bluuebunny/crossref_metadata_2025 huggingface dataset:
Reducing clothing production volumes by design: a critical review of sustainable fashion strategies
Authors: Maldini I.  , Balkenende A.R.
Published: February 2025

Based on a literature review, this article discusses how the challenge of diminishing clothing production volumes has been approached within the field of sustainable fashion. We identify six common strategies in literature and discuss the approach of user involvement in the process of design and/or manufacture of garments in detail. A critical analysis of the state of the art in the field points out that these strategies have been constructed, studied and promoted without empirical validation. The article concludes with a recommendation to move forward from conceptual to empirical studies. Analyses of existing initiatives and their results in terms of consumer buying behavior and obsolete inventory are recommended as first steps towards validation.
Dynamics of social capital in relation to the development of a sustainable product-service system applied to distributed production
Authors: Menezes J.P.  , Castillo L.A.G.
Published: February 2025

Due to the people's lack of engagement on sustainability, the impacts of industrial production continue to increase. Many people see sustainable development as a distant reality and dependent on factors exclusively linked to technology, ignoring that the transition to more sustainable life styles requires a social learning process marked by collaboration and participation of people, i.e. through social capital. Sustainable Product-Service Systems and Distributed Production are approaches that favor behavioral change about artifacts' production and consumption. However, few empirical data has been generated about these promising models and their potential to promote sustainability. This paper presents data collected in the UM PASSO project, one of these initiatives located in the state of Pernambuco, Brazil. Through observation, questionnaires and interviews, the possibility of social capital strengthening related to these new practices of design, production and consumption is investigated. The results indicate that replication of local initiatives can promote a discontinuity in consolidated behavior, combating individualism and lack of cooperation, opening a debate and strengthening essential aspects for sustainable development.
Planned obsolescence: the government's choice?
Authors: Ober E.  , Dell'Anno B.  , Drèze J.-R.  , Herrmann L.  , Luciano A.  , Maltry R.  , Oehme I.  , Schmon B.  , Ventère J.-P.
Published: February 2025

Representatives of five Environmental Ministries and attached Agencies examined political instruments to ban products with built-in defects designed to end the product's life-cycle. The focus primarily laid on better consumer information. A lack of information concerning the durability and repairability of products creates an asymmetry in the market balance between producers and consumers. The need for common actions on EU-level was highlighted. Among these political instruments are voluntary measures and innovative economic models. The European legal framework for consumer protection has to be further evaluated, more specifically the concept of warranty law. With political feasibility being considered, conclusions were drawn unanimously. However, the authors take into consideration the findings of different national studies that could not validate the accusation of planned obsolescence.
Interdisciplinary educational approaches to clothing longevity
Authors: Mclaren A.  , Claxton S.  , Hill H.  , Cooper T.  , Oxborrow L.  , Goworek H.
Published: February 2025

How do we encourage and enable interdisciplinary systems thinking approaches to sustainable fashion design and business education? In preparation for a workshop at the 2017 PLATE conference, this paper introduces the context of a toolkit – The Clothing Durability Dozen (Cooper at al, 2016b) – aimed at enabling students to collaborate and learn about clothing longevity across disciplines and creating a better understanding of the roles that different departments can play in placing sustainable design strategies at the heart of the clothing industry. In line with education for sustainable development (ESD) principles, objectives include stimulating learning and promote core competencies, such as critical and systemic thinking, collaborative decision-making, and taking responsibility for present and future generations. In the workshop, participants will trial and contribute to the development of the toolkit and any necessary supporting material, the final version of which will be available for use as an educational tool.
Approximation of Parabolic Fuzzy Numbers
Authors: Jagadeeswari M.  , GomathiNayagam V. Lakshmana
Published: February 2025

Most of the real world problems are complex that is raised from uncertainity in the form of ambiguity. To overcome the ambiguity and impreciseness, Zadeh [20] introduced fuzzy set theory in the year 1965. The underlying power of fuzzy sets is that linguistic variables can be used rather than quantitative variables to represent imprecise concepts which are continuous transitions. The continuous imprecise concepts need to be modeled by continuous fuzzy numbers instead of intervals and real numbers. Many researchers applied triangular and trapezoidal fuzzy numbers which are piecewise linear and continuous for imprecise concepts because of its convenience and ease. But imprecise variables in the fields of decision making, data analysis, fuzzy control, image processing, fuzzy clustering etc., need not be linear always. So parabolic fuzzy numbers can be used for modeling nonlinear fuzzy concepts to have a better accuracy in real life problems. Since parabolic fuzzy numbers are not completely studied till now and the triangular fuzzy numbers have been thoroughly studied, triangular approximations of parabolic fuzzy numbers may be used as supplementary tool along with parabolic fuzzy numbers. Approximation is a kind of de-fuzzification that has to change non-linear functions into linear functions which reduces the fuzziness for the undefined information and produces a better accuracy. So the study of approximation of parabolic fuzzy numbers is necessary and hence, in this paper, the problem of triangular approximations of parabolic fuzzy numbers is attempted using distance function in terms of α-cuts. Some properties of the parabolic fuzzy numbers which are useful in multi criteria decision making (MCDM) have also been discussed. Finally pertinent illustrations and applications of the approximation of parabolic fuzzy number have also been given.
Design of Dual-Band Bandpass Filter Based on Substrate Integrated Waveguide Structure
Authors: Li XuanXuan  , Wei Sheng  , Li MinQuan  , Wei YaDong  , Wu YingBo
Published: February 2025

