Reviewerly/exHarmony huggingface dataset:
Cooperatives and the Use of Artificial Intelligence: A Critical View

Digital Transformation (DT) has become an important issue for organisations. It is proven that DT fuels Digital Innovation in organisations. It is well-known that technologies and practices such as distributed ledger technologies, open source, analytics, big data, and artificial intelligence (AI) enhance DT. Among those technologies, AI provides tools to support decision-making and automatically decide. Cooperatives are organisations with a mutualistic scope and are characterised by having participatory cooperative governance due to the principle of democratic control by the members. In a context where DT is here to stay, where the dematerialisation of processes can bring significant advantages to any organisation, this article presents a critical reflection on the dangers of using AI technologies in cooperatives. We base this reflection on the Portuguese cooperative code. We emphasise that this code is not very different from the ones of other countries worldwide as they are all based on the Statement of Cooperative Identity defined by the International Cooperative Alliance. We understand that we cannot stop the entry of AI technologies into the cooperatives. Therefore, we present a framework for using AI technologies in cooperatives to avoid damaging the principles and values of this type of organisations.
A Causality-Inspired Approach for Anomaly Detection in a Water Treatment Testbed

Critical infrastructure, such as water treatment facilities, largely relies on the effective functioning of industrial control systems (ICSs). Due to the wide adoption of high-speed network and digital infrastructure technologies, these systems are now highly interconnected not only to corporate networks but also to the public Internet, mostly for remote control and monitoring purposes. Sophisticated cyber-attacks may take advantage the increased interconnectedness or other security gaps of an ICS and infiltrate the system with devastating consequences to the economy, national security, and even human life. Due to the paramount importance of detecting and isolating these attacks, we propose an unsupervised anomaly detection approach that employs causal inference to construct a robust anomaly score in two phases. First, minimal domain knowledge via causal models helps identify critical interdependencies in the system, while univariate models contribute to individually learn the normal behavior of the system's components. In the final phase, we employ the extreme studentized deviate (ESD) on the computed score to detect attacks and to exclude any irrelevant sensor signals. Our approach is validated on the widely used Secure Water Treatment (SWaT) benchmark, and it exhibits the highest F1 score with zero false alarms, which is extremely important for real-world deployment.
Towards Optimal Planning for Green, Smart, and Semantically Enriched Cultural Tours

This concept paper presents our viewpoint regarding the exploitation of cutting-edge technologies for the delivery of smart tourism cultural tours. Specifically, the paper reports preliminary work on the design of a novel smart tourism solution tailored to a multiobjective optimization system based on factors such as the preferences and constraints of the tourist/visitor, the city’s accessibility and traffic, the weather conditions, and others. By optimizing cultural tours and delivering comfortable, easy-to-follow, green, acceptable visiting experiences, the proposed solution, namely, OptiTours, aims to become a leading actor in tourism industry transformation. Moreover, specific actions, applications, and methodologies target increasing touring acceptance while advancing the overall (smart) city impression. OptiTours aims to deliver a novel system to attract visitors and guide them to enjoy a city’s possible points of interest, achieving high visitor acceptance. Advanced technologies in semantic trajectories’ management and optimization in route planning will be exploited towards the discovery of optimal, smart, green, and comfortable routes/tours. A novel multiscale and multifactor optimization system aims to deliver not only optimal personalized routes but also alternative routes, ranked based on visitors’ preferences and constraints. In this concept paper, we contribute a detailed description of the OptiTours approach for ICT-based smart tourism, and a high-level architectural design of the solution that is planned to be implemented in the near future.
Information Security Awareness Among Postgraduate Students

Information security prevents unauthorized access to using, disclosing, altering, inspecting, recording, or destroying information. Information security programs are designed to achieve three main goals: confidentiality, integrity, and availability of information. The study highlights awareness and perceptions of information security among postgraduate students. To make everyone aware of the opportunities and difficulties that exist in today's threat landscape, modify risk behaviour, and develop or strengthen a secure corporate culture, information security awareness training is required to be effective. This paper aims to assess information security awareness among Postgraduates of Mangalore University. A survey was conducted, and appropriate tools and technologies were used to collect and interpret the data. Based on the results, suggestions for information security among postgraduate students were made.
Complete hierarchy of linear systems for certifying quantum entanglement of subspaces

