Reviewerly/exHarmony huggingface dataset:
Efficient visual monitoring of offshore windmill installations with online image annotation and deep learning computer vision

The number of offshore windmills keeps growing around the world since they are an essential part of any strategy to produce energy without using nuclear power plants or burning fossil resources. Due to the growing number of installations new monitoring and inspection strategies and routines need to be developed that are not only effective but efficient. In this work we will present first results from a joint project of industry partners and academia that aims at the development of new approaches making use of modern imaging technologies and new methods from artificial intelligence and computer vision research. We investigate three different strategies for collecting digital photos or video and posterior computational analysis using Convolutional Neural Networks (CNN). Our results indicate, that patterns of interest like rust or coating damage can be detected and classified with F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> scores between 0.80 and 0.94 in photos collected by inspectors. In videos collected with unmanned aerial vehicles (UAV) rust patterns, oils spills and coating damage were detected with F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> = 0.91. To support users in the time-consuming task of visual data exploration, we present a new visualization method referred to as “virtual twin”. Using hand - recorded image collections, a data-driven 3D model of a windmill is generated, that can be used to investigate damage patterns in the full context of the windmill and in full detail. Altogether our results indicate, that although each single approach has its limitations, a combination of different imaging methods, deep learning computer vision algorithms and sophisticated visual data exploration by experienced users appears to have potential to overcome the bottleneck in data analysis, interpretation and decision making in the context of the future inspections of offshore windmills, platforms or other constructions.
Using Dirichlet Marked Hawkes Processes for Insider Threat Detection

Malicious insiders cause significant loss to organizations. Due to an extremely small number of malicious activities from insiders, insider threat is hard to detect. In this article, we present a Dirichlet Marked Hawkes Process (DMHP) to detect malicious activities from insiders in real-time. DMHP combines the Dirichlet process and marked Hawkes processes to model the sequence of user activities. The Dirichlet process is capable of detecting unbounded user modes (patterns) of infinite user activities, while, for each detected user mode, one set of marked Hawkes processes is adopted to model user activities from time and activity type (e.g., WWW visit or send email) information so that different user modes are modeled by different sets of marked Hawkes processes. To achieve real-time malicious insider activity detection, the likelihood of the most recent activity calculated by DMHP is adopted as a score to measure the maliciousness of the activity. Since the majority of user activities are benign, those activities with low likelihoods are labeled as malicious activities. Experimental results on two datasets show the effectiveness of DMHP.
Sensitivity and Positive Prediction of Secured Electrocardiograph (ECG) Transmission using Fully Homomorphic Encryption Technique (FHE)

Health connected is a technology that links medical devices, telecommunications and security techniques. It empowers patients to be observed and treated remotely from their homes. Patient's healthcare records with a connected healthcare system should be stored securely before transmitted for further investigation and interpretation. Electrocardiogram (ECG) is the clinical method utilized to screen heart execution and utilized for the detection of various arrhythmias. For diagnostic purposes, individuals with a background of heart diseases have long records of ECGs, which results in the requirement of a large amount of storage space and labor. Hence, there is a requirement for a system that involves digital signal processing and signal security so that the spared information is made sure about at one spot and an only authentic individual can see and utilize this ECG signal for additional findings. This study presents a set of security solutions that can be deployed in a connected healthcare territory, which includes the fully homomorphic encryption (FHE) techniques used to secure the ECG signals. The study helps the medical provider to record ECG signals confidentially and to prevent mistreatment. The study focuses on Pan and Tompkins algorithm methods for the detection of the ECG Signal. As a result, the output of the Pan and Tompkins algorithm for ECG signal processing with the FHE technique shows a sensitivity of 92.59% and a positive prediction of 90.00%.
Winning the new channel war on Amazon and third-party platforms

The top online marketplaces in the world, including Alibaba, Amazon, and eBay, sold $2.7 trillion in 2020, or 62% of global web sales that year. Despite the promise of unfettered access to customers, the reality is that most sellers struggle with unprecedented and unauthorized rogue resellers. These often anonymous, third-party individuals and firms may not provide the quality experiences aligned with customers' expectations of a brand and often market their goods in competition with trusted partners. This requires a systematic battle plan to effectively protect the seller's brand equity, service promise, and authorized distribution network. We describe the channel war that is at work and the forces fueling its development. We explain how rogue reseller activities drive down pricing, increase channel competition and conflict, impact the customer experience, and harm brand equity. We recommend revising product policies, procedures, and agreements to begin to differentiate products from those sold by unauthorized parties. We also recommend a legal strategy to complement these efforts. Critical steps include regular and active enforcement as well as implementing services, benefits, and quality controls for the brand's products that cannot be replicated by unauthorized sellers and requiring authorized distributors and retailers to maintain those benefits and controls.
Research on video Surveillance System used for smart Community