This paper presents a novel dual-band bandpass filter design based on hybrid structure of substrate integrated waveguide (SIW) and complementary split ring resonator (CSRR). SIW is a novel structure with the combination of rectangular waveguide and microstrip transmission line. CSRR capacitive gaps are etched on the metallic surface to form a resonant cell. By combining SIW with CSRR structure, a dual-band bandpass filter has been designed and fabricated. The center frequencies of the filter are 2.4GHz and 3.5GHz, the maximum passband insertion loss is 0.5dB and the return loss is larger than 25dB. Transmission zeros appear at 3GHz, 4GHz and 5GHz, respectively, which make the band rejection larger than 40dB. Good agreements between the simulation and the experimental results are observed
An Improved Fast Decision Tree Algorithm Based on Attribute Deviation
Authors: Liu Dan  , Zhang Yue  , Sui Xin  , Zeng Yan  , Wang Huan  , Li Li
Published: February 2025

This paper proposed a decision tree algorithm based on attribute deviation. The algorithm classified the target attribute values and replaced the “entropy” in the ID3 algorithm with a new classification rule. The new rules considered the deviation of the sample size for different attributes in the data set, completing a quick and detailed classification. At the same time, this classification rule solved the deviation caused by the decision tree bias selects multi-valued attributes. In this paper, a simulation experiment was conducted in UCI data set. The results showed that the attribute deviation algorithm has higher classification accuracy and shorter computation time than ID3 algorithm.
Application of Data Mining Software to Predict the Alum Dosage in Coagulation Process: A Case Study of Vientaine, Lao PDR
Authors: Ladsavong Khoumkham  , Chawakitchareon Petchporn  , Kiyoki Yasushi
Published: February 2025

This paper presents an alum dosage prediction in coagulation process by using Weka Data Mining Software. The data in this research had been collected from Dongmarkkaiy Water Treatment Plant (DWTP), Vientiane capital, Laos PDR from 1st January 2008 to 31st October 2016. The total number of collected data were 2,891 records. In this research, we compared the results from multilayer perceptron (MLP), M5Rules, M5P, and REPTree method by using the root mean square error (RMSE) and mean absolute error (MAE) value. Three input independent variables, i.e. turbidity, pH, and alkalinity were used. The dependent variable was alum added for the coagulation process. Our experimental results indicated that the MLP method yielded the highest precision method in order to predict the alum dosage.
Bed Posture Classification Using Noninvasive Bed Sensors for Elderly Care
Authors: Viriyavit Waranrach  , Sornlertlamvanich Virach  , Kongprawechnon Waree  , Pongpaibool Panita
Published: February 2025

This study proposes bed posture classification using a Neural Network and a Bayesian Network for elderly care. The data are collected in a hospital. The on-bed postures are analyzed into five types, those are, out of bed, sitting, lying down, lying left, and lying right, by using signals from a sensor panel (composed of piezoelectric sensors and pressure sensors). The sensor panel is placed under a mattress in the thoracic area. To eliminate the effect of weight and the bias between different types of sensors, the sensing data are normalized into a range of 0 to 1 by the unity-based normalization (or feature scaling) method. In addition, a Bayesian Network is adopted to estimate the likelihood of consecutive postures. The results from both a Neural Network and Bayesian Network estimation are combined by the weighted arithmetic mean. The experimental results yield the maximum accuracy of posture classification when the coefficient of Bayesian probability and a Neural Network are set to 0.7 and 0.3 respectively.
A Super-Simple Run-Time for CSP-Based Concurrent Systems
Authors: Goldsby Michael E.
Published: February 2025

MicroCSP is a run-time system written in C supporting process-oriented concurrency. It features CSP-style synchronous communication over point-to-point channels and alternation (including timeouts) and implements preemptive priority process scheduling. The MicroCSP programmer writes the logic of a process as an ordinary function which, barring preemption, runs to completion every time the process is scheduled. To make such an approach feasible requires that the programmer cast each process's logic in normal form: a loop controlling a single choice of guarded events, with event-free computation following each event. A context switch requires a mere function call, with the exception of those that occur as the result of an interrupt. The system is memory-efficient, fast and has a narrow hardware interface. The intended target of MicroCSP is bare microcontroller hardware, and its efficient use of the stack makes it particularly suitable for microcontrollers with restricted memory. The current version runs on a single processor over a Linux implementation of the hardware interface and serves as a prototype for the microcontroller implementations. A multicore implementation appears to be possible.
Guppy: Process-Oriented Programming on Embedded Devices
Authors: Barnes Frederick R.M.
Published: February 2025

