Reviewerly/exHarmony huggingface dataset:
Cost-oriented Information Construction for Enterprise Based on ERP System

In the context of the information age, cost control has received extensive attention from enterprises in the whole society, which is related to the competitiveness and sustainable development ability of enterprises in related fields. To further account and control the cost of the enterprise, the ERP system is introduced as an enterprise management tool in the enterprise management process. However, the current research on the ERP system still has structural and application deficiencies. This paper first sorts out the theoretical basis of the ERP system and completes the research content by defining the concepts of cost accounting and cost control informatization. On this basis, an enterprise information cost control system based on the ERP system was established, and the information flow, enterprise logistics, value flow, cost accounting procedures, etc. integrated in view of ERP system were optimized, and get analyzed and summarized in the perspective of system program operation.
Analyzing the Application of Blockchain and Artificial Intelligence in New Energy Vehicle Transactions from a Data Security Perspective

This article studies the application of blockchain and artificial intelligence technology in new energy vehicle transactions. From the perspective of data security, it studies the charging location and charging time of new energy vehicles as the main factors limiting their development. At the same time, some remote charging stations and electricity Electric vehicles that are fully idle cannot be used effectively. In response to this problem, a technology platform that integrates new energy vehicle charge and discharge transactions with alliance blockchain is proposed, and uses the decentralized characteristics of blockchain and artificial intelligence algorithms to conduct in-depth discussions on new energy vehicle charge and discharge transactions and development prospects. The result is an increase of 4.6%.
Cyber-Physical System Enabled Path Planning Simulation for Collaborative Industrial Robots

Collaborative industrial robot is becoming more and more important in the manufacturing industry. Thanks for a variety of high-precision sensors integrated by the Cyber-Physical System, the collaborative industrial robot can model the working environment and know its own state in real time. Because of this, CPS enables the robot to plan a feasible path in a virtual simulation environment. In this paper, a two-dimensional working space and a three-dimensional working space is constructed and set as a virtual environment model constructed by CPS. Q-learning algorithm is used to plan a path in the working space. A feasible path is found by adjusting the number of iteration times of the algorithm. Further more, the learning rate α of the Q-learning algorithm is also adjusted and the results demonstrate that the increase of α will accelerate the convergence speed of the algorithm within the set range.
Proactive Smart Home Assistants for Automation—User Characteristic-Based Preference Prediction with Machine Learning Techniques

AI-powered smart homes bring high-quality intelligent services to occupants with digital virtual assistants. Through interactions with occupants, the smart home assistants (SHAs) can develop occupants’ profiles using a number of personal characteristic features for tailored and smart interactions. Based on these profiles, smart home systems can proactively offer automation services while conserving occupants’ comfort and convenience. In this study, we have sought to investigate characteristic features that affect occupants’ perception of the proactive concept, as well as their preferences for modes of interactions through an application of automation for energy efficiency management. Upon a data collection through an online experiment on campus, 58 valid responses with personal characteristic features were utilized to develop predictive machine learning models. These models can predict participants’ general attitude towards proactive SHAs, as well as their preferences for interaction modes with good performance (accuracy between 0.67 and 0.82 and F-score between 0.66 and 0.74). Various features were identified to have considerable significance, including personal beliefs of taking actions and energy expenses, as well as environmental protection values. The findings of this study provide an insight into the design of learning processes for virtual assistants in smart home ecosystems and the effect of the individual characteristics on the users’ preferences for interactions with SHAs.
Blockchain-Based Secure Evidence-Management Police Assistance System

The distributed and decentralized property of Blockchain technologies is very much suitable for those applications where integrity, confidentiality and proper tracking of operations is at highest priority. In this chapter, a Blockchain-based secure evidence management system for tracking police complaints and forensic reports is proposed. The proposed system provides security and privacy preservation of important documents used as evidence in the police system. The proposed scheme provides a Ethereum-based DApp to track a police complaint starting from FIR to when the complaint gets resolved, and also to provide security to forensic reports as security, confidentiality and integrity are at the highest priority. The proposed application supports forensic investigation with authenticity, immutability, traceability, resilience and distributed trust between evidential entitles. Details of tracking police complaints, further investigation, forensic report preservation and analysis will be recorded in chains of block. The proposed system is validated through various experimental results for security, privacy and sensitivity of important documents used as evidence by a police department.
Prediction of Breast Cancer using Machine Learning Algorithm's

