bluuebunny/crossref_metadata_2025 huggingface dataset:
Making Sense of Empathy with Social Robots
Authors: Redstone Josh
Published: February 2025

Social robots exploit human-like behaviors so that people might form emotional bonds with them. Ostensibly, such bonding is an empathic response on the part of the person toward the robot. However, as philosopher Catrin Misselhorn points out, it's conceptually problematic to say that people empathize with robots, for the social robots of the present arguably don't possess human-like emotions. To address this concern, Misselhorn proposes that empathy with robots is possible owing to a sort of interplay between perception and imagination that she calls “imaginative perception.” In this paper, I shall make a preliminary sketch of a conceptual framework that, I argue, serves as a clearer, more conceptually straight-forward alternative to imaginative perception. On this framework, empathy with social robots is the result of a kind of perceptual illusion, rather than the result of the imaginative perception of emotion.
Sociality Without Prior Individuality
Authors: Levine Alex
Published: February 2025

The philosophical tradition offers us numerous variations on the claim that for human agents, individuality is prior to sociality: what I will call the Priority of Individuality Thesis (POI). As an implicit presupposition, I argue that it vitiates attempts to account for sociality, but also undermines the category of the individual person itself. This paper problematizes POI. Though I ultimately want to show that sociality is prior to or at least co-constitutive of individuality, my primary objective here will be critical. I will challenge POI on two grounds. First, in most of its variants, POI turns on an equivocal notion of priority. There are at least three distinct kinds of priority at work in the philosophical literature: epistemic priority, ontological priority, and diachronic temporal priority. There are also numerous problematic equivocations among them. Second, I consider two examples from the philosophical canon in which POI can be seen to have pathological, even paradoxical consequences. By individuating moral agency by recourse to autonomous individual human subjects, both Kantian and Utilitarian accounts of morality erase the possibility of morally significant differences among such subjects. Furthermore, two classic solutions to the problem of other minds both presuppose that the problem of other minds is the epistemological problem of identifying individual subjects as such. And as Merleau-Ponty has suggested, both “solve” the problem of other minds by essentially erasing otherness; there may be multiple instances of mind, but qua instances of mind, there are no significant differences between them.
Classify Blog Articles Using Queried Keywords
Authors: Chen Yi-Hui  , Lu Eric Jui-Lin  , Wu Tsai Ying  , Lin Tsung Hau
Published: February 2025

According to a Technorati report, 40% of blog readers agree that blog content is better than content in mainstream media; 48% of bloggers believe network users will obtain knowledge through blogs within the next five years. However, blogs still exist like isolated islands. One approach to break this barrier is to use a clustering technique to link similar articles together. Traditionally, clustering methods utilize keywords extracted from whole articles. Because queried keywords represent user's intentions, we believed they provide a better foundation for clustering methods.The experimental results show that the average precision and recall values of clustering methods based on queried keywords perform better than those of using social tagging and keywords extracted from whole articles. In addition, FCA+HAC using queried keywords outperforms other studied methods.
An efficient exact algorithm for P
Authors: Chang Maw-Shang  , Chen Li-Hsuan  , Hung Ling-Ju
Published: February 2025

Given a graph G = (V, E), a P2-packing 𝒫 is a collection of vertex disjoint copies of P2s in G where a P2is a simple path with three vertices and two edges. The MAXIMUM P2-PACKING problem is to find a P2-packing 𝒫 in the input graph G of maximum cardinality. This problem is NP-hard for cubic graphs. In this paper, we give a branch-and-reduce algorithm for the MAXIMUM P2-PACKING problem in cubic graphs. We analyze the running time of the algorithm using measure-and-conquer and show that it runs in time O*(1.4366n) which is faster than previous known exact algorithms where n is the number of vertices in the input graph.
Fast Seriation of Multiple Homogeneous-content Videos Using Audio-visual Features
Authors: Zeng Yi-Chong  , Chang Wen-Tsung
Published: February 2025

Facing a large number of homogeneous-content videos shared on the internet, it is essential to seriate videos with prior information. In this paper, we propose fast video seriation (ViSA) scheme based on audio-visual features. The features, mel-frequency cepstral coefficients (MFCCs) and color histogram are extracted as audio feature and visual feature, respectively. Principal component analysis (PCA) is exploited for dimensionality reduction as well as time consumption reducing during feature matching. Subsequently, the proposed fast feature matching computes difference between features, in the meantime, estimates time difference between two videos. The chronological ordering establishes relationship among videos, and then seriates videos by synchronizing video time. The experiment results will show the proposed scheme reduces more 50% computing time than brute force approach does in feature matching. The error of time difference between groundtruth and the estimated result is very small. The experiment results will demonstrate that the proposed scheme is efficient in video seriating.
2D to 3D Video Conversion via Depth Inference
Authors: Kuo Tien-Ying  , Hsieh Cheng-Hong  , Wan Kuan-Hung  , Chen Yan-Jhu
Published: February 2025