Guppy is a new and experimental process-oriented programming language, taking much inspiration (and some code-base) from the existing occam-π language. This paper reports on a variety of aspects related to this, specifically language, compiler and run-time system development, enabling Guppy programs to run on desktop and embedded systems. A native code-generation approach is taken, using C as the intermediate language, and with stack-space requirements determined at compile-time.
Communicating Process Architectures in the Light of Parallel Design Patterns and Skeletons
Authors: Chalmers Kevin
Published: February 2025

This work presents some initial thoughts and future directions when considering Communicating Process Architectures in light of parallel design patterns and algorithmic skeletons. Consideration of where CPA aligns with current work in the parallel application design world is considered. Proposals are made on how the scope of CPA can expand to better support the current trends in parallel application development are presented and proposed as future work.
Model-Driven Design of Simulation Support for the TERRA Robot Software Tool Suite
Authors: Lu Zhou  , Bezemer Maarten  , Broenink Jan
Published: February 2025

Model-Driven Development (MDD) – based on the concepts of model, meta-model and model transformation – is an approach to develop predictable and reliable software for Cyber-Physical Systems (CPS). The work presented here concerns a methodology to design simulation software based on MDD techniques, supporting the TERRA tool suite to describe and simulate process communication flows. TERRA is implemented using MDD techniques and Communicating Sequential Process algebra (CSP). Simulation support for TERRA helps the designer to understand the semantics of the designed model and, hence, to increase the probability of first-time-right software implementation. A new simulation meta-model is proposed, abstracting the simulation process of a TERRA model. With this new meta-model and our previously designed CSP meta-model, a simulation model can be transformed from its TERRA source. The Eclipse Modelling Framework (EMF) is used to implement the meta-model. The Eclipse Epsilon Framework includes the Epsilon Transformation Language (ETL) and the Epsilon Generation Language (EGL), used for modelto-model and model-to-text transformation. The simulation support is shown using an example, in which the generated trace text is shown as well. Further work is to implement an animation facility showing the trace text in the TERRA graphical model using colours.
Code Specialisation of Auto-Generated GPU Kernels
Authors: Blum Troels  , Vinter Brian
Published: February 2025

In this work we explore and evaluate the effect of automatic code specialisation on auto-generated GPU kernels. When combining the high productivity coding environment of computational science with the Just-In-Time compilation nature of many GPU runtime systems there is a clear cut opportunity for code optimisation and specialisation. We have developed a hybrid kernel generation method which is shown to be useful and competitive across very different use cases, and requires minimal knowledge of the overall structure of the program. Stencil codes which are commonly found at the core of computer simulations are ideal candidates for this type of code specialisation. For exactly this type of application we are able to achieve speedups of up to 2.5 times with the implemented strategy.
Communicating Processes and Processors (1975–2025)
Authors: May M. David
Published: February 2025

The ideas that gave rise to CSP, occam and transputers originated in the UK around 1975; occam and the Inmos transputer were launched around 1985. Thousands of computer scientists and engineers learned about concurrency and parallel computing using the occam-transputer platform and the influence is evident in many current architectures and programming tools. I will reflect on the relevance of these ideas to-day – after 30 years – and talk about communicating processes and processors in the age of clouds, things and robots. Slides used in the presentation can be downloaded from [1].
Connecting Two Robot-Software Communicating Architectures: ROS and LUNA
Authors: van der Werff W. Mathijs  , Broenink Jan F.
Published: February 2025

Two current trends in modern robotics and other cyber-physical systems seem to conflict: the desire for better interaction with the environment of the robot increases the needed computational power to extract useful data from advanced sensors. This conflicts with the need for energy efficiency and mobility of the setups. A solution for this conflict is to use a distribution over two parallel systems: offloading a part of the complex and computationally expensive task to a base station, while timing-sensitive parts remain close to the robotic setup on an embedded processor. In this paper, a way to connect two of such systems is presented: a bridge is made between the Robotic Operating System (ROS), a widely used open source environment with many algorithms, and the CSP-execution engine LUNA. The bridge uses a (wireless) network connection, and provides a generic and reconfigurable way of connecting these two environments. The design, implementation in both environments, and tests characterizing the bridge are described in this paper.
Extensions to the Concurrent Communications Library
Authors: Skovhede Kenneth  , Vinter Brian
Published: February 2025

