Reviewerly/exHarmony huggingface dataset:
Multi-agent Pathfinding with Local and Global Guidance

Multi-agent path finding (MAPF) exists in many practical applications, such as intelligent warehouses. In this type of scenario, the agents need to cooperate with each other and eventually reach the target point without collision. The existing multi-agent path planning algorithms are mainly centralized algorithms, such as Conflict-Based Search (CBS). However, this kind of approach is difficult to solve the problem in real time and its scalability is poor. In this work, we focus on solving MAPF problem in intelligent warehouse. To address this problem, instead of using time-consuming search based algorithms, we propose a novel decentralized multi-agent pathfinding method based on deep reinforcement learning. Combined with curriculum learning, the algorithm uses local and global guidance mechanisms to help agents plan feasible paths. As a result, the success rate of the algorithm has been significantly improved. Experimental results show that our algorithm generalizes well and it still performs well when the scale of problem increases. The solution efficiency is close to the centralized algorithms.
Cyber-Risk Management Planning Using NIST CSF v1.1, NIST SP 800-53 Rev. 5, and CIS Controls v8

With the use of information systems as a means for supporting the success of missions and objectives of organizations increased, the protection towards assets against cyber risks needs to be considered and paid more attention. Cyber-risk management planning can be carried out as a means or approach to protect assets from the risks of cyber-attacks. As a supporting unit in XYZ, the IT Unit has the responsibility to manage the information systems, information technology, and their infrastructure and services within the XYZ system. However, the IT Unit has never conducted a cybersecurity evaluation so it does not yet have a plan for cybersecurity risk management. In this study, we tailored a cyber-risk plan for the IT Unit of XYZ using NIST CSF as the main framework and CIS Controls v8 and NIST SP 800-53 Rev 5 for defining controls and action recommendations. As the results, we found 42 risk scenarios in the IT Unit in which 12 are accepted and 30 are mitigated. There are 14 actions recommendation for the IT Unit to reach tier 3 based on 18 controls of CIS and 20 controls of NIST SP 800-53 rev 5 that can be applied to control the current cyber-risk.
Blockchain Interoperability Issues in Supply Chain: Exploration of Mass Adoption Procedures

Today's supply chains are becoming more data-driven with the impact of big data, but there are many challenges that need to be overcome in big data for better service operations management in supply chains. Blockchain has the great potential to improve big data services and applications with its decentralisation and security nature. However, blockchain interoperability is critical to realising more value creation in blockchain networks and achieving promising results for global supply chains that intersect with multiple business ecosystems and blockchain platforms. In addition, it is unclear how to address interoperability issues for mass adoption. In this chapter, a three-step approach is applied to analyse blockchain interoperability in supply chains for mass adoption. First, a literature review is conducted to explain blockchain technology and the widely used methodologies for blockchain interoperability in supply chains. Then, four real-case blockchain use cases in supply chains from different industry segments are analysed in terms of their technical capabilities addressing interoperability concerns. Finally, we discuss results of use case analysis based on the comments of interviewees. The analysis reveals that REST-APIs with a common interface and GS1 standards are very useful to integrate with blockchain applications in supply chains for mass adoption.
Visualization as infrastructure: China’s data visualization politics during COVID-19 and their implications for public health emergencies

In this article, we analyze the rise of data visualization in social and political contexts. Against the background of the COVID-19 pandemic, we consider a case in Shenzhen, China, and demonstrate the impact of visualization as intermediation for data on policies and society. We propose an epistemology of visualization as infrastructure. In China, visualization has been developed as a technological system to support information dissemination and collective action in public crises. Three characteristics are proposed to describe China’s data visualization politics during the pandemic, in particular, to summarize the power relationships between visual brokers, policymakers, and public users in data visualization. Further, based on the conceptualization of the links between visualization, infrastructure, and data politics, visualization serves as an important component of information infrastructure, and is an easily overlooked technical process. The creation and use of data may seem like normalized actions, but data visualization in fact supports the perception, participation, proposal, and critique of data politics issues. Via a focus group discussion with members of the Shenzhen public health sector and in-depth interviews with Shenzhen residents, we developed a qualitative effectiveness measurement framework highlighting the potential for the involvement of visualization in politics and activism.
PyMTDEvaluator: A Tool for Time-Based Moving Target Defense Evaluation: Tool description paper

