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6th International Conference on Recent Innovations in Computing, ICRIC 2023

作者： Easin Arafat, Md. Asuah, Georgina Saha, Sourav Orosz, Tamas Data Science and Engineering Department Faculty of Informatics Eötvös Loránd University Pázmány Péter Str. 1/A Budapest1117 Hungary Department of Computer Science and Engineering United International University United City Madani Avenue Dhaka1212 Bangladesh

ISBN: (纸本)9789819734412

Real-time integration within the SAP HANA (High-Performance Analytics Appliance) environment through open-source components signifies a substantial financial investment in information technology (IT) projects. The prevalence of budget overruns, delays in implementation, and imprecise forecasts remain significant factors contributing to project failures. This has led many companies to adopt the usage of real-time insights in planning and decision-making facilities. By keeping the fundamental components of SAP, businesses can upgrade their information systems without having to completely reconfigure them, which proves to be cost-effective. This study initiates an exploration of the intricate integration of large language model (LLM), LangChain, and SAP HANA, underlining the intrinsic value of real-time insights and their multi-faceted applications. The study's main objective is to provide individuals with a road map for utilizing these essential components, customized to their enterprise's specific phase of development. This study highlights how important this integration is and provides a strong push in the direction of operational effectiveness and long-term expansion. The study equally outlines the challenges and considerations of such components in operational effectiveness and long-term expansion. The integrated framework offers the following key benefits, linguistic precision, seamless language-technology integration, and robust analytics infrastructure. As the integrated framework develops, it becomes a revolutionary instrument that changes how real-time insights and decision-making are conducted across a variety of industries. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Budget control

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LLM-based DatalogMTL Modelling of MiCAR-compliant Crypto-Assets Markets 5

LLM-based DatalogMTL Modelling of MiCAR-compliant Crypto-Ass...

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5th International Workshop on the Resurgence of Datalog in Academia and Industry, Datalog-2.0 2024

作者： Colombo, Andrea Baldazzi, Teodoro Bellomarini, Luigi Gentili, Andrea Sallinger, Emanuel Politecnico di Milano Dipartimento di Elettronica Informazione e Bioingegneria Milan Italy Università Roma Tre Department of Computer Science and Engineering Rome Italy Banca d’Italia Rome Italy TU Wien Faculty of Informatics Vienna Austria University of Oxford Department of Computer Science Oxford United Kingdom

Recent extensions of Datalog that consider the temporal dimension as a first-class citizen have unlocked the possibility of using its temporal variants, such as DatalogMTL, to model and reason about complex financial domains. Very relevant ones are crypto-activity markets, which, according to the recent Markets in Crypto-Assets Regulation (MiCAR) of the EU, are described by white papers published by crypto-assets issuers. In particular, the issuers publish semi-structured information about the assets they are willing to offer. Then, the assets are implemented in decentralized finance contexts (i.e., in a blockchain) as executable scripts known as smart contracts. However, these scripts are often criticized for their complexity, which makes them challenging to understand and communicate. On the other hand, in our experience, the availability of a declarative and executable representation of a crypto-activity market fosters a better understanding of that market as well as improved transparency, reproducibility and, as a consequence, increased fairness. These characteristics are of major interest to the financial authorities for example for supervision purposes. In this paper, we study the problem of automatically translating textual descriptions of crypto-assets, written according to the MiCAR specifications, into DatalogMTL programs that represent and capture the respective crypto-activity market. To this end, we opt for a machine translation approach and leverage a Large Language Model. We discuss promising techniques and preliminary experimental results. © 2024 Copyright for this paper by its authors.

