Reviewerly/exHarmony huggingface dataset:
Synthetic Data for Semantic Segmentation in Underwater Imagery

Semantic segmentation is a critical component of scene understanding in underwater environments. Deep learning models have shown the best performance in semantic segmentation tasks. Deep learning benefits from large amounts of labeled imagery for training. However, there is a significant gap in size between the above-ground and underwater datasets. This work addresses the dataset size and class imbalance problems of underwater datasets by creating synthetic images to increase effective dataset size. Using the semantic segmentation mask, we can cut and paste objects from one image into another. A synthetic dataset created this way could also address class imbalances by automatically adding more instances of underrepresented classes. Doing this can create a subpixel boundary around inserted objects that networks will learn to detect instead of the intended targets. To solve this problem, we compare two different blurring methods, Gaussian and Poisson, to reduce the impact of this effect. We evaluate different methods, such as Gaussian and Poisson blurring and the number of targets added to synthetic images, to find the best way to blend inserted objects in underwater imagery. In addition, we assess the different methods to see which one produces the most realistic training set by comparing the performance metrics of various semantic segmentation algorithms after training on synthetic datasets employing each method.
Profile Fusion in Social Networks: A Data-Driven Approach

User matching across various social networks has received a significant attention in the recent years. Several approaches have been evaluated including discrete user attributes, text mining, network analysis, and more recently machine learning. However, there is a lack of publicly available labeled datasets for this task. Our contribution is twofold. Firstly, we create an open-source framework that collects profiles from various social networks and identifies the true pairs of accounts corresponding to the same user by leveraging user attributes and computer vision. We present a case study dataset that encompasses more than 27k anonymized profile pairs from Quora and Twitter with their corresponding content: 33M tweets and 1.1M Quora answers. Secondly, we evaluate different user linkage schemes and text representation models for the identification of users across these two social networks and discuss the limitations of each approach. Our experiments show that users can be identified with up to 84% accuracy when they have a sufficient amount of generated content in their social accounts.
Lightweight Web3D Crowd Rendering for Online Massive Conferencing

Massive virtual online conferences have been encountering technical challenges and limitations, such as huge data volume, slow network transmission and low rendering frame rate. To tackle these limitations, in this paper we propose a Lightweight Online Virtual conferencE (LOVE) framework. A pseudo instantiation technique is first applied to reduce the weight of massive dynamic scenarios. LOVE realizes a progressive transmission of pseudo instanced objects, that speeds up the network loading speed of dynamic scenarios. Moreover, an intelligent UV mapping module for rendering pseudo instanced objects is developed to improve the diversity of dynamic crowd. Our experiments on several scenes of large crowd have demonstrated that LOVE is able to keep high FPS when the size of crowd increases.
Implementation of value based curiosity mechanism in Reinforcement Learning algorithm based on A3C

Intrinsic motivation for RL agents was shown to be useful as it helps to explore better, to learn better and to handle sparse reward environments. In this work we present a new variant of intrinsic motivation in the form of an additional reward given for exploration of states whose value is hard to predict with the simplified critic module. This reward bonus is summed up with the extrinsic reward from the environment and the agent learns from the combined reward. We test the asynchronous actor-critic algorithm (A3C) augmented with our intrinsic reward on Atari Pong task and compare it with the vanilla A3C, showing faster convergence while having generally similar architecture and the number of trainable model parameters.
Hybrid Inference with Invertible Neural Networks in Factor Graphs

This paper bridges the gap in the literature between neural networks and probabilistic graphical models. Invertible neural networks are incorporated in factor graphs and inference in this model is described by linearization of the network. Consequently, hybrid probabilistic inference in the model is realized through message passing with local constraints on the Bethe free energy. We provide the local Bethe free energy for the invertible neural network node, which allows for evaluation of the performance of the entire probabilistic model. Experimental results show effective hybrid inference in a neural network-based probabilistic model for a binary classification task, paving the way towards a novel class of machine learning models.
Face Recognition Using VGG16 CNN Architecture for Enhanced Security Surveillance—A Survey

A review of the web camera surveillance, face recognition, convolution neural network (CNN), digital images are presented in this work. Previous works on face recognition systems for enhanced surveillance-based security are presented together with relevant deep learning concepts and theories relating to convolutional neural networks. In-depth analysis is summarized and presented in concise way.
New Families of MDS Symbol-Pair Codes From Matrix-Product Codes