This work developed a robust 2D to 3D digital video conversion technique that allows traditional 2D video content to be displayed as 3D video. The main principle of this work is to infer an accurate depth map for each frame from the 2D video sequence as with the inter-frame consistence. First, we infer three depth maps for each frame: initial block-wise depth map, initial pixel-wise depth map, and region-wise propagated depth map. Then, Kalman filter is introduced to fuse three depth maps to a final depth map. Once the depth map of the 2D video is obtained, we can generate the left and right images with it through DIBR algorithm, and display it on 3D stereoscopic display.
Combining SURF Features with GMM Classifier in Multi-Pose Face Recognition
Authors: Lin Shinfeng D.  , Wang Ding-En  , Chuang Chih-Yao
Published: February 2025

This paper aims to investigate a multi-pose face recognition system based on an effective design of classifier using SURF feature. In training phase, the proposed method utilizes SURF features to calculate similarity between two images from different poses of the same face. Face recognition model (GMM) is trained using the robust SURF features from different poses. In testing phase, feature vectors corresponding to the test images are input to all trained models for the decision of the recognized face. Experimental results are presented to demonstrate the comparative performance of the proposed method.
Vision-based Detection of Steel Billet Surface Defects via Fusion of Multiple Image Features
Authors: Hsu Chao-Yung  , Kang Li-Wei  , Lin Chih-Yang  , Yeh Chia-Hung  , Lin Chia-Tsung
Published: February 2025

Automatic inspection techniques have been widely employed to achieve high productivity while ensuring high-quality products in steel making industry. In this paper, a vision-based detection framework for automatically detecting different types of steel billet surface defects is proposed. The defects considered in this study includes cratches, corner cracks, sponge cracks, slivers, and roll marks. In the proposed framework, to improve the quality of image acquisition for billet surface, two preprocessing techniques, i.e., automatic identification of ROI (region of interest) and HDR (high dynamic range)-based image enhancement techniques, are proposed. Then, DWT (discrete wavelet transform)-based image feature is extracted from the image to be detected and fused with the other two extracted local features based on variance and illumination to identify each defect on the billet surface. Experimental results have verified the feasibility of the proposed method.
Spatial-Temporal FAST Corner Detector for Videos
Authors: Lee Chia-Chun  , Chang Yun-Jung  , Lin Yen-Yu  , Huang Chun-Rong
Published: February 2025

Due to the increase of large-scale videos, efficient and effective corner detectors are necessary to facilitate video processing and the underlying applications. Designing a good corner detector often faces an accuracy/efficiency trade-off problem. To address this problem, a highly efficient and effective approach named spatial-temporal FAST corner detector is proposed to detecting corners for videos. Our approach considers both the spatial and temporal evidence to extract corners, each of which is an extreme when comparing it with the pixels on the surface of the spatial-temporal tube centered on it. To efficiently obtain spatial-temporal FAST corners, a two-step procedure is adopted. The FAST detector [1] is firstly used to filter out most non-corner pixels. Then, temporal consistency is checked for FAST corners to identify if these corners are spatial-temporal FAST corners. As a result, the detected corners are spatial-temporal coherent. In the experiments, seven the state-of-the-art corner detectors are compared. The results show the superiority of our approach in both repeatability and efficiency.
Construction of GPU Virtualization Using PCI Direct Pass-Through in Cloud
Authors: Yang Chao-Tung  , Ou Wei-Shen  , Lo Yu-Shang  , Huang Chao-Wei  , Chang Chih-Hung
Published: February 2025

The graphics processor is able to play an important role in cloud computing in a cluster environment, because it can be used to build a high-performance computing environment. In the cloud architecture, virtualization plays a very important part. Any virtual machine built with the nVIDIA graphics card will have the CUDA high-performance computing ability. This makes the virtual machine have not only virtual CPUs, but also physical graphics processors to do computations, resulting in much improvement in performance of the virtual machine.
Analysis of exercise ventilation with autoregressive model and Hilbert-Huang transform
Authors: Chu Hsueh-Ting  , Fu Tieh-Cheng
Published: February 2025

In this study, we propose a method for the quantitative analysis of exercise ventilation signal that is an important diagnostic tool to evaluate the prognosis of chronic heart failure (CHF) patients. An autoregressive (AR) model is used to filter the breath-by-breath measurement of ventilation. Then the signals before reaching the most ventilation are decomposed into intrinsic mode functions (IMF) by Hilbert-Huang transform (HHT). The average amplitude of the second IMF component AmpC2 is used as an indicator about pulmonary function for chronic heart failure patients.
Auto-Tuning for GPGPU Applications Using Performance and Energy Model
Authors: Lin Chih-Sheng  , Teng Shih-Meng  , Hsiung Pao-Ann
Published: February 2025