关键词： Decentralized finance

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CSA-VC: Contrastive Learning With Selective Attention in Non-Parallel Voice Conversion

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1183-1195页

作者： Bima Prihasto Yi-Xing Lin Phuong Thi Le Jia-Ching Wang Chung-Hsien Wu Department of Informatics Institut Teknologi Kalimantan Kota Balikpapan Indonesia Department of Computer Science and Information Engineering National Central University (NCU) Taoyuan City Taiwan Department of Computer Science and Information Engineering Fu Jen Catholic University New Taipei City Taiwan Department of Computer Science and Information Engineering National Cheng Kung University (NCKU) Tainan Taiwan

This work introduces a novel approach to non-parallel voice conversion (VC) through contrastive learning with selective attention (CSA). Unlike traditional methods that suffer from inconsistent spectral feature mapping, our CSA model enhances spectral feature analysis and comparison by employing an attention matrix to measure spectral distances and select anchor points for contrastive loss. We assess our model's performance across various training schemes, including one-to-one and many-to-one voice conversions, gender differences, data scarcity, and accent variations, without adding extra parameters to maintain efficiency. Our evaluations, both subjective and objective, demonstrate superior sound quality in voice synthesis over baseline methods. Additionally, we explore the impact of selective attention in an ablation study, further validating our model's effectiveness in real-world voice conversion applications.

关键词： Generators Training Speech processing Spectrogram Contrastive learning Generative adversarial networks Feature extraction Entropy Time-frequency analysis Real-time systems

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A Probabilistic Public-Key Encryption with Ensuring Data Integrity in Cloud Computing 4

A Probabilistic Public-Key Encryption with Ensuring Data Int...

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4th International Conference on Control, Artificial Intelligence, Robotics and Optimization, ICCAIRO 2023

作者： Kara, Mostefa Karampidis, Konstantinos Papadourakis, Giorgos Laouid, Abdelkader Alshaikh, Muath University of El Oued LIAP Laboratory El Oued El Oued39000 Algeria Hellenic Mediterranean University Department of Electrical and Computer Engineering Greece College of Computing and Informatics Saudi Electronic University Computer Science Department Riyadh11673 Saudi Arabia

ISBN: (纸本)9798350300918

Researchers are working hard to face various challenges in the fast-moving field of network security and data exchange. When it comes to encryption techniques, efforts vary to address multiple issues such as computational speed, data size requirements, application scope, etc. that are driven by the purposes of collecting and sharing sensitive data that must be stored and processed securely. This paper proposes an efficient asymmetric cryptosystem that bases on the multiplication of plaintext by a variable public key. The origin of this key is constant, but for each encryption, a random number must be added to it. Therefore, a new public key each time is generated;in other words, a simulation of a one-time pad system. For each message, the sender generates a new secret twin of the public key;The receiver can decrypt all messages encrypted by different twins with a single private key. The proposed probabilistic scheme is linear coding and hence is lightweight, easy to execute, and practical in many areas. We consider the cloud to be an untrusted part that tries to disclose data when decoded by systems, the proposed method provides the additive homomorphic property and ensures data integrity when a homomorphic operation is performed by the cloud;thus, we realize verifiable partial homomorphic encryption. © 2023 IEEE.

关键词： Network security

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Semantically-Disentangled Progressive Image Compression for Deep Space Communications: Exploring the Ultra-low Rate Regime

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IEEE Journal on Selected Areas in Communications 2024年 1-1页

作者： Zhang, Weicheng Liu, Yajing Chen, Lingyu Shi, Jianghong Hong, Xuemin Wang, Xianbin School of Informatics Xiamen University Xiamen China Department of Electrical and Computer Engineering University of Western Ontario London ON Canada

While sensing imagery in space missions has broad applications, the growing image resolution and data volume have caused a major challenge due to limited deep space channel capacities. To address this challenge, semantics-aware image compression becomes a promising direction. This paper is motivated to explore lossy compression at the ultra-low rate regime, which is a deviation from the high-fidelity- oriented tradition. Specifically, we propose an ultra-low rate deep image compression (DIC) codec by synthesizing multiple neural computing techniques such as style generative adversarial network (GAN), inverse GAN mapping, and contrastive disentangled representation learning. In addition, a residual-based progressive encoding framework is proposed to enable smooth transitions from the ultra-low rate regime to near- lossless regime. Experiments on the FFHQ and DOTA dataset demonstrate that compared with existing DICs, the proposed DIC can push the minimum rate boundary by about one order of magnitude while preserving the semantic attributes and maintaining a high perception quality. We further elaborate the design considerations for cross-rate-regime progressive DIC. Our study confirm that a semantically disentangled DIC holds the promise to bridge multiple rate regimes. IEEE

关键词： Deep neural networks

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MAX CUT in Weighted Random Intersection Graphs and Discrepancy of Sparse Random Set Systems 32

MAX CUT in Weighted Random Intersection Graphs and Discrepan...