This paper presents updates and measurements for the Concurrent Communications Library, CoCoL, which is a CSP inspired library targeting C# and other languages running on the Common Language Runtime, also known as .Net. We describe the new library interface methods that simplify writing correct, encapsulated and compositional networks. We also describe an extension to the library, which enables communication over network connections and measure the performance.
Adding CSPm Functions and Data Types to CSP++
Authors: Garner Daniel  , Roggenbach Markus  , Gardner W.B.
Published: February 2025

This work extends the subset of CSPm that can be translated to C++ using the tool CSP++. Specifications can now contain user defined functions, make limited use of set and sequence data, and utilise built-in CSPm functions that operate on such data. An extended development paradigm is suggested, based on applying transformational methods from UML to C++. All techniques are demonstrated over three case-study examples.
The π-Calculus for SoS: Novel π-Calculus for the Formal Modeling of Software-Intensive Systems-of-Systems
Authors: Oquendo Flavio
Published: February 2025

A major research challenge in the architectural design of a software-intensive System-of-Systems (SoS) is to enable the formal modeling of its evolutionary architecture. One of the main issues is that SoS architectures evolve dynamically, during run-time, in unexpected ways while producing emergent behavior. To address this issue, this paper proposes a novel process calculus, called “the π-Calculus for SoS”, defined as a novel variant of the π-Calculus based on concurrent constraints and inferred channel bindings for enabling the formal modeling of software-intensive SoSs, meeting their challenging architectural characteristics.
OpenTransputer: Reinventing a Parallel Machine from the Past
Authors: Amaya García Andrés  , Keller David  , May David
Published: February 2025

The OpenTransputer is a new implementation of the Transputer first launched by Inmos in 1985. It supports the same instruction set, but uses a different microarchitecture that takes advantage of today's manufacturing technology; this results in a reduced cycle count for many of the instructions. Our new Transputer includes support for channels that connect to input-output ports, enabling direct connection to external devices such as sensors, actuators and standard communication interfaces. We have also generalised the channel communications with the support of virtual channels and the design of a message routing component based on a Beneš switch to enable the construction of networks with scalable throughput and low latency. We aim to make the OpenTransputer and switch components available as open-source designs.
Automated Detection of Lung Nodules Using HOG Technique with Chest X-Ray Images
Authors: Raghavendra U.  , Gudigar Anjan  , Rao Tejaswi N.  , Fujita Hamido  , Acharya U. Rajendra
Published: February 2025

Lung disease is a growing disease and hence needs lot of attention. It is difficult to delineate the boundary of the lung when it is imaged through X-ray due to poor resolution. Hence, computer aided diagnosis (CAD) is preferred as it assists the radiologists in efficient diagnosis. In this work, a novel supervised classification technique is proposed using histogram of oriented gradient (HOG) and neighborhood preserving embedding (NPE). Our method is evaluated using 2000 chest X-ray images and can efficiently classify normal and abnormal classes with a promising performance of 97.95% accuracy, using support vector machine (SVM) classifier.
Mathematical Model of Master Production Schedule for Kalak Refinery Plant
Authors: Sadiq Shereen S.  , Abdulazeez Adnan Mohsin  , Haron Habibollah
Published: February 2025

Oil refinery production planning is a complex system and crucial. The optimization for production plan needs a mathematical model to represent the production system that can be used by the optimization model. This paper presents a mathematical model of Master Production Scheduling (MPS) for Kalak Refinery System that can be used for future especially in optimizing its production system. In spite of the fact that MPS has been broadly studied but it has been very few in refinery system maybe because of its complexity in limited production capacity and flexible fabrication routings. In the mathematical model, steps in deriving the model are explained started from the block diagram of the production system, definition of all variables, list of constraints and lastly the final mathematical model with the objective and its general constraints. The model has been validated by comparing the actual production volume with the estimated volume calculated by the model. This paper successfully presents and validates a mathematical model for multi-objective decision model.
Identification of Coordination Strategies and Indicators for Global Software Development Projects: Interview Outcome
Authors: Subbarao Anusuyah  , Mahrin Mohd Naz'ri
Published: February 2025

Generally, software measurement is considered as a management tool which if conducted in an effective manner, helps the project manager and the entire software team to take decisions that leads to successful completion of the project. Many indicators have been projected and validated in the field of software engineering but indicators related to Global Software Development (GSD) projects still need to be explored. Identification of indicators may guide the project managers to assess the coordination processes effectively between collocated and distributed team in GSD environment. The aim of this paper is to identify the coordination strategies and related indicators from the perspectives of GSD practitioners. We carried out semi structured interview sessions among project managers and consultants from GSD industries to accomplish this task. The collected data were analysed using Thematic Analysis and Nvivo software. The results of this study are a set of coordination strategies and related indicators that can be a guideline for project managers to assess the coordination processes in GSD projects development towards its success.
Timeline: A Chinese Event Extraction and Exploration System
Authors: Li Chenguang  , Cheng Hao  , Xiao Yanghua  , Xie Chenhao  , Jiang Haiyun  , Feng Suo
Published: February 2025

Event extraction plays a significant role in information extraction (IE). Compared with previous works on event extraction in English, relatively little effort has been made to extract Chinese events. Existing Chinese event extraction systems have two main drawbacks. First, they can only extract a limited number of events. Second, they don't organize their extracted events by entities or date or demonstrate them in a user-friendly way. In this paper, we propose Timeline, a Chinese event extraction system that extracts massive events from Chinese online encyclopedias . Our proposed system extracts event triples (entity, date and event description) from huge numbers of articles meanwhile generating the largest Chinese structured event base. Our system also automatizes event validation and normalization procedures to harvest high-quality events, and then organize events by corresponding entities and dates. Furthermore, we have also developed an interactive web portal that encodes events along a visual timeline, which satisfies the process of exploring historical events. We also designed comprehensive experiments to show the effectiveness of our extraction and validation work. Both extracted event triples and timeline web portal are published.
Unconscious Patterns in Argument: Fractal Dimension in Oratory
Authors: Dunne Paul E.
Published: February 2025

We consider a long-established approach to text analysis applying it to the specific structure of oratory and spoken arguments. This method consists of deriving measures of the so-called “Fractal dimension”. Building on previous work linking “aesthetic appeal” to fractal dimensions of a particular value from the fields of music and literary study, we present empirical analyses of a number of different “persuasive texts” against a range of different choices for determining fractality. Initial results suggest that distinctive “oratorical aims” as may be represented in a text become evident via distinctive fractal dimension.
Probabilistic Graded Semantics
Authors: Thimm Matthias  , Cerutti Federico  , Rienstra Tjitze
Published: February 2025

We propose a new graded semantics for abstract argumentation frameworks that is based on the constellations approach to probabilistic argumentation. Given an abstract argumentation framework, our approach assigns uniform probability to all arguments and then ranks arguments according to the probability of acceptance wrt. some classical semantics. Albeit relying on a simple idea this approach (1) is based on the solid theoretical foundations of probability theory, and (2) complies with many rationality postulates proposed for graded semantics. We also investigate an application of our approach for inconsistency measurement in argumentation frameworks and show that the measure induced by the probabilistic graded semantics also complies with the basic rationality postulates from that area.
Structural Constraints for Dynamic Operators in Abstract Argumentation
Authors: Wallner Johannes P.
Published: February 2025

Many recent studies of dynamics of formal argumentation in AI focus on the well-known formalism of argumentation frameworks (AFs). Despite their use-fulness in many areas of argumentation, their abstract notion of arguments creates a barrier for operators that modify a given AF, namely in the case that dependencies between arguments have been abstracted away that might be subsequently missed. In this paper we aim to support development of dynamic operators on formal models in abstract argumentation by providing constraints imposed on the modification of the structure that can be used to incorporate information that has been abstracted away. Towards a broad reach, we base our results on the general formalism of abstract dialectical frameworks (ADFs) in abstract argumentation, and study the complexity of the proposed structural constraints. To show applicability, we adapt an extension enforcement operator on AFs to ADFs that is allowed to only add support relations between arguments. We show feasibility of our approach by an experimental evaluation of an implementation of this operator.
Advanced Rich Transcription System for Estonian Speech
Authors: Alumäe Tanel  , Tilk Ottokar  , Ullah Asad
Published: February 2025

This paper describes the current TTÜ speech transcription system for Estonian speech. The system is designed to handle semi-spontaneous speech, such as broadcast conversations, lecture recordings and interviews recorded in diverse acoustic conditions. The system is based on the Kaldi toolkit. Multi-condition training using background noise profiles extracted automatically from untranscribed data is used to improve the robustness of the system. Out-of-vocabulary words are recovered using a phoneme n-gram based decoding subgraph and a FST-based phoneme-to-grapheme model. The system achieves a word error rate of 8.1% on a test set of broadcast conversations. The system also performs punctuation recovery and speaker identification. Speaker identification models are trained using a recently proposed weakly supervised training method.
Web Pattern Navigation Profiling for Online Marketing Campaigns Design Support Under a Data Science Approach
Authors: Palomino Arturo  , Gibert Karina
Published: February 2025

Internet is a huge source of knowledge where people search entertainment and information for several reasons while leaving an invisible blueprint of their preferences, derived from their behavior while navigating and visiting several websites in a specific sequence of domains. This paper proposes a scalable algorithm for discovering the most probable contiguous sequences of domains visited by people from a given socio-demographic profile, of major utility to design marketing campaigns where the advertisement is given in several successive banners. The proposal helps to identify a sequence of websites to distribute these banners. The user patterns of visits are inferred on the basis of a user-centric database containing user's behavior. A combination of data mining methodologies provides evidence of differentiated navigation habits for specific household characteristics and provides decision support tools to assist the marketing designers.
Enhancing Text Spotting with a Language Model and Visual Context Information
Authors: Sabir Ahmed  , Moreno-Noguer Francesc  , Padró Lluís
Published: February 2025