According to the novel Coronavirus epidemic spread that broke out this year and the severe epidemic prevention and control problems faced by the resumption of work and production in the factory area in the post-epidemic period, in this paper, an intelligent early-warning management system for personnel status in the production area of the factory was established, which realized the environmental monitoring and personnel monitoring and management, in addition, the data processing and storage of face recognition, data transmission between edge terminal and cloud platform, and task sharing of distributed edge terminal are optimized, intelligent security is realized, at the same time can effectively ensure the health and safety of workers.
A Design System of Cultural and Creative Models Based on Image Retrieval and 3D Relief

It is an interesting question for members of public to design 3D creative models based on cultural heritage, which can propagate the traditional culture. In order to use the existing cultural resources, we proposed a method to design cultural and creative models based on image retrieval and the generation of relief model technologies. It contains three steps: first, we adopted image retrieval technology to find the most similar image from image databases by calculating the similarities of color and geometric features among different images. We used shape from shaping algorithm to calculate depth to generate 3D relief from a single image. Finally, a 3D model, which is created by Structure from Motion as the base model, is integrated with the generated 3D relief to create the cultural and creative model. A prototype of a cultural and creative model design system using Android is implemented. The experimental results show that the system is effective and robust.
Fredkin Gate Simulation

The article deals with the study of options for implementing a reversible Fredkin Gate based on a LUT FPGA per variable.
Building an application framework to connect NGOs and Volunteers

The NGO sector struggles to get volunteers and yet individuals interested in volunteering some of their time to socially meaningful causes are not able to find suitable opportunities. This paper aims to set up platforms to allow NGOs to communicate these volunteering opportunities and for volunteers to enroll for those that match their preferences. The platform is developed both as an Android application and a Web-based interface. The paper also explores how gamification can be used to make the process fun and competitive to increase engagement with the volunteers.
Text Classification in Emergency Calls Management Systems

In this chapter text mining approach for emergency calls management systems is investigated. A data mining approach of emergency calls classification based on the machine learning decision tree method was proposed. As an input dataset for model building the data with 1.6 million textual emergency events descriptions was taken. Approbation results, finally, led to the conclusion that high effectiveness and a possibility of practical use in the emergency calls management information systems.
Enhancing Investigative Pattern Detection via Inexact Matching and Graph Databases

Tracking individuals or groups based on their hidden and/or emergent behaviors is an indispensable task in homeland security, mental health evaluation, and consumer analytics. On-line and off-line communication patterns, behavior profiles and social relationships form complex dynamic evolving knowledge graphs. Investigative search involves capturing and mining such large-scale knowledge graphs for emergent profiles of interest. While graph databases facilitate efficient and scalable operations on complex heterogeneous graphs, dealing with incomplete, missing and/or inconsistent information and need for adaptive querying pose major challenges. We address these by proposing an inexact graph pattern matching method, which is implemented in a graph database with a scoring mechanism that helps identify hidden behavioral patterns. PINGS ( <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">P</u> rocedures for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IN</u> vestigative <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">G</u> raph <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> earch), a graph database library of procedures for investigative graph search is presented. Results presented demonstrate the capability of detecting individuals/groups meeting query criteria as well as the iterative query performance in graph databases. We evaluate our approach on three datasets: a synthetically generated radicalization dataset, a publicly available patient’s ICU hospitalization stays dataset, and a crime dataset. These varied datasets demonstrate the wide-range applicability and the enhanced effectiveness of observing suspicious or latent trends in investigative domains.
Neural synchronization of optimal structure-based group of neural networks

