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SSRN 2023年

作者： Kynych, Frantisek Cerva, Petr Zdansky, Jindrich Svendsen, Torbjørn Salvi, Giampiero Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Studentska 2 Liberec461 17 Czech Republic Department of Electronic Systems Norwegian University of Science and Technology Torgarden TrondheimNO-7491 Norway KTH Royal Institute of Technology School of Electrical Engineering and Computer Science Brinellvägen 8 StockholmSE-100 44 Sweden

This manuscript deals with the task of real-time speaker diarization (SD) for stream-wise data processing. Therefore, in contrast to most of the existing papers, it considers not only the accuracy but also the computational demands of individual investigated methods. We first propose a new lightweight scheme allowing us to perform speaker diarization of streamed audio data. Our approach utilizes a modified residual network with squeeze-and-excitation blocks (SE-ResNet-34) to extract speaker embeddings in an optimized way using cached buffers. These embeddings are subsequently used for voice activity detection (VAD) and block-online k-means clustering with a look-ahead *** described scheme yields results similar to the reference offline system while operating solely on a CPU with a low real-time factor (RTF) below 0.1 and a constant latency of around 5.5 seconds. In the next part of the work, our research moves toward much more demanding and complex real-time processing of audio-visual data *** this purpose, we extend the above-mentioned scheme for audio data processing by adding an audio-video module. This module utilizes SyncNet combined with visual embeddings for identity tracking. Our resulting multi-modal SD framework then combines the outputs from audio and audio-video modules by using a new overlap-based fusion strategy. It yields diarization error rates that are competitive with the existing state-of-the-art offline audio-visual methods while allowing us to process various audio-video streams, e.g., from internet or TV broadcasts, in real-time and with the same latency as for audio data processing. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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A novel transfer learning-based model for diagnosing malaria from parasitized and uninfected red blood cell images

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Decision Analytics Journal 2023年 9卷

作者： Qadri, Azam Mehmood Raza, Ali Eid, Fatma Abualigah, Laith Institute of Computer Science Khwaja Fareed University of Engineering and Information Technology Rahim Yar Khan 64200 Pakistan Technology Management Stony Brook University 11794 NY United States Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos 13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan MEU Research Unit Middle East University Amman 11831 Jordan Applied Science Research Center Applied science private university Amman 11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang 11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya 27500 Malaysia

Malaria represents a potentially fatal communicable illness triggered by the Plasmodium parasite. This disease is transmitted to humans through the bites of Anopheles mosquitoes that carry the infection. This disease has significant and devastating consequences on the health systems of fragile countries, particularly in sub-Saharan Africa. Malaria affects red blood cells by invading and replicating within them, destroying them, and releasing toxic byproducts into the bloodstream. The parasite's ability to stick and modify the surface of red blood cells can cause them to become sticky, obstructing blood flow in vital organs such as the brain and spleen. Therefore, efficient approaches for the early detection of malaria are critical to saving patients’ lives. The main aim of this study is to develop an efficient model for early malaria diagnosis. We used malaria images based on parasitized and uninfected red blood cells for the study experiments. We applied neural network-based approaches such as Neural Search Architecture Network (NASNet) and compared its performance with machine learning techniques. Moreover, we proposed a novel NNR (NASNet Random forest) method for feature engineering. The proposed NNR approach first extracts spatial features from input malaria images, then class prediction probability features are extracted from these spatial features. The feature set obtained from the data extraction trains machine learning models. Our comprehensive experiments show that the support vector machine outperformed state-of-the-art models, achieving a high-performance score of 99% and having an inference time near 0.025 s. We validated the performance using k-fold cross-validation and optimized the hyperparameters through tuning. Our proposed research has improved the early diagnosis of malaria and can assist medical specialists in reducing the mortality rate. © 2023 The Author(s)

关键词： Deep learning Machine learning Malaria diagnosis Neural search architecture network Red blood cells Transfer learning

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Machine Learning-Based Security Mechanism for Detecting Phishing Attacks

Machine Learning-Based Security Mechanism for Detecting Phis...