The general-purpose graphic processing unit (GPGPU) is a popular accelerator for general applications such as scientific computing because the applications are massively parallel and the significant power of parallel computing inheriting from GPUs. However, distributing workload among the large number of cores as the execution configuration in a GPGPU is currently still a manual trial-and-error process. Programmers try out manually some configurations and might settle for a sub-optimal one leading to poor performance and/or high power consumption. The state-of-the-art methods for addressing this issue are mainly based on the heavy profiling of computation kernels. This paper presents an auto-tuning approach for GPGPU applications with the performance and power models. First, a model-based analytic approach for estimating performance and power consumption of kernels is proposed. Second, an auto-tuning framework is proposed for automatically obtaining a near-optimal configuration for a kernel computation. In this work, we formulate the problem as the constraint optimization and solve it using the optimization algorithms. Experimental results show the fidelity of our proposed models for performance and energy consumption, and prove the rationality of adopting the auto-tuning method with the models for minimizing overhead. Further, the efficiency of tuning procedure and the quality of outcome configuration in our proposed method show the superiorities over the previous methods.
Detection and Classification of MS Lesions in Multispectral MR Images
Authors: Chen Hsian-Min  , Chai Jyh-Wen  , Chen Clayton Chi-Chang  , Ouyang Yen-Chieh  , Yang Ching-Wen  , Lee San-Kan  , Chang Chein-I
Published: February 2025

Quantitative analysis of patients with multiple sclerosis (MS) is an important issue in both diagnosis and therapy monitoring. We propose a new spectral signature detection approach for quantitative volumetric analysis of multispectral MRI. It is called constrained energy minimization (CEM) method, which is derived from the hyperspectral imaging processing. The CEM makes use of a finite impulse response (FIR) filter to linearly constrain a desired object while minimizing interfering effects caused by other unknown signal sources. The results show that the CEM method is a promising and effective spectral technique for lesions detection in multispectral MRI.
A Vision-based Virtual Keyboard Design
Authors: Weng Shiuh-Ku  , Hsieh Chaur-Heh  , Chy You-Zen
Published: February 2025

This paper intends to use image processing technology to develop the vision-based virtual keyboard. The proposed system uses a camera, a paper panel keyboard and the detection of user's hand gestures on the paper keyboard to achieve the input operation. The camera captures and analyzes the behavior of the user's hand gestures, which is a new noncontact input method. The experimental results show that the system is robust enough.
The Research of Experimental State Feedback Mechanism Based on Windows API – Take Cloud Security Experimental Platform for Instance
Authors: Ching Yun-Ting  , Chen Yi-Ming
Published: February 2025

With the rapid development of Internet, many personalized services also rise and bring many business opportunities. In addition to leveraging personalized service recommendation in e-commerce, the demand of personalized services also appear in personalized e-learning applications. Though there are many academic literatures and mechanisms about how do we access and identify user's action behaviors, still this information is not enough to give the details of user's operating behavior to be used to provide personalized services. To overcome this shortcoming, in this paper, we present a method to capture the users operating behaviors in Windows environments. We name it the Experimental Status Feedback Mechanism (ESFM). We apply the ECSM in the Cloud Security Experimental Platform (CSEP) which is an e-learning platform for exercising network security attacks and defenses and supports interactive teaching. As ESFM could capture user's action when they do either mouse or keyboard operations on the virtual machine, it will send the captured information to the CSEP server which in turn will presents appropriate steps for users to continue the security exercise. Our experiments showed that the overhead of ESFM is acceptable in the CSEP interactive learning environment and better than the traditional user state capture mechanisms, like Sikuli.
A Comparative Survey on RFID Grouping Authentication Protocols
Authors: Shen Jian  , Tan Haowen  , Ji Sai  , Ren Yongjun  , Wang Jin
Published: February 2025

Radio Frequency Identification (RFID) is a kind of communication technology using radio signal to identify specific targets and exchange data. With wide reading range and rapid identification speed, it is not necessary for the RFID reader to establish mechanical or optical contact very close to the tag. As a matter of fact, RFID tag is proved to be very potential and useful in many applications and assumed to replace the barcodes completely. In this paper, we give a comparative survey on RFID grouping authentication protocols and compare them in terms of different factors. Finally, the comparison results are presented.
Data Visualization in the Semantic Web
Authors: Ma Xiaogang  , Chen Yu  , Wang Han  , Zheng Jin Guang  , Fu Linyun  , West Patrick  , Erickson John  , Fox Peter
Published: February 2025