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32nd International Symposium on Algorithms and Computation, ISAAC 2021

作者： Nikoletseas, Sotiris Raptopoulos, Christoforos Spirakis, Paul Computer Engineering and Informatics Department University of Patras Greece Computer Technology Institute Patras Greece Computer Engineering and Informatics Department University of Patras Greece Department of Computer Science University of Liverpool United Kingdom

ISBN: (纸本)9783959772143

Let V be a set of n vertices, M a set of m labels, and let R be an m × n matrix of independent Bernoulli random variables with probability of success p;columns of R are incidence vectors of label sets assigned to vertices. A random instance G(V, E, RT R) of the weighted random intersection graph model is constructed by drawing an edge with weight equal to the number of common labels (namely [RT R]v,u) between any two vertices u, v for which this weight is strictly larger than 0. In this paper we study the average case analysis of Weighted Max Cut, assuming the input is a weighted random intersection graph, i.e. given G(V, E, RT R) we wish to find a partition of V into two sets so that the total weight of the edges having exactly one endpoint in each set is maximized. In particular, we initially prove that the weight of a maximum cut of G(V, E, RT R) is concentrated around its expected value, and then show that, when the number of labels is much smaller than the number of vertices (in particular, m = nα, α Θ(1)n), we show that with high probability, a majority type randomized algorithm outputs a cut with weight that is larger than the weight of a random cut by a multiplicative constant strictly larger than 1. Then, we formally prove a connection between the computational problem of finding a (weighted) maximum cut in G(V, E, RT R) and the problem of finding a 2-coloring that achieves minimum discrepancy for a set system Σ with incidence matrix R (i.e. minimum imbalance over all sets in Σ). We exploit this connection by proposing a (weak) bipartization algorithm for the case m = n, p = Θ(1)n that, when it terminates, its output can be used to find a 2-coloring with minimum discrepancy in a set system with incidence matrix R. In fact, with high probability, the latter 2-coloring corresponds to a bipartition with maximum cut-weight in G(V, E, RT R). Finally, we prove that our (weak) bipartization algorithm terminates in polynomial time, with high probability, at least w

关键词： Graph theory

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Integrating Deep Learning and Ant Colony Optimization for Improved Shortest Route Problem Solving 5

Integrating Deep Learning and Ant Colony Optimization for Im...

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5th International Conference on Big-data Service and Intelligent Computation, BDSIC 2023

作者： Turky, Ayad Hussain, Abir Bettayeb, Anissa M. Serhani, Mohamed Adel Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Information Technology Center University of Sharjah Sharjah United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400708923

Determining the best shortest path between locations in intelligent transportation systems is crucial but challenging. Traditional approaches, which assume fixed travel times, fall short of accurately reflecting dynamic factors such as weather, time, and day. This paper introduces a novel two-stage approach that integrates deep learning and ant colony optimization to address this limitation. Firstly, deep learning is employed to predict travel times, incorporating weather conditions as input for improved accuracy. Secondly, an ant colony optimization algorithm is utilized to identify the shortest route based on the predicted travel times. We evaluated the proposed approach using the New York City taxi benchmark dataset and observed superior performance compared to current state-of-the-art algorithms. © 2023 ACM.

关键词： Travel time

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Category Based Sentiment Analysis for Basic Skin-Cares Using LDA and SVM Approach 6

Category Based Sentiment Analysis for Basic Skin-Cares Using...

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6th International Conference on Electrical, Telecommunication and computer engineering, ELTICOM 2022

作者： Masaling, Nikita Ananda Putri Lubis, Tasnim Amalia, Amalia Lubis, Arif Ridho Lydia, Maya Silvi Universitas Sumatera Utara Department of Computer Science Medan Indonesia Universitas Sumatera Utara Department of Linguistic Medan Indonesia Politeknik Negeri Medan Department of Computer Engineering and Informatics Medan Indonesia