Cancer in whole have become a new normal in the ‘disease’ world and es pecially in this growing generation. Many are contributing to the risk phenomenon such as dietary conditions. Lifestyle too plays a major role here because many regret to do, eat or make something of a good will. Almost no one is surveying these factors and these have led to a rapid growth on this tally for he past 20 years, or more. In the varied population of the Americas and to a wider as pect, this has become an inevitable circums tance. In this case, female aging 40 and above are more prone to two inexorable circumstances being Urinary Tract Infections In hand and Breast Cancer in the other. This has become a frequently researched and scary topic among not only the physicians and researchers but with the youth population too. Till day there is not even a slightest cure to the deadliest disease among them all. As from the earlier times, Inhibiting is is better than cure. this still fits to these days among us all. There are many kinds of tests and therapies to treat almost every time of cancers but till this day, a cure is the biggest question. This has been the case since its inception. Awareness is being created in the form of printing warning signs on the front of cigarette packets, chewing gums etc., but they must be mandatorily imposed upon the people to create a widespread impact. Breast cancer like all other cancer counterparts has no cure but to remove the infected or whole of the infected organ or gland. Early detection is the principal way of escaping almost any disease not only cancer. Here in this paper Detection of breast cancer is easily elaborated to ease up the process before going professionally to get a small view on the prediction of the disease. The need to detect this disease earlier has been of course a growing concern among the people of every nation. This Breast Cancer Prediction system is mainly aimed at predicting the accuracy on how furious the cancer have spread or how not at all. This code describes if the patient have cancer or not at all using the given input, predicting the accuracy.
Ship target detection based on adverse meteorological conditions

ship target detection is significant for the marine economy and safe driving of marine autonomous systems. However, recent ship target detection is hardly suitable to adverse meteorological conditions. Different weather conditions will disturb the clarity of taken pictures and hurt the performance of the ship detection. Consequently, accurately classifying and locating the ship target is a big challenge. To address the problems, we propose a ship detection system that contains an adaptive weather classification algorithm, adaptive enhancement algorithm, and RetinaNet. Specifically, we construct a classification network for distinguishing fog, low illumination, and clear pictures. Then, the adaptive enhancement algorithm is proposed to eliminate meteorological interference and restore clear pictures. Finally, the RetinaNet network is used to detect the ship targets with fine processed pictures. Extensive experiments show that our ship system consistently exceeds the strong baseline and improves the performance for ship detection based on adverse meteorological conditions.
Immersive Storytelling in Creative Media, Smart Manufacturing and Healthcare

The paper explores interactive storytelling in Augmented and Virtual Reality. It is based on the project ANONYMIZED that bridges the gap between technological developments and their application and aims to combine art, economy and science. Five use cases employ an interdisciplinary research design in the areas of Creative Media, Smart Manufacturing and Healthcare. They all develop prototypical applications but also attend to their contextualization with focus on narrative elements: Each use case defines a ”story” in its own context, the purpose of its narration, and its media specificity. Due to their increased availability, virtual and immersive technologies have not only led to a resurgence of their popularity but also to intriguing new perspectives regarding their use in different contexts. Our research project employs state-of-the art AR and VR technology in five use cases about VR Interaction, Motion Tracking in Artistic VR, Auditory AR for Industry, AR Interfaces for Industry and AR Education for Patients. All of them inquire prototypical applications but relate to the central concept of interactive storytelling. The project addresses three distinct dimensions of storytelling in each use case: the production of a coherent story, the collection and transfer of knowledge, and the specificity of the used technology. Those dimensions are widely known and employed. However, they are seldomly intertwined in one interdisciplinary project focusing on immersive media. Furthermore, the notions of interactivity and immersion play significant roles in the study of storytelling in conjunction with VR an AR. Following papers about immersive storytelling are significant: John Bucher (2017) e. g. examines the timeless principles of storytelling and how they are applied, transformed, and transcended in VR. Marie-Laure Ryan (2015) combines the concepts of immersion and interactive storytelling into a Poetics of Immersion including spatial, temporal and emotional categories. She highlights underlying interactive structures such as the Complete Graph, the Network, the Flowchart. Moya Baldry (2018) discusses the role of the author in complex narratives of immersive environments. Covering the areas of Creative Media, Smart Manufacturing and Healthcare leads to a very specific research design, as each area is connected to different research contexts. Therefore, the paper focuses on the above-mentioned dimensions of storytelling and addresses the following questions: How is the “story” defined in each context and what is its purpose? Which methods and technologies are used to convey it? Which narrative elements show potential and how can they be developed further for future applications? The paper reflects on the outcome of the use cases and potential future applications of interactive storytelling in immersive media.
Research on obstacle detection algorithm based on YOLOX