The widespread propagation of networked computers in Earth and space science and especially the representation of scientific data in formats that can be shared, analyzed and visualized has given rise to exciting new opportunities for exploration and discovery. Data are the representation of some facts. We can see data of various subjects, types and dimensions, such as a geologic map of New York State, records of sulfur dioxide concentration in the plume of Turrialba Volcano, Costa Rica, and the global mean sea level time series derived from radar altimeter records of the TOPEX and Jason satellite series. A unique feature of most data in Earth and space science is that they are georeferenced, which means they contain positional values below, on, and/or above the surface of the Earth. Information is the meaning of data as interpreted by human beings, such as, a geologist may discover some initial clues for shale gas exploration by using a geologic map, a volcanologist may detect a few abnormal sulfur dioxide concentration values in the plume of a volcano, and a climatologist may find that the global mean sea level has been rising in the past twenty years. People use their knowledge to discover information from data. The knowledge is their expertise or familiarity with one or more subjects under working. The discovery process of data to information in turn may make new contribution to people's knowledge.
An Approach to Tracing Information Flows of Untrustworthy Data in Programs
Authors: Lim Hyun-il
Published: February 2025

As the role of software increases in computing environments, issues in software security become more important problems. Taint analysis is a technique to trace and manage tainted data originated from untrustworthy sources in a program. This analysis can be applied to software security verification as well as software behavior understanding, testing unexpected errors, or debugging. In this paper, we present a method for tracing information flows of untrustworthy data in a base language. In addition, we verify the correctness of the proposed algorithm by implementing an analyzer, and show that propagation of untrustworthy data can be traced by the proposed algorithm. The proposed trace algorithm can be applied to understand the analysis procedures of information flows of untrustworthy data, and it can be used in software analysis to detect security problems that is caused by misuse of information in software or system.
Sliding Window Approach for Effective Mining of Transitional Patterns
Authors: Huang Yo-Ping  , Zwane Thanduxolo Shannon  , Dlamini Siphamandla Musa  , Dlamini Mbuso Gerald
Published: February 2025

Frequent pattern mining research has raised an interest in studying the dynamic behavior of patterns whose frequency significantly alters over time periods. This paper presents an investigation into using a sliding window approach in exploring the dynamic behavior of frequent patterns in consecutive time periods where they occur in a data set. This approach gives a promising alternative to the existing techniques being used for decision makers to know the exact period in which a pattern's frequency significantly alters and therefore responds appropriately. The results will help in detecting and reporting significant changes in the frequency of patterns sequentially from one time period to another.
Transformation-based Computation and Impossibility Results
Authors: Pankova Alisa  , Laud Peeter
Published: February 2025

In this chapter we study transformation-based approaches for outsourcing some particular tasks, based on publishing and solving a transformed version of the problem instance. First, we demonstrate a number of attacks against existing transformations for privacy-preserving linear programming. Our attacks show the deficiencies of existing security definitions; we propose a stronger, indistinguishability-based definition of security of problem transformations that is very similar to IND-CPA security of encryption systems. We study the realizability of this definition for linear programming and find that barring radically new ideas, there cannot be transformations that are information-theoretically or even computationally secure. Finally, we study the possibility of achieving IND-CPA security in privately outsourcing linear equation systems over real numbers, and see that it is not as easy as for linear equations over finite fields for which it is known that efficient information-theoretically secure transformations do exist.
Social Need for Secure Multiparty Computation
Authors: Kanger Laur  , Pruulmann-Vengerfeldt Pille
Published: February 2025

The aim of this chapter is to introduce and discuss the potential socio-technical barriers that might be hindering the adoption of Secure Multiparty Computation (SMC) techniques. By investigating the conditions of adoption of technology under development, we are able to find different solutions that support the technology adoption process. We set out to interview the potential future users of SMC technologies to investigate the most likely adopters and to find the best directions for future developments of SMC. SMC is compared with the existing practices of data handling to demonstrate its advantages as well as disadvantages. In the current development phase, the focus of SMC advances needs to be on the usefulness and on finding an appropriate niche for the technology.
Harmonising the public service models of the Points of Single Contact using the Core Public Service Vocabulary Application Profile
Authors: Loutas Nikolaos  , De Keuzer Michiel  , Tarabanis Konstantinos  , Alvarez-Rodriguez Miguel  , Burian Peter
Published: February 2025

In the process of implementing the Services Directive, Member States have implemented electronic Points of Single Contact (PSCs) offering public services categorised according to business events. The objective of this paper is to present a commonly agreed data model for describing business events and public services with a particular focus on the electronic PSCs. The paper describes the definition and verification of this data model, called Application Profile of the ISA Core Public Service Vocabulary (CPSV-AP), which includes 16 classes and 85 properties. Moreover, the paper describes the model's mapping to the data models used in the PSCs of 10 MS.
Can predicate invention compensate for incomplete background knowledge?
Authors: Cropper Andrew  , Mugglteon Stephen
Published: February 2025

In machine learning we are often faced with the problem of incomplete data, which can lead to lower predictive accuracies in both feature-based and relational machine learning. It is therefore important to develop techniques to compensate for incomplete data. In inductive logic programming (ILP) incomplete data can be in the form of missing values or missing predicates. In this paper, we investigate whether an ILP learner can compensate for missing background predicates through predicate invention. We conduct experiments on two datasets in which we progressively remove predicates from the background knowledge whilst measuring the predictive accuracy of three ILP learners with differing levels of predicate invention. The experimental results show that as the number of background predicates decreases, an ILP learner which performs predicate invention has higher predictive accuracies than the learners which do not perform predicate invention, suggesting that predicate invention can compensate for incomplete background knowledge.
The Power Laws of the Italian Constitutional Court, and Their Relevance for Legal Scholars
Authors: Agnoloni Tommaso  , Pagallo Ugo
Published: February 2025

The paper examines two different sets of rulings by the Italian Constitutional Court (“ICC”), namely the in via incidentale (“IVI”), and the in via principale (“IVP”), rulings, vis-à-vis the web of scholarly opinions (“WSO”), devoted to such cases. On the one hand, the paper shows that all such networks, i.e. IVIN, IVPN, and WSO, follow power laws patterns of informational distribution. On the other hand, this stance allows us to deepen the meaning of legal relevance. In addition to the ICC decisions widely debated by scholars, we should take into account cases that are relevant in both IVIN and IVPN and still, WSO scarcely debates them. This is the class of legal cases that scholars ignore at their own risk, since the higher a case is ranked in the citation network of the court, the higher the probability that scholars will have to reflect on such case soon.
Implementation of ECLI – State of Play
Authors: Van Opijnen Marc  , Ivantchev Alexander
Published: February 2025

In 2010 the Council of the European Union laid down the standard for the European Case Law Identifier (ECLI). In this paper we assess to what extent and how ECLI has been implemented by national and European courts, as well as the state of play regarding the ECLI search engine on the European e-Justice Portal. With a lot of work to be done, the ongoing developments justify a positive outlook.
Simulation of Cyber-Physical Attacks on Water Distribution Systems with EPANET
Authors: Taormina Riccardo  , Galelli Stefano  , Tippenhauer Nils Ole  , Salomons Elad  , Ostfeld Avi
Published: February 2025

In this work, we discuss the use of EPANET to simulate the effects of malicious cyber-physical attacks on water distribution systems. EPANET—a standard numerical modeling environment developed by the US Environmental Protection Agency—models hydraulic and water-quality behavior of pressurized pipe networks. EPANET promises to be well suited to show the effects of direct attacks on hydraulic actuators, such as pumps, or the effects of attacks on sensors. Using the C-Town benchmark network, we show that EPANET has some limitations when modeling these attacks, and we report the workarounds needed to overcome these limitations. In particular, we describe attacks that change the control strategies of pumps, and attacks that alter the tank water level reported by sensors.
Q-Floid: Android Malware detection with Quantitative Data Flow Graphs
Authors: Castellanos John Henry  , Wüchner Tobias  , Ochoa Martín  , Rueda Sandra
Published: February 2025

Due to the rapid proliferation of Android malware, conventional anti-malware signature-based solutions face significant challenges to battle against cybercrime. In this work, we propose to use quantitative data flow profiles between system entities such as processes, files and sockets to detect malicious applications on Android, an approach which has been shown to be promising for detecting Windows malware. Our approach uses features based on graph-theoretical metrics as a basis for the analysis. Those features are trained through multiple machine learning algorithms obtaining malware detection rates of up to 93% for variants of known (trained) families and detection of new families of up to 84%.
Accurate in-network file-type classification
Authors: Divakaran Dinil Mon  , Liau Yung Siang  , Thing Vrizlynn L.L.
Published: February 2025

Accurate classification of file types carried by network traffic aids in securing a network against various types of malicious activities such as malware infection, data exfiltration, botnet communication, etc. An important challenge here is to accurately classify files without slowing down network traffic. Therefore, the cost of accurate file-type classification has to be known. In this work, we carry out a preliminary but extensive investigation to evaluate different sets of features for file-type classification. The objective is to detect not only file types under normal scenario, but also files that are transferred with obfuscated headers. Our experiments show that the feature vector consisting of unigram frequencies leads to high accuracy; yet, combining this feature set with entropy feature vector leads to improvement in accuracies.
Data Driven Physical Modelling For Intrusion Detection In Cyber Physical Systems
Authors: Junejo Khurum Nazir  , Yau David
Published: February 2025