This paper addresses the problem of detecting and recognizing text in images acquired ‘in the wild’. This is a severely under-constrained problem which needs to tackle a number of challenges including large occlusions, changing lighting conditions, cluttered backgrounds and different font types and sizes. In order to address this problem we leverage on recent and successful developments in the cross-fields of machine learning and natural language understanding. In particular, we initially rely on off-the-shelf deep networks already trained with large amounts of data and that provide a series of text hypotheses per input image. The outputs of this network are then combined with different priors obtained from both the semantic interpretation of the image and from a scene-based language model. As a result of this combination, the performance of the original network is consistently boosted. We validate our approach on ICDAR'17 shared task dataset.
Brain MR Image Segmentation Using Multiphase Active Contours Based on Local and Global Fitted Images
Authors: Akram Farhan  , Singh Vivek Kumar  , Sarker Md. Mostafa Kamal  , Garcia Miguel Angel  , Puig Domenec
Published: February 2025

The study of white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF) regions in the brain magnetic resonance (MR) images can be useful for determining different brain disorders, assisting brain surgery, post-surgical analysis, saliency detection and for studying regions of interest. In this paper, a novel hybrid region-based multiphase (four-phase) active contours method is proposed to partition a brain MR image into three distinct regions i.e., GM, WM and CSF. The proposed energy functional is formulated by combining local and global fitted images in a multiplicative fashion. Both fitted images are defined by integrating two-phase global and local intensity from Chan-Vese (CV) and local binary fitted (LBF) models, respectively. In this paper, a post processing (pixel correction) method is also devised which improves the accuracy of the segmented WM, GM and CSF regions in a brain MR image. Different thresholds are decided based on averages of all three regions. According to the computed thresholds, a binary value (0 or 1) is then assigned to each pixel. Experimental results using both two-dimensional (2D) and three-dimensional (3D) brain MR images show that the proposed method outperforms the state-of-the-art both qualitatively and quantitatively.
Hybr-iT Project: Initial Steps Towards Contextual Robotic Manipulation for Human-Robot Teams in Industrial Environments
Authors: de Gea Fernández José  , Niemann Nils  , Stock Sebastian  , Günther Martin  , Mronga Dennis  , Wiese Hendrik  , Kirchner Elsa  , Stiene Stefan
Published: February 2025

This paper describes initial efforts and ideas towards a robot-agnostic contextual control software architecture for robots cooperating with humans in factory environments. The robot architecture, through the interplay with a middleware (resource-oriented architecture), should enable existing factory IT systems and robots to be interconnected. The aim is that the use of contextual information directly coming from the IT systems together with background knowledge should allow more flexible robot skills which are adaptive to the current context, situation or ongoing task.
Fuzzy Quantified Subgraph Patterns Matching over Fuzzy RDF Graph
Authors: Li Guanfeng  , Zhang Peng
Published: February 2025

Fuzzy quantified queries have been studied over several types of data and allow to sum up large volumes of data in a very intuitive manner. However, few works have been led on Resource Description Framework (RDF) graph databases. In this paper, we introduce the notion of fuzzy quantified queries in a (fuzzy) RDF database context. We firstly show how these queries can be defined and implemented in the quantified graph pattern, which is an extension of graph patterns by supporting linguistic quantifier on edges. Then, we develop an algorithm for evaluating quantified RDF graph patterns, that is based on a backtracking strategy which incrementally finds partial solutions by adding joinable candidate vertices or abandoning them when it determines they cannot be completed. Finally, we show some experimental results on real-life data and synthetic data that show the efficiency and scalability of our approach.
Regression-Based Estimation of Covariance Matrix of Stock Returns
Authors: Kim Saejoon  , Kim Soong
Published: February 2025

Covariance matrix estimation is an important problem in various fields of social science including financial economics. In this paper, we consider the estimation problem in the regression framework in order to resolve the deficiencies of the traditional methods. In particular, we establish the regression framework using support vector regression for the in-sample-based and the shrinkage-based estimation methods. Empirical results will indicate that our proposed covariance matrix estimation methods sufficiently perform superior to the two traditional estimation methods.
Jumping Fuzzy Finite Automata and Their Languages
Authors: Martinek Pavel
Published: February 2025

The paper introduces concepts of jumping fuzzy finite automata and jumping fuzzy grammars. Relations among some families of fuzzy languages accepted by the automata and generated by the grammars are described.
Content-Based Download Cooperation Scheme in VANET: A Brief Review
Authors: Zhu Jun  , Wang Ting  , Li Kai  , Fang Weidong  , Zhang Wuxiong
Published: February 2025