In this paper, a neural synchronization of the optimal structure-based group of neural networks is proposed. Asymmetric cryptography is widely used to generate a key amongst two parties and to exchange the key through an insecure channel. However, since the methods that used this strategy, like RSA, have been compromised, new methods for producing a key that can offer security must be discovered. A new group of cryptography known as neural cryptography was created to solve this issue. The primary aim of neural cryptography is to produce a secret key over an unreliable medium. The optimal neural network architecture for creating and defining a secret key between the two authorized individuals is examined in this article. Furthermore, studies into the coordination of a group of neural networks are uncommon. For the design of the public key exchange protocol, synchronization of a cluster of neural networks with Three Layer Tree Parity Machine (TLTPM) is proposed. To calculate the synchronization time, steps taken, and the number of times the attacking neural network could replicate the actions of the two accepted networks, more than 15 million simulations were run. Various parametric experiments have been conducted on the proposed methodology. Simulations of the approach show that it is correct, according to the results of the paper.
Farmer-Assistive Chatbot in Indian Context Using Learning Techniques

According to the 2011 Agricultural Census of India, 61.5% population depends on Agriculture for livelihood. Agriculture contributes to only 23% of the country’s population. The low performance economically is primarily because of a lack of proper information and resources to obtain genuine information. The farmers’ queries are hardly solved and heard by the concerned individuals thus leading to chaos and confusion in their minds regarding their farming activities. Proper attention and implementation are thus needed to assist the farmers of India. We propose to build a smart chatbot using learning techniques to get correct information to farmers quickly. A farmer can interact with the chatbot in their local language as our chatbot is multi-linguistic. We use the dataset from Kisan Call Centre to train the chatbot. We use the k-Nearest Neighbors algorithm and sequence-to-sequence framework to enable Natural Language Processing as well as Natural Language Generation. Farmers will get real-time answers to their questions through our system assisted by Machine Learning.
Knowledge Based Hyperbolic Propagation

There has been significant progress in utilizing heterogeneous knowledge graphs (KGs) as auxiliary information in recommendation systems. However, existing KG-aware recommendation models rely solely on Euclidean space, neglecting hyperbolic space, which has already been shown to possess a superior ability to separate embed-dings by providing more "room". We propose a knowledge-based hyperbolic propagation framework (KBHP) which includes hyperbolic components for calculating the importance of KG attributes relative to achieve better knowledge propagation. In addition to the original relations in the knowledge graph, we propose a user purchase relation to better represent logical patterns in hyperbolic space, which bridges users and items for modeling user preference. Experiments on four real-world benchmarks show that KBHP is significantly more accurate than state-of-the-art models. We further visualize the generated embeddings to demonstrate that the proposed model successfully clusters attributes that are relevant to items and highlights those that contain useful information for the recommendation.
A double gimbal optical image stabilizer driven by piezoelectric stacks

With the advantages of high accuracy and fast response, piezoelectric actuators are used to drive optical image stabilizers. As a piezoelectric stack is not tensile, preload voltage is required for a traditional piezoelectric driven optical image stabilizer, which wastes electricity and adversely affects the stack’s performance. In this paper, we propose a double gimbal optical image stabilizer, whereupon a prototype is machined and its performance is assessed. Two piezoelectric stacks are used to produce the forward and reverse actuation, respectively, in which case the preload voltage is not essential. Owing to the isolation of the gimbal, the output coupling of the two piezoelectric actuators is less than 1%. The presented stabilizer has the actuation range of ±50 µm and effectively tracks the sinusoidal signals with the frequency below 46 Hz.
Design of a Hardware Accelerator for Zero-Knowledge Proof in Blockchains

With the popularization and maturity of blockchain technology, more and more industries and projects are gradually trying to combine blockchain technology, including digital currency, Internet of Things, 5G new infrastructure. The most important thing for these applications is to require its safety. These security services are usually provided by cryptographic protocols, and zero-knowledge proof is such a core technology to provide the bottom layer of security services. However, the most widely used protocol named zk-SNARK, involves solving multiple large-scale examples of tasks related to polynomial arithmetic on large prime fields of cryptography and multi-exponentiations on elliptic curve groups. Complicated and huge calculations bring longer prover time, which hinders the implementation of some applications. In this paper, we propose a design of hardware accelerator based on FPGA for zero-knowledge proof. The zk-SNARK engine which is combined of multiple FFT, MAC and ECP units reduces the prover time by 10x and provides the possibility for future blockchain terminals based on mobile devices.
Algorithm of ML-based Re-scheduler for Container Orchestration System