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Communications, Computing, Cybersecurity, and informatics (CCCI), International Conference on

作者： Pragya Bhatt Mohammad S. Obaidat Gagan Dangwal Ashok Kumar Das Mohammad Wazid Balqies Sadoun Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India KASIT University of Jordan Amman Jordan University of Science and Technology Beijing Beijing China SRM University Kattankulathur India Amity University Noida India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad India College of Engineering Al-Balqa’ Applied University Al-Salt Jordan

ISBN: (数字)9798350349832

ISBN: (纸本)9798350349849

Phishing attacks pose a serious threat to online users by impersonatina trustworthy entities and stealing sensitive data. This can result in reputational damage, financial loss, ransomware, or additional spyware outbreaks. Phishing scams take into consideration roughly 22% of all data violations, making it one of the most prominent cybercrimes, in accordance with the FBI's 2021 IC3 report. According to phishing email statistics, roughly 1.2% of all emails received are fraudulent, resulting in 3.4 billion phishing emails per day. One out of every 4,200 emails sent is almost certainly a phishing attempt. Cognitive methods, such as machine learning (ML) and deep learning (DL), are widely used in cybersecurity to extract information and detect possible intrusions and phishing attempts. This research investigates the procedure of employing machine learning methods for recognizing URLs that are used for phishing attacks. We have used the Pristine and Malicious URLs Dataset to train, test, and evaluate the performance of a set of algorithms to develop a robust binary class URL phishing attack detection mechanism. The obtained results show that the proposed mechanism outperforms the other existing competing mechanisms with 99.6% accuracy.

关键词： Uniform resource locators Training Accuracy Machine learning algorithms Phishing Electronic mail Classification algorithms Telecommunication computing computer crime Random forests

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Challenges and working solutions in agile adaptation: Experiences from the industry 30

Challenges and working solutions in agile adaptation: Experi...

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Joint 30th International Workshop on Software Measurement and the 15th International Conference on Software Process and Product Measurement, IWSM-Mensura 2020

作者： Özcan-Top, Özden Demirors, Onur McCaffery, Fergal Graduate School of Informatics Middle East Technical University Ankara Turkey Department of Computer Engineering Izmir Institute of Technology Izmir Turkey Regulated Software Research Centre Dundalk Institute of Technology Dundalk Ireland STATSports Group Newry Ireland

Challenges in agile adaptation is inevitable in software development projects and have to be dealt with by software practitioners. The pathway to excellence in agility requires experience of challenges, failure of process scenarios;and the discovery of working solutions by software development teams. The major purpose of this study is to highlight both the challenges organizations faced when implementing agile techniques and the solutions adopted that proved successful. In order to specify these challenges and working solutions, we performed a multiple case study by using the Software Agility Assessment Reference Model (AgilityMod). In this paper, we describe two cases that achieve the highest levels of agility among eight cases and describe their experiences in achieving a good adaptation through the challenges that they faced and the solutions that were found for these challenges. Additionally, we provide two challenges that have not been resolved yet and are subject to further discussions. Copyright © 2020 for this paper by its authors.

关键词： Software design

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PhenoProfiler: Advancing Phenotypic Learning for Image-based Drug Discovery

arXiv

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arXiv 2025年

作者： Li, Bo Zhang, Bob Zhang, Chengyang Zhou, Minghao Huang, Weiliang Wang, Shihang Wang, Qing Li, Mengran Zhang, Yong Song, Qianqian PAMI Research Group Department of Computer and Information Science University of Macau Taipa China Beijing Key Laboratory of Multimedia and Intelligent Software Technology Beijing Institute of Artificial Intelligence Beijing University of Technology Beijing China Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida Florida United States Faculty of Applied Sciences Macao Polytechnic University Taipa China School of Intelligent Systems Engineering Sun Yat-sen University Guangdong China Department of Cancer Biology Wake Forest School of Medicine Winston SalemNC United States

In the field of image-based drug discovery, capturing the phenotypic response of cells to various drug treatments and perturbations is a crucial step. This process involves transforming high-throughput cellular images into quantitative representations for downstream analysis. However, existing methods require computationally extensive and complex multi-step procedures, which can introduce inefficiencies, limit generalizability, and increase potential errors. To address these challenges, we present PhenoProfiler, an innovative model designed to efficiently and effectively extract morphological representations, enabling the elucidation of phenotypic changes induced by treatments. PhenoProfiler is designed as an end-to-end tool that processes whole-slide multi-channel images directly into low-dimensional quantitative representations, eliminating the extensive computational steps required by existing methods. It also includes a multi-objective learning module to enhance robustness, accuracy, and generalization in morphological representation learning. PhenoProfiler is rigorously evaluated on large-scale publicly available datasets, including over 230,000 whole-slide multi-channel images in end-to-end scenarios and more than 8.42 million single-cell images in non-end-to-end settings. Across these benchmarks, PhenoProfiler consistently outperforms state-of-the-art methods by up to 20%, demonstrating substantial improvements in both accuracy and robustness. Furthermore, PhenoProfiler uses a tailored phenotype correction strategy to emphasize relative phenotypic changes under treatments, facilitating the detection of biologically meaningful signals. UMAP visualizations of treatment profiles demonstrate PhenoProfiler’s ability to effectively cluster treatments with similar biological annotations, thereby enhancing interpretability. These findings establish PhenoProfiler as a scalable, generalizable, and robust tool for phenotypic learning, offering transformative advancement

关键词： Benchmarking

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Supply Chain Management Using Blockchain: Opportunities, Challenges, and Future Directions

Supply Chain Management Using Blockchain: Opportunities, Cha...

