Reviewerly/exHarmony huggingface dataset:
Data Analytics on Medical Images with Deep Learning Approach

The world is moving toward seeking the support of the healthcare system in today’s scenario. Healthcare has the opportunity not only to save lives but also foster scientific and technological advances. Medical images play a significant role in diagnosis and experimentation. For years, many experimentations and analyses have been made on medical images with a broad scope of achieving the desired results. Medical modalities like MRI, CT, PET, X-ray, ultrasound, etc. are popular in representing internal organ pictures of human bodies. Such volumetric images are stored and accessed with a huge demand in storage space. Compressing the images solves the problem of storage and transmission and also becomes beneficial for the telemedicine application. Since medical images depict the human body’s detailed and vital information, these need to be compressed without distorting the details present over them. The lossless image compression approach works efficiently in representing the information present in the medical images without distorting the details. Deep learning neural networks proved to be efficient in analyzing medical images in recent years. Achieving a better image-archiving system with an image segmentation approach of selecting region-based medical image compression is experimented with and analyzed in this chapter. Experimental analysis with the CNN, autoencoder, etc. is considered in this chapter. Performance of the architecture is compared in terms of the number of epochs, peak signal–noise ratio, bits per pixel, compression ratio, and computational time are considered in terms of performance metrics.
Spatial Solar Energy Potential of Photovoltaic Panels Surrounded by Protected Mountain Ranges

High growth of human population and dynamic socio-economic development have contributed to mounting demand for electric energy. Currently, electric energy is mainly generated from mined and combusted fossil fuels and by the nuclear power plants. The current geopolitical crisis forces mankind to reflect upon the search for alternative energy sources. In this paper analyses of the potential solar radiation volume for 12 months and annual total have been made and visualized on the maps. Additionally, the energy volume gained by the photovoltaic systems in a 12-month period and the annual total as well as underutilised solar radiation energy were calculated. It was found that the tested sites had a solar potential in the range from 113 kWh·m&lt;sup&gt;−2&lt;/sup&gt; to 1314 kWh·m&lt;sup&gt;−2&lt;/sup&gt;. For process reasons, only 18 to 203 kWh·m&lt;sup&gt;−2&lt;/sup&gt; could be converted into electric energy, which accounts approximately for 1/5 of the total radiation. The results can be useful to show the best investment site for commercial plants and households in the mountainous area. Surplus energy output should be stored or sold to the power grid. Moreover, the energy source from photovoltaic panels in these areas is only one of the options for green energy generation.
Emergence of Crypto Currency and the Digital Financial Landscape

The reality of the new normal induced by the pandemic has resulted in a radical transformation of the financial sector, where it is undergoing a dramatic digital change. With this backdrop, the purpose of this study is conceptualizing the role of crypto currencies and the effects on the digital financial (DiFi) landscape in India and worldwide. It has led to disruptive technological innovation and accelerated change in the financial system. In this light, the present study attempts to highlight the different digital initiatives undertaken by various developed and developing countries and the pros and cons associated with the same. This research study shall serve as a primer to demonstrate the effects of use of digitalization and technology-enabled initiatives for crypto currencies and is also aimed to encapsulate the restraining and facilitating forces that drive adoption of digital financial models and the hybrid role of government as we usher in the new financial landscape.
Multiple extended target tracking based on distributed multi‐sensor fusion and shape estimation

Abstract Distributed multi‐sensor fusion based on the generalised covariance intersection (GCI) fusion has been widely integrated into the Random Finite Set theory, which is promising for multi‐target tracking with an unknown number of targets. However, it has not been widely investigated in the multiple extend target tracking (METT) field, and there is still an open problem on how to solve the inconsistency of label space among the sensors. For these problems, we first introduce the GCI fusion into the METT and proposed an association algorithm by considering the estimated shapes and the target positions to avoid the phenomenon of the label inconsistency as well as to reduce the computational burden. Simulation results show that the proposed algorithm has a better tracking performance than the traditional METT algorithms.
Behavioral recommendation engine driven by only non-identifiable user data

Most recommendation systems utilize personal data to device personalized recommendations for users. Even though it seems favorable, security risks like data breaches are inevitable. This research proposes a novel reinforcement learning 'approach' to recommend users without collecting identifiable data. With only user activity on a session, our proposed method can model and track user behavior and formulate a recommendation system. We conclude that our algorithms demonstrate positive results in capturing user behavior without collecting private data of any kind from the user. The research is two folds. On one hand, we experiment using traditional reinforcement learning techniques (MDP, Q-learning), and on the other hand, we use deep reinforcement learning algorithms (DQN, DDQN, and D3QN) on a movie recommendation scenario. Interestingly, we observe that MDP and D3QN works comparatively better on movie recommendations.
Dynamical System-Based Computational Models for Solving Combinatorial Optimization on Hypergraphs

