Reviewerly/exHarmony huggingface dataset:
Path Planning of Tube-Sheet Girth Welding by Discrete Firefly Algorithm

The path planning of tube-sheet welding can be approximately equivalent to traveling salesman problem (TSP), which belongs to NP-hard problem. To solve this problem, the standard firefly algorithm is discreted, and a new firefly distance pattern is proposed to represent the distance between two fireflies. At the same time, a new random rule is introduced into the iteration rule for stochastic search. To implement the algorithm, the fitness function of the shortest welding path considering the thermal deformation based on the Gauss heat distribution law is constructed in this paper. The experiment of path planning of tube-sheet welding is taken by applying of the proposed DFA, the results show that the proposed algorithm is efficiency and the fitness function is feasible.
Lines of Play

'Lines of Play' is a domino art application showcasing recent advancements in augmented reality (AR) technology. Unlike previous mobile AR applications that were limited to horizontal and vertical object placement, these game objects interact with real objects in the player's environment with surface-level resolution. Features of the player's environment provide kinematic constraints (on collision) and serve as obstructions both visually (occlusion) and during gameplay. Using single camera depth information, players can use familiar vector drawing tools to rapidly create and modify lines, rings, paths, and turns of domino tiles. We thereby provide a method for placing a plurality of gaming objects by converting raycasted anchor points into paths of game objects. This opens up novel possibilities of authoring tools specially adapted for AR game development or game-integrated level editing.
Research on the Integration of High and Low Voltage Power Distribution Cabinet and Fault Monitoring Device Based on PLC and Inverter

With the rapid economic growth and the continuous increase in power load, users have increasingly demanded power quality, and the importance of fault-free monitoring of distribution networks has also increased. This article introduces the hardware of the control system. According to the actual speed range and load of the original DC speed control system, the appropriate frequency conversion asynchronous motor, frequency converter and encoder for measuring the speed are selected. In this paper, the overall design of the online monitoring system for the integrated equipment of high and low voltage distribution cabinets is carried out. The online monitoring system of the fault monitoring device is composed of intelligent capacitors, power information collection terminals and the host station of the host computer. The power information collection terminal collects the working condition and alarm information of each capacitor through the RS485 communication line, and then uploads it to the host station of the upper computer through GPRS. The staff can monitor the reactive power compensation device in real time on the main station operation page. By analyzing the influence of the control parameters and load size of the rectifier and inverter sides of the system on the fault diagnosis effect and the simulation results of the fault diagnosis on both sides, a robust fault characteristic index is proposed for reliable and rapid diagnosis.
CEnTrA: City-scale Energy Transaction Application for the Smart Grid

Under the broader aegis of smart grid, the use of Distributed Ledger Technology to promote privacy, trust and security in peer-to-peer (P2P) energy sharing is gaining attention globally. However, implementation is limited to microgrid or neighbourhood level due to the challenges of scalability and performance associated with increase in number of prosumers. Therefore, the inclusion of stakeholders- producers to prosumers-require a scalable solution for advancing the information and energy exchange objectives. To this end, we propose CEnTrA, an application of sharding in blockchain to develop a novel hierarchical model capable of processing P2P energy sharing transactions at city-scale. CEnTrA is based on ChainSpace and takes advantage of the structure of the electrical grid to create a scalable network. The hierarchical model allows the use of customized transaction policies at different levels and locations of the grid. The results show that sharding increases transaction throughput by upto 59.52% in comparison to no sharding. Additionally, a location-based sharding model is presented that improves performance of multi-input transactions by 39.57% for 250 inputs in comparison to random sharding.
Modelling Ontologies for Biorefinery Processes - A Case Study

This study introduces the systematic modelling approach of “visual modelling” to biorefinery processes by applying it to a concept from the demonstration project KomBiChemPRO. The process consists of a hydrothermal conversion of hemicellulose-derived oligo sugars into monomeric C5 sugars, combined with purification steps of adsorption and nanofiltration. The experimental studies determine on model effluents hydrothermal conversion kinetics, adsorption isotherms and kinetics, as well as separation efficiencies of the nanofiltration. The paper shows an efficient graphical representation of the process topology, which is combined with an equation ontology.
Webometric Analysis of Websites of Hindi Newspapers in India

