Reviewerly/exHarmony huggingface dataset:
Cyber-Risk: Cyber-Physical Systems Versus Information Technology Systems

In this chapter, we define cyber-risks, summarize their properties, and discuss the tools of cyber-risk management. We provide comparative analysis of cyber-risks and other (natural disasters, terrorism, etc.) risks. Importantly, we divide networked systems into two domains: traditional Information Technology Systems (ITSs) and Cyber-Physical Systems (CPSs), and compare and contrast their cyber-risks. We demonstrate that these domains have distinct cyber-risk features and bottlenecks of risk management. We suggest that this dichotomy is a useful tool for cyber-risk analysis and management. In the companion chapter on Cyber-insurance, we apply this classification to simplify the exposition of Cyber-insurance for CPS.
Multiagent System for Home Appliances on Internet of Things (MAS-HAIoT)

Currently, there are various smart devices for homes, which have different functions that support domestic tasks such as keeping the pantry always full, accessing recipes, and creating shopping list. These devices have a high cost, for this reason, it arises in creating a low-cost smart device, which contains the options described above. In addition to having a module called “nutrition”, with which you can keep a record of meal plans and a diet provided by a nutritionist at a cost minor, for this, a mobile application was created called: Mobile application for smart appliances (MoASa) given the current situation due to the COVID-19 pandemic. It has resulted in many people taking shelter in their homes and taking care measures such as avoiding going out to non-essential situations, this pretends to be a support to carry out grocery shopping and avoid going out as little as possible. The analysis and design of the device contain several agents, this article described the nutrition agent, the food ontology, its semantic network, hardware, and software for communication with multiple applications that support the user.
Telugu Scene Text Detection Using Dense Textbox

This paper presents an end-to-end trainable Scene Text Detector and a Script Classification Network to filter Telugu text. The Scene Text Detector named Dense Textbox efficiently makes accurate predictions using Densenet as a base architecture. It introduces K-Means clustering to set aspect ratios of default boxes and predicts at vertical offsets [−0.125, −0.25, 0.125, 0.25] similar to Textboxes and at a resolution of \(512\times 512\) to produce more text candidate proposals which are filtered using non-maximum suppression. SSD Focal loss is used instead of traditional SSD loss to accurately differentiate text regions from the background. Dense Textbox outperforms other similar frameworks on the IndicSceneText dataset using no preprocessing and standard ICDAR2013 dataset using a single pass at a single resolution. The framework has no problem in detecting text containing various scripts like the images present in IndicSceneText dataset. Traditional Scene Text Detection frameworks fail to classify the type of script Ex: Telugu, English present in an Image. We introduce a Script Classification network that is used to filter images that contain Telugu script from a non-Telugu script based on CRNN network, which uses both convolutional and Recurrent networks to classify the type of script present. Classifying and filtering text regions helps to send the text regions to appropriate Scene Text Recognition modules. The network identifies the type of script with 92.4% accuracy.
Joint Spatial-Spectral Encoder For Hyperspectral Image Denoising

Hyperspectral image(HSI) has a wide range of application prospects because it contains rich image information. However, in a large amount of information, it usually contains a lot of noise, which will have a great impact on image processing and application. The biggest difficulty of hyperspectral image denoising lies in how to combine the information of many bands instead of denoising only one band. In this paper, a joint band hyperspectral image denoising model (HSI-UBDAE) based on the encoder is proposed, which makes full use of the correlation between bands and combines the spatial domain with the spectral domain to denoise HIS image effectively. Experiments on several real data sets show that the proposed method is superior to some mainstream denoising methods.
Design Space Exploration for High-Speed Implementation of the MISTY1 Block Cipher

This paper proposes 2 × unrolled high-speed architectures of the MISTY1 block cipher for wireless applications including sensor networks and image encryption. Design space exploration is carried out for 8-round MISTY1 utilizing dual-edge trigger (DET) and single-edge trigger (SET) pipelines to analyze the tradeoff w.r.t. speed/area. The design is primarily based on the optimized implementation of lookup tables (LUTs) for MISTY1 and its core transformation functions. The LUTs are designed by logically formulating S9/S7 s-boxes and FI and {FO + 32-bit XOR} functions with the fine placement of pipelines. Highly efficient and high-speed MISTY1 architectures are thus obtained and implemented on the field-programmable gate array (FPGA), Virtex-7, XC7VX690T. The high-speed/very high-speed MISTY1 architectures acquire throughput values of 25.2/43 Gbps covering an area of 1331/1509 CLB slices, respectively. The proposed MISTY1 architecture outperforms all previous MISTY1 implementations indicating high speed with low area achieving high efficiency value. The proposed architecture had higher efficiency values than the existing AES and Camellia architectures. This signifies the optimizations made for proposed high-speed MISTY1 architectures.
Regulatory Trends in Blockchain Technologies