The existence of a content-based download cooperation scheme in intermittently connected VANET not only satisfies the needs of vehicle users for security information services, but also facilitates the implementation of network value-added services. It enables users to download large amount of data files or watch online video in the driving vehicle, and so on. Meanwhile, the performance index of VANET is improved, such as increase downloaded throughput and spectral efficiency, reduce the data transmission delay and complexity of cooperation scheme, etc. Although there are many researches on the current cooperation schemes, but they are short of a systematic review. Therefore, the cooperative communication schemes in VANET will be divided into three categories. The cooperation schemes are introduced in detail in the cooperation conditions, communication modes and cooperative strategies, respectively. Based on the above classification, this paper focuses on the study, comparison and analysis of the latest cooperative communication schemes from wireless access technology, cooperation direction, download data volume, etc. In addition, some problems and future research directions of cooperation scheme in the current VANET are proposed.
An Evaluation Framework for Groups' Clustering Algorithms in Social Networks
Authors: Rodas Britez Marcelo  , Lissoni Davide  , Marchese Maurizio
Published: February 2025

Older adults are increasingly using Social Networks Sites to support their social interactions. Moreover, the popularity of such network sites, the availability of datasets and recent progress in the computing systems and machine learning areas have made social network analysis a current important area of research. In this context, our current research aim is to investigate how these technological platforms are affecting the lifestyle of older adults. In this paper, we propose a Java oriented framework that can assist researchers in this area in the analysis of social networks groups, specifically in the comparison of user's groups creation from existing cluster-based algorithms. To validate the proposed framework we use a dataset extracted from the Meetup social network – a website providing members software services to schedule events using a common platform. For our study, we filtered the data for the specific group of older adults. The framework proposes several ways of evaluating the quality of the data, and is extensible to other clustering algorithms and evaluation metrics. Currently, we have tested our framework with the following well known clustering algorithms: k-means, fuzzy k-means, and affinity propagation. We report some preliminary results obtained by using the proposed framework and the above clustering algorithms using the extracted Meetup dataset.
Dynamic Soft Sensor Based on Impulse Response Template and Deep Neural Network for Industrial Processes
Authors: Liu Lei  , Wang Kangcheng  , Shang Chao  , Yang Fan  , Lyu Wenxiang  , Huang Dexian
Published: February 2025

In recent years, deep learning has been applied to build soft sensor models. Compared to the classical latent variable models, the model based on deep learning has a good performance in tackling nonlinearity in process data. However, static soft sensor based on deep neural network (SSSDNN) fails to take into account the dynamic characteristics, which are unavoidable in some applications. To improve the performance of the soft sensor, a novel dynamic soft sensor model based on impulse response template and deep neural network (DSSDNN) is proposed by means of Wiener structure, and an iterative algorithm will be used to train this novel model. A case study based on real debutanizer column data demonstrates the desirable prediction performance of DSSDNN and shows that the DSSDNN is able to obtain better approximation accuracy than the static soft sensor model in nonlinear processes with dynamic characteristics.
Multi-Granularity Soft Rough Fuzzy Sets and Its Application
Authors: Liu Jianbo  , Zhao Pengyun  , Zhang Yanyan  , Zhang Lichao
Published: February 2025

In order to analyze fuzzy data efficiently, this paper mainly introduces the multi granularity soft rough set model, which is a new method to deal with uncertain information and describe fuzzy concepts. Furthermore, an algorithm is put forward which can be used in multi attribute decision making. Finally, we provide a concrete example in order to verify its effectiveness.
A Distributed Management System for Massive GIS Data Using HBase
Authors: Liu Dawei  , Li Xuemei  , Wang Haiyang
Published: February 2025

Data organization, storage, management, analysis and mining are the core of wisdom in smart city. With the development of big data technology and the promotion of smart city construction, how to manage more massive geographic information data has become a hot topic of research. This paper designs a GIS data management system based on distributed database HBase. The system optimizes the generation and storage process of raster data and writes massive raster data to HBase storage and index directly. At the same time, in view of the storage, index and retrieval of vector spatial data, a new design of rowkey is proposed. Considering both the latitude and longitude, and the type and attribute of the spatial data, it can quickly locate the data to be returned through the rowkey of HBase when the vector geographic information is retrieved in space position, and the data will be carried out. Fast filtering can achieve the purpose of retrieving vector spatial data at high speed. The above methods are verified with real GIS data in the cluster environment of HBase. The results show that the proposed system has high data storage and retrieval performance and achieves high efficiency when perform storage and real-time high-speed retrieval of massive geographic information data.
Legal Fictions and the Essence of Robots: Thoughts on Essentialism and Pragmatism in the Regulation of Robotics
Authors: Fossa Fabio
Published: February 2025

