PluraMath: Extending Mathematical Reasoning Evaluation Beyond High-Resource Languages

Authors: Daryna Dementieva, Nikolay Babakov, Kathy Hämmerl, Ilseyar Alimova, Jindřich Libovický, Shu Okabe, Miras Baisbay, Lukas Edman, Abrorkhon Inomkhujaev, Antonia Karamolegkou, Mateusz Lango, Volkan Özer, Nikola Selic, Subhankar Swain, Tsedeniya Kinfe Temesgen, Galit Bary Weisberg, Alexander Fraser

Abstract: Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resource languages, with English and Chinese dominating both pre-training corpora and evaluation suites. The recently released PolyMath (Wang et al., 2025) dataset represents a significant step forward, yet its coverage is stil… ▽ More Mathematical reasoning has become a central task for evaluating and tuning reasoning Large Language Models (LLMs), yet existing benchmarks remain heavily biased toward high-resource languages, with English and Chinese dominating both pre-training corpora and evaluation suites. The recently released PolyMath (Wang et al., 2025) dataset represents a significant step forward, yet its coverage is still limited to 18 only high-resource languages. To address this gap, we introduce PluraMath, an extension of PolyMath to 18 additional {underrepresented languages spanning 6 language families -- ranging from mid-resource to extreme low-resource settings. We constructed the dataset through a human-curated pipeline, where native speakers thoroughly validated pre-computed translations. Using PluraMath, we then benchmark 27 reasoning LLMs across four model scales -- small, mid-size, large, and closed-source ensembles -- probing the multilingual mathematical reasoning capabilities of state-of-the-art models under diverse linguistic conditions. Our fine-grained analysis confirms a persistent gap in mathematical reasoning performance between high-resource and underrepresented languages, with stronger results largely associated with better instruction-following ability. We fully open-source our dataset, data acquisition pipeline, and evaluation framework, with the goal of lowering the barrier to multilingual benchmark development for underrepresented communities. △ Less

Submitted 7 July, 2026; originally announced July 2026.

A Modular Architecture for Typologically Controlled Lexicon Generation

Authors: Sankalp Tattwadarshi Swain, Dhruv Kumar

Abstract: Constructing artificial lexicons that are pronounceable, typologically plausible, and semantically structured remains an open challenge in computational linguistics. Existing conlang generators either lack formal phonotactic guarantees or delegate generation to opaque, non-reproducible LLM-based pipelines. We propose a modular framework that samples phoneme inventories from PHOIBLE, generates word… ▽ More Constructing artificial lexicons that are pronounceable, typologically plausible, and semantically structured remains an open challenge in computational linguistics. Existing conlang generators either lack formal phonotactic guarantees or delegate generation to opaque, non-reproducible LLM-based pipelines. We propose a modular framework that samples phoneme inventories from PHOIBLE, generates word forms under interchangeable phonological grammars (deterministic, OT, and MaxEnt), and assigns meanings via a Swadesh--Leipzig--Jakarta ontology with explicit form--meaning alignment. Evaluation on character $n$-gram perplexity, log-likelihood, and KL divergence against PHOIBLE across lexicon sizes of 100-5,000 forms shows that probabilistic grammars consistently outperform deterministic and random baselines on both phonotactic coherence and typological realism. △ Less

Submitted 7 April, 2026; originally announced May 2026.

Cloud Computing Review: A Decade of Research

Authors: Smruti Rekha Swain

Abstract: The popularity and rapid development of Cloud Computing in recent years has led to a vast number of publications capturing the accumulated knowledge in this field. Due to the interdisciplinary nature and significant relevance of cloud computing research, it has become increasingly challenging to comprehend the overall structure and progress of this field without employing analytical methods. While… ▽ More The popularity and rapid development of Cloud Computing in recent years has led to a vast number of publications capturing the accumulated knowledge in this field. Due to the interdisciplinary nature and significant relevance of cloud computing research, it has become increasingly challenging to comprehend the overall structure and progress of this field without employing analytical methods. While the evaluation of scientific research has a long tradition in many fields, we have identified a lack of comprehensive scientometric studies specifically focused on cloud computing. This study applies scientometric techniques to empirically examine cloud computing research evolution and current state from a macroscopic perspective. We employed the CiteSpace tool for visual analysis, exploring topics related to cloud computing by retrieving papers published between 2014 and 2023 from the Web of Science Core database. Our approach involved constructing collaboration networks among authors, institutions, and countries to pinpoint the most prolific contributors in each category. Through the analysis of core journal distributions via journal co-citations, document co-citation networks, and clustering analysis, we uncovered the underlying research topics and knowledge structure. The results of this study enhance our understanding of patterns, trends, and other critical factors, offering a foundation for guiding research activities, sharing knowledge, and fostering collaboration in the field of cloud computing research. △ Less

Submitted 23 May, 2026; originally announced May 2026.

