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Obsolescence Forecasting of Intel Processors: A Transformers Approach 5th

Obsolescence Forecasting of Intel Processors: A Transformers...

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5th International Conference on Innovative Intelligent Industrial Production and Logistics

作者： Nouar, Manelle Souifi, Amel Decocq, Bertrand Zolghadri, Marc Orange Innovat Ave Republ Chatillon France Quartz Lab Rue Fernand Hainaut St Ouen Sur Seine France

ISBN: (纸本)9783031807596;9783031807602

This study investigates the issue of electronic product obsolescence, focusing on Intel processors within Orange's data centers. As digitization advances, managing stock and addressing supply chain disruptions become critical, particularly when dealing with the obsolescence of key components like servers. A proactive approach to obsolescence management is explored, emphasizing the importance of predicting the life cycle of server components to ensure operational continuity. The research highlights the use of advanced predictive techniques, specifically transformers, to forecast the life cycles of electronic devices. The study aims to provide insights into mitigating the risks associated with obsolescence, thereby aiding in the formulation of effective long-term strategies for maintaining and managing essential electronic components.

关键词： Deep learning Obsolescence Life cycle stages Predictive analysis Transformers

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Feature Extraction and Dimensionality Reduction to Evaluate Decision Tree Ensembles for Diabetic Retinopathy Detection 4th

Feature Extraction and Dimensionality Reduction to Evaluate ...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Lahmar, Chaymaa Idri, Ali Mohammed V Univ Rabat SPM ENSIAS Rabat Morocco Mohammed VI Polytech Univ Fac Med Sci Ben Guerir Morocco

ISBN: (纸本)9783031821554;9783031821561

Diabetic Retinopathy (DR) is an eye disease associated with chronic diabetes. It remains the primary cause of visual impairment and blindness among the global working-age population. Early detection of DR is crucial for ensuring timely diagnosis and effective treatment. This paper proposes a new homogeneous ensemble-based approach constructed using a set of hybrid architectures as base learners and two combination rules (weighted and hard voting) for referable DR detection, using fundus images from the Messidor-2, Kaggle DR, and APTOS datasets. The hybrid architectures are created using deep feature extraction techniques, dimensionality reduction techniques to reduce the size of the extracted features, and a decision tree algorithm (DT) for classification. The results showed the potential of the proposed new approach which achieved high accuracy values over the three datasets: 90.65%, 93.01%, and 83.32% using the APTOS, Kaggle DR, and Messidor-2 datasets respectively. Therefore, we recommend using the proposed approach since it is impactful for referable DR classification, and it represents a promising tool to assist ophthalmologists in diagnosing DR.

关键词： computer-Aided Diagnosis Diabetic Retinopathy Machine learning Deep learning Ensemble learning Image Processing

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Non-monotonic Extensions to Formal Concept Analysis via Object Preferences 5th

Non-monotonic Extensions to Formal Concept Analysis via Obje...

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5th Southern African Conference for Artificial Intelligence Research

作者： Carr, Lucas Leisegang, Nicholas Meyer, Thomas Rudolph, Sebastian Univ Cape Town Cape Town South Africa CAIR Cape Town South Africa Tech Univ Dresden Dresden Germany Ctr Scalable Data Analyt & Artificial Intelligenc Dresden Germany

ISBN: (纸本)9783031782541;9783031782558

Formal Concept Analysis (FCA) is an approach to creating a conceptual hierarchy in which a concept lattice is generated from a formal context. That is, a triple consisting of a set of objects, G, a set of attributes, M, and an incidence relation I on G x M. A concept is then modelled as a pair consisting of a set of objects (the extent), and a set of shared attributes (the intent). Implications in FCA describe how one set of attributes follows from another. The semantics of these implications closely resemble that of logical consequence in classical logic. In that sense, it describes a monotonic conditional. The contributions of this paper are two-fold. First, we introduce a non-monotonic conditional between sets of attributes, which assumes a preference over the set of objects. We show that this conditional gives rise to a consequence relation that is consistent with the postulates for non-monotonicty proposed by Kraus, Lehmann, and Magidor (commonly referred to as the KLM postulates). We argue that our contribution establishes a strong characterisation of non-monotonicity in FCA. To our knowledge, this is a novel view of FCA as a formalism which supports non-monotonic reasoning. We then extend the influence of KLM in FCA by introducing the notion of typical concepts through a restriction placed on what constitutes an acceptable preference over the objects. Typical concepts represent concepts where the intent aligns with expectations from the extent, allowing for an exception-tolerant view of concepts. To this end, we show that the set of all typical concepts is a meet semi-lattice of the original concept lattice. This notion of typical concepts is a further introduction of KLM-style typicality into FCA, and is foundational towards developing an algebraic structure representing a concept lattice of prototypical concepts.