Aiming at the problem that the computing power of current computing equipment cannot meet the needs of emerging algorithms, an improved YOLOX obstacle detection method is proposed. This algorithm adds an attention mechanism to the YOLOX detection algorithm based on the original algorithm, which reduces the computing scale of hardware devices and achieves high speed and high accuracy for obstacle recognition. By improving the feature extraction method, the detection effect of the target in the video is improved. The experimental results show that the detection accuracy of the improved algorithm is increased from the original 89.47% to 91.21%, and the mAP (mean Average Precision) index is increased from the original 83.4% to 84.1%. The improved result is better than the original algorithm.
Scene flow estimation based on Light Field

Light field camera can sample multiple views of the same scene with a single exposure, which has unique advantages in dynamic scene analysis. Existing scene flow estimation algorithms ignore the processing of image noise in natural scenes. To solve this problem, a scene estimation algorithm is designed using the light field focal stack. The bidirectional defocusing in the light field focal stack selects the optimal depth estimation cost and integrates noise processing into the depth estimation. The depth map is applied to the optical flow estimation through full focusing, so that the light rays in all directions of the spatial point are gathered into their respective angular domains, and the complete structural information and direction information of the spatial point can be obtained. When the spatial pixel points are matched, the constraints of the light in different directions of the spatial points are added, which greatly improves the matching accuracy of the algorithm. The anti-noise cost of depth estimation is diffused into the scene flow through all-focus, so that the scene flow algorithm has strong anti-noise performance. The experimental results show that the proposed method has lower sensitivity to noise.
Major traumatic injury sustained during use of a virtual reality (VR) headset

We report the first documented case of significant, potentially life-changing trauma sustained during use of virtual reality (VR). A male in his fifties fell from standing whilst immersed in a VR game and sustained injuries including bilateral occipital condyle fractures, spinal cord contusion resulting in central cord syndrome, right vertebral artery occlusion, and left hypoglossal nerve palsy. His injuries were managed conservatively with a lengthy recovery period. The patient made a good recovery with full resolution of his symptoms and has returned to full-time employment. This case discusses how these injuries are commonly related and considers the implications of VR on a person’s balance and whether the headset may make neck extension injuries more likely.
Triple Steps for Verifying Chemical Reaction Based on Deep Whale Optimization Algorithm (VCR-WOA)

An Intelligent computing has become a basis in all life facilities and is applied in almost all sciences due to accurate results it provides and the reduction in human effort and time, Computer operations have become the basis for investigating the accuracy of the results that are reached, and this does not diminish the importance of the human effort exerted in the laboratories to conduct experiments, select materials, and appropriate working conditions, and on the basis of the principle that the advancement of any nation starts from the accuracy of its products and the exploitation of the capabilities and resources available with the least human effort and the most appropriate cost. This chapter deals with building an optimization model for accurately predicting chemical reactions, since obtaining sensitive data in this field, necessary for training and testing various algorithms, is a challenge. To ensure accuracy and reliability of the source data, we have entered the data ourselves, which are accurate and weighted equations from the sixth-grade textbook. The proposed model consists of five basic stages. The first stage is a process of collecting and preparing data so that it is suitable for the decision-making stage, including data collection that included 100 chemical chains, each chain representing a correct and weighted chemical reaction. The second step is Tokenization that included the process of dividing each chain into segments of different sizes ؤ Token = 1, 2, 3, 4, and 5 according to the periodic table, and storing results in five buffers. The third step included coding those tokens based on their sequence in the periodic table and the buffer number that contained them. The final step included dividing the data into training data (60% of the data volume) and test data (the remaining 40%). The second stage included the process of searching for the best number of pivots by applying one of the selection algorithms called deterministic selection algorithm, as this algorithm depends on the principle of division, arrangement, and selection until the best pivots are found. The third stage involved implementing one of the optimization algorithms called Whale after determining the number of search agents from which to start searching based on the previous step (number of search agent = number of pivots), in order to reduce the search time for predictive optimal chemical reaction outputs. The fourth stage involved the verification of the accuracy of the results of the proposed model named VCR-WOA based on five parameters of the collision matrix, namely accuracy, recall, and precision F and Fb. The results were promising for the training data despite the accuracy of the data and acceptable for the test data. The proposed model was able to reduce the number of calculations and the time required to implement the optimization algorithm compared to the traditional methods that start from random number of search agents.
Application and Research of Image Denoising for Oil Field Security Monitoring

