on Undergraduate Students' Acquisition of Conceptual Learning, Problem Solving, and Model Building of Electronic Circuits

We investigated whether an intelligent tutoring system, ElectronixTutor, improved students&#x0027; types of knowledge, including conceptual learning, problem-solving, and model building in the domain of electronic circuits. Specific research questions were (1) Can students improve their deep learning through interacting with ElectronixTutor through conceptual learning, problem-solving, and model building modules? And (2) What are student learning characteristics as they proceed through each model and learning mode? We conducted a recorded interview procedure with 10 participants as they learned with ElectronixTutor, and gave them a pre-, mid-, and posttest in order to assess their overall learning gain as well as learning transfer. We found that, although there were no significant effects due to our small sample size, the results from the means suggest both learning gain and learning transfer. Qualitative analyses were conducted on each of the three different learning modes. Collectively we found that participants were very good at problem-solving using equations and writing formulas but struggled on the application or conceptual questions. Our study suggests that there is a need for building or improving more sophisticated learning or tutoring technologies on STEM domains that focus on the integration of knowledge and skills.
Neural network-based models of binomial time series in data analysis problems

This article is devoted to constructing neural network-based models for discrete-valued time series and their use in computer data analysis. A new family of binomial time series based on neural networks is presented, which makes it possible to approximate the arbitrary-type stochastic dependence in time series. Ergodicity conditions and an equivalence relation for these models are determined. Consistent statistical estimators for model parameters and algorithms for computer data analysis (including forecasting and pattern recognition) are developed.
Comparative assessment of cyber-physical threats to megacities

By 2030, forecasts suggest that urban areas will house 60 percent of the world’s population and one in every three people will live in cities with at least half a million inhabitants. Within the same time frame, the number of global megacities is expected to jump from 33 today to 43 in 2030 [1]. Underpinning these large urban areas will be an interconnected network of critical physical infrastructures reliant on Internet-connected Industrial Control Systems and susceptible to increasingly sophisticated, e.g., AI-enabled, cyber threats. In hand, the cyber threat landscape is shifting rapidly. We are seeing a sharp rise in the number of cyberattacks on critical infrastructure [2] with significant impacts cascading across multiple sectors and causing disruption to the provisioning of essential goods and services. Security scholars suggest that these impacts are not always equitable and that disruption to critical infrastructure can affect vulnerable groups differently [3], which further emphasizes the need to improve cybersecurity between critical infrastructure sectors [4]. Through structured analysis of city statistics, demographic information, cyber incidents, and current cyber policy, our presentation will articulate potential social implications of megacity growth through the lens of cyber-physical infrastructure disruption. We investigate the largest 15 megacities in the world and find that megacities continue to grow in population but not in cyber policy. We highlight recent examples of cyber-physical disruption in Mumbai and New York City with focus on implications for vulnerable populations. Our work suggests the need for future research on social responsibility regarding security of these critical infrastructure sectors and on the need for technology-focused law, policy, and regulation guidelines.
Study on the design of similar model test of parallel rectangular pipe jacking construction

In order to study the interaction of parallel rectangular pipe jacking construction and its influence on surrounding strata, a similar model test design of parallel rectangular pipe jacking construction was carried out based on an engineering project. The main factors affecting the mechanical properties of rectangular pipe jacking construction process were analyzed, and the basic physical quantities were selected to deduce the similarity between model and prototype. Based on the similarity theory, the similar relations among the various physical quantities are analyzed through dimensional analysis method, the similarity ratio relationship suitable for pipe jacking construction model is obtained. Rectangular pipe jacking is realized by meeting geometric and elastic modulus similarity ratio, and model soil is realized by satisfying cohesion and elastic modulus similarity ratio. The data measurement and acquisition scheme of model test was designed, and the surface settlement, earth pressure and pipe jacking strain can be measured and analyzed. The key points in the model test process are pointed out through the simulation of the test process. The experimental design can meet the test requirements and achieve the test goal of parallel rectangular pipe jacking construction.
The System for Eliminating Blurring of Text Images Using Convolutional Neural Networks

Image blur degrades the quality of human perception of images and significantly complicates their subsequent analysis using computer vision systems. Real blurs tend to have unknown, spatially complex, and unique blur kernels and usually are even more complicated by noise and other artifacts. The above factors determine the relevance of developing, analyzing, and improving deblurring methods based on convolutional neural networks. A thorough study of existing solutions in the field of machine learning and convolutional neural networks for restoring blurry images was conducted. This work provides a detailed description of the key elements of convolutional neural networks, describes the loss function used for training image recovery models, and provides an overview of perception loss. As a result of the thesis, a new architecture of deep convolutional neural network called CNN was proposed, which, while having 25 times fewer parameters, achieves similar results to the CNN-L15 network on PSNR, SSIM metrics, and during visual comparison of resulting images. Also, modifications to the CNN-L15 network architecture and usage of perceptual loss function are proposed, which help the network achieve significantly better results on metrics, and subjective visual clarity of images, while being trained the same number of epochs
A Comparison between Term-Independence Retrieval Models for Ad Hoc Retrieval

In Information Retrieval, numerous retrieval models or document ranking functions have been developed in the quest for better retrieval effectiveness. Apart from some formal retrieval models formulated on a theoretical basis, various recent works have applied heuristic constraints to guide the derivation of document ranking functions. While many recent methods are shown to improve over established and successful models, comparison among these new methods under a common environment is often missing. To address this issue, we perform an extensive and up-to-date comparison of leading term-independence retrieval models implemented in our own retrieval system. Our study focuses on the following questions: (RQ1) Is there a retrieval model that consistently outperforms all other models across multiple collections; (RQ2) What are the important features of an effective document ranking function? Our retrieval experiments performed on several TREC test collections of a wide range of sizes (up to the terabyte-sized Clueweb09 Category B) enable us to answer these research questions. This work also serves as a reproducibility study for leading retrieval models. While our experiments show that no single retrieval model outperforms all others across all tested collections, some recent retrieval models, such as MATF and MVD, consistently perform better than the common baselines.
CD-SR: A Real-time Anomaly Detection Framework for Continuous Concept Drift

System administrators need to monitor various metrics (network traffic, NTP offset, etc.) of their internal services in real-time as a way to determine whether anomalies occur in the system. Traditional Spectral Residual (SR) anomaly detection methods do not take into account the interference of certain human factors (e.g., changes in personal preferences) in certain scenarios, i.e., concept drift. In these scenarios, the accuracy of anomaly detection is bound to be affected. In order to guarantee the availability and stability of network services, we propose an intelligent and pervasive anomaly detection strategy, CD-SR. First, we use the traditional SR model and the SVM method to train the time series that have not drifted to determine the threshold value. Then, to solve the problem of the pervasiveness of application scenarios, we use a drift detection model to find the time series where concept drift occurs. Finally, the sequence where the concept drift occurs is imported into the drift adaptation model to complete the replacement of the old and new concepts, the data is processed in real-time, and the replaced data is detected again in the detection model for anomalies. In the experimental stage, we obtained several data metrics using the cloud platform system built by Openstack, and by comparing several mainstream anomaly detection algorithms, our method obtained superior results.
Ransomware operators arrested in Ukraine amid wave of new campaigns and malware

A co-ordinated law enforcement operation mounted by the Ukrainian National Police, the French National Gendarmerie and the FBI, with co-operation from Interpol's Cyber Fusion Centre and Europol's European Cybercrime Centre, has resulted in the arrests of two people in Kyiv, Ukraine.
Second Sight: MobileNet v1 Integration in Dynamic and Time Critical Scenarios

When contending with the safety and awareness of active shooters or one who may be carrying an instrument of destruction, a solution exists which may enable others to quickly respond and become aware of the presence of a firearm. For additional insight and spatial awareness of potentially dangerous activities, machine learning and computer vision further augment the span of abilities to increase one's sense when navigating through an environment. Convolutional Neural Network (CNN) architectures provide incredible accuracy in object recognition when trained unto a specific subject domain through feature extraction. However, CNNs may become computationally expensive and require indefinite amounts of hardware to support real-time vision propagation -which imposes difficulty if one was adorned with computer vision equipment such as body cameras. MobileNet is a computer vision paradigm specifically designed to perform on compact computational devices while retaining object recognition performance. This paper proposes the analysis and implementation of MobileNet in low computational capacity devices -such as body cameras-and still enacts high object recognition performance in dynamic and time-critical scenarios to further augment an officer's insight into potentially dangerous situations. A custom firearm dataset was produced of over 10,000 images and the model performed over 85% on weapon validation accuracy when provided and trained on a variety of different dynamic and active environments containing the presence of firearms.
All the World's a Stage

This chapter provides a case study where a systematic, organized method of storytelling, presented as the Story-based Learning model, is used to design a series of integrated and engaging activities for cybersecurity training (to protect computer systems and networks) that fosters deliberate practice and improves performance. To address the talent shortage in the global cybersecurity workforce, the client developed a blended curriculum designed to provide practical experience to prospective cybersecurity professionals. A key component of this curriculum was the capstone exercises, activities focused on application of the content introduced in the courseware. Essentially, this is a story of using stories, one of humanity's oldest technologies, to solve the problem of training and cultivating expertise in future cybersecurity personnel. Based on solid prior evidence supporting the use of stories to increase engagement and retention, this case study focuses on detailing the thought process used to reach this set of solutions, as captured by the Story-based Learning model.
DBias: Predicting attribute effectiveness using biased databases

With the fast-moving world, the prominence of Artificial Intelligence (AI) algorithms and methodologies has also reached new heights. Modern algorithms are being implemented in each field, including cyber threat Intelligence (CTI). However, multiple frameworks are present in this space, each using its own AIs for more efficient detection. Understanding these algorithms is the key to realise how these algorithms compute each factor, and many Explainable AI (XAI) implementations have been moderately successful in doing so. However, on most occasions, these XAI implementations do not agree with each other when decoding a black-box model. The primary idea put forward in this paper is to determine if the present XAI interpretations of complex AI models have attributes that unfairly contribute more than they should. The paper adopts a novel approach of using biased databases to train multiple models using the same methodology and then simulates them for each scenario. For the current implementation, research was carried out using a popular CTI database.