We introduce a hierarchy of linear systems for showing that a given subspace of pure quantum states is entangled (i.e., contains no product states). This hierarchy outperforms known methods already at the first level, and it is complete in the sense that every entangled subspace is shown to be so at some finite level of the hierarchy. It generalizes straightforwardly to the case of higher Schmidt rank, as well as the multipartite cases of completely and genuinely entangled subspaces. These hierarchies work extremely well in practice even in very large quantum systems, as they can be implemented via elementary linear algebra techniques rather than the semidefinite programming techniques that are required by previously-known hierarchies.
ExplAInable Pixels: Investigating One-Pixel Attacks on Deep Learning Models with Explainable Visualizations

Nowadays, deep learning models enable numerous safety-critical applications, such as biometric authentication, medical diagnosis support, and self-driving cars. However, previous studies have frequently demonstrated that these models are attackable through slight modifications of their inputs, so-called adversarial attacks. Hence, researchers proposed investigating examples of these attacks with explainable artificial intelligence to understand them better. In this line, we developed an expert tool to explore adversarial attacks and defenses against them. To demonstrate the capabilities of our visualization tool, we worked with the publicly available CIFAR-10 dataset and generated one-pixel attacks. After that, we conducted an online evaluation with 16 experts. We found that our tool is usable and practical, providing evidence that it can support understanding, explaining, and preventing adversarial examples.
“I Can Feel It in My Hand”: Exploring Design Opportunities for Tangible Interfaces to Manipulate Artefacts in AR

This paper shows how generic proxies as tangible interfaces can provide an intuitive and realistic interaction method to manipulate artefacts in AR. The domain we selected was cultural heritage, specifically the interaction with historical artefacts. We explored four interfaces: 1) a standard touch screen using a web 3D viewer, 2) a traditional flat AR marker, 3) a generic wooden cylinder, and 4) a 3D-printed replica of the digital artefact. Our results show that a 3D-printed replica represents the most realistic interaction method to manipulate artefacts in AR. However, we further found that using a generic object such as the cylinder can deliver a more immersive experience, offering an advantage over the standard touch screen and flat AR marker. As high-fidelity physical objects can be costly to produce. We argue that a more generic physical proxy is a feasible middle ground when 3D-printed replicas are not viable. We then discuss design opportunities for using generic objects to explore different artefacts in AR.
Local Path Planning for Unmanned Surface Vehicles based on Hybrid A* and B-spline

Unmanned Surface Vehicles (USVs) have witnessed a vigorous growth in the past decades and have been applied in various applications in both commercial and military domains. Central to the control of USVs, local path planning is one of the crucial technologies in the process towards autonomy. This paper investigates the application of a novel path planning algorithm in combination with modified Hybrid A* and several path manipulators. First, the heuristic strategy and node expansion method of standard Hybrid A* have been adapted for the algorithm. Second, node cutting technique and B-spline path smoother are applied to enhance the path quality. Simulations have been carried out to illustrate the excellent performance of the proposed method comparing to several state-of-the-art methods. Furthermore, the new algorithm is tested in the USV model, and the results have demonstrated that it can perfectly coordinate with the USV control system. Therefore, the proposed algorithm can be considered as a reliable method dealing with local path planning problem for USV.
Compensation Method of Robotic Arm Positioning Error Under Extreme Cold and Large Temperature Difference Based on BP Neural Network

Aiming at the problem that the robotic arm is deformed by temperature and affects the positioning accuracy in the extremely cold and large temperature difference environment, a positioning compensation method of the robotic arm based on BP neural network is proposed. Taking the robotic arm UArm Swift Pro as the research object, the thermal deformation of the robotic arm in the extremely cold and large temperature difference environment is analyzed first, which shows that the temperature has a great influence on the positioning accuracy of the robotic arm, and the necessity of error compensation is proved; The kinematic model of the robotic arm is established by the D-H method; the positioning error compensation model based on the BP neural network algorithm is established through experiments, and the error comparison curve before and after compensation is obtained. The results demonstrate the effectiveness and feasibility of the positioning error compensation method of the robotic arm.
Multi-UAVs Trajectory Planning Method with Coordinated Attack Angle-Time Constraints

Multi unmanned aerial vehicles (multi-UAVs) coordinated ground attack is an important part of regional sealing and suppression tasks. This paper takes this task as the background to carry out research on multi-UAVs cooperative trajectory planning. Based on this, a multi-UAVs trajectory planning method considering cooperative attack angle and time constraints is proposed. First, the problem is described as a directed graph based on graph theory, the UAV is equivalent to the Dubins Car model, and the constraint model of trajectory avoidance, obstacle avoidance, attack time, and attack angle is given. The total track length is taken as the optimization objective, and the genetic algorithm is designed to solve the problem. The simulation results show that the genetic algorithm can solve the problem, and the designed method has good engineering application value.
Deep Learning Methods for Objective Detection