关键词： Formal Concept Analysis Non-monotonic reasoning Rational consequence relations Defeasible reasoning

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An Improved Static Analysis Approach for Malware Detection by Optimizing Feature Extraction Combining Different ML Algorithms 4th

An Improved Static Analysis Approach for Malware Detection b...

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4th International Conference on Intelligent Systems and Pattern Recognition-ISPR

作者： Barzev, Iliyan Borissova, Daniela Bulgarian Acad Sci Inst Informat & Commun Technol Sofia 1113 Bulgaria

ISBN: (纸本)9783031821523;9783031821530

One of the essential factors in cyber defense is malware analysis. Attackers use various social engineering techniques along with one or more zero-day vulnerabilities to deploy malicious software. In this regard, various malware detection algorithms are being developed to deal with this type of threat faced not only by individual users but also by organizations/nations. Low classification accuracy, computational complexity, and resource usage are among the factors influencing malware evaluation methods. Therefore, our efforts are also focused on improving the detection and classification of malware. To this end, an improved static analysis approach for malware detection is proposed by optimizing feature extraction by combining different machine learning algorithms. Three hybrid algorithms based on the combination of Naive Bayes, K Nearest Neighbors, and Random Forest are defined. To measure the accuracy of these hybrid algorithms a proper formula is presented. The applicability of the proposed hybrid algorithms is tested on a subset of the IoT-23 dataset, generated by the Avast AIC laboratory using Zeek Net-work Security Monitor. The conducted experiments and the obtained results about the hybrid algorithms' performance are not only accurate but also precise and reliable in detecting true positives while minimizing false positives and negatives.

关键词： Malware Detection Static Analysis Hybrid Algorithms Feature Extraction

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Lattices for Many-Logics Modal Logic: Constructions and Representations 4th

Lattices for Many-Logics Modal Logic: Constructions and Repr...

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4th International Conference on Artificial Intelligence Logic and Applications

作者： Freire, Alfredo Madeira, Alexandre Martins, Manuel A. Univ Brasilia Brasilia DF Brazil Univ Aveiro Dept Math CIDMA Aveiro Portugal

ISBN: (纸本)9789819603534;9789819603541

To reason about complex systems, logical tools and results provide a fundamental framework for studying regularities in such systems. However, standard approaches, though very successful, assume that transitions can occur only between states that behave according to a single logic. To reason about behaviors of some complex systems, it is worth considering different logics in different states. The issue is how to meaningfully communicate values in different lattices semantics of states' logics. In this paper, we propose a method, based on the direct product construction, to produce a natural common lattice background in which the relation between the original lattices can be represented.

关键词： computer circuits

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Digital Platform for Strengthening Accessibility of Tourist Destinations in the Pieria Region 5th

Digital Platform for Strengthening Accessibility of Tourist ...

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5th 5th Olympus - International Conference on Supply Chain

作者： Achillas, Charisios Aidonis, Dimitrios Folinas, Dimitrios Kostavelis, Ioannis Tsolakis, Naoum Triantafyllou, Dimitrios Nikolaou, Miltiadis Dimou, Vasileios Int Hellen Univ Katerini 60100 Greece Reg Cent Macedonia Thessaloniki 54110 Greece Pieria Org Tourism Dev & Promot Katerini 60100 Greece

ISBN: (纸本)9783031693502;9783031693519

Accessibility problems often prevent people with special needs from enjoying facilities and services in tourist destinations. It is an institutional obligation, as well as a social necessity, to "open the doors" of tourism for everyone, within the concept of tourism-for-all concept. Moreover, accessible tourism is considered an important economic opportunity for touristic destinations, taking also into account the ageing of population over the last decades. In order to strengthen the touristic brand of the Pieria Region and promote the area as an "Accessible Tourist Destination", an online platform has been developed under the responsibility of the Pieria Organization for Tourism Development and Promotion, with the scientific and technical support of the International Hellenic University. The platform provides complete information on accessibility infrastructure in respect to route accessibility (obstacles in pavements, ramps, etc.) accessibility issues for points of cultural and tourist interest (PoIs). In this paper, the key functionalities of the platform are presented through the case of Platamonas, Greece, where the platform has been tested and results have been validated.

关键词： Accessibility Tourism-for-All Pieria

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Towards Interaction Design with Active Inference: A Case Study on Noisy Ordinal Selection 5th

Towards Interaction Design with Active Inference: A Case Stu...