This paper presents PyMTDEvaluator, a tool for evaluating the effectiveness of time-based Moving Target Defense (MTD) against availability attacks (e.g., Denial of Service - DoS, resource starvation attacks). PyMTDEvaluator is based on simulation runs of an extended deterministic Stochastic Petri Net (SPN) and offers a user-friendly interface where it is possible to analyze and compare MTD policies with different parameters. The SPN design relies on knowledge obtained from empirical observation. PyMTDEvaluator provides results such as probability of attack success, availability, and system capacity to support MTD design decision making. The tool allows analyzing and comparing several scenarios in the same evaluation, thus enabling the study of the pros and cons of different MTD deployment alternatives. PyMTDEvaluator aims to be part of the toolset for MTD policies design. It is also valuable for sensitivity analysis of MTD-enabled system parameters.
Trends in Cybersecurity Management Issues Related to Human Behaviour and Machine Learning

The number of organisational cybersecurity threats continues to increase every year as technology advances. All too often, organisations assume that implementing systems security measures like firewalls and anti-virus software will eradicate cyber threats. However, even the most robust security systems are vulnerable to threats. As advanced as machine learning cybersecurity technology is becoming, it cannot be solely relied upon to solve cyber threats. There are other forces that contribute to these threats that are many-a-times out of an organisation&#x0027;s control i.e., human behaviour. This research article aims to create an understanding of the trends in key cybersecurity management issues that have developed in the past five years in relation to human behaviour and machine learning. The methodology adopted to guide the synthesis of this review was a systematic literature review. The guidelines for conducting the review are presented in the review approach. The key cybersecurity management issues highlighted by the research includes risky security behaviours demonstrated by employees, social engineering, the current limitations present in machine learning insider threat detection, machine learning enhanced cyber threats, and the underinvestment challenges faced in the cybersecurity domain.
Experimental Optical Setup to Measure Power Loss versus Fiber Bent Radius for Tapping into Optical Fiber Communication Links

Optical fiber communication was widely believed that an optical network was a good way to prevent hacking through wiretaps which intercept network data. However, fiber optic cable is not particularly secure. Various tapping methods can be used to extract data from fiber optic networks with little or no risk of detection. One of the easiest ways of intruding into a fiber optic communication link is creating a bend on the fiber cable. Even small percentage of the transmitted light acquired with the bending method is enough for recovering the data by the attacker. Though there are some techniques and devices that can track the power and intensity inside the fiber cable, these methods cannot detect an attack exploiting small leakage. Therefore, it is critical to determine a range of radii of curvature for which tapping attacks are not detectable. Though there are some studies to quantify the power loss both theoretically and numerically, as of the authors&#x0027; knowledge, there has not been any experimental validation of those studies. In this respect, an experimental optical setup and a technique relying on the ADC conversion capability of Arduino UNO to measure power loss resulting from different bending radii are proposed in this paper. A lower limit for radius of curvature for an attack to be undetected is obtained and the estimations in the literature for power loss resulting from fiber bending is validated.
IoTSE-based open database vulnerability inspection in three Baltic countries: ShoBEVODSDT sees you

This study aims to analyze the state of the security of open data databases, i.e. being accessible from the outside of organization, representing both relational databases and NoSQL of three Baltic countries - Latvia, Lithuania, Estonia. This is done by using previously proposed tool for non-intrusive detection of vulnerable data sources called ShoBEVODSDT (Shodan- and Binary Edge-based vulnerable open data sources detection tool). ShoBEVODSDT is based on the use of Internet of Things Search Engines (IoTSE). It is found to be suitable for this study since it conducts the passive assessment, which means that its use does not harm the databases but rather checks for potentially existing bottlenecks or weaknesses which, if the attack would take place, could be exposed. It allows for both comprehensive analysis for all unprotected data sources falling into the list of predefined data sources - MySQL, PostgreSQL, MongoDB, Redis, Elasticsearch, CouchDB, Cassandra and Memcached, or to define IP range to examine what can be seen from the outside of the organization about the data source. Although some data sources can be described as following the security-by-design principle, some of them face serious challenges in this respect. The study carries out cross-country comparative study on 8 data sources. We inspect both, (1) the most vulnerable data sources and (2) countries characterized by the highest number of open data sources and the highest degree of &#x201C;value&#x201D; of data being available to external actors.
An Experimental Study of the Efficacy of Augmented Reality in Chinese Kindergarten-Level Students' Learning of English Vocabulary