ISBN: (纸本)9798350320718

Aspect Based Sentiment Analysis (ABSA) is a sentiment analysis technique of identifying sentiment value towards a specific aspect associated with a given target. However, in some cases, several aspects can be classified into one category group. This is because some words have different spellings, even though they refer to the same aspect. The problem is the dataset for category-based categories is still limited, especially for low-resource languages such as Bahasa Indonesia (Indonesian Language). In fact, dataset generation is the most time-consuming step rather than the process of building the classification model. This study aims to build a category-based sentiment analysis model starting from the dataset generation, model classification generation, and model evaluation. The case study in this research is sentiment analysis for the basic skin-care category in Bahasa Indonesia. The dataset is generated from raw data, which is the user's review on the female-daily website (***). We implemented the Latent Dirichlet Allocation (LDA) algorithm to extract the aspect from each review and implemented TextBlob library to annotate the polarity of each review. The research uses a Support Vector Machine algorithm to generate a model classification. Based on the research, the LDA can extract and also can group the same aspect into the same category. The support Vector Machine method is a fairly accurate method for conducting sentiment analysis. In this study, the accuracy value of 86.9% for training data and 83.9% for test data from all categories were obtained using the K-Fold Cross Validation evaluation method with 5 folds. © 2022 IEEE.

关键词： Sentiment analysis

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Dynamics Personalized Learning Path Based on Triple Criteria using Deep Learning and Rule-Based Method 38

Dynamics Personalized Learning Path Based on Triple Criteria...

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38th IEEE Region 10 Conference, TENCON 2023

作者： Imamah Laili Yuhana, Umi Djunaidy, Arif Hery Purnomo, Mauridhi Institut Teknologi Sepuluh Nopember Department of Electrical Engineering Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Informatics Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Information Systems Surabaya Indonesia Institut Teknologi Sepuluh Nopember Department of Computer Engineering Surabaya Indonesia

ISBN: (纸本)9798350302196

Personalized learning paths are designed to optimize learning time and improve student learning performance by providing an appropriate learning sequence based on the unique characteristics of each student. A common method for constructing personalized learning paths is based on the student's knowledge but disregards the student's interest in the subject matter. This research employs a deep learning and rule-based approach to recommend suitable material based on the topic's difficulty, student interest, and knowledge level. The difficulty level of the topic is predicted using deep learning. A questionnaire is used to determine the level of student interest, which is then processed using a rule-based approach to generate a learning path. Modeling a dynamic learning path requires measuring student knowledge in each topic and updating the learning path accordingly. Comparing the learning outcomes of students who utilized conventional e-learning versus those who followed a personalized learning path constitutes the evaluation. The results demonstrated that students scored 29% higher, or 15.06 points, than those who utilized conventional e-learning. © 2023 IEEE.

关键词： Students

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OBVIOUSLY STRATEGY-PROOF MECHANISMS WITHOUT MONEY FOR SCHEDULING

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SIAM Journal on Discrete Mathematics 2025年第2期39卷 1102-1122页

作者： Kyropoulou, Maria Ventre, Carmine Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Department of Informatics King's College London London United Kingdom

We consider the scheduling problem when no payments are allowed and the machines are bound by their declarations. We are interested in a notion of incentive compatibility, stronger than the (standard) strategy-proofness, termed obviously strategy-proof (OSP), and explore its possibilities and limitations. OSP formalizes the concept of strategy-proofness for agents/machines with a certain kind of bounded rationality by making an agent's incentives to act truthfully obvious in some sense: roughly speaking, the worst possible outcome after providing information about her true type is at least as good as the best possible outcome after misreporting information about her type. Under the weaker constraint of strategyroofness, Koutsoupias [Theoret. Comput. Syst., 54 (2014), pp. 375-387 proves a tight approximation ratio of (Formula presented) for the makespan for one task, under the monitoring paradigm. We wish to examine how this guarantee is affected by the strengthening of the incentive compatibility constraint. The main message of our work is that there is essentially no worsening of the approximation guarantee corresponding to the significant strengthening of the guarantee of incentive compatibility from strategy-proofness to OSP, as long as the mechanism designer can implement a particular notion of monitoring. To achieve this, we introduce the notion of max-monitoring and prove that weaker monitoring frameworks do not suffice, thus providing a complete picture of OSP with monitoring in the context of scheduling a task without money. © 2025 Society for Industrial and Applied Mathematics.

关键词： Digital arithmetic

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