The intrinsic energy minimization in dynamical systems offers a valuable tool for minimizing the objective functions of computationally challenging problems in combinatorial optimization. However, most prior works have focused on mapping such dynamics to combinatorial optimization problems whose objective functions have quadratic degree (e.g., MaxCut); such problems can be represented and analyzed using graphs. However, the work on developing such models for problems that need objective functions with degree greater than two, and subsequently, entail the use of hypergraph data structures, is relatively sparse. In this work, we develop dynamical system-inspired computational models for several such problems. Specifically, we define the ‘energy function’ for hypergraph-based combinatorial problems ranging from Boolean SAT and its variants to integer factorization, and subsequently, define the resulting system dynamics. We also show that the design approach is applicable to optimization problems with quadratic degree, and use it develop a new dynamical system formulation for minimizing the Ising Hamiltonian. Our work not only expands on the scope of problems that can be directly mapped to, and solved using physics-inspired models, but also creates new opportunities to design high-performance accelerators for solving combinatorial optimization.
Acceleration Techniques for Automated Design of Approximate Convolutional Neural Networks

The main issue connected with using approximate components such as approximate multipliers in deep convolutional neural networks (CNN) during the design process is the necessity to emulate them due to the lack of native support for approximate operations in modern CPUs and GPUs, which is computationally expensive. To accelerate the emulation of approximate operations of CNNs on GPUs, we propose TFApprox4IL, a software library supporting both symmetric as well as asymmetric quantization modes, approximate 8xN bit multipliers emulated using lookup tables, a new type of approximate layer known as approximate depthwise convolution, and quantization-aware training. The TFApprox4IL performance is extensively evaluated in the simulation of approximate implementations of MobileNetV2 and ResNet networks on Nvidia Pascal and Tesla GPU architectures. Furthermore, TFApprox4IL is also evaluated in neural architecture search (NAS) algorithms to automatically design CNN architectures that directly employ approximate multipliers. On two different NAS methods, EvoApproxNAS and Google Model Search (GMS), we show how approximate multipliers can effectively be incorporated into the CNN design process. To estimate the energy consumption of the approximate CNNs, AxMultAT tool based on Timeloop and Accelergy is introduced. Contrasted to the highly optimized GPU-based CNN simulation implemented using exact arithmetic operations available within TensorFlow, the average overhead of the inference and training, introduced by TFApprox4IL, is <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$13.6\times $ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$8.0\times $ </tex-math></inline-formula> , respectively, considering ResNet50V2 and MobileNetV2 CNNs on ImageNet and CIFAR-10 data sets. This overhead was reduced by one order of magnitude with respect to previous methods.
Proof-of-Monitoring (PoM): A Novel Consensus Mechanism for Blockchain-based Secure Service Level Agreement Management

In the current 5th Generation (5G) networking paradigm, the enforcement of Service Level Agreements (SLAs) is a non-trivial measure to ensure the scope and the quality of services and standards between tenants and service providers (SPs).On top of this, Secure Service Level Agreements (SSLA) are introduced to ensure that SPs deliver the most critical and required security-related standards defined in the contract, such as integrity, confidentiality, availability, nonrepudiation, and privacy assurance.However, with the tendency for more distributed and multi-stakeholder networking architectures in next-generation networks, the management process of such SSLAs will be challenging due to the diversified security vulnerabilities and complexity of underlying technologies.Although blockchain is emerging as a platform to facilitate such distributed SSLA/SLA management frameworks, its currently available consensus mechanisms are more generic.Still, they need to improve in terms of applying in multi-stakeholder networks.Therefore, this paper presents a novel consensus mechanism called Proof-of-Monitoring (PoM) for a blockchain-based novel SSLA management framework.Moreover, we provide details about the prototype implementation of our proposed consensus algorithm and SSLA management framework.It is proven by comparing our proposal with the other existing solutions that our solution outperforms in many aspects, such as energy consumption, computation cost, and security features.
What contextual factors affect information poverty? Evidence from ethnic villages in rural Southwest China

To solve the problem of information poverty, it is necessary to understand the factors influencing it. Past studies have examined information poverty from the perspectives of economic poverty and deficiency or deprivation. However, these studies do not fully explain the formulation of information poverty because they may overemphasize the economic and material side of information and neglect the social and communicative aspects. A quantitative study was conducted to examine how contextual factors (i.e., external situations, social relations, individual situations, and social interactions) affected the information poverty of ethnic villages in rural Southwest China. Research results showed that geographic locations, information assets, individual literacy, and individual psychology significantly influenced information poverty. Social norms and beliefs also affected information poverty; however, this influence was insignificant. These findings extend existing studies on information poverty and provide practical guidance for stakeholders (e.g., governments, libraries, and intellectual works) to alleviate information poverty.
Privacy-Preserving AGV Collision-Resistance at the Edge Using Location-Based Encryption