The blockchain is a technology that was initially designed as an alternative to the banking system aimed to decentralize money circulation. It has become apparent that the invention has a vast potential to improve various industries and fields of human activities. The blockchain industry generated a variety of ideas of how to use this technology: to crowdfund business with Initial Coin Offerings (ICO), to tokenize and manage assets online, to improve public administration and state-owned registries, or even to use it for electronic voting. Multiple applications of this technology became possible since the industry started experimenting with overlay technologies on blockchains, for example, Colored Coins, known as tokens on Bitcoin; Namecoin, a blockchain as a decentralized infrastructure for Top-Level Domain “.bit,” but the significant step forward happened when Ethereum proposed their concept of smart contracts on blockchain. There are several platforms that offer users the ability to design their own private and public distributed ledgers (Hyperledger, Azure). It is not easy to mention all of the projects in the field of blockchain industry; however, at this stage, it has become clear that the technology (along with some other innovations, like Artificial Intelligence) is penetrating spheres that are traditionally highly regulated and centralized with a high level of human participation: securities and exchanges, public registries (notary, real estate, business entities and other registries), public finances, elections, etc. This discussion is devoted to the issues of regulations for the activities that arise from the blockchain.
Cloud-Based Lightweight Collaborative Editing Algorithm for Mobile Devices

With the burgeoning of mobile devices, collaborative editing through mobile devices has become an alternative choice to traditional desktop PCs. However, mobile collaborative editing has to face some technical challenges. How to achieve an efficient mobile collaboration while maintaining the shared document copies consistency is one of the core issues in collaborative computing area. This paper proposes a cloud-based lightweight collaborative algorithm for mobile devices. Firstly, a cloud-based mobile collaborative editing framework is proposed for collaborating between cloud platform and mobile devices. Secondly, a collaborative editing task allocation model is constructed to assign editing operations for cloud platform and mobile devices. Thirdly, a cloud-based lightweight collaborative editing approach is presented to achieve the data synchronization and the consistency maintenance for the shared document copies. Finally, experiments evaluations prove the effectiveness of the proposed approach.
High-speed electronic speckle pattern interferometry for analysis of thermo-mechanical behavior of electronic components

The electronics industry is creating complex miniaturized devices with steadily higher power density. The increase of maximum operating temperatures affects the thermo-mechanical load and imposes greater requirements on the quality of electronic packages. Fast and reliable methods for inspecting the quality of electronic components can help to improve production quality and to reduce waste and environmental burden. We present a compact optical sensor based on Electronic Speckle Pattern Interferometry (ESPI) that provides a possibility to carry out such control in a fast, precise and non-contact manner and can be integrated directly in a production line. Analysing thermo-mechanical deformations of objects under study, the system is capable of identifying common defects in electronic modules, such as die attachment delamination.
The positioning principle of global positioning system and its application prospects

Abstract Due to the continuous advancement of science and technology, more and more construction fields have realized the transformation from science and technology to productivity. Including GPS positioning technology. GPS (Global Positioning System) is a system that uses positioning satellites to achieve precise global positioning and can also navigate. The United States developed and used a satellite navigation and positioning system in 1994. The application of this technology has basically covered all areas of the national economy. GPS is widely used in ground surveying, engineering surveying, aerial photogrammetry, topographic surveying, etc. in the field of surveying. The application prospect of GPS system is introduced.
Cultivation of College Students Innovative Ability in Mathematics Based on Constructivism

College mathematics course is an important general course in higher education. It is an important task of mathematics course to cultivate students' applied mathematical knowledge, ideas and methods to solve practical problems. Starting from the four characters of constructivism learning theory: situations, cooperation, dialogue and meaning construction, analysising the situation for the training of mathematical application ability, the paper proposes that the training of innovative application ability should be carried out from the following four aspects: Firstly, creating problem situations to strengthen students' dominant position in solving cognitive conflicts; Secondly, a collaborative team should be established to acquire knowledge and cultivate teamwork awareness during the discussion. Thirdly, it should designed mathematical experiments to cultivate the ability of inquiry and discovery in the process of reducing knowledge. Fourthly, it should actively construct knowledge system to improve innovation application ability in the process of meaning construction.
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom

Deep Neural Network (DNN) based super-resolution algorithms have greatly improved the quality of the generated images. However, these algorithms often yield significant artifacts when dealing with real-world super-resolution problems due to the difficulty in learning misaligned optical zoom. In this paper, we introduce a Squared Deformable Alignment Network (SDAN) to address this issue. Our network learns squared per-point offsets for convolutional kernels, and then aligns features in corrected convolutional windows based on the offsets. So the misalignment will be minimized by the extracted aligned features. Different from the per-point off-sets used in the vanilla Deformable Convolutional Network (DCN), our proposed squared offsets not only accelerate the offset learning but also improve the generation quality with fewer parameters. Besides, we further propose an efficient cross packing attention layer to boost the accuracy of the learned offsets. It leverages the packing and unpacking operations to enlarge the receptive field of the offset learning and to enhance the ability of extracting the spatial connection between the low-resolution images and the referenced images. Comprehensive experiments show the superiority of our method over other state-of-the-art methods in both computational efficiency and realistic details. Code is available at https://github.com/MKFMIKU/SDAN
Designing a tangible tabletop installation and enacting a socioenactive experience with TangiTime

Abstract Contemporary computational technology (tangible and ubiquitous) are still challenging the mainstream systems design methods, demanding new ways of considering the interaction design and its evaluation. In this work, we draw on concepts of enactivism and enactive systems to investigate interaction and experience in the context of the ubiquity of computational systems. Our study is illustrated with the design and usage experience of TangiTime: a tangible tabletop system proposed for an educational exhibit. TangiTime was designed to enable a socioenactive experience of interaction with the concept of “deep time.” In this paper, we present the TangiTime design process, the artifacts designed and implemented, in its conceptual, interactional, and architectural aspects. Besides that, we present and discuss results of an exploratory study within an exhibition context, to observe how socioenactive aspects of the experience potentially emerge from the interaction. Overall, the paper contributes with elements of design that should be considered when designing a socioenactive experience in environments constituted by contemporary computational technology.
The case for adding privacy-related offloading to smart storage

It is important to ensure that personally identifiable information (PII) is protected within large distributed systems and is used only for intended purposes. Achieving this is challenging and several techniques have been proposed for privacy-preserving analytics, but they typically focus on the end hosts only. We argue that future storage solutions should include, in addition to emerging compute offload, also privacy-related operators. Since many privacy operators, such as perturbation and anonymization, take place as the very first step before other computations, query offload to a Smart Storage device might be only feasible in the future if privacy-related operators can also be offloaded.
Sixty seconds on . . . cough in a box

Don’t worry, we’re not talking about wrapping up covid symptoms. This is the news that the UK government is looking into whether artificial intelligence (AI) can be used to detect covid-19 by analysing the sound of someone’s cough.I’m not quite sure where that fits in. The website Politico , which first reported the story, said that the …
Prediction of Anime Series' Success using Sentiment Analysis and Deep Learning

Viewers' feelings and satisfaction are among the most important influences on movies and other entertainment programs' success. The entertainment industry is one of the most important fields of our time. This paper proposes sentiment analysis to help business leaders make their decisions about investing in this field and the Anime producers to understand viewers' feelings and how satisfied they are. Deep learning techniques are utilized to develop a text sentiment analysis model to classify reviews into positive and negative classes. A data set containing 50,000 reviews from the MyAnimeList dataset is used to train and evaluate a Convolutional Neural Network model. The model is evaluated using different metrics whereby it achieved an accuracy of 0.95, precision of 0.96, recall of 0.98, and F1-score of 0.97.
Object Detection for Autonomous Vehicles Using Deep Learning Algorithm

Self-driving cars is recently gaining an increasing interest from the people across the globe. Over 33,000 Americans are killed in car accidents every year and lots of those accidents are often avoided by implementing the autonomous vehicle detection. Different ways are developed to manage and detect the road obstacles with the help of the techniques like machine learning and artificial intelligence. To resolve the issues associated with the existing vehicle detection like vehicle type recognition, low detection accuracy, and slow speed, many algorithms like the fast and faster region-based convolutional neural networks (RCNNs) are implemented but those were not supportive in real time because of the speed at which they compute and its two-step architecture with the faster RCNN, which is the enhanced version of RCNNs that runs at a speed of 7 frames per second. As it is observed that the CNN family has two steps (object detection and classification), which can reduce the response time in real time with good accuracy and high image resolution. So, the vehicle detection model like YOLOv2 and YOLOv3 is taken into consideration in this paper as they are very useful in real-time detection with a comparatively higher frame rate. As YOLO family of algorithms will mostly use the single step detection and classification. YOLO has an FPS rate of 45 which is pretty good in the real-time scenarios. We had an average of 90.4 using the taken algorithm for each image in this paper with a lower resolution image alone.
The Application of Computer-based BIM Technology in Garden Architecture Design