In emerging storage technologies, the outputs of the channels consist of overlapping pairs of symbols. The errors are no longer individual symbols. Controlling them calls for a different approach. Symbol-pair codes have been proposed as a solution. The error-correcting capability of such a code depends on its minimum pair distance instead of the usual minimum Hamming distance. Longer codes can be conveniently constructed from known shorter ones by a matrix-product approach. The parameters of a matrix-product code can be determined from the parameters of the ingredient codes. We construct a new family of maximum distance separable (MDS) symbol-pair matrix-product codes. Codes which are permutation equivalent to matrix-product codes may have improved minimum pair distances. We present four new families of MDS symbol-pair codes and a new family of almost MDS symbol-pair codes. The codes in these five new families are permutation equivalent to matrix-product codes. Each of our five constructions identifies permutations that can increase the minimum pair distances. We situate the new families among previously known families of MDS symbol-pair codes to highlight the versatility of our matrix-product construction route.
Creation of a Knowledge Space by Semantically Linking Data Repository and Knowledge Management System - a Use Case from Production Engineering

The seamless documentation of research data flows from generation, processing, analysis, publication, and reuse is of utmost importance when dealing with large amounts of data. Semantic linking of process documentation and gathered data creates a knowledge space enabling the discovery of relations between steps of process chains. This paper shows the design of two systems for data deposit and for process documentation using semantic annotations and linking on a use case of a process chain step of the Tailored Forming Technology.
Hybrid Boustrophedon and Partition Tree Group Algorithm for Coverage Path Planning Problem with Energy Constraints

Coverage path planning (CPP) finds the shortest feasible paths passing through all points of an environment while avoiding obstacles. Previous studies usually assume that a robot has infinite power and can fully cover the workspace with only one charge, but most mobile robots run under a limited energy budget. This study focuses on the CPP problem for a mobile robot with constrained energy power that satisfies two main optimization objectives: the total distance and the number of repeated cells. We propose a hybrid algorithm between the Boustrophedon and the partition tree group algorithms, namely B-WZone, for the CPP with energy constraints. The performance of this algorithm is compared with existing methods. The experimental results showed that the B-WZone algorithm helps the robot reduce energy consumption and traveling time in all the tested environments.
MemSAC: Memory Augmented Sample Consistency for Large Scale Domain Adaptation

Practical real world datasets with plentiful categories introduce new challenges for unsupervised domain adaptation like small inter-class discriminability, that existing approaches relying on domain invariance alone cannot handle sufficiently well. In this work we propose MemSAC, which exploits sample level similarity across source and target domains to achieve discriminative transfer, along with architectures that scale to a large number of categories. For this purpose, we first introduce a memory augmented approach to efficiently extract pairwise similarity relations between labeled source and unlabeled target domain instances, suited to handle an arbitrary number of classes. Next, we propose and theoretically justify a novel variant of the contrastive loss to promote local consistency among within-class cross domain samples while enforcing separation between classes, thus preserving discriminative transfer from source to target. We validate the advantages of MemSAC with significant improvements over previous state-of-the-art on multiple challenging transfer tasks designed for large-scale adaptation, such as DomainNet with 345 classes and fine-grained adaptation on Caltech-UCSD birds dataset with 200 classes. We also provide in-depth analysis and insights into the effectiveness of MemSAC. Code is available on the project webpage https://tarun005.github.io/MemSAC .
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications

Guaranteeing real-time and accurate object detection simultaneously is paramount in autonomous driving environments. However, the existing object detection neural network systems are characterized by a tradeoff between computation time and accuracy, making it essential to optimize such a tradeoff. Fortunately, in many autonomous driving environments, images come in a continuous form, providing an opportunity to use optical flow. In this paper, we improve the performance of an object detection neural network utilizing optical flow estimation. In addition, we propose a Lyapunov optimization framework for time-average performance maximization subject to stability. It adaptively determines whether to use optical flow to suit the dynamic vehicle environment, thereby ensuring the vehicle’s queue stability and the time-average maximum performance simultaneously. To verify the key ideas, we conduct numerical experiments with various object detection neural networks and optical flow estimation networks. In addition, we demonstrate the self-configurable stabilized detection with YOLOv3-tiny and FlowNet2-S, which are the real-time object detection network and an optical flow estimation network, respectively. In the demonstration, our proposed framework improves the accuracy by 3.02%, the number of detected objects by 59.6%, and the queue stability for computing capabilities.
Comparative Analysis of Risk Assessment During an Enterprise Information Security Audit