Object detection is one of the most fundamental computer vision tasks that needs further research. With the rapid evolution of the Deep Neural Networks(DNN) model, Object detection has become a heated discussion among people and thus becoming the research hotspot. However, the research on object detection has made significant progress, with few reviews on this task. As a result, the goal of our work is to provide a comprehensive overview of recent achievements. This paper introduces many aspects of objection detection: benchmarks, metrics, recent research, and neural network models with the help of the research contributions in this field. We also focus on new technologies, so we mainly write two relatively new neural network models called Graph Convolutional Networks(GCN) and Graph Attention Networks(GAT). We finish the survey by listing some long-standing challenges for follow-up research and identifying intriguing future research directions.
An Adaptive Local Path Planning Algorithm for Multi-robot Systems

An improved dynamic window method (DWA) is proposed for the path planning of multi-robot systems for rescue in harsh environments. For a single robot unit, a cost function based on sensors' perception is constructed and added to the evaluation function to upgrade the performance of path evaluation. To develop the self-adaptability of the algorithm, fuzzy control is introduced to fine-tune the weights of the evaluation function to obtain effective path in different environments. For the multi-robot system, to enhance the rescue efficiency, the path reuse method is employed to reduce the planning time when the paths of multiple robots overlap. Finally, the proposed algorithm has been tested in indoor and corridor simulation environments to verify its feasibility and effectiveness in obstacle environments.
Key Information Extraction from Mobile-Captured Vietnamese Receipt Images using Graph Neural Networks Approach

Information extraction and retrieval are growing fields that have a significant role in document parser and analysis systems. Researches and applications developed in recent years show the numerous difficulties and obstacles in extracting key information from documents. Thanks to the raising of graph theory and deep learning, graph representation and graph learning have been widely applied in information extraction to obtain more exact results. In this paper, we propose a solution upon graph neural networks (GNN) for key information extraction (KIE) that aims to extract the key information from mobile-captured Vietnamese receipt images. Firstly, the images are pre-processed using U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net, and then a CRAFT model is used to detect texts from the pre-processed images. Next, the implemented TransformerOCR model is employed for text recognition. Finally, a GNN-based model is designed to extract the key information based on the recognized texts. For validating the effectiveness of the proposed solution, the publicly available dataset released from the Mobile-Captured Receipt Recognition (MC-OCR) Challenge 2021 is used to train and evaluate. The experimental results indicate that our proposed solution achieves a character error rate (CER) score of 0.25 on the private test set, which is more comparable with all reported solutions in the MC-OCR Challenge 2021 as mentioned in the literature. For reproducing and knowledge-sharing purposes, our implementation of the proposed solution is publicly available at https://github.com/ThorPhamlKey_infomation_extraction.
Method of Obtaining OD Matrix for Bus Passenger Flow Based on Pedestrian Re-identification Algorithm

In public transport, the OD (origin-destination) matrix of each passenger's starting and ending points is of great importance for bus scheduling and network optimization. The difficulty is matching passengers getting off at the back door with those getting on at the front door in real-time. The leading technology used here is re-identification. The current re-identification method is mainly used in the security field of pedestrian re-identification, vehicle filling discrimination, and although the accuracy is high, the real-time performance is poor. In this paper, the classical method FastReID is improved by changing its attention module using GCNet to improve accuracy and by adding a fully connected layer to the BNNeck structure to classify other essential attributes of pedestrians while calculating the distance between the same passenger attributes; in the loss function, constraints are applied according to the distance between this attributes to reduce the intra-class distance. Finally, passenger features are stored in categories by attributes to improve the final retrieval efficiency. Experiments show that the method in this paper improves accuracy by 3.2 % and detection speed by 14.73% (69.58ms) per 100 retrievals compared to FastReID.
Implementation of Data Analysis and Document Summarization in Social Media Data Using R and Python