Abstract With the development of computer technology, all walks of life have begun to combine computer technology for industry development, including the garden industry. The garden industry mainly uses computer-aided technology for garden design. The main technology currently used in garden planning and design is BIM technology. It is an important means to achieve two-dimensional construction drawings and three-dimensional landscape models in garden planning and design.
Double Clustering Based Neural Feedback Method for Unstructured Text Data

Text clustering has now a days become a very major technique in many fields including data mining, Natural Language Processing etc. It’s also broadly used for information retrieval and assimilation of textual data. Majority of the works which were carried out previously focuses on the clustering algorithms where feature extraction is done without considering the semantic meaning of word based on its context. In the given work, we introduce a double clustering algorithm using K -Means, by using in conjuction, a Bi-directional Long Short-Term Memory and a Convolutional Neural Network for the purpose of feature extraction, so that the semantic meaning is also considered. Recurrent neural network (RNN) has the ability to study long-term dependencies prevailing in input whereas CNN models are for long known to be effective in feature extraction of local features of given input data. Unlike all the works previously carried out, this proposed work considers and carries out extraction of features and clustering of documents as one combined mechanism. Here result of clustering is send back to the model as feedback information thereby optimizing the parameters of the network model dynamically. Clustering in a double-clustering manner is implemented, which increases the time efficiency.
Conceptual State Analysis

Conceptual State Analysis (CSA) is the process of defining, exploring, and reasoning about state attributes and state spaces in complex system architectures. Model-Based State Analysis enhances CSA with formal modeling and analysis methods. We propose a process for generating the state vector of a given system architecture, based on a graph representation of the architecture’s model in Object-Process Methodology. A robust graph data structure that represents the model is queried to produce the architecture’s state space. The process facilitates analysis, reasoning, and model revision based on improved understanding of state vectors and state spaces. State attribute refinements within the model allow for model validity, viability, and reusability as the architecture evolves, with multiple agents, attributes, and attribute state values. The CSA approach advocates and facilitates careful, dynamic, and interactive state space exploration in lieu of exhaustive upfront enumeration of state space permutations. The results can be fed into other analysis, simulation, or visualization tools. We demonstrate CSA on driver assistance technology.
Multi-Camera Collaborative Network Experimental Study Design of Video Surveillance System for Violated Vehicles Identification

Abstract In this paper, we propose an experimental study of multi-camera collaborative network for surveillance the highway traffic turn in real life scenario, the target of surveillance based offline video processing is capturing the violated vehicles that driving violated paths in many cases specified by user-defined rules. Best topology of the experiment zone is considered and covered by four pillars; each has two (fixed and motorized) cameras that casing the entire specific effective field of view. As to author knowledge, there is no such available experiment, and hence, it could serve researchers that interested in. However, the experiment is done for around 180 recorded hours for 8 videos during 9 days, each video for one camera. It is designed based on the collaborative cameras principle for intelligent video surveillance systems and the outcomes show that the surveillance and the tracking of violated vehicles have been successes in most user-defined rules cases for more than 90 % cases.
Real Time Moving Vehicle Congestion Detection and Tracking using OpenCV

In this era people using vehicles is getting increased day by day. To plan, monitor and also controlling of these vehicles is becoming a big challenge. A system is to be implemented without altering the infrastructure, so a video- based vehicle capturing and analysis of that video without affecting the traffic is required, by which traffic accidents and congestion can be determined. In this paper, we have come up with a solution for the above problem using the video surveillance considering the video data from the traffic cameras. We have used adaptive thresholding method, Gaussian based background subtraction with tracking methods such as blob tracking and virtual detector. The implementation was done using OpenCV Python as a tool. Our proposed system can identify, track the congestion and help in counting the objects precisely.
A Forensic Evidence Recovery from Android Device Applications

In a computerized world, indeed unlawful conduct and/or violations may be named as advanced. This world is expanding. Getting to be versatile, where the fundamental computation and communication substances are Little Scale Computerized Gadgets (SSDDs) such as standard versatile phones, individual computerized collaborators, smartphones, and tablets. The ought to recoup information, which might refer to illegal and unethical exercises gave rise to the teaching of portable forensics, which has ended up a fundamental portion of computerized forensics. The literature relevant to Smartphone forensics, as explored in this paper, focuses on the architecture of Smartphone operating systems. It also addresses the digital evidence of Smartphone applications. in this paper undertakes practical experiments to identified sources for evidence that can later be used in the judiciary system. In this research, I'll use open-source Tools that can recover deleted data from the application.