This article discusses a threat and vulnerability analysis model that allows you to fully analyze the requirements related to information security in an organization and document the results of the analysis. The use of this method allows avoiding and preventing unnecessary costs for security measures arising from subjective risk assessment, planning and implementing protection at all stages of the information systems lifecycle, minimizing the time spent by an information security specialist during information system risk assessment procedures by automating this process and reducing the level of errors and professional skills of information security experts. In the initial sections, the common methods of risk analysis and risk assessment software are analyzed and conclusions are drawn based on the results of comparative analysis, calculations are carried out in accordance with the proposed model.
From broad reality to the Metaverse: Critical reflection on the contribution of education

&lt;p&gt;Augmented reality is a concept that includes virtual reality, augmented reality and hybrid reality. It has made remarkable progress in recent years and has a great impact on understanding education. Based on two key concepts: immersion and existence, the study of extended reality is helpful to improve teaching. However, these contributions need to be reviewed on a key theoretical basis to avoid falling into technophobia without scientific evidence. The purpose of this article is to understand and evaluate the contribution of a wide &lt;span style="font-family: 'Times New Roman';"&gt;ra&lt;/span&gt;nge of realities to education and the educational challenges faced before the development of &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse. To the end, RST defines each type of reality and critically evaluates their application in the field of education. Second, it puts forward critical arguments on the advantages and disadvantages of these technologies in educational p&lt;span style="font-family: 'Times New Roman';"&gt;ra&lt;/span&gt;ctice. Third, it describes the challenges faced by &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse in education. This analysis shows that augmented reality contributes to the innovative development and improvement of the teaching process, despite the lack of teacher t&lt;span style="font-family: 'Times New Roman';"&gt;ra&lt;/span&gt;ining to design the learning experience according to augmented reality, and the lack of interdisciplinary coope&lt;span style="font-family: 'Times New Roman';"&gt;ra&lt;/span&gt;tion in technology and education. These results have affected the development of &lt;span style="font-family: 'Times New Roman';"&gt;M&lt;/span&gt;etaverse, which is another step in expanding the evolution of reality and will have a significant impact on the field of education. The survey results show that it is necessary to design an ethics code, develop a culture and protect the identity of users.&lt;/p&gt;
Secure and Privacy-Preserving Human Interaction Recognition of Pervasive Healthcare Monitoring

A cloud-based Artificial Intelligence (AI) service has recently empowered the Internet of Medical Things (IoMT) in many applications on the remote Human Interaction Recognition of Pervasive Healthcare Monitoring (HIR-PHM). In this work, a framework of the skeleton-based HIR-PHM under secure Edge-Fog-Cloud computing (EFCC) was proposed to manage the computation and storage resources, latency, cyber-attack, and privacy-preserving simultaneously. At the Edge, IoMT with a camera, record the human interaction as videos and sends them to the Fog, which installed a human pose estimation model, PoseNet, to convert the videos into human skeleton data. At the Cloud, a skeleton-based Spatial-Temporal Graph Convolution Network with Pairwise Adjacency Matrix (STGCN-PAM) was employed to recognize the human interaction. A Hybrid of One Time Password (OTP), Hashed Messages Authentication Code (HMAC), and symmetric cipher were employed to secure the skeleton data. The UT-Interaction dataset was used to evaluate the proposed framework. Besides, the computation performance and latency under EFCC were compared with Edge-Fog and Edge-Cloud deployments. Experimental results confirmed two major contributions: 1) the proposed framework has promising performance compared with other methods on the dataset. 2) The skeleton-based HIR-PHM with secure EFCC shows the advantages of better computation performance in terms of frame per second.
Quantum circuit optimization for multiple QPUs using local structure

Interconnecting clusters of qubits will be an essential element of scaling up future quantum computers. Operations between quantum processing units (QPUs) are usually significantly slower and costlier than those within a single QPU, so usage of the interconnect must be carefully managed. This is loosely analogous to the need to manage shared caches or memory in classical multi-CPU machines. Unlike classical clusters, however, quantum data is subject to the no-cloning theorem, which necessitates a rethinking of cache coherency strategies. Here, we consider simple strategies of using EPR-mediated remote gates and teleporting qubits between clusters as necessary – generally expensive operations that we seek to minimize. Crucially, we develop optimizations at compile-time that leverage local structure in a quantum circuit, so as to minimize inter-cluster operations at runtime. We benchmark our approach against existing quantum compilation and optimization routines, and find significant improvements in circuit depth and interconnect usage.
Composite FORCE Learning of Chaotic Echo State Networks for Time-Series Prediction