In today's internet environment, people mostly use smartphones, laptops, and other peripheral devices as communication devices to connect with people. The emerging different uses of social media platforms also necessitates a significant research attention in this field. Analysis of data from various social media resources, analysis of each user's activity, analysis of social media post content, and analysis of social media discussion message are recently gaining a significant research interest. These statistical analysis were performed based on the summary data and it can be classified as positive and negative. Python and R are two programming languages that may be used to perform this study. R is a programming language primarily created for performing data analysis. R is the key to unlock the door between the data challenges that needs to be solved and the answers required to reach the solution. A new strategy based on well-known hierarchical Bayesian title model is introduced in this study. Bayesians argue that the relevant information regarding decision-making and updating beliefs cannot be ignored and that hierarchical modeling has the potential to overrule classical methods in applications, where the respondents provide multiple observational data. The suggested contextual topic model will effectively detect the sentiments present in the articles that have been shared widely on social media. It might be helpful to identify any prohibited or violent content and then the data will be sent to the cloud for performing an additional review. The proposed model outperforms HLDA and LDA in document modelling, according to the quantitative assessment findings. It might be used to track out who sends how many messages and forwards a significant number of them. Furthermore, a practical example shows the implementation of the proposed model in a summary system and how it greatly increases the summarizing performance and bring it up to pace with current-generation summarizing systems. Finally, Exploratory Data Analysis (EDA) is a statistical method or approach for reviewing data sets to explain its important and major qualities utilizing visual aids to illustrate the categorised data from the dataset to be studied using R programming. Basic data pre-processing steps like null value imputation and removal of unwanted data.
Reliable Data Storage and Sharing using Block chain Technology and Two Fish Encryption

Digital data that has been certified by a respected institution is valuable and can be saved or transmitted over the internet. However, the issues are ensuring the security and reliability of stored and shared data, as well as ensuring a secure, transparent, and fair data sharing process. As a result, The schemas for data production, storage, and sharing is suggested. A group signature scheme in the data production schema for a collection of respected organisations that provide similar services is used. A member of the group develops precious digital data from RD (raw data) provided by a DO (data owner) and then provides a certification based on the encipher of that data. The system saves the data's access address in the database after data owner uploading his or her data to the system in the data storage schema. Before submitting a data sharing proposal to the DO, every person on the system might validate the quality of shared data. The data sharing mechanism is carried out via a smart contract to incentivize honesty. The schemas assure the security properties of data storage and exchange, which include confidentiality, integrity, privacy, and non-repudiation.
Research and Application of Pointer Meter Reading Algorithm Based on Attention U<sup>2</sup>-Net

As one of the most common measuring instruments, the pointer-type analog meter is widely used in factories and substations because of its simple structure and high stability. However the mechanical structure makes it almost impossible to provide digital interfaces directly, thus the most common way of getting reading is still recording by individual, but this could cause high labor costs and lack of reliability. To address this issue, we proposed a compact attention based salient object detection model, which combined the U <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Net with a spatial attention module to make the model pay more attention to the pointer and scale area. And the model has been trained and tested on the collected pointer meter datasets from a power substations. In addition, the model has been deployed on the AIoT device to achieve a set of high accuracy and robust remote pointer meter reading system. The test on real scenarios proves the accuracy and robustness of our system.
Sentiment Analysis on Online Education During Covid Pandemic

“Sentiment Analysis on Online Education during Covid Pandemic” is the title of this research. This is the most common method for determining and classifying people's view on any services or products. It is the process to determine if a specific piece of information is positive, negative, or neutral. During COVID-19 pandemic lockdowns, educational institutions close, resulting in online schooling. The impact of online education is explored in this research by studying people's reactions to it. Such feedbacks are collected and analyzed by using Lexicon Approach includes (Text Blob, Vader Analysis, and Senti Word Net) to get a better-analyzed result.
Image Pre-Processing based Sign Language Recognition using Convolutional Neural Network

The domain of the project is Image pre-processing. One of the most commonly used ways of expressing oneself to others is communication. Communication is a virtue of Life. Still a correspondence between a visually challenged individual and a Hearing challenged and Speaking challenged individual is a nightmare. A Visually challenged person can communicate to normal people easily by speech, whereas the Hearing challenged and speaking challenged people can communicate using Sign language. But the communication among them is not possible in this advancing technology. Visually challenged person cannot understand the Sign language, which Hearing challenged and Speaking challenged people use. And Hearing challenged and speaking challenged people cannot hear what visually challenged person says. Hence, we propose a system for communication between a visually challenged, Hearing challenged and Speaking challenged people using HSV (Hue Saturation Value). Herein we use a Sign language to detect what Hearing challenged and speaking challenged people say to visually challenged person and vice versa. To begin with, we make a classifier model utilizing the signs utilizing the Keras execution of convolutional neural organization utilizing python. Then, at that point, we measure another continuous framework which utilized skin division to discover the Region of Interest in the casing which shows the jumping box. The classifier observed to be improving with various foundations and hence the point of the picture caught.