The purpose of this paper is to offer some critical remarks on the so-called pragmatist approach to the regulation of robotics. To this end, the article mainly reviews the work of Jack Balkin and Joanna Bryson, who have taken up such approach with interestingly similar outcomes. Moreover, special attention will be paid to the discussion concerning the legal fiction of ‘electronic personality’. This will help shed light on the opposition between essentialist and pragmatist methodologies. After a brief introduction (1.), in 2. I introduce the main points of the methodological debate which opposes pragmatism and essentialism in the regulation of robotics and I examine how legal fictions are framed from a pragmatist, functional perspective. Since this approach entails a neat separation of ontological analysis and legal reasoning, in 3. I discuss whether considerations on robots' essence are actually put into brackets when the pragmatist approach is endorsed. Finally, in 4. I address the problem of the social valence of legal fictions in order to suggest a possible limit of the pragmatist approach. My conclusion (5.) is that in the specific case of regulating robotics it may be very difficult to separate ontological considerations from legal reasoning—and vice versa—both on an epistemological and social level. This calls for great caution in the recourse to anthropomorphic legal fictions.
The Value of Necessity
Authors: Jenkins David
Published: February 2025

The changes in work being augured by advanced robotics radically changes what we mean by necessity. With massive reductions in the amount of time spent doing things we do because we have to, the idea of our social, political and economic environments making demands of us becomes much harder to inculcate into the daily habits, routines and dispositions of people. In this paper, I argue that the notions of necessity and involuntary engagements, although in tension with the significant goods proffered by expanded freedoms, are nevertheless important parts of social and ethical life and should be given due consideration.
Kinetic Values, Assistive Robotics, and Smart Urban Environments
Authors: Parviainen Jaana  , Särkikoski Tuomo
Published: February 2025

In this paper, we introduce a novel idea of kinetic values to examine challenges regarding assistive robots that are expected to support human movement in smart homes and mobility in urban environments. By problematizing the medical-engineering paradigm in care robotics for aging people, we argue that assistive robotics has been based on an idealized and anaemic vision of human motility. Drawing on the phenomenological theory of kinaesthesia, we analyse recent assistive robotics innovations based on four key conceptualisations: ‘self-motion’, ‘being moved’, ‘co-motion’, and ‘united movement’. We conclude that the phenomenological approach provides a better understanding of the interplay among assistive robots, kinaesthesia and urban technological infrastructure.
Best Energy Mixture Model Based on Simulation Analysis on Electricity Generation – A Case Study for Latvia with Combination of Wind and Biomass Power
Authors: Endrjukaite Tatiana  , Dudko Alexander  , Okano Kunihiko  , Yamamoto Hiromi
Published: February 2025

In this paper we proposed a new best energy mixture model and electricity generation simulation approach. This approach is explained based on Latvia case example where we use existing gas power station with combination of introduced hybrid bio-wind energy system. The goal is to replace imported fossil fuels for electricity generation with locally produced renewable sources of energy, to secure and increase independence of the country and to reduce CO2 emissions. The introduced best energy mix is based on two renewable sources: biomass and wind power stations. To make use of both advantages we combine these two types of power stations into one hybrid power system that gives a stable power output to supply country's energy needs.
Constraint Programming Based on Matrix-Like Representation of Qualitative Constraints
Authors: Zuenko Alexander  , Oleynik Yirii
Published: February 2025

The paper proposes an original approach to solving the problem of ineffective processing of qualitative constraints of a subject domain using the constraint programming technique. The approach is based on the use of specialized matrix-like structures providing a “compressed” representation of constraints over finite domains, as well as on the use of the authors' inference techniques on these structures. In comparison with the prototypes using the typical representation of multi-place relations in the form of a table, the techniques make it possible to more efficiently reduce the search space. The paper presents practical aspects of implementation of user-developed types of constraints and corresponding algorithms-propagators, with the help of constraint programming libraries. The algorithms performance has been assessed by means of the above matrix structures to clearly demonstrate the advantages of representation and processing of qualitative constraints of a subject domain.
Modelling Legal Knowledge for GDPR Compliance Checking
Authors: Palmirani Monica  , Governatori Guido
Published: February 2025

In the last fifteen years, Semantic Web technologies have been successfully applied to the legal domain. By composing all those techniques and theoretical methods, we propose an integrated framework for modelling legal documents and legal knowledge to support legal reasoning, in particular checking compliance. This paper presents a proof-of-concept applied to the GDPR domain, with the aim to detect infringements of privacy compulsory norms or to prevent possible violations using BPMN and Regorous engine.
Classifying Semantic Types of Legal Sentences: Portability of Machine Learning Models
Authors: Glaser Ingo  , Scepankova Elena  , Matthes Florian
Published: February 2025

Legal contract analysis is an important research area. The classification of clauses or sentences enables valuable insights such as the extraction of rights and obligations. However, datasets consisting of contracts are quite rare, particularly regarding German language.