Echo state network (ESN), a kind of recurrent neural networks, consists of a fixed reservoir in which neurons are connected randomly and recursively and obtains the desired output only by training output connection weights.First-order reduced and controlled error (FORCE) learning is an online supervised training approach that can change the chaotic activity of ESNs into specified activity patterns.This paper proposes a composite FORCE learning method based on recursive least squares to train ESNs whose initial activity is spontaneously chaotic, where a composite learning technique featured by dynamic regressor extension and memory data exploitation is applied to enhance parameter convergence.The proposed method is applied to a benchmark problem about predicting chaotic time series generated by the Mackey-Glass system, and numerical results have shown that it significantly improves learning and prediction performances compared with existing methods.
Cryptocurrency Value Prediction with Boosting Models

Ensemble learning is a methodology that entails integrating a number of inefficient entities to achieve significantly improved performance. Boosting is a significant category of ensemble learning that involves the consecutive aggregate input of weak learners. The benefits of boosting approaches in processing tabular data with a significant quantity of information and resistance to overfitting can be very useful in estimating the market value of digital currency or cryptocurrency. The goal of this work is to examine and comprehend the capabilities of major boosting techniques such as XGBoost, AdaBoost, and CatBoost in cryptocurrency forecasting. The work examines the long-term forecasts of two major cryptocurrencies, Bitcoin and Ripple, for this purpose. The results indicate that AdaBoost and XGBoost have comparable predicting efficiency, followed by CatBoost. This implies that AdaBoost’s simpler boosting strategy is effective at achieving outcomes that are comparable to those of more recent boosting algorithms like XGBoost and CatBoost. The study has emphasized the similarities in achieving the best cryptocurrency prediction outcomes from each model. According to the research, a more straightforward boosting tactic is just as effective as or even more effective than the other most recent boosting strategies.
A Coulomb Force Inspired Loss Function for High-Performance Pedestrian Detection

Pedestrian detection has received considerable research interest due to its wide application and has made significant progress along with the development of deep neural networks. However, crowd occlusion still remains a significant challenge to current state-of-the-art pedestrian detectors due to the complication in formulating interactions between occluded instances. Inspired by the Coulomb force, we in this work set each proposal as a single electric charge and define the attractive and repulsive forces to model the interaction between ground truths and assigned proposals. This design is driven by two motivations: the attractive force pulls bounding boxes toward their assigned targets, aggregating them compactly around the ground truths. The repulsive force pushes bounding boxes away from other instances, preventing them from shifting to surrounding pedestrians. With this insight, we propose a novel bounding box regression loss and achieve more robust localization performance in crowded scenes without introducing any computational overhead. Extensive experimental evaluations on the CityPersons and CrowdHuman benchmarks demonstrate consistent state-of-the-art performance.
Visual Analytics Platform for Centralized COVID-19 Digital Contact Tracing

The Covid-19 pandemic and its dramatic worldwide impact have required global multidisciplinary actions to mitigate its effects. Mobile phone activity-based digital contact tracing (DCT) via Bluetooth Low Energy (BLE) technology has been considered a powerful pandemic monitoring tool, yet it sparked a controversial debate about privacy risks for people. In order to explore the potential benefits of a DCT system in the context of Occupational Risk Prevention, this paper presents the potential of Visual Analytics methods to summarize and extract relevant information from complex DCT data collected during a long-term experiment at our research centre. Visual tools were combined with quantitative metrics to provide insights into contact patterns among volunteers. Results showed that crucial actors such as participants acting as bridges between groups could be easily identified - ultimately allowing for making more informed management decisions aimed at containing the potential spread of a disease.
Prototype of Temperature and Humidity Control System for Oyster Mushroom Cultivation Using Arduino Uno Based on The Internet of Things

The Oyster mushroom is one of the most widely consumed mushrooms and has many health benefits. Cultivation of this mushroom requires quite complicated care. The difficulties include regulating the temperature and humidity in the mushroom kumbung. This problem makes farmers have to check mushroom kumbung frequently. Creating a temperature and humidity control system based on the Internet of Things is necessary to overcome this problem. The main components used are the Dht11 sensor to determine temperature and humidity, the LDR sensor for monitoring light intensity on the mushroom kumbung, an Arduino Uno Atmega 328 as a control, and the Esp8266-01 module as a wifi module connected to the internet network. The ideal conditions needed in the cultivation of oyster mushrooms include a temperature of 22-30 °C and a humidity of 80% - 90% at the incubation time. The fruiting body formation phase requires a temperature between 16 - 30 C and light intensity between 200-300 lux. Testing the prototype of the temperature and humidity control system was carried out on oyster mushroom kumbung using a fan as an actuator to lower the temperature and a water pump to increase humidity, and a 5-watt lamp to increase light intensity. The results obtained are an average time duration of 12.75 minutes to lower the temperature, while it takes an average of 3 minutes to increase the humidity in oyster mushroom kumbung.