The use of augmented reality (AR) within educational settings has seen substantial growth in recent years. The highly immersive, engaging nature of AR offers a novel approach to second-language teaching. This quasi-experimental study investigated the efficacy of using AR in children's second-language acquisition (SLA). 52 kindergarten children participants were divided into experimental and control groups. Results revealed the use of AR to be effective, with those in the AR group exhibiting significantly greater performance on both same-day and 3-day delayed productive recognition tests, and intrinsic motivation whilst learning vocabulary than the control group. Given these findings, the paper speculates on why AR might improve kindergarten students' acquisition of English vocabulary, whilst providing suggestions for future research into the use of AR for SLA by kindergarten students.
Exploration, Sentiment Analysis, Topic Modeling, and Visualization of Moroccan Twitter Data

Twitter is used more and more by Internet users as a means of expressing their opinions. In this research work, we are interested in analyzing the tweets published by Moroccan users to generate some useful statistics, identify different sentiments, and extract then visualize predominant topics. First, we collected 52 939 tweets using Twitter API and python language and stored them in MongoDB database. Stored tweets were preprocessed by applying natural language processing techniques (NLP) using the NLTK library. Then, we performed sentiment analysis, which classifies the polarity of Twitter comments into negative, positive, and neutral categories. Finally, we applied topic modeling over the tweets using Latent Dirichlet Allocation (LDA) and visualize the most discussed topics with LDAvis.
Wikuliner - Regional Culinary Recommendation System Based on The Web Using Extreme Programming Method

The Indonesian economy in the post-pandemic period has the opportunity to grow, especially in the tourism sector. The potential of the tourism sector, especially the culinary industry, is immense. The development of the tourism industry in the culinary field, both small, medium, and prominent in the area wrapped in local wisdom, has increased the surrounding community's economy. The utilization of information technology in supporting the promotion of culinary tourism is needed to accelerate culinary tourism development. This study built a web-based regional culinary tourism selection recommendation application named wikuliner.com using the Extreme Programming method and Skyline Query applied to a simple query in a relational database system based on the user's preferences. The application was developed using the Web Framework - PHP Yii2 Framework and MySQL Database. The web application is solved in two iterations. The first iteration completed five user stories with a team velocity of 25. The second iteration ended with six user stories with a team velocity of 29. Some of the features that have been successfully implemented are authentication, registration, and review of culinary shops, user profile settings, and searching based on user preferences.
Task-agnostic representation learning of multimodal twitter data for downstream applications

Abstract Twitter is a frequent target for machine learning research and applications. Many problems, such as sentiment analysis, image tagging, and location prediction have been studied on Twitter data. Much of the prior work that addresses these problems within the context of Twitter focuses on a subset of the types of data available, e.g. only text, or text and image. However, a tweet can have several additional components, such as the location and the author, that can also provide useful information for machine learning tasks. In this work, we explore the problem of jointly modeling several tweet components in a common embedding space via task-agnostic representation learning, which can then be used to tackle various machine learning applications. To address this problem, we propose a deep neural network framework that combines text, image, and graph representations to learn joint embeddings for 5 tweet components: body, hashtags, images, user, and location. In our experiments, we use a large dataset of tweets to learn a joint embedding model and use it in multiple tasks to evaluate its performance vs. state-of-the-art baselines specific to each task. Our results show that our proposed generic method has similar or superior performance to specialized application-specific approaches, including accuracy of 52.43% vs. 48.88% for location prediction and recall of up to 15.93% vs. 12.12% for hashtag recommendation.
Special Object Detection Based On Mask Rcnn

Mask RCNN is committed to improving the accuracy of target detection. Mask RCNN target detection algorithm belongs to regional convolutional neural network, it can not only detect the target, but also use deep semantic segmentation target. This method makes the location of special objects more accurate and expression more vivid. When it detect the target, will produce a high quality split mask. The mask adds an additional branch to fast RCNN to predict the target. So its detection speed is faster than fast RCNN. Experiment shows that Mask RCNN have high recognition rate and accurate segmentation rate. it can identify and detect the target object effectively.
Improving efficiency in convolutional neural networks with 3D image filters

The effective performance of deep networks has provided the solution to various state-of-the-art problems. Convolutional Neural Network (CNN) is accepted as an accurate, effective, and reliable practice in image-based applications. However, there is a need to use pre-trained models in case of insufficient data in CNN. This study aims to present an alternative solution to this problem with the proposed 3D image-based filter generation approach with simpler CNNs for the classification of small datasets. In this study, a novel 3D image filters-based CNN (Hist3DCNN) is proposed. The proposed filter generation approach is based on 3D object images taken from different perspectives. The efficiency of Hist3DCNN is shown on a novel histological dataset that contains blood, connective, epithelium, muscle, and nerve tissue images. Various case studies are carried out with generated filters assigned as the initial value to AlexNet and the designed Hist3DCNN model that is simpler than AlexNet. Based on results, the classification accuracy of AlexNet with proposed filters used in convolution layers were 84.65% and 85.34%. The accuracy was increased to 85.47% by Hist3DCNN on the histological image classification. Moreover, four different benchmark datasets were tested to demonstrate the robustness of Hist3DCNN on various datasets. This study provides a new aspect to literature due to 3D image-based filter generation approach to initialize convolution filters. Experimental results validate that Hist3DCNN can be used as a filter value initialization method with simple CNN models that contain less learnable parameters for the classification task of small datasets.
Visual-Thermal Fusion-Based Object Tracking via a Granular Computing Backed Particle Filtering

The proposed research aims to enhance the capabilities of information fusion-based object tracking using thermal and visible imaging. An essential motivation behind this technique is to carve out a framework that may utilize many attributes from several sensors. The built-in particle filter is adapted with the granular computing concept for weighing the particles. Furthermore, the algorithm is extended to obtain source-level fusion after the attribute weight adaptation. After comparing with the state-of-the-art methods, the results obtained suggest that it outperforms the compared trackers from the literature.
JomNFC: Zero Effort Intelligent Access System

Multiple unfortunate accidents in hotels ranging from theft to crimes due to unsupervised access. This paper aims to implement a smart hotel access system based on high-security attributes using the NFC Host-Card Emulation application for authentication in hotels to replace the traditional access ways. Analysis of the technologies and market were made to enlarge the knowledge and benefits of the proposed application. A mixed-mode evaluation was introduced based on surveys and interviews in two different geographical areas. The proposed system is an anti-contagious and contactless technology of smartphone applications for a convenient safe visit. The highest positive responses regarding cost-effectiveness were from Le Quadri hotel oppositely Four Seasons Hotel had larger positive responses in regards the contactless technology. Moreover, both locations had the same average of 95&#x0025; positive comments given the competency and trust the application provided for eliminating contagiousness of covid-19.
MQTPP – Towards Multiple Q-Table based Path Planning in UAV Environments

This paper introduces an original multi destination path planning approach for Unmanned Aerial Vehicles (UAVs) named MQTPP (Multi Q-Table Path Planning).MQTPP aims to reduce the computational burden of cyclical/continuous path planning through a Q-learning planning process whilst overcoming the fixed path origin problem.The preliminary performance evaluation results indicate that MQTPP performs well for longer paths, and allows for more efficient re-planning should mission objectives or environmental topography change.
GFO: The General Formal Ontology

The General Formal Ontology (GFO) is a top-level ontology that is being developed at the University of Leipzig since 1999. Besides introducing some of the basic principles of the ontology, we expound axiomatic fragments of its formalization and present ontological models of several use cases. GFO is a top-level ontology that integrates objects and processes into a unified framework, in a way that differs significantly from other ontologies. Another unique selling feature of GFO is its meta-ontological architecture, which includes set theory into ontology and which accounts for its specific role in common representation approaches. The second level of that architecture starts from the distinction of categories and individuals, which forms the backbone of the world’s structure. Furthermore, GFO comprises several kinds of categories, among them universals and concepts, and it considers several ontological regions and levels. In the context of this special issue paper, we study five pre-determined use cases from the perspective of GFO. The results of these analyses yield insights into how the ontology treats several important notions. Very abridged, this covers material objects and their composition; roles and social entities; properties with their relations to objects and processes, and their changing; changes of processes, including a functional perspective; and, eventually, the nature and changing of concepts as well as terminology. A final part summarizes application projects that use GFO in various contexts.
Neural Networks

This chapter is devoted to the presentation of neural-network models in the context of control systems design. It is divided into four parts. The first two parts introduce the reader to the theory of static and dynamic neural network structures. These parts can be treated as a quick review of already developed and well-documented neural network architectures, giving an insight into their properties and the possibility of their application in control theory. The third part is focused on the problem of model design. As the majority of control system designs are model based, developing an accurate model of a plant is of crucial importance, especially for nonlinear systems. Two modelling approaches are discussed: forward and inverse modelling. Moreover, the problem of a training of feed-forward and recurrent neural models is described in the context of parallel and series-parallel identification schemes. The fourth part discusses a very important issue of uncertainty associated with the model. This notion is crucial when dealing with robust and fault-tolerant control. We describe the methods that could be used in estimating the uncertainty associated with neural network models, namely the set-membership identification, model error modelling and statistical approaches.