Edge computing fundamentally changes the architecture in which applications are deployed and how resources are managed, especially in the 5G/6G era. Automated guided vehicles (AGV) are critical means of transportation in future society. We aim at investigating a privacy-preserving AGV collision-resistance paradigm leveraging edge computing. We define a model containing all potential collision occasions and propose a method to handle collisions using “virtual” traffic lights generated by edge servers. The proposed paradigm is AGV-centered, for AGVs ought not to care about where the nearby edge servers are in a broadcasting environment. In case of privacy leakage, we incorporate ciphertext-policy attribute-based encryption (CP-ABE) to protect AGV information. Besides, the use of CP-ABE leads to a location-based encryption (LBE) scheme, where AGV encrypts its running state information leveraging its current position rather than an edge server's public key. That is achievable due to the support of integer comparison in the CP-ABE access policy. To our knowledge, we are the first to implement an LBE scheme based on CP-ABE. Part of the paradigm has been deployed in a wharf yard in Shanghai. Throughout the article, we take a yard as an example and conduct concrete experiments to highlight the feasibility and efficiency.
Conceptualisation of Decentralized Blockchain-Based, Open-Source ERP Marketplaces

In order to reduce the misunderstandings between the providers/implementers of the ERP software solutions and the clients, but also to achieve marketplace fairness and coopetition, we suggest that an overarching, large, but decentralized system can offer important improvements. The main goal we would like to achieve is a decentralized ERP solution, and the current blockchain technology and open-source approaches offer a substantial foundation for conceptualizing a solution. We are portraying a decentralized ERP solution which is both open source and decentralized, by using blockchain technology, and designing its marketplace, business model, functionalities, and mechanisms. The Open-Source aspect of the ERP software solution means it can be updated and maintained by independent software teams, independent implementation teams, and the clients who are many (many-to-many, not one-to-one). Within the blockchain technology, we find fit in EOS/Reef operative systems, their decentralization, their dispersion as well as possibility to use smart contracts, providing opportunities to overcome the above-mentioned burning issues. In such a decentralized system, the users will be able to use various implementation teams for the same software solution, also use various teams for software production and various for software updates. The basis for remuneration can be tokens for deliberately introduced coins. Another very important component is a dashboard on which the users will publish their needs as well as state how many tokens will be transferred to the implementer team, the cumulative number of tokens offered from many users with the same needs will be the incentive for the teams to uptake an assignment and fulfill it with best possible quality. That will maintain a high level of motivation, quality, and user service as well as a mechanism for improvement of the ERP on a continuous basis, as a very important feature of any system. When a team considers the number of tokens sufficient for their work, they will be able to complete the task and obtain the tokens, on the first-come-first-served principles. This allows decentralization and liberation of the process of acquiring, production and implementation of any software solution. The teams that would work on maintenance and improvement of the software based on user needs or its implementation would have to freeze a certain number of tokens and will be in position to uptake tasks that are in number maximum 20 times higher than their frozen tokens. The smart contract will allow distribution of the tokens to the team who has completed the task, if and only if it is done properly and with the expected quality. The teams who receive negative references for bad performance will not be able to uptake future tasks or assignments or will be excluded as teams from the decentralized systems. Such a framework, based on blockchain technology would allow more fair coopetition (collaboration and competition) and position of both providers/implementers and users.
Shoppers Interaction Classification Based on An Improved DenseNet Model Using RGB-D Data

This study aims to present a deep learning approach utilizing transfer learning and an RGB-D dataset termed HADA (Hands dataset) acquired by a depth sensor from a top-view configuration capable of monitoring customers and classifying their interaction in intelligent retail settings. With the intention of developing an automated RGB-D approach for video analysis, we provide an innovative, intelligent technology that can comprehend customer behavior, in particular their interactions with items on the shelves. The camera system identifies the presence of humans and classifies their interactions with products accurately. Through the RGB and depth frames, the system determines consumer interactions with shelf objects and identifies if a product is picked up, taken and subsequently returned, or if there is no touch at all. Our approach obtained good accuracy, precision, and recall, demonstrating the efficiency of the proposed model, and testing findings have proved that its performance in real-world conditions is adequate.
Improved Dynamic Window Approach using the jerk model