Intelligent security monitoring is often used to detect all kinds of irregularities in oil field operation. Because of the influence of the environment, the pictures captured by digital devices contain noise, which degrades the quality and affects the subsequent analysis. Therefore, the research of image denoising has strong theoretical significance and application value. In this paper, the Restormer denoising network and HINET denoising network are compared and analyzed respectively, the implementation process and application of these two neural networks are briefly described, and the advantages and disadvantages of these two networks in image denoising are compared.
Crowd detection and estimation for an earthquake early warning system using deep learning

Earthquakes, and their cascading threats to economic and social sustainability, are a common problem between China and Chile. In such emergencies, automatic image recognition systems have become critical tools for preventing and reducing civilian casualties. Human crowd detection and estimation are fundamental for automatic recognition under life-threatening natural disasters. However, detecting and estimating crowds in scenes is nontrivial due to occlusion, complex behaviors, posture changes, and camera angles, among other issues. This paper presents the first steps in developing an intelligent Earthquake Early Warning System (EEWS) between China and Chile. The EEWS exploits the ability of deep learning architectures to properly model different spatial scales of people and the varying degrees of crowd densities. We propose an autoencoder architecture for crowd detection and estimation because it creates compressed representations for the original crowd input images in its latent space. The proposed architecture considers two cascaded autoencoders. The first performs reconstructive masking of the input images, while the second generates Focal Inverse Distance Transform (FIDT) maps. Thus, the cascaded autoencoders improve the ability of the network to locate people and crowds, thereby generating high-quality crowd maps and more reliable count estimates.
Anchor Prediction: A Topic Modeling Approach

Networks of documents connected by hyperlinks, such as Wikipedia, are ubiquitous. Hyperlinks are inserted by the authors to enrich the text and facilitate the navigation through the network. However, authors tend to insert only a fraction of the relevant hyperlinks, mainly because this is a time consuming task. In this paper we address an annotation, which we refer to as anchor prediction. Even though it is conceptually close to link prediction or entity linking, it is a different task that require developing a specific method to solve it. Given a source document and a target document, this task consists in automatically identifying anchors in the source document, i.e words or terms that should carry a hyperlink pointing towards the target document. We propose a contextualized relational topic model, CRTM, that models directed links between documents as a function of the local context of the anchor in the source document and the whole content of the target document. The model can be used to predict anchors in a source document, given the target document, without relying on a dictionary of previously seen mention or title, nor any external knowledge graph. Authors can benefit from CRTM, by letting it automatically suggest hyperlinks, given a new document and the set of target document to connect to. It can also benefit to readers, by dynamically inserting hyperlinks between the documents they’re reading. Experiments conducted on several Wikipedia corpora (in English, Italian and German) highlight the practical usefulness of anchor prediction and demonstrate the relevancy of our approach.
Collision Cone Based Collision Avoidance for Hexa-Rotors

This paper presents a novel collision cone based collision avoidance algorithm for hexa-rotors. The work conducted shows a speed based and a heading based collision avoidance method that could be implemented with minimal computational burden and minimal sensing. The dynamics of the hexa-rotor is considered and a complete dynamic model is presented. The validity and the applicability of the work is confirmed by both simulations and real-life experiments.
“See with the app's eyes what your real eyes cannot see”: design potentials for Mobile Augmented Reality at home

Immersive technologies such as Augmented Reality afford to enrich the physical environment with a layer of digital information, providing new experiences that can support and improve the daily life of people in their homes. We present insights from a field study with nine families to understand user experience and expectations of Mobile Augmented Reality at home and reveal emerging designs. We deployed a mobile application that provided a contextual representation of the WiFi signal through visualizations integrated in the physical space. Each family lived with the prototype for one week, with a pre- and post-study interview session. We found that Augmented Reality has potential to (1) foster people's autonomy and control (e.g., configuration of technology and problem solving), as well as (2) enhance people's knowledge and awareness about domestic technology (e.g., understanding technical terms and awareness of invisible infrastructures).