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informatics (ICI), International Conference on

作者： Rajchandar K A. Shameem Piali Biswas B.T. Geetha J.R. Arunkumar Prasanna Kumar Lakineni Department of CS & AI SR University Warangal Telangana India AMET Business School AMET University Department of Commerce Jamshedpur Co-Operative College Kolhan University Jamshedpur Jharkhand Department of ECE Saveetha School of Engineering SIMATS Tamil Nadu India Department of Computer Science and Engineering Modern Institute of Technology and Research Centre Alwar Rajasthan Department of CSE GITAM School of Technology GITAM University Visakhapatnam

Blockchain innovation is being proclaimed as a unique advantage. Numerous examiners see blockchain innovation as a troublesome creation. Blockchain innovation has grabbed the eye of scholastics, chiefs, and specialists the whole way across the world. Blockchain is widely regarded by these entities as a highly disruptive technology that is already reshaping businesses and their supply chain models. Blockchain has the potential to transform supply chain (SC) processes in various ways, such as enhancing SC provenance, improving security, and reengineering business operations. An increasing number of recent studies have explored the use of blockchain in supply chains. Some have shown significant awareness of blockchain's role in functional transparency across various sectors, including e-commerce, agriculture, and public administration. We emphasize the identification of valuable opportunities, potential cultural implications, the current state of the art in advancements, as well as significant trends and challenges. We research a few modern areas, including transportation, fabricating, and auto, flight, finance, innovation, energy, medical care, horticulture and food, online business, and schooling, among others, that can be effectively overhauled with blockchain. The main aim of the study presents a complete evaluation of 178 examination distributions and examines different components of blockchain innovation in SC and coordinated operations the board.

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Online carpooling using expander decompositions 40

Online carpooling using expander decompositions

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40th IARCS Annual Conference on Foundations of Software technology and Theoretical computer Science, FSTTCS 2020

作者： Gupta, Anupam Krishnaswamy, Ravishankar Kumar, Amit Singla, Sahil Computer Science Department Carnegie Mellon University PittsburghPA United States Microsoft Research Bengaluru India Department of Computer Science and Engineering Indian Institute of Technology Delhi India Department of Computer Science Princeton University NJ United States

ISBN: (纸本)9783959771740

We consider the online carpooling problem: given n vertices, a sequence of edges arrive over time. When an edge et = (ut, vt) arrives at time step t, the algorithm must orient the edge either as vt → ut or ut → vt, with the objective of minimizing the maximum discrepancy of any vertex, i.e., the absolute difference between its in-degree and out-degree. Edges correspond to pairs of persons wanting to ride together, and orienting denotes designating the driver. The discrepancy objective then corresponds to every person driving close to their fair share of rides they participate in. In this paper, we design efficient algorithms which can maintain polylog(n, T ) maximum discrepancy (w.h.p) over any sequence of T arrivals, when the arriving edges are sampled independently and uniformly from any given graph G. This provides the first polylogarithmic bounds for the online (stochastic) carpooling problem. Prior to this work, the best known bounds were O(√n log n)discrepancy for any adversarial sequence of arrivals, or O(loglog n)-discrepancy bounds for the stochastic arrivals when G is the complete graph. The technical crux of our paper is in showing that the simple greedy algorithm, which has provably good discrepancy bounds when the arriving edges are drawn uniformly at random from the complete graph, also has polylog discrepancy when G is an expander graph. We then combine this with known expander-decomposition results to design our overall algorithm. © Anupam Gupta, Ravishankar Krishnaswamy, Amit Kumar, and Sahil Singla;licensed under Creative Commons License CC-BY.

关键词： Stochastic systems

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An efficient performance analysis using collaborative recommendation system on big data 5

An efficient performance analysis using collaborative recomm...