The Dynamic Window Approach (DWA) method is a collision avoidance strategy for mobile robots that is optimized to take a robot to its goal while maintaining a minimum distance from any obstacle. However, owing to its velocity invariance assumption, it generates narrow paths that may not enable it to avoid obstacles. In addition, it is impossible to control thejerk. To address these issues, we propose a method for generating speed change paths based on jerk models. First, we limited the maximum jerk in the model. Second, we used the model to generate variable speed sequences to obtain a wider range of paths. We added jerk as a term in the evaluation function to prevent consistently large jerks. We verified the effectiveness of the proposed method by comparing the results with the conventional method through simulation experiments.
Anomaly Detection of an Air Compressor from Time-series Measurement Data

In railway vehicles, air compressor operates and stops aperiodically according to the pressure usage. LSTM-AE and anomaly score can be used to detect abnormality of an air compressor using time-series measurement data. However, it is hard to detect abnormal symptom in a short-term, without considering tendency of time-series data, because slight abnormal symptom occurs repetitively in the system if there is little failure. In this paper, a smoothing method using anomaly-score-moving average (ASMA) is proposed for anomaly detection from time-series measurement data. LSTM-AE model is trained from actual air compressor data at a railway vehicle and ASMA-based anomaly detection is performed using abnormal data.
Cable Instance Segmentation with Synthetic Data Generation

We propose a bottom-up approach for the instance segmentation of cables (commonly referred in the literature as deformable linear objects). While the state of the art instance segmentation techniques propose a bounding box and perform foreground segmentation within each proposed bounding box, we adopt a bottom-up approach as cables can span a considerable part of the image or even the entire image, and therefore, cannot be well localized in a bounding box. In this paper, we show that several operations in the top-down instance segmentation approaches are only applicable for certain classes (i.e., compact objects) such as cars but they are a poor approximation for objects with highly overlapping bounding boxes such as cables. In particular, the non-maximum suppression and RoIPool/RoIAlign operations limit the generalizability of proposal-based instance segmentation methods to such datasets. Furthermore, we introduce a synthetic data generation technique that can also be applied to other popular public datasets such as COCO, Pascal VOC, and Cityscapes.
Improved Robustness of Reinforcement Learning Based on Uncertainty and Disturbance Estimator

This paper proposes a method to improve the robustness of RLs based on model-free uncertainty and disturbance estimator (RL-based UDE). In the real environment, instead of using optimal trajectory and control techniques to perform complex tasks, it learns through RL and supplements robustness by using uncertainty and disturbance estimator (UDE). From UDE, the robotics system can be improved the stability by appropriately canceling the uncertainty and disturbance without efforts to obtain model information; hence the UDE can compensate for the performance degradation of RL when system is non-stationary. In addition, the performance can be improved by reducing the sensor noise from low-pass filter of UDE. It is shown through an experiment that the proposed RL-based UDE provides robustness.
Adaptive Sequential Gaussian Sum Approximation for High-Resolution Radar Target Tracking with Non-Gaussian Multi-Detection

This paper proposes a practical nonlinear target tracking filter using high-resolution radar measurements with non-Gaussian distribution. To cope with the numerical instability and computational burden of the existing nonlinear filters, an adaptive sequential Gaussian sum approximation is used in designing a tracking filter. As an efficient way to restrain the mode inflation of the a posterior probability density function, the modes with the same measurement update history are merged at every time instant. Accordingly, the proposed filtering scheme is suitable for real-time implementation while maintaining satisfactory target tracking performance. Through simulations of a typical re-entry ballisitic target tracking scenario, the superior performance and computation efficiency of the suggested filter are demonstrated.
A Secured Lossless Visual Secret Sharing for Color Images Using Arnold Transform

Nowadays, with the rapid growth in information, a fast and secure method is eagerly needed to share images. (n, n)-Visual Secret Sharing (VSS) is used to share a secret image into n shares, where the secret can only be recovered using all the n shares and the recovery must be fast with low computational complexity. This paper proposes a secured lossless (n, n)-VSS system based on Arnold transform and pixel vectorization suitable to be used with binary, grayscale and color images. Multiple security tests were performed such as entropy, correlation, Mean Squared Error (MSE), National Institute of Standards and Technology (NIST) SP-800-22 statistical suite, and differential attacks, which demonstrate the good security of the proposed system. In addition, the time complexity and runtime of the recovery system indicate good efficiency.
A Survey on Infrastructure-as-Code Solutions for Cloud Development

Cloud software is increasingly written according to the DevOps paradigm, where use of virtualization and Infrastructure-as-Code is prevalent. This paper surveys the state of the art of IaC cloud development, and proposes a combination of Cloud-Native software to build an on-premise PaaS for a Security Lab.