Newspapers have great influence on society. They are important for communication and as a source of information to user. Using Alexa internet webometric analysis and evaluation of top Indian Hindi newspaper websites is the main purpose of this study. All newspaper websites searched in Alexa internet databank and related essential statistical data for traffic ranking, pages view, downloading speed and percentage of bounce, time spend on site, searching rate, and Indian and foreign visitor percentage were measured. Latter on this statistical data were analyzed. The final result of this study shows that NavBharat Times is having good traffic rank in India and globally also having highest links (63,775) and foreign user percentages (23.4%). Lokmat Samachar having fastest downloading speed (1.318 sec), maximum page views (12.90), lowest bounces rate (10.30%), highest estimated daily time spent on site (10:17) and highest percentage of visit from search engine (48.70%).
Multi-Dimensional Power Marketing Linkage Analysis and Intelligent Monitoring Based on Visualization Technology

In order to monitor the power purchase and consumption of users in power plants or power companies in real time, this study analyzes the power linkage marketing situation. The research combines the information entropy decision tree model and the clustering algorithm in data mining to realize the visualization of power high-dimensional data, so as to realize the intelligent monitoring of power. The results show that the electricity transaction volume between users and the company accounts for about 40% of the total transaction volume. Data visualization based on information entropy decision trees and clustering algorithms can effectively monitor the purchase and use of electricity by users. It is hoped that this research will provide certain reference and reference for the power marketing of power companies and the monitoring of users' power consumption.
Life-size Sequential Photography in a Mixed Reality Environment

We visualize life-size sequential photographs of sports activities in a mixed reality (MR) environment. Wearing a video-see-through head-mounted display, an observer records the motions of a player using a handheld camera. Our system then places billboards in the three-dimensional MR space, on which the sequential photographs of the player’s motion are presented at life-size. In a user study, we found that the observers perceived the size of the motions more accurately than when viewing sequential photographs on a monitor display.
Semantic Regularization for Blind image Deconvolution in Medical Images with extended sparseness

Blind Image deconvolution is one of the key areas of research and is a challenging task in data restoration. Many deconvolution techniques have been proposed and their evaluation as a successful counterpart in the area of medical imaging is still void. Unknown cause for degradation and amount of degradation is of main concern and is solved by regularization of image features using semantic nets with extended sparseness in our proposed model. A degraded image is convolved using a kernel matrix based on Latent semantic analysis and the resultant co-occurrence matrix is regularized using Tikhonov regulariser. In order to reduce the reconstruction error a non-negative matrix factorization approach is used resulting in a better quality image post regularization. Non negative matrix factorization aids to retrieve high level semantic features. Based on this assumption matrix factorization is used to model the blur model (Point spread function (PSF) is known, kernel matrix is factorized to find the semantic features) and the extracted features are related with the existing features for further extraction. Improved semantic networks with extended sparseness are analyzed for two most popular convolutional semantic techniques namely Latent semantic analysis and probabilistic latent semantic analysis. Feasibility of extended sparseness for blind image deconvolution over the two methods is tested on real time ultrasound and dermatology datasets and the performance of the same is justified using performance metrics PSNR, SSIM, BIF and VSNR. Our proposed method results in images with better PSNR and structural similarity index values compared to original images.
Gathering People’s Happy Moments from Their Seeing Capabilities

Digital technologies have dramatically changed our daily real world. Human wellbeing is one of the most important factors in our daily lives. Reminding happy moments increases human wellbeing significantly so memorizing the past happy moments and presenting the moments timely is a promising direction for making our life more flourishing. This paper shows our current ongoing work on CollectiveEyes that allows us to share collective people’s eyes and ears, and describes the use of CollectiveEyes to memorize and present people’s visual perspectives containing various their happy moments for increasing human wellbeing.
Hidden Markov-based LDA Internet Sensitive Information Text Filtering

We are in an era of rapid development of Internet information [1], generating billions of text every day. Under the test of such a “digital torrent”, how to ensure the ecological security and healthy development of the Internet has become a technical challenge. Rocchio[2] put forward a linear classifier which is a classification algorithm based on linear vector space model. With the development of hardware devices, the machine learning model has become the mainstream. The main training method is linear regression [3], K-Nearest Neighbor[4], Neural Network Model[5] and Support Vector Machine[6]. This paper raises a hidden Markov model based on feature keywords-themes. This is a statistically based approach. We use the Textrank algorithm [7] to extract feature words from a large number of data sets. Using the Apriori algorithm [8] to quantify the implicit relationship between feature words, we can generate a feature word confidence level matrix and establish a HMM-LDA correlation model. According to the text document which can produce an associated state matrix and a probability state transition matrix, we can confirm the conversion probability of the visible state chain. Thus we can filter and identify sensitive Internet information text.(Abstract)
Sparse Matrix Implementation on Personal News Recommendation for Anonymous User