Due to the gradual growth of the number of companies that use cloud technologies, there is an increase in the number of enterprises deploying and using an internal private cloud. Due to this trend, there is growth of interest in various technologies that ensure the efficiency of the cloud infrastructure. One of such technologies is the orchestration technology, the core of which is a scheduler - a special component that allows efficiently distribute virtualized entities with running tasks across computational nodes. However, schedulers usually only plan the locations schemes of tasks that was not started yet; often they do not plan to make changes to the arrangement of already running entities. To create the plan of changing the state of already running tasks deschedulers and reschedulers are additionally used. This article proposes a solution using a Reinforcement Learning based rescheduler and an algorithm of its preparation.
Impact of Lexical Features on Answer Detection Model in Discussion Forums

Online forums have become the main source of knowledge over the Internet as data are constantly flooded into them. In most cases, a question in a web forum receives several responses, making it impossible for the question poster to obtain the most suitable answer. Thus, an important problem is how to automatically extract the most appropriate and high-quality answers in a thread. Prior studies have used different combinations of both lexical and nonlexical features to retrieve the most relevant answers from discussion forums, and hence, there is no standard/general set of features that could be effectively used for relevant answer/reply post classification. However, this study proposed an answer detection model that is exclusively relying on lexical features and employs a random forest classifier for classification of answers in discussion boards. Experimental results showed that the proposed answer detection model outperformed the baseline technique and other state-of-the-art machine learning algorithms in terms of classification accuracy on benchmark forum datasets.
Co-Inflow: Coarse-grained Information Flow Control for Java-like Languages

Coarse-grained dynamic information-flow control (IFC) is a good match for imperative object-oriented programming languages such as Java. Java language abstractions align well with coarse-grained IFC concepts, and so Java can be cleanly extended with coarse-grained dynamic IFC without requiring significantly different design patterns or excessive security annotations, and without excessive performance overhead.We present Co-Inflow: an extension of Java with coarse-grained dynamic IFC. By careful design choices and defaults, a programmer typically needs to add very few annotations to a Java program to convert it to a Co-Inflow program with relatively good precision. Additional annotations can improve precision. We achieve this tradeoff between precision and annotation burden by instantiating and specializing recent advances in coarse-grained IFC for a Java-like setting, and by using opaque labeled values: a restriction of labeled values that the Co-Inflow runtime automatically and securely creates and uses.We have captured the essence of Co-Inflow in a middle-weight imperative calculus, and proven that it provides a termination- insensitive non-interference security guarantee. We have a prototype implementation of Co-Inflow and use it to evaluate the precision, usability, and potential performance of Co-Inflow.
Development of Augmented Reality-Based Application to Search for Electric Appliances and Furniture

This study developed an application to promote purchases through functions that are not available in existing shopping applications, based on the expansion and growth of e-commerce sites and the increase in the conversion of physical stores to e-commerce. The developed application uses augmented reality (AR) to place virtual objects in real space and allow the user to set the desired size of the item. The application can also search for products based on the size of the retrieved object data and present the actual product.
Breast Tumor Detection in MR Images Based on Density

Breast cancer is dangerous in women. It is generally found after the symptoms appear. Detecting the breast cancer at an early stage and understanding the treatment are the most important strategies to prevent death from cancer. Generally, for detection of breast cancer, breast Magnetic Resonance Image (MRI) takes place. It is one of the best approaches to detect tumor in women. In this research paper, a combination of selection methods for seed region growing image segmentation is suggested to detect breast tumor. The suggested method has been divided into following parts: First, the pre-processing of breast image is performed. Second, the automatic threshold for binarization process is calculated. Third, the number of seed points and its position in the breast image are determined automatically using density of pixels value. Fourth, a method for calculation of threshold value is proposed for the purpose of region creation in seed region growing. For the evaluation purpose, the proposed method was applied and tested on the RIDER MRI breast dataset from National Biomedical Imaging Archive (NBIA). After the test was performed, it was observed that proposed algorithm gives 90% accuracy, 88% True Negative Fraction, 91% True Positive Fraction, 10% Misclassification Rate, 94% Precision and 86% Relative Overlap which is better than other existing methods. It not only gives better evaluation measure but also provides segmentation method for multiple tumor detection.