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5th International Workshop on Active Inference

作者： Stein, Sebastian Williamson, John H. Murray-Smith, Roderick Univ Glasgow Sch Comp Sci Glasgow Lanark Scotland

ISBN: (纸本)9783031771378;9783031771385

This paper explores active inference for user interfaces. We implement an active inference approach for 1-of-N selection, a fundamental building block of interactive systems. In this setup, users provide noisy discrete inputs and the interface sequentially identifies an intended target. This problem has an optimal solution (Horstein's algorithm) where the channel noise is iid and known a priori, but is an open problem where the noise is unknown or varying. We reformulate the problem as free energy minimisation and derive a practical active inference implementation. Active inference with a flat noise prior performs comparably to Horstein with conservative noise assumption in the first interaction sequence and as well as Horstein with perfectly calibrated noise thereafter, demonstrating fast adaptation. We also show that active inference can infer the input polarity, offering an extra degree of freedom to users, and adapt to non-stationary noise. The application of active inference to interaction is novel, and we hope this example establishes the groundwork for the community to explore active inference in human-computer interaction. Code available at https://***/drsstein/iwai2024

关键词： Active Inference Computational Interaction Design Ordinal Selection Adaptive Interfaces Human computer Interaction

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A Method for Identifying Fraudulent Behavior in Electricity Bill Collection Based on Nearest Neighbor Metric Matching 5th

A Method for Identifying Fraudulent Behavior in Electricity ...

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5th International Conference on Neural Computing for Advanced Applications (NCAA)

作者： Han, Juan Yu, Limei Li, Aiqing Jiang, Yuanmeng Jiang, Shuai Tian, Mengyu State Grid Shandong Elect Power Co Mkt Serv Ctr Measurement Ctr Jinan 250001 Peoples R China

ISBN: (纸本)9789819770038;9789819770045

In view of the current problems of very few samples of electricity bill collection fraud and difficulty in identifying abnormal power payment users, this paper proposes a method to identify electricity bill collection fraud based on nearest neighbor metric matching. First, by analyzing the characteristics of multi-source data on users' electricity consumption, the user payment behavior portrait categories are depicted. Subsequently, Gaussian process regression was used to build a user payment behavior prediction model based on normal user payment sample data. Finally, combined with dynamic multi-objective gravity search to obtain their nearest neighbor groups with different metrics, through abnormal probability distributionmatching, the abnormal payment thresholds and abnormal payment cycle thresholds of categories without abnormal payment user records are obtained, thereby generating electricity bill collection fraud screening rules and storing them on the blockchain as smart contracts, the accounts of abnormal transactions are checked through smart contracts. The experimental results show that the proposed method can promptly and accurately identify abnormal paying users, thereby effectively reducing fraud in electricity bill channels.

关键词： payment behavior portrait Gaussian process regression dynamic multi-objective gravity search electricity bill collection identification nearest neighbor metric matching

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Cascading Power 1st

Cascading Power

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1st International Workshop on Mechanism Design in Social Networks

作者： Agotnes, Thomas Christoff, Zoe Univ Bergen Bergen Norway Shanxi Univ Taiyuan Peoples R China Univ Groningen Groningen Netherlands

ISBN: (纸本)9789819602131;9789819602148

Cascading phenomena in social networks happen when the adoption of some behaviour by initial adopters causes some of their immediate friends to adopt which again causes some of their friends' friends to adopt, and so on. Who the initial adopters are, or rather how they are positioned in the network, is of crucial importance for the potential cascade. In this paper we look at the relative importance of different agents as initial adopters in a network, for a given cascading goal such as a complete cascade: which groups of agents are sufficient, which groups are necessary, and which agents are the most important to have as initial adopters in order for the goal to be achieved? For the latter question, we look to cooperative games and power measures to identify agents who are pivotal for a group of inital adopters with respect to the goal. We characterise the computational complexity of resulting decision and counting problems, when the goal is represented using a propositional logical formula. We also draw connections to abduction in logic programming.

关键词： Social network analysis Threshold models Cascades Power measures Propositional logic Logic Programming Computational complexity

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Data Augmentation of Gujarati Texts for Sentiment Analysis 4th

Data Augmentation of Gujarati Texts for Sentiment Analysis

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4th International Conference on Advanced Network Technologies and Intelligent Computing-ANTIC-Annual

作者： Desai, Nikita Dabhi, Vipul Dharmsinh Desai Univ Dept Informat Technol Nadiad Gujarat India

ISBN: (纸本)9783031837920;9783031837937

Sentiment analysis (SA) is a crucial natural language processing (NLP) tool for understanding user sentiments, particularly in business intelligence. However, the lack of labeled data in low-resource languages like Gujarati limits the effectiveness of such models. We propose a novel data augmentation pipeline specifically designed for Gujarati movie review datasets to balance the class distribution. This paper presents manually tagged SA datasets for Gujarati movie reviews, annotated with three sentiment classes: positive, negative, and neutral. IndoWordNet is used to generate new data points for under-represented classes by replacing words with their synonyms, while IndicBERT embeddings are utilized to verify the semantic similarity of the augmented sentences. After balancing the dataset, standard classifiers such as SVM, Naive Bayes, Logistic Regression, and KNN were used to evaluate improvements in sentiment classification performance. An increase of 21% in the macro F1 score was empirically observed when the augmented balanced dataset was used for the classification task.

关键词： NLP Indian text movie review sentiment classification sentiment analysis Data Augmentation

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