Cyber physical systems are critical to the infrastructure of a country. They are becoming more vulnerable to cyber attacks due to their use of off the shelf servers and industrial network protocols. Availability on World Wide Web for monitoring and reporting, has further aggravated their risk of being attacked. Once an attacker breaches the network security, he can affect the operations of the system which may even lead to a catastrophe. Mathematical and formal models try to detect the departure of the system from its expected behaviour but are difficult to build, and are sensitive to noise. Furthermore they take a lot of time to detect the attack. We here propose a behaviour based machine learning intrusion detection approach that quickly detects attacks at the physical process layer. We validate our result on a complete replicate of the physical and control components of a real modern water treatment facility. Our approach is fast, scalable, robust to noise, and exhibits a low false positive (FP) rate with high precision and recall. The model can be easily updated to match the changing behaviour of the system and environment.
A Framework for Large-Scale Collection of Information from Smartphone Users based on Juice Filming Attacks
Authors: Meng Weizhi  , Lee Wang Hao  , Krishnan S.P.T.
Published: February 2025

Cyber security refers to protecting computers, networks, programs and data from unauthorized access, change or destruction. With the wide adoption of smartphones, a lot of sensitive information can be cleaned from users' interaction with their smartphones and tablets. In our previous effort, we have developed a type of charging attacks called juice filming attacks, which can sniff smartphone users' information when they are interacting with their phones during charging. With the increasing number of public charging stations, we notice that this type of charging attacks may become a big threat to compromise users' privacy. In this work, we propose a framework of collecting smartphone information in large-scale based on juice filming attacks. Then, we show that such charging attacks work well not only on Android phones, but also on the newly released iPhones. Our work points out that the proposed framework is feasible to threaten users' privacy in practice.
Hausdorff Measure-Based Attribute Reduction of Intuitionistic Fuzzy Decision Information Systems
Authors: Hao Wen-Juan  , Li Zhi-Wei
Published: February 2025

An indiscernibility relationship between intuitionistic fuzzy sets is constructed by using a kind of hausdorff measure on intuitionistic fuzzy sets at first. Also, the operators of lower approximation with accuracy ϵ are introduced by this indiscernibility relation, and its properties are discussed. Then, the intuitionistic fuzzy rough set model is defined based on the indiscernibility relation and lower approximate reductions, and a kind of attribute reduction algorithm of this model is given. An example is employed to show its application.
Reconstructed Phase Space Method for Event Prediction based on Sugeno-Type Fuzzy Inference
Authors: Kovalev Sergey  , Sukhanov Andrey
Published: February 2025

This paper proposes the modified time series data mining framework applying Reconstructed Phase Space to construct clusters from the temporal patterns, which are predictive of interesting events. Cluster objective function used in the presented technique is defined not only by cluster internal predictive patterns but also by estimation of the efficacy of cluster to characterize the predictive clusters. For prediction stage, framework uses initial both information about predictive clusters and expert knowledges by applying Sugeno-type fuzzy inference. Experimental results demonstrate presented framework can reach more effective results than existed algorithms, which utilize reconstructed phase space.
Lightweight Search Engine Based on Text-Mining
Authors: Liu Chao  , Yin Shi Qun  , Sun Meng Meng  , Gao Sheng
Published: February 2025

The search engine discussed in this paper is a directory search engine. It is used to solve issues of cache and performance optimization. It provides website search services for education resources of primary and secondary schools. Optimizations from the architecture design to underlying details of each level are as follows: TCP protocol and epoll multiplexer were used, search words correction mechanism was adopted for error correction and association recommendation, multiple index coordination mechanisms were used instead of the traditional single index, Redis was used to handle query history cache instead of traditional memcached systems. The experimental data is from Fudan University corpus. The test method is a comparison between longitudinal and traversal tests. It is found that the average service time has been reduced from 5ms to less than 1ms; the actual performance has been improved nearly 100 times when compared with the previous average results.
A Non-recursive Agorithm Based on Binary for Hanoi Problem
Authors: Pan Mei-Qin  , Pan Kai-Xiang
Published: February 2025

According to the Hanoi problem, this paper proposes a simple non-recursive algorithm based on binary number. First, it constructs a recursive definition tree for the problem. Next, it provides a non-recursive procedure by determining three factors of mobile elements; analysis of the relationship between a binary number and mobile order provides the non-recursive algorithm of the Hanoi problem. Based on the non-recursive algorithm, the algorithm for its solution is any natural number and location condition.
Big Data in Practice: A Study on Cloud Data Collection and Storage Services
Authors: Wang Chin-Wei  , Chang Shuchih Ernest
Published: February 2025

With the rapid growth of emerging services and applications, a variety of data to be processed become huge. And the increasing demand for cloud services, including cloud storage, computing and backup, more and more cloud services are emerging. Although many free cloud services (such as Google Drive, Dropbox, etc.) are available on the Internet, there is still much room for improvement. To store data in enterprises, General cloud storage space isn't suitable to deal with large and complex data. A NoSQL database would be the solution. The purpose of this study is to construct a cloud data collection and storage service via free cloud NoSQL database and relational database. We also test the performance and make comparison between NoSQL database and relational database. The benefit of our research is forming an architecture that could enhance the knowledge of using RDB and NoSQL database and let us clearly understand and realize the difference between them. We can also learn the detail process of collecting and storing big data from this study.
Applying Metaheuristic Algorithm to the Admission Problem as a Combinatorial Optimisation Problem
Authors: Okewu Emmanuel  , Misra Sanjay
Published: February 2025

Prominent among the factors militating against quality education is poor student intake standards. This in the long run has multiplier effect on the quality of a nation's human capital. The role of human capital in national transformation cannot be overemphasized. However, the process of developing human resource for socio-economic transformation, particularly in an optimal sense, means conscious and concerted efforts must be geared towards meritocracy. Secondary education is reputed for bridging the gap between primary education and tertiary education. It particularly provides the gateway for career development as subjects taken at the senior level are tailored towards future career choices. To ensure that resources invested in education are well utilized, students' admission process has to be streamlined to secure best candidates specifically in gifted schools where competition is high. This paper formulated a computational strategy to upgrade admission process by reducing cost and time associated with it. Using a Nigerian University Secondary School as case study, the researchers applied a Metaheuristics search algorithm to the admission problem of securing the best candidates from a pool of applicants. The results supported the claims that Metaheuristics algorithms are capable of optimizing an admission process in terms of cost and speed.
Modals as Predicates of Modal Objects
Authors: Moltmann Friederike
Published: February 2025

This talk will outline a novel semantics of modals based not on possible worlds and quantifiers ranging over them, but on what I will call ‘modal objects’, entities of the sort of permissions, obligations, needs, abilities, and essences. According to that semantics, modal predicates take modal objects as their implicit (Davidsonian) argument and the complement clause (or prejacent) of the modal acts as a predicate characterizing the modal object in terms of its satisfiers (truthmakers) and possibly violators (falsifiers), in roughly the sense of Fine's recent truthmaker semantics. On this approach, the difference between modals of necessity and possibility is made a matter of ontology as are inferential relations among modal sentences.
Challenges and Benefits in an Open Data Initiative – A Local Government Case Study of Myths and Realities
Authors: Melin Ulf
Published: February 2025

This paper investigates the myths and realities of open data at local government (a focused municipality) level. There are many expectations related to open government data (OGD) covering e.g. public transportation, car parks, public committee minutes and air quality measurements and the effects of more open public agencies and commercial possibilities, together with citizen benefits. Expectations are often uncritical and expressed in terms of rationalized myths. The purpose of this paper is to investigate myths and realities in a case study and to present lessons learned from focusing such dimensions in an ongoing and emerging local government OGD initiative. This study confirms previous research on open data myths, challenges and benefits from a local government perspective. The conclusions also illustrate three important findings directed to the existing body of research regarding the importance of alliances of stakeholders in OGD initiatives, aspects of heterogeneous organizations launching open data and reflections on the division of labour between public and private actors when handling different communication channels. Implications for research and practice are also outlined together with limitations and further research.
Towards Successful e-Government Initiatives: Exploring the Adaptation Strategies of Public Sector Middle Managers
Authors: Binsaif Nasser  , Adam Frederic  , Grace Audrey
Published: February 2025

Over the last two decades, research on adoption and technology acceptance of new information systems by users has provided very valuable insights. Most of this research has focused on the impact on citizens in a bid to measure the improvements in the quality and speed of the services provided. However, there is still a lack of understanding of internal users' reactions to new information systems, and in particular to new e-government systems. In light of this deficit, this research study draws on the Coping Model of User Adaption (CMUA) to examine the adaptation strategies of middle managers in public bodies when new e-government initiatives are implemented in their work place. We report on the preliminary findings of an initial qualitative case study of one such initiative. We believe that studying internal users' adaptation strategies is beneficial because these strategies influence the extent to which benefits arise from new electronic services provided by governments.
Smart Cities Through Implicit Participation: Using Gamification to Generate Citizen Input for Public Transport Planning
Authors: Johannessen Marius Rohde  , Berntzen Lasse
Published: February 2025

In this paper, we present a case study of the mobile app and ecosystem Trafpoint. Trafpoint is a system for registering when and where people travel by public transport, using gamification in an attempt to convince more people to travel in environmentally friendly ways. We argue that the Trafpoint app is a good example of what we call “implicit participation”, where user-generated data from volunteers generate valuable input for the political decision-making process. With the growth of sensors, smartphones being ubiquitous, and the growing interest in the Internet of Things, this form of participation has the potential to become very valuable for decision-makers in the coming years.
A Serious Game Prototype to Encourage Citizens to Use e-Government in Libya
Authors: Ahmed Alsanossi M.  , Buckley Kevan A.  , Moreton Robert  , Elmaghraby Adel
Published: February 2025