Online news are being spread through social media by news agencies to encourage people to read news from their site. After users log in to their site, users will continue to read the news if it is relevant to their personalized news recommendation. However, nowadays, a personalized recommendation could be provided to users if the site has records of users' browsing history, and users must have logged in to the news site. This could be problematic if the readers are anonymous users, and the site does not have enough browsing history records of these users. It will be difficult for news agencies to increase the number of daily readers on their site, especially if they have to compete with other agencies for providing a good personalized recommendation. Therefore, a personalized recommendation system is necessary for anonymous users who only visit the news site occasionally so that the website can recommend the news they prefer to read by implementing CSR Sparse Matrix Vector Multiplication and proximity processing as sparse matrix applied method. The Bayesian framework for user interest will also be implemented with the modification in its term. This research proved that personalized news recommendations could still be provided to readers with limited knowledge of the users' searching history.
Variable lateral shearing Sagnac interferometer with dove prism

Sagnac interferometer is an important structure of lateral shearing interferometer. The shearing distance of the interferometer affects the measurement sensitivity of the interferometer and the stripe density of the fringes. A Sagnac interferometer with dove prism with variable lateral shearing distance is proposed. A dove prism is placed in the traditional optical path between the two mirrors in the Sagnac interferometer. The shearing distance of the interferometer can be adjusted by rotating the dove prism continuously. This structure can get the shearing distance over a wide range and reduce the influence of vibration on the system in the Sagnac interferometer during adjustment. The availability of this interferometer was confirmed after simulation and experiment.
Research on The Framework of The GIS-document Workflow System

Geographic information system (GIS)-document workflows are formed by combining workflow and GIS, and have the capabilities of geographic data processing, analysis, and visualization. They are widely used in areas with “integration of map and document.” An increasing number of complex business processes have great demands for business logic control and geographic data processing. Therefore, we established the framework of GIS - document workflow system in order to face the complex business processes and large geographic data processing processes.
Energy-sensitive Scheduling for Cloud Data Centers Prone to Failures

With the rapid growth of cloud computing, its energy waste and excessive energy consumption have become a big issue. Cloud infrastructure is built on a large number of servers and devices. In the execution processes of computing tasks, faults of different components may occur in server hardware/software at any time. We propose a task scheduling method for high performance computing considering failures of servers and the transmission of task datasets in data centers. This approach optimizes two conflicting objectives: minimizing energy consumption during computation and transmission, and reducing application rejections or violations due to failures. The proposed method can also improve resource utilization. The experimental simulations via large scale parallel working datasets show that this method can obtain good energy saving benefit and high quality of service.
Euphonia

This paper reflects on the design process for a work-in-progress AI-powered voice-controlled narrative game created by Innovation for Games and Media Enterprise (InGAME). This paper describes the steps which led to the final design decisions, and how the background research, research questions and initial prototyping may be traced through to the work-in-progress game. The design process is then reviewed for its suitability as a practice-based research and development workflow, before finally suggesting next steps the project will take.
Detecting Gurmukhi and Hindi text object present in images

This paper resolves the trouble detecting Gurmukhi and Hindi text objects present in images. Therefore, we propose a new region-based bottom-up method to extract text from images, where we first identify elementary substructures using connected components and edges and then merge them successively. As each character counter has high contrast as compared to its neighbours, all character pixels and several non-character pixels which exhibit excessive neighbourhood. Although a lot of noise is removed by this step a second pass filter is applied based on stroke width. The stroke width of Hindi and Gurmukhi characters are approximately uniform. The candidates with similar properties are aggregated into chains, according to the observation that the figures of a book. For performance evaluation, we created our own dataset containing 60 images of varied size, resolution and type. The performance is evaluated on this dataset containing caption, document, and scene text images using different performance metrics.
Unknown Protocol Identification Based on Improved K-Means++ Algorithm

Abstract In recent years, the gradual popularization of mobile terminals and the vigorous development of the network have spawned the birth of a new Internet structure and promoted the growth of network traffic. Behind such a large network, effective supervision of network traffic is the cornerstone of network security protection. At present, many studies on the direction of network supervision focus on the analysis of unknown network protocol types. The protocol identification method combined with machine learning is a hot topic in this kind of research. This method extracts data stream features and builds data sets, using machine learning algorithms. The model analyzes unknown network traffic and can obtain better recognition results than traditional network protocol analysis methods. Aiming at the problem of unknown traffic identification, this paper proposes a reasonable unknown traffic identification algorithm. The feature normalization preprocessing, feature selection, LOF outlier analysis, etc. are introduced. The clustering process uses the K-Means++ algorithm, and the maximum local reachable density point in the outlier analysis is used to realize the initial cluster center point. Accurate positioning.
Adversarial Level Agreements for Two-Party Protocols