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5th International Conference on Trends in Electronics and informatics, ICOEI 2021

作者： Deepak, V. Rajesh Khanna, M. Dhanasekaran, K. Prakash, P. G. Om Babu, D. Vijendra Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Veltech Multitech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai62 India Mailam Engineering College Department of Electronics and Communication Engineering Villupuram604304 India Aarupadai Veedu Institute of Technology Vinayaka Mission's Research Foundation Department of Ece Chennai603 104 India

ISBN: (纸本)9781665415712

In all the technological fields, the data size increases very rapidly and also database becomes very bulk in size. Users using bulk databases confront several challenges, such as determining which query produces the most relevant results. As the number of users has increased dramatically in recent years, there have been various competitions for recommendation systems. For enhancing or building recommendation systems all most or commonly everybody come up with an idea of collaborative filtering technique. When database or the data size increases it also reflect the processing time consumed and as well as the proposals will have potential. It is the best errand to give proposal to huge scope issues to create high greatness suggestions. Nonetheless, several approaches for the expansion of the recommender framework have been presented. Perhaps, the most and famous popular framework in for modern large datasets is Map Reduce, due to the outstanding features as gullibility, fault-tolerance, ease and effective of programming, flexibility. This paper aims to state the enlightening the status of effective and parallel query processing using Apache Mahout, Map Reduce and collaborative filtering. © 2021 IEEE.

关键词： Recommender systems

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A Swarm Artificial Intelligence Approach for Effective Treatment of Chronic Conditions

A Swarm Artificial Intelligence Approach for Effective Treat...

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International Workshop on Design of Reliable Communication Networks (DRCN)

作者： Kitty Kioskli Spyridon Papastergiou School of Computer Science and Electronic Engineering University of EssexInstitute of Analytics and Data Science (IADS) Essex United Kingdom Gruppo Maggioli Research and Development Lab Athens Greece trustilio B.V. Amsterdam Netherlands Department of Informatics Piraeus University of Piraeus Greece Focal Point Brussels Belgium

Long-term conditions or chronic diseases are multifaceted and challenging. Current treatment options, for patients with long-term conditions, are mainly pharmacological, causing numerous adverse drug events and pressing for alternative management strategies such as personalized interventions. Areas of machine learning, such as deep learning, would enable researchers to develop predictive modelling algorithms, using continuous monitoring and allowing assessing the medical risk for long-term conditions and their related complications. In this paper, we claim that harmonization of data, novel machine learning algorithms, swarm-based technologies, and the involvement of the entire healthcare community will lead to acceptable and effective personalized healthcare. Our proposed approach aims to amplify the intelligence of the healthcare community. Based upon the patients’ characteristics empowers better decisions, personalised medical risk prediction and recommendations of acceptable and effective interventions. Our future work includes the validation of the SwarmAI framework by actively engaging relevant stakeholders.

关键词： Drugs Machine learning algorithms Medical services Pressing Predictive models Reliability engineering Prediction algorithms

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Towards Integrated Sensing and Communications for 6G: A Standardization Perspective

arXiv

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arXiv 2023年

作者： Kaushik, Aryan Singh, Rohit Dayarathna, Shalanika Senanayake, Rajitha Di Renzo, Marco Dajer, Miguel Ji, Hyoungju Kim, Younsun Sciancalepore, Vincenzo Zappone, Alessio Shin, Wonjae The School of Engineering & Informatics University of Sussex United Kingdom The Department of Electronics & Communication Engineering Dr. B. R. Ambedkar National Institute of Technology India The Department of Electrical & Electronic Engineering University of Melbourne Australia The CentraleSupelec Paris-Saclay University France The Futurewei Technologies United States Samsung Research Samsung Electronics Korea Republic of The NEC Laboratories Europe GmbH Germany The Department of Electrical and Information Engineering University of Cassino and Southern Lazio Italy The Department of Electrical and Computer Engineering Ajou University Korea Republic of The Department of Electrical Engineering Princeton University United States

The radio communication division of the International Telecommunication Union (ITU-R) has recently adopted Integrated Sensing and Communication (ISAC) among the key usage scenarios for IMT-2030/6G. ISAC is envisioned to play a vital role in the upcoming wireless generation standards. In this work, we bring together several paramount and innovative aspects of ISAC technology from a global 6G standardization perspective, including both industrial and academic progress. Specifically, this article provides 6G requirements and ISAC-enabled vision, including various aspects of 6G standardization, benefits of ISAC co-existence, and integration challenges. Moreover, we present key enabling technologies, including intelligent metasurface-aided ISAC, as well as Orthogonal Time Frequency Space (OTFS) waveform design and interference management for ISAC. Finally, future aspects are discussed to open various research opportunities and challenges on the ISAC technology towards 6G wireless communications. © 2023, CC BY.

关键词： Standardization

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