See, Explain, and Intervene: A Few-Shot Multimodal Agent Framework for Hateful Meme Moderation

Authors: Naquee Rizwan, Subhankar Swain, Paramananda Bhaskar, Gagan Aryan, Shehryaar Shah Khan, Animesh Mukherjee

Abstract: In this work, we examine hateful memes from three complementary angles - how to detect them, how to explain their content and how to intervene them prior to being posted - by applying a range of strategies built on top of generative AI models. To the best of our knowledge, explanation and intervention have typically been studied separately from detection, which does not reflect real-world conditio… ▽ More In this work, we examine hateful memes from three complementary angles - how to detect them, how to explain their content and how to intervene them prior to being posted - by applying a range of strategies built on top of generative AI models. To the best of our knowledge, explanation and intervention have typically been studied separately from detection, which does not reflect real-world conditions. Further, since curating large annotated datasets for meme moderation is prohibitively expensive, we propose a novel framework that leverages task-specific generative multimodal agents and the few-shot adaptability of large multimodal models to cater to different types of memes. We believe this is the first work focused on generalizable hateful meme moderation under limited data conditions, and has strong potential for deployment in real-world production scenarios. Warning: Contains potentially toxic contents. △ Less

Submitted 8 January, 2026; originally announced January 2026.

Talking with Oompa Loompas: A novel framework for evaluating linguistic acquisition of LLM agents

Authors: Sankalp Tattwadarshi Swain, Anshika Krishnatray, Dhruv Kumar, Jagat Sesh Challa

Abstract: Existing evaluation studies on linguistic competence of large language models (LLM agents) have focused primarily on vocabulary learning, morphological rule induction, syntactic generalization, pragmatic inference, and cross-linguistic transfer. However, none assess whether LLM agents can acquire a language through pattern recognition and interactive feedback, a central feature of human language a… ▽ More Existing evaluation studies on linguistic competence of large language models (LLM agents) have focused primarily on vocabulary learning, morphological rule induction, syntactic generalization, pragmatic inference, and cross-linguistic transfer. However, none assess whether LLM agents can acquire a language through pattern recognition and interactive feedback, a central feature of human language acquisition. We propose a novel experimental framework in which an LLM agent is evaluated on its ability to acquire and use a newly constructed language (Tinkatongue) in conversation with a bot that understands only Tinkatongue. Our findings show that LLM agents fail to establish a conversation within 100 responses, yet they adopt distinct strategies that mirror human approaches to language learning. The results suggest a new direction for evaluation benchmarks and open pathways to model designs that learn more effectively from interactive feedback. △ Less

Submitted 9 September, 2025; originally announced September 2025.

STEMTOX: From Social Tags to Fine-Grained Toxic Meme Detection via Entropy-Guided Multi-Task Learning

Authors: Subhankar Swain, Naquee Rizwan, Vishwa Gangadhar S, Nayandeep Deb, Animesh Mukherjee

Abstract: Memes, as a widely used mode of online communication, often serve as vehicles for spreading harmful content. However, limitations in data accessibility and the high costs of dataset curation hinder the development of robust meme moderation systems. To address this challenge, in this work, we introduce a first-of-its-kind dataset - TOXICTAGS consisting of 6,300 real-world meme-based posts annotated… ▽ More Memes, as a widely used mode of online communication, often serve as vehicles for spreading harmful content. However, limitations in data accessibility and the high costs of dataset curation hinder the development of robust meme moderation systems. To address this challenge, in this work, we introduce a first-of-its-kind dataset - TOXICTAGS consisting of 6,300 real-world meme-based posts annotated in two stages: (i) binary classification into toxic and normal, and (ii) fine-grained labelling of toxic memes as hateful, dangerous, or offensive. A key feature of this dataset is that it is enriched with auxiliary metadata of socially relevant tags, enhancing the context of each meme. In addition, we propose a novel entropy guided multi-tasking framework - STEMTOX - that integrates the generation of socially grounded tags with a robust classification framework. Experimental results show that incorporating these tags substantially enhances the performance of state-of-the-art VLMs in toxicity detection tasks. Our contributions offer a novel and scalable foundation for improved content moderation in multimodal online environments. Warning: Contains potentially toxic contents. △ Less

Submitted 16 March, 2026; v1 submitted 6 August, 2025; originally announced August 2025.

Revisiting CroPA: A Reproducibility Study and Enhancements for Cross-Prompt Adversