bluuebunny/crossref_metadata_2025 huggingface dataset:
An Evaluation Model of Credibility Calculation System for Natural Disasters
Authors: Honda Ken  , Yoshida Naofumi
Published: February 2025

In this paper, an evaluation model of credibility calculation system is introduced. This system calculates information credibility by comparing target information with various reliable information resources on World Wide Web. The feature of the method is calculating information credibility by sensor data (earthquake, weather, railway sensors) besides primary sources (government, Ministry, press relation, supporting organization). The information credibility is calculated by degree of objectivity. Therefore, the system integrates high quality data from resources to derive objectivity. This paper presents a model of evaluation for credibility calculation system. The main contribution of this paper is to present evaluation model for credibility calculation system by recall and precision as a degree of false positive and false negative. And this paper shows the feasibility of the model by experiments of recall and precision for credibility calculation system.
Temporalised Belief Revision in the Law
Authors: Tamargo Luciano H.  , Martinez Diego C.  , Rotolo Antonino  , Governatori Guido
Published: February 2025

This paper presents a belief revision operator for legal systems that considers time intervals. This model relates techniques about belief revision formalisms and time intervals with temporalised rules for legal systems. Our goal is to formalise a temporalised belief base and corresponding timed derivation, together with a proper revision operator. This operator may remove rules when needed or adapt intervals of time when contradictory norms are added in the system.
Process-Based Aho-Corasick Failure Function Construction
Authors: Strauss Tinus  , Kourie Derrick G.  , Watson Bruce W.  , Cleophas Loek
Published: February 2025

This case study is embedded in a wider project aimed at investigating process-based software development to better utilise the multiple cores on contemporary hardware platforms. Three alternative process-based architectures for the classical Aho-Corasick failure function construction algorithm are proposed, described in CSP and implemented in Go. Empirical results show that these process-based implementations attain significant speedups over the conventional sequential implementation of the algorithm for significantly-sized data sets. Evidence is also presented to demonstrate that the process-based performances are comparable to the performance of a more conventional concurrent implementation in which the input data is simply partitioned over several concurrent processes.
C++11 CSP
Authors: Chalmers Kevin
Published: February 2025

This fringe presentation focuses on development undertaken on a library to support CSP ideas within the new C++11 standard. The library has been developed to undertake development of a MPI layer for CPA Networking, with C and C++ being simpler languages to build up such a framework. This fringe presentation will generally look at how we can write applications using C++11 CSP.
Concurrency: Quit Obsessing About Your Code and Start Worrying About Your Data!
Authors: Vinter Brian
Published: February 2025

Concurrency is commonly used for coordinating events that are non-deterministic in nature, sensors, transaction systems and games to mention a few. However, in the era of Big Data, concurrency has a whole new dimention to the need for concurrency. With thousands of concurrent operations with highly asynchronous completion times, an efficient concurrency control system is essential for performance. This talk will show the challenge and introduce one idea towards concurrency as a mechanism for Big Data access. Slides used in the presentation can be downloaded from [1].
Distributed Computing on a (Tiny) Budget: Building a Raspberry Pi Zero Microcluster
Authors: Miller Richard
Published: February 2025

The small, low-cost and tinkering-friendly Raspberry Pi computer board has been used as the basis for a variety of distributed computing clusters built by research groups and individuals for experimental and pedagogical use. The new Raspberry Pi Zero model is smaller, consumes less power, and costs only $5.00 (when supplies are available), but lacks the built-in ethernet interface of its larger predecessors. Making a virtue of necessity, an Altera Cyclone II FPGA on an inexpensive development board can be used to provide the communication fabric for a pocket-sized Raspberry Pi Zero cluster, avoiding the need for bulky network cables and routers, and enabling experimentation with different networking architectures which may be more suited to fine-grained closely-coupled distributed computations than the usual TCP/IP over commodity ethernet. (Work in progress.)
Semantic Image-Based Profiling of Users' Interests with Neural Networks
Authors: Wieczorek Szymon  , Filipiak Dominik  , Filipowska Agata
Published: February 2025

We propose a method for creating a semantic profile of user's interests emerging from pictures by application of neural networks trained for object recognition. We use BabelNet, an online encyclopaedic dictionary, to generalise object names into categories of interests. Our method is evaluated with ground-truth data based on social tagging mechanism. Experiments are conducted entirely on original data containing 60,000 images crawled from Flickr, evenly distributed among 300 users. Results show that object recognition methods combined with object category generalisation can be effectively used to predict user's interests. The accuracy of the presented method seems to change with the neural network used for object recognition (5 NN tested in total), therefore it has a strong potential for further development. ResNet-50 turned out to be the most accurate network in our experiment.
From Rankings to Ratings: Rank Scoring via Active Learning
Authors: O'Neill Jack  , Delany Sarah Jane  , Mac Namee Brian
Published: February 2025

In this paper we present RaScAL, an active learning approach to predicting real-valued scores for items given access to an oracle and knowledge of the overall item-ranking. In an experiment on six different datasets, we find that RaScAL consistently outperforms the state-of-the-art. The RaScAL algorithm represents one step within a proposed overall system of preference elicitations of scores via pairwise comparisons.
Biomedical Image Data Segmentation with Using of Clustering Driven by Genetic Algorithms
Authors: Kubicek Jan  , Penhaker Marek  , Augustynek Martin  , Cerny Martin  , Oczka David
Published: February 2025

The clustering algorithms, like is the K-means algorithm, are commonly utilized for the biomedical image regional segmentation. One of the major limitations of the clustering algorithms is a definition of the initialization phase. When the initialization distribution of the centroids is improperly set the K-means algorithm is not able to achieve a reliable approximation of the tissues, thus the convergence of such segmentation procedure is significantly limited. Furthermore, when the biomedical image data are corrupted either by the noise, or artefacts, an effectivity of the segmentation is limited as well. We have analyzed a multiregional segmentation model based on the hybrid approach of the K-means algorithm which is driven by the ABC genetic algorithm. We suppose that the initialization distribution of the each cluster's centroid should reflect minimal variation towards the pixels lying inside the cluster. More the variation is increasing, worse results we obtain. Therefore, we define the fitness function minimizing the inter-cluster variance to obtain an optimal distribution of the image clusters within a predefined number of the ABC algorithm iterations. We have tested the segmentation procedure on a sample of the CT and MR image data, and verified this procedure against standard clustering algorithms.
Recent Advances on Fog Health – A Systematic Literature Review
Authors: Ibrahim Wan Nur Hidayah  , Selamat Ali  , Krejcar Ondrej  , Chaudhry Junaid
Published: February 2025

Fog Computing is a part of edge computing that define as intermediate layer between “Things” and the Cloud. Fog Health is an implementation of fog Computing's concept in health care and its related area. The purpose of this study is to extract and analyze the concept and application of Fog Health. The goal of this study are to identify the trend or patterns in Fog Health publications, to identify the application domain of Fog Health and to identify the research gap and future direction of implementing Fog Computing in health care related areas. Search term with relevant keywords were used to identify primary study related to the topic. About 53 of primary study were identified and selected. 46% (the largest portion) of the selected paper were journal articles. 47% of the publications were published by IEEE and 25 of the publications were published in 2017. We have found that the most three major issues that mostly discussed in fog Computing literatureas are related to the implementation of real-time system with minimum delay, the issues related to the performance on complex data processing that not affecting the system performance and the security & privacy issues related to the Fog Health implementations in the medical related facilities.
Fuzzy Methodology for Recommendation Based on Sentiment Analysis and Content Tools
Authors: Serrano-Guerrero Jesus  , Lopez-Herrera Antonio Gabriel  , Jimenez Pablo  , Olivas Jose A.  , Herrera-Viedma Enrique
Published: February 2025

Most of the well-known recommender systems are based on a proprietary database and a finite and well-known set of elements to recommend. This article presents a methodology that allows us to make recommendations on broad domains where we do not have our own database of recommendable items. This methodology proposes the definition of the characteristic aspects of the domain treated and the extraction of information based on online content tools. These tools will provide the system with subjective and objective information that will make it possible to characterize the recommended elements and thus, adapt the recommendations to the user's preferences.
Automatic Generation of Android SQLite Database Components
Authors: Musleh Iman  , Zain Samer  , Nawahdah Mamoun  , Salleh Norsaremah
Published: February 2025

Mobile applications (apps) are becoming ubiquitous and at the same time getting more complex to develop. Specific development tools and techniques are always essential to facilitate the development of reliable and cost effective mobile apps. However, a large fraction of Android app developers are known to be novice and come from non-computing background. To assist developers, this paper addresses the problem of automatic generation of Android database components, and presents a technique for creating SQLite database and APIs. The technique is based on a tool named Android SQLite Creator (ASQLC) that can automatically generate Android SQLite database as well as the API classes that perform read/write operation on that database. To evaluate the tool, a preliminary experiment was conducted by junior computer science students in building a small size Android database. This preliminary evaluation shows that our tool can be usable and promising particularly for novice developers.
Adapting Recommender Systems to the New Data Privacy Regulations
Authors: Tejeda-Lorente Álvaro  , Bernabé-Moreno Juan  , Herce-Zelaya Julio  , Porcel Carlos  , Herrera-Viedma Enrique
Published: February 2025

Recommender systems are key enablers to provide personalization and to make systems be adapted to users' needs. Both, users and content or commercial providers benefit from these techniques. While user profiling is required during the recommendation process, it can also introduce additional threats to the user's privacy. New regulations come into place to palliate the misuse of personal information from companies and public institutions. However, there are no clear rules defined for recommender systems. We find in the literature different proposals to privacy-preserving recommender system, but none of them tackle the compliance with the General Data Protection Regulation (GDPR). In this work we suggest a set of guidelines to assess and implement GDPR compliant recommender systems. Recommender providers shall follow our guidelines to make sure that their systems are not only privacy-preserving, but also GDPR compliant.
Hierarchical Fuzzy Situational Networks for Online Decision Support in Distributed Cyber-Physical Systems
Authors: Kotenko Igor  , Saenko Igor  , Ageev Sergey
Published: February 2025

The paper suggests a new approach to create online decision support systems (ODSSs) to control distributed cyber-physical systems (DCPSs). We consider ODSSs which are based on applying hierarchical fuzzy situational networks together with fuzzy mathematical programming and fuzzy logical inference. These methods allow one to make efficient decisions in the conditions of dynamically changing external factors. The conceptual principles to create ODSSs are provided. The generalized algorithm of ODSS' functioning is developed. As a case study we consider a multi-service communication network which is a communication basis of DCPSs and one of the most complicated subsystem operating with fuzzy data. The performed evaluation of the proposed approach demonstrates its significant gain in comparison with known statistical methods and methods based on the application of reference situations. Based on the results of the evaluation, the structure of intelligent agents' software implementing ODSS for DCPSs is developed.
A Deep Gait Classification Approach for an Early Recognition of Huntington Diseases
Authors: Paragliola Giovanni  , Coronato Antonio
Published: February 2025

Huntington disease (HD) is a progressive disorder of motor, cognitive, and psychiatric disturbances. A general lack of coordination and an unsteady gait often follow motor speed, fine motor control, and gait are affected. Gait disturbance is one of the main factors contributing to a negative impact on quality of life of patients. The state-of-the-art of assessment approaches for the evaluation and recognition of this type of disease are expensive ambulation-based performed under the supervision of clinicians. Our research aim at overcoming these issues by defining an in-house self-test mobile solution able to detect anomalies in the gait dynamics of elderly. In this paper, we present the preliminary results of our research exploring a deep learning-based model for the automatic assessment of the gaits dynamics of elderly people. The gait dynamics signal is measured by means of a temporal time series of the acceleration values of the patient's acceleration movements along the (x,y,z) axes. Our experiments show classification results reaching a good accuracy rate at 0.75% with a recall an precision rate at 0.70% and 0.75%.
Classifying Types of Ethos Support and Attack
Authors: Duthie Rory  , Budzynska Katarzyna
Published: February 2025

Endorsing the character of allies and destroying credibility of opponents is a powerful tactic for persuading others, impacting how we see politicians and how we vote in elections, for example. Our previous work demonstrated that ethos supports and attacks use different language, we hypothesise that further distinctions should be made in order to better understand and implement ethotic strategies which people use in real-life communication. In this paper, we use the Aristotelian concept of elements of ethos: practical wisdom, moral virtue and goodwill, to determine specific grounds on which speakers can be endorsed and criticised. We propose a classification of types of ethos supports and attacks which is empirically derived from our corpus. The manual classification obtains a reliable Cohen's kappa κ=0.52 and weighted κ=0.7. Finally, we develop a pipeline to classify ethos supports and attacks into their types depending on whether endorsement or criticism is grounded in wisdom, virtue or goodwill. The automatic classification obtains a solid improvement of macro-averaged F1-score over the baseline of 10%, 25%, 9% for one vs all classification, and 16%, 18%, 10% for pairwise classification.
Ontological Foundations of Competition
Authors: Sales Tiago Prince  , Porello Daniele  , Guarino Nicola  , Guizzardi Giancarlo  , Mylopoulos John
Published: February 2025

It is widely recognized that accurately identifying and classifying competitors is a challenge for many companies and entrepreneurs. Nonetheless, it is a paramount activity which provide valuable insights that affect a wide range of strategic decisions. One of the main challenges in competitor identification lies in the complex nature of the competitive relationships that arise in business environments. These have been extensively investigate over the years, which lead to a plethora of competition theories and frameworks. Still, the concept of competition remains conceptually complex, as none of these approaches properly formalized their assumptions. In this paper, we address this issue by means of an ontological analysis on the notion of competition in general, and of business competition, in particular, leveraging theories from various fields, including Marketing, Strategic Management, Ecology, Psychology and Cognitive Sciences. Our analysis, the first of its kind in the literature, is grounded on the Unified Foundational Ontology (UFO) and allows us to formally characterize why competition arises, as well as to distinguish between three types of business competitive relationships, namely market-level, firm-level and potential competition.
NLP-PIPE: Latvian NLP Tool Pipeline
Authors: Znotiņš Artūrs  , Cīrule Elita
Published: February 2025

The paper introduces a modular pipeline that allows to combine multiple Natural Language Processing tools into a unified framework. It aims to make NLP technology more accessible for researchers, non-experts and software developers. The paper describes the architecture of NLP-PIPE and presents publicly available NLP components for Latvian.
The Speech Rhythm of the Lithuanian and Latvian Languages
Authors: Kazlauskienė Asta  , Zigmantaitė Aistė
Published: February 2025

Studies on the rhythm of living Baltic languages are scarce (especially comparative ones) and their conclusions are usually ambiguous or even controversial. The aim of this research was to determine the values of the acoustic correlates of Lithuanian and Latvian languages and to compare them with the values found in other languages, identified by researchers as belonging to particular rhythm groups. The empirical material of this study consisted of 5 Lithuanian and 5 Latvian native speakers reading Aesop's fable The North Wind and the Sun in their respective native languages. The acoustic analysis of the audio recordings was performed using Praat and Correlatore applications. The analysis showed that the values of the acoustic rhythm correlates in both Baltic languages were found unequal. Nevertheless,according to the studied acoustic correlators, Lithuanian belongs to stress-timed languages, while Latvian is closer to syllable-timed languages.
A Method to Integrate Semantic Criteria into a Recommender System Based on ELECTRE Outranking Relations
Authors: Buira Sílvia  , Valls Aida
Published: February 2025

This paper presents a new approach to semantic recommender systems. The data analysed consists on a set of alternatives described by means of a list of tags. Each tag corresponds to one concept in a domain ontology. The first contribution of this work is the use of a multi-criteria outranking method to evaluate, rank and filter a set of alternatives. The second contribution is a procedure for exploiting the topology of the ontology to create a set of numerical criteria and represent the user's preferences about the concepts related to these criteria. The solution proposed reduces the computing time and converts a semantic problem into a multi-criteria numerical problem that can be solved using the ELECTRE method. The paper shows a case study in the field of touristic destinations recommendation with some tests with different user's profiles.
An Argumentation Approach for Agreement Analysis in Reddit Debates
Authors: Alsinet Teresa  , Argelich Josep  , Béjar Ramón  , Martínez Santi
Published: February 2025

Reddit is a social platform that has become the meeting point for millions of people every day. Reddit allows its users to debate about any opinion or news, by posting comments and answers to previous comments of other users. Our aim is to analyze real-world debates from Reddit to check what are the winning positions and the level of discrepancy between users. To analyze the agreement or disagreement between user comments in Reddit debates, we propose an argumentation based analysis that represents a debate as a two-sided debate graph. In this graph, comments are divided in two groups, the ones that agree with the root comment of the debate, and the ones that disagree with it. The edges of the graph represent disagreement between comments of the two groups. Finally, we model the problem of obtaining the accepted comments of the debate, as a valued argumentation problem, where comments are arguments, the attack relation is the disagreement relation, and values for arguments are computed from the votes of users to comments. The accepted comments are computed using the ideal semantics of abstract argumentation. Next, the agreement is analyzed comparing the comments accepted from the two sides of the debate graph. To test our system, we analyze a test set of Reddit debates where we obtain the accepted comments from both sides of the debate and we compare the differences between both sets.
Tweet Sentiment Visualization and Classification Using Manifold Dimensionality Reduction
Authors: Grimaldo Francisco  , López-Iñesta Emilia  , Garcés-Iniesta Juan José  , Gómez-Sanchis Juan  , García-Costa Daniel  , Marqués-Benítez Eusebio  , Soria-Olivas Emilio
Published: February 2025

The growth of online content generated by users on the Web and social media has turned Sentiment Analysis into one of the most active research areas of Natural Language Processing aimed to computationally identify the underlying opinions, attitudes or feelings of a given text. In particular, a variety of methods have emerged that focus on Twitter due to its challenging traits, being a central issue how to provide a proper feature-based representation for its short and incomplete messages, i.e. tweets. The contribution of this paper is twofold: on the one hand, we present an approach to perform sentiment tagging of tweets based on text and emoji polarity; on the other hand, a set of manifold dimensionality reductions are carried out that allow a convenient 3D visualization and a rapid prototyping of sentiment patterns. We then compare the performance obtained with such reduced feature spaces when applied to the classification of sentiments of a collection of tweets from a real-life case study of people experiencing the celebration of a massive festivity.
Sorting Hams Using Bagged Decision Trees in a Commercial Pig Slaughterhouse
Authors: Masferrer Gerard  , Carreras Ricard  , Marti-Puig Pere  , Serra Moises
Published: February 2025

Classification of pig carcasses according to its characteristics is a determining factor in slaughterhouses, since allows the optimization of production and improves the performance in cutting plants and in other subsequent processes. Usual criteria in carcasses classification are the weight and the fat content of the ham, especially in regions such as Spain where a significant proportion of hams are used for curing to produce Jamón Serrano. The objective of this study is to compare different models based on Decision Trees and using intrinsic data of pigs to classify hams in four groups according to their predicted weight. The model presented is based on Bagged Decision Trees and use as input: the weight and the lean meat percentage (LMP) of the carcass, the sex and the breed of the pig and the manual classification of the ham according to the thickness of the subcutaneous fat. The results show a success rate of 81.7%, improving by 4.4% the results obtained with a more straightforward decision tree based only on the weight and the LMP.
On an Application of Fuzzy Logic for Radar ATR of Maritime Targets
Authors: Tien Vu Viet  , Hop Tran Vu  , Kien Tran Trung  , Nam Luu Hoai  , Loi Nguyen Van  , Thanh Tran Thi  , Thang Nguyen Xuan  , Duong Nguyen Duc  , Chung Nguyen Xuan
Published: February 2025

A new ATR tool for coastal surveillance radars is proposed. This tool is based on new features from target range, azimuth profiles and an adaptive neuro-fuzzy classifier with linguistic hedges (ANFC-LH). The tests with real data show that the ANFC-LH used in our study has a better classification result in comparison with several other ensemble classification algorithms such as bagged decision trees and adaptive boosting. Moreover, it is shown that the proposed ATR tool with ANFC-LH has a better performance than that one given in a recent publication.
Relationship Between Weight Correlation of the Convolution Kernels and the Optimal Architecture of CNN
Authors: Wang Qi  , Lu Yonggang
Published: February 2025

Currently, deep learning has been one of the most popular research topics, and it has already been successfully applied in many fields such as image recognition, recommendation systems and so on. Convolutional neural network (CNN), as an important application in the field of deep learning classification, has attracted a lot of research interests. The performance of CNN is greatly affected by the number of convolution kernels. However, how to determine the optimal number of convolution kernels automatically in each convolution layer is still an unsolved problem. In this work, a simple CNN composed of a single convolution layer followed by a pool layer is studied. It is found that the correlation between the weights of the convolution kernels is related with the optimal number of kernels in the CNN. Usually, with the increasing of the kernel size, a lower weight correlation threshold will correspond to the optimal number of kernels. Furthermore, it is found that the weight correlation threshold is only affected by the kernel size, but is not affected by different kinds of datasets. These results imply that the weight correlation of the convolution kernels is an important indicator for determining the optimal architecture of CNN.
A Partitioning Algorithm of Social Network Based on Dimensionality Reduction
Authors: Wang Lina  , Gao Bo  , Liu Yu
Published: February 2025

In order to solve the social network members' distribution is no longer confined to the two-dimensional plane and social network partitioning process involves groups of content privacy problems, this paper provides an improved CURE algorithm based on principal component analysis to reduce the dimension of social network division, which named DRICURE algorithm. First, the concept of principal component analysis could be used to reduce the dimension of social network distribution and simplify the calculation method. Second, the distance between nodes is measured by the closeness among members. Finally, DRICURE algorithm is used to cluster until the number of categories meets the requirements, and uses similarity to solve the attribution of outlier to members of social networks. Experiments show that the algorithm reduces the dimension of social network distribution and improves the time and space efficiency distinctly, and also does not involve the privacy of social network members. The results show that the quality of community structure obtained in this paper is higher, and the separation of isolated members is considered effectively.
An Implementation of a Chip Security Mechanism
Authors: Xu Pingjiang  , Yuan Yanfang  , Fu Qingqin  , Yuan Yuan
Published: February 2025

A method of chip security protection is proposed in this paper. According to the application characteristics of the chip, a security state machine is designed. The state machine is driven by the chip instructions. Only the security state meets the preset file access condition, the file access is permitted. At the same time, in order to isolate access permissions of different applications, the environment state machine and application state machine are defined. Through using these state machines together, the security of the chip is greatly enhanced.
The Application of Big Data to Improve the Users' Attention to We-Media
Authors: Liu Jianghui  , Huang Weibo  , He Wen Jing  , Ruan Lingxi  , Li Xiaodan
Published: February 2025

This study is to help We-Media to improve the users' attention by using Big Data technology. A new TAM model is optimzed based on the TAM model proposed by Shein Bowman and Chirs Willis. The model can better obtain the users' data and optimize We-Media to attract more attention. The paper took Guangdong University of Foreign Studies as an example and carried out experiments. We applied MapReduce and Hadhoop to process the Big Data, such as the users' interests, hobbies and browse habits of We-Media users with SPSS. The above processes are completed by using literature research, empirical research, quantitative analysis and qualitative analysis. The experiment result can help We-Media to improve its content. We-Media can optimize the released content according to the users' Big Data, so as to obtain the users' higher attention, assist people to recognize things positively and promote the direction of We-Media.
An Imaging Algorithm for Rotation Invariant Automated Macula Localization in Digital Fundus Images
Authors: Bhattacharya Sayantan  , Sehgal Jai  , Sengar Namita  , Dutta Malay Kishore  , Travieso-González Carlos M.
Published: February 2025

The Macula is one of the important parts of the retina of the human eye, responsible for sharp central vision. Detection of the macula is an important task for an automated screening tool for different ocular pathologies. The proposed method presents an imaging method detect macula from fundus images automatically and which is invariant to image rotation. The proposed method includes a strategic region based method based on an adaptive arc which is used to detect macula efficiently. For experimentation purposes, normal and affected fundus images were collected from a local eye hospital to test the performance of the proposed method and achieved encouraging results. The method proposed for the detection of the macula is accurate, efficient and hence can be used in real time automated screening of various eye diseases.
Socio-Technical Imaginaries and Human-Robotics Proximity—The Case of Bestic
Authors: Mossfeldt Nickelsen Niels Christian
Published: February 2025

Political/managerial levels intend to reform Denmark through digitization. Assistive feeding robotics is a welfare technology, relevant to citizens with no or low function in their arms. Despite national endorsement and dissemination strategies, it proves difficult to recruit suitable citizens as first users of this new technology. This study discusses how the socio-technical imaginaries of governmental institutions, technology developers, and affected stakeholders distribute agencies and what this means for the implementation of this robotics. The principle of ANT (Actor-Network Theory) of ‘following the actor’ inspired the study reported here that took place as multi-sited ethnography at different locations in Denmark and Sweden. The study contributes to the area of Science and Technology Studies (STS) by providing an empirical analysis that views together, and holds up against each other, (i) political imaginaries, (ii) the assumptions of technology developers, and (iii) the possibilities and difficulties arising for care providers and users.
Capitalism, Labor and the Totalising Drive of Technology
Authors: Toivakainen Niklas
Published: February 2025

In this paper I try to illustrate, quite roughly and indicatively, the interconnections between automation technology and social organization. Central to this analysis are the notions of automation, increased productivity in a capitalist society, labor, equality, global inequality and the modern culture of technology. I will end the paper with brief critical remarks on the question of ‘robot rights’.
Goal-Oriented Adaptive and Extensible Study-Process Creation with Optimal Cyclic-Learning in Graph-Structured Knowledge
Authors: Konishi Ryosuke  , Nakamura Fumito  , Kiyoki Yasushi
Published: February 2025

We herein present a method that dynamically generates the curricula specialized to the learning circumstances of individual learners, given prior learning goals and learning objects. A generated curriculum encourages the selection of learning behaviors according to the learning objects that may be feasibly acquired within a limited timeframe. Our method evaluates the circumstances of an individual learner; by dynamically selecting feasible learning objects based on the individual's learning behaviors and their past records, it finds the best learning tasks within the constraints of time, circumstances, and activities. Using prior known rules and strengths of causal/dependency relations between learning items, our method enables, from individual test results, the discovery of the learning objects that are important, and how they should be ordered, on an individual basis. This enables an effective support in choosing the most appropriate learning behaviors, tailored to the individual learner. It also enables the selection of effective learning behaviors by examining the behavior records of other individuals, treating the influence of their prior learning behaviors on subsequent learning behaviors as experience quotients and using them by converting them into expected scores for the individual's learning behaviors. Accordingly, we evaluate whether the learning behaviors selected by the individual are indeed learning tasks that would correspond to anticipated learning results, conduct prior assessment of the influence that the results of this intervention would have upon the learning circumstances and thus prioritize more effective learning behaviors. When implemented, our method assesses the changes in the individual's learning circumstances based on their learning behaviors on a timeline and subsequently adjusts the recommended behaviors. The method can provide effective support for individual learners: along with effective feedback on learning task selection in response to the individual's circumstances, it dynamically generates an individualized curriculum by measuring the relationships between the individual's learning circumstances and learning items. We herein present a method for dynamically generating the curricula in response to an individual's learning circumstances through measuring the causal/dependency relations between learning items, thus enabling the calculation of the relationship between an individual's past learning record, and the learning behaviors and learning objects available to be chosen by the individual. We investigate its efficacy and achievability through empirical testing by using actual data.
Supportive Data Management for Collaborative Research
Authors: Kropp Yannic Ole  , Thalheim Bernhard
Published: February 2025

Collaboration is a crucial part of joint research. Especially in projects, where multiple academic domains and disciplines are involved, this is challenging. Different research foci and individual perspectives are often mismatching. Heterogeneous data, diverging working habits, and differing standards are common. Research itself is agile, dynamic, and evolving. Changes and revisions of data structures, requirements, and actual data values arise continuously. And so on…
Shopping Mall Environmental Impact Evaluation Based on Microscopic Traffic Flow Simulation
Authors: Savrasovs Mihails  , Karakikes Ioannis  , Pticina Irina
Published: February 2025

The goal of the paper is to demonstrate the approach of using microscopic traffic flow simulation for evaluation of the environmental impact of new Shopping mall in the urban area. The results of the research demonstrate, that implementation of the such attraction point as Shopping mall could significantly influence the environment in the area, this should be considered during development of the Shopping mall.
An Environmental-Semantic Computing System of Multispectral Imagery for Coral Health Monitoring and Analysis
Authors: Wijitdechakul Jinmika  , Kiyoki Yasushi  , Koopipat Chawan
Published: February 2025

The global environmental analysis system is a new platform to analyze environmental multimedia data that acquired from nature resources. This study aims to realize and interpret coral reefs phenomena and changes occurring that happening in global scale by utilizing Acropora coral as a bioindicator or natural sensing. This paper presents a new environmental-semantic computing system of multispectral imagery for automated coral health monitoring and analysis to realize and recognize coral condition in actual situation. Multispectral semantic-image space for coral monitoring and analysis can be utilized for ocean environment monitoring and assessment by measuring coral reef health which highly beneficial to the current ocean pollution problem. Our method applies semantic distance calculation to measure similarity between multispectral image data and context images including three coral conditions (healthy, bleaching, and dead). In our experiments, we applied the SPA function which is an effective concept to design environmental systems with Physical-Cyber integration. This paper presents case study of Acropora coral monitoring and assessment at Man-nai Island, Rayong province, Thailand. Therefore, an additional objective of this research is to apply the Artificial Intelligence (AI) and Environmental monitoring system for combatting the ocean pollution problem by transferring the knowledges and technology from computer science fields to fundamental marine science research.
A Sensor Selection and Learning Method for Credibility Assessment Using Sensor Data
Authors: Honda Ken  , Yoshida Naofumi
Published: February 2025

We herein introduce a sensor selection and learning method for credibility assessment using sensor data. To assess the credibility of information, comparing with primary resources and integrating sensor data are effective to derive the objectivity of the information. However, when we are using sensor data, if incorrect sensors are selected or if the necessary sensors are not referred, it implies that the credibility of the credibility assessment system is uncertain. This paper presents a sensor selection and learning method for information credibility assessment using sensor data. In this method, several discriminators select sensors through weighting. Additionally, the result is feedbacked to the discriminators to learn the precision of the discriminators. The primary contribution of this method is the proposal of a method of sensor selection from several sensors that is opened on the World Wide Web by weighting from discriminators such that the discriminators can learn their precision from the feedback of the selection result. This paper shows the feasibility and usefulness of the method through experiments. An application method for credibility assessment technology is also introduced.
An Environmental Semantic Search-Space Creation Method for News-Article Search Systems with Context-Oriented Interpretations
Authors: Fujioka Hanako  , Sasaki Shiori  , Watanabe Toshihiro  , Otsuka Kyohei  , Ishii Masayuki  , Kiyoki Yasushi
Published: February 2025

Metadata space creation for semantic associative search is an essential process for semantic searching for news article. We apply semantic computing methodology to realize an Environmental-News-Article Search System with Context-Oriented Interpretations. This paper presents a method for creating an environmental-news-article metadata space for applying semantic computing to environmental news-resources. Our creation process for realizing an environmental-news-article semantic space is (1)select news article regarding environment from news-resources, (2)extract news article that have keyword-tag “Global Warming” or “Warming” and set them as news article of “Global Warming,” (3)obtain all keyword-tag that news article of “Global Warming” have, (4)Count appearance frequency of each keyword-tag in news article of “Global Warming” and determine frequency thresholds of keyword-tag for composing metadata space, (5)create the environmental-news-article metadata space using the keyword-tag creamed off. We compared between several experiments with changed search phenomenon or search context for semantic associative search, and we made certain that our Environmental-News-Article Search System extracts the metadata space properly depending on the context and retrieves phenomena and documents semantically close to the search phenomenon or the search context. Our evaluation shows the effectiveness of the method for creating an environmental-news-article metadata space for applying semantic computing to environmental news-resources.
Using Agreement Statements to Identify Majority Opinion in UKHL Case Law
Authors: Valvoda Josef  , Ray Oliver  , Satoh Ken
Published: February 2025

This paper is concerned with the task of finding majority opinion (MO) in UK House of Lords (UKHL) case law by analysing agreement statements (AS) that explicitly express the appointed judges' acceptance of each other's reasoning. We introduce a corpus of 300 UKHL cases in which the relevant AS and MO have been annotated by three legal experts; and we introduce an AI system that automatically identifies this AS and MO with a performance comparable to humans.
Study on the Traffic Impact Assessment of Large-Scale Stadiums and Gymnasiums – From the Perspective of Meta-Analysis Theory
Authors: Zhang Min  , Heng Wei
Published: February 2025

Based on the meta-analysis theory, a model is established to discuss the traffic impact assessment of large-scale stadiums and gymnasiums. Seven assessment indexes of large-scale stadiums and gymnasiums are selected, including the level, building area, and location of the large-scale stadiums and gymnasiums, the saturation degree of the road section in the region, the distance away from stadiums and gymnasiums, the level of traffic intelligence and games-organizing arrangements. The influence of large-scale stadiums and gymnasiums on surrounding road network is classified into large, medium and small degrees. By establishing the node domain matrix and classical domain matrix, as well as calculating the proportion of each assessment index and the comprehensive correlation of subordinate degree, the influence of large-scale stadiums and gymnasiums on the surrounding road network is measured. In addition, the application of the assessment model is stimulated with examples.
A Concurrent Data Collection Environment for WAsteful COMmunication SAtellite System
Authors: Skovsgaard Tor  , Sørensen Patrick Dyhrberg  , Dickson Lawrence J.  , Quarrie Lindsay O'Brien  , Vinter Brian
Published: February 2025

WACOMSAS (WAsteful COMmunication SAtellite System) uses the Transterpreter to demonstrate a finite, static occam-based design for a ground station that communicates with at most four passing satellites at a time. There may be an indefinite number of satellites, each of which may never return. The satellites are not coded in occam, and external channels with mobile channel ends are used to communicate with them according to a switchboard analogy applicable both to hardware and software interfaces. The occam solution is compared to similar implementations in Go and Python/PyCSP, and environment that simulates satelites with Arduinos is set up and used for eksperiments.
Building a Hand-Held Cluster for £120
Authors: Chalmers Kevin
Published: February 2025

Cluster computers are seen as expensive, space consuming, high energy platforms hidden away in universities and tech companies. Having your own cluster computer to explore parallelism ideas was not realistic for the majority of people. With the release of the Raspberry Pi things changed, and now it is possible to build a five node machine powered by USB that you can hold in your hand for as little as £120. This talk will describe how such a machine can be built with very little technical knowledge, and then provide an example of setting up MPI on the machine to allow simple distributed parallel applications to be explored. The system is not fast, and communicates via a shared USB 2.0 connection, but the principles of distributed parallelism can be explored. An example system will be available for demonstration purposes.
Rigorous Timing, Static occam, and Classic CSP: Mathematical Ground Truth
Authors: Dickson Lawrence J.  , Martin Jeremy M.R.
Published: February 2025

This Fringe presentation will continue the comparison of the two approaches from our first talk (“Formal Verification for IoT”) in rigorous mathematical detail. We introduce the mathematical concept of a Covering-with-Boundary (CwB), prove the equivalence of cycle-counted occam on a Transputer with this path diagram, and proceed to prove cases that avoid both divergence and effective divergence by finite restrictions. Due to static characteristics of occam-and-Transputer-based “Ground Truth,” we are also able to prove Hardware-Software Equivalence, and reach IoT conclusions that extend to non-digital payloads.
Solving the Santa Claus Problem Over a Distributed System
Authors: Marchant David  , Kerridge Jon
Published: February 2025

A working solution to the Santa Claus Problem is demonstrated that operates over a distributed system. It is designed and modelled using the client/server model to maintain a deadlock and livelock free architecture. The system was developed using the JCSP library for Java and demonstrated over a network of twenty desktop PCs. Each Santa, Reindeer and Elf process is broken down into numerous sub-processes, and are each designed according to the client/server model. A novel approach to the Elves is presented using a chain-based architecture to allow non-determined sub-groups to communicate and consult with Santa in sets of three.
Formal Analysis of a Video Encoding Application Within Map/Reduce
Authors: Ruiz M. Carmen  , Perez Diego  , Gruska Damas
Published: February 2025

Cloud computing is the infrastructure of choice for compute–intensive and data–intensive systems providing a flexible resources for software applications; that is, the processing capacity assigned to an application can be adapted to its needs. Nevertheless, in a cloud pay–per–use model, the number of demanded resources must be taken into account in order to minimise the costs. Our main goal is to reason about a cloud–aware application's resource usage by means of our Timed Process Algebra called BTC (Bounded True Concurrency), that is, to study the trade–offs between an application's response time and resource usage.
T42 – Transputer in FPGA
Authors: Mielke Uwe  , Zabel Martin  , Bruestle Michael
Published: February 2025

The T42 Transputer in FPGA is a full binary-code compatible open-source VHDL implementation of the Inmos IMS-T425 32bit microprocessor. The T42 is mainly targeted for education and exploration purposes and many interlinked cores can be loaded onto any suitable sized FPGA board. The construction of larger parallel computing systems based on the Transputers distributed memory architecture is possible (e.g. within student semester projects). The programming model of occam can be evaluated and compared to other languages.
Space Invaders Game Written in SME, Running on an FPGA
Authors: Johnsen Carl-Johannes
Published: February 2025

Given the presentation of the Pong inspired game at last years conference, we bring you another, more complex arcade game. This time we have partially implemented Space Invaders running on an FPGA (Field-Programmable Gate Array), which renders the screen directly to the VGA output of the board. Furthermore, to improve the usability of the game, we have connected the wires of an NES controller directly to the board, in order to control the game.