Adversaries in cryptography have traditionally been modeled as either semi-honest or malicious. Over the years, however, several works have investigated the design of cryptographic protocols against rational adversaries. The most well-known example are covert adversaries in secure computation (Aumann & Lindell, TCC '07) which are adversaries that wish to deviate from the protocol but without being detected. Protocols secure against such covert adversaries guarantee that deviations are detected with probability at least - which is known as the deterrence factor.
Hand posture classification with convolutional neural networks on VGG-19 net Architecture

Abstract This study aims to classify the image depth data Hand Posture. Hand Posture is a form of hand and movement used to communicate. Hand Posture is difficult to classify because various human hand objects are complex articulation objects. The model used in this study is Convolutional Neural Networks using the VGG-19 Net architecture. Based on the results shows an increase in the percentage of classification accuracy in each subject is 0.9976, 1.0, 0.9984, 1.0, and 0.9992 respectively.
Multi-view RGB-D System for Person Specific Activity Recognition in the context of holographic communication

Activity recognition is a key component of context-aware holographic communication to support optimal quality flow of data, but conventional approaches often lack in semantic information and context-awareness due to problems such as difficulty identifying the activity which the invidiual performs; overfitting when building activity models; collection of a large amount of labeled data from each end user. This paper presents a fully developed multi-view RGB-D system based on user-specific metrics - facial features coupled with a body skeleton. The system employs a skeleton-based approach. We test the performance of the proposed architecture in a controlled environment.
Visual anomaly detection by distributed deep learning

Anomaly detection with visual information by distributed deep learning is proposed in the paper. First, visual anomalies are defined in a special application domain, which are very important and critical for safe operation. Secondly, deep convolutional neural network is chosen as detector for visual anomalies. Thirdly, detection results from different visual sources are fused to get higher accuracies and lower false alarm rate. Experimental results demonstrate that the visual anomaly detection framework proposed can achieve high performance and provide satisfactory security assurance.
Einsatz der PXRF-Analytik zur petrographischen Klassierung eines umfassenden Fundinventars an Gesteinsartefakten

Archaeological rock artefacts are often very fine-grained and an unequivocal visual classification may thus be difficult, even for geologists. Geoscientific analytical methods would allow an unambiguous classification; however, they are not free of destruction. In a pilot study, portable X-ray fluorescence analytics (pXRF) was applied, in addition to standard optical examination, to study a larger lithic find inventory of the Neolithic to early Bronze Age settlement of Cham- Bachgraben. The aim of the study was a correct petrographic classification of the finds. The study demonstrates that non-destructive pXRF analytics allows a far more reliable classification than optical methods alone. Otherwise hardly distinguishable rock types like jadeitite, nephrite, serpentinite and (meta)mafica can be discriminated with confidence. The method is, however, not yet fully exploited.
Class Representation Networks for Few-Shot Learning

In this paper, we proposed a novel network referred as Class Representation Networks (CRNs) to solve the few-shot learning problems. In the proposed CRNs, a high-quality class representation is learned by training a set-based neural network. In addition, a network with fully connected layers was constructed for learning distance metric instead of using a predefined distance metric. Compared with recent methods for few-shot learning, our network achieves state-of-the-art performance for few-shot learning. Extensive experiments on three benchmark datasets validate the effectiveness of our proposed model.
FlashAttention: Data-centric Interaction for Data Transformation Using Programming-by-Example

Programming-by-example (PBE) can be a powerful tool for reducing manual work in repetitive data transformation tasks. However, having few examples often leaves ambiguity and may cause undesirable data transformation by the system. This ambiguity can be resolved by allowing the user to directly edit the synthesized programs, but this is difficult for non-programmers. Here, we present a novel approach: data-centric interaction for data transformation, where users resolve the ambiguity in data transformation by examining and modifying the output rather than the program. The key idea is focusing on the given set of data the user wants to transform instead of pursuing the synthesized program's completeness. Our system provides interactive visualization that allows users to efficiently examine and fix the transformed outputs, which is much simpler than understanding and modifying the program itself. Our experiment shows that the number of candidates is much smaller than the number of synthesized programs, which implies the effectiveness of the proposed method.