Citizen engagement was identified as one of the main factors in e-government success, and many projects failed due to a lack of citizen engagement, particularly in developing countries. The benefits of utilizing serious games in education and training and their positive impacts in the field are expected to be the same in an e-government context, hence, it is argued that the use of serious games to expand knowledge, training, build confidence and trust among citizens can improve their use of e-government service. This research paper discusses a study conducted with the aim of developing a “e-Reservation” service as a serious game that expands knowledge and trains Libyan citizens on how to act when using the actual e-service. The proposed serious game is dedicated to familiarizing players with all rules and system requirements. Results show that the use of serious games has a positive impact on citizens' motivation to engage with e-government.
Current Practice and Challenges of Data Use and Web Analytics in Online Participations
Authors: Serov Igor  , Leitner Maria  , Rinderle-Ma Stefanie
Published: February 2025

Information system design and implementation are key factors for electronic participatory processes and procedures. How information systems are designed does not only affect the procedures but also influences the trust building between organizers, operators and participants. In addition, the implementation often has to adhere to legal standards. In this paper, we aim to investigate current practice of data use in online participations. In particular, a qualitative analysis is conducted and 18 online participations are investigated on their data use, i.e. use of participant information, cookies and web analytics. The results show that most projects require and request data during site visits (e.g., IP address, browser type) and for active participation (e.g., name, email). The real benefit, however, for the use of web analytics is often unclear. Furthermore, often proprietary solutions for web analytics are used, even tough open source solutions (i.e. that store data locally) exist. For future projects, it is recommended to not only define but also keep privacy policies updated (according to the used technology) and to specify the purpose and goals of using web analytics.
Knowledge-Based Programs with Defaults in a Modal Situation Calculus
Authors: Claßen Jens  , Neuss Malte
Published: February 2025

We consider the realistic case of a GOLOG agent that only possesses incomplete knowledge about the state of its environment and has to resort to sensing in order to gather additional information at runtime, and where the agent is controlled by a knowledge-based program in which test conditions explicitly refer to the agent's knowledge (or lack thereof). In this paper, we propose a formalization of knowledge-based agents that extends earlier proposals by a form of non-monotonic reasoning that includes Reiter-style defaults. We present a reasoning mechanism that enables us to reduce projection queries about future states of the agent's knowledge (including nesting of epistemic modalities) to classical Default Logic, and provide a corresponding Representation Theorem. We thus obtain the theoretical foundation for an implementation where reasoning subtasks can be handed to an embedded off-the-shelf reasoner for Default Logic, and that supports a (in some respects) more expressive epistemic action language than previous solutions.
Entity Embeddings with Conceptual Subspaces as a Basis for Plausible Reasoning
Authors: Jameel Shoaib  , Schockaert Steven
Published: February 2025

Conceptual spaces are geometric representations of conceptual knowledge in which entities correspond to points, natural properties correspond to convex regions, and the dimensions of the space correspond to salient features. While conceptual spaces enable elegant models of various cognitive phenomena, the lack of automated methods for constructing such representations have so far limited their application in artificial intelligence. To address this issue, we propose a method which learns a vector-space embedding of entities from Wikipedia and constrains this embedding such that entities of the same semantic type are located in some lower-dimensional subspace. We experimentally demonstrate the usefulness of these subspaces as approximate conceptual space representations by showing, among others, that important features can be modelled as directions and that natural properties tend to correspond to convex regions.
Exact Particle Filter Modularization Improves Runtime Performance
Authors: Edgington Padraic D.  , Maida Anthony S.
Published: February 2025

Bayesian filters provide a robust and powerful technique for integrating noisy information in dynamic environments. However, the computational cost of the filtering algorithm depends on the size of the problem, and an effective solution may be constrained by execution time. This paper applies basic concepts of clustering and message passing to particle filters making them substantially faster to compute, while still maintaining the original accuracy. An example from vehicle state estimation is provided to illustrate how to implement the technique. Our results indicate that modularization can produce a speed up of over 28 times even on this small problem.
Description Logics Reasoning w.r.t. General TBoxes Is Decidable for Concrete Domains with the EHD-Property
Authors: Carapelle Claudia  , Turhan Anni-Yasmin
Published: February 2025

Reasoning for Description logics with concrete domains and w.r.t. general TBoxes easily becomes undecidable. However, with some restriction on the concrete domain, decidability can be regained. We introduce a novel way to integrate concrete domains 𝒟 into the well-known description logic, we call the resulting logic. We then identify sufficient conditions on 𝒟 that guarantee decidability of the satisfiability problem, even in the presence of general TBoxes. In particular, we show decidability offor several domains over the integers, for which decidability was open. More generally, this result holds for all negation-closed concrete domains with the EHD-property, which stands for ‘the existence of a homomorphism is definable’. Such technique has recently been used to show decidability of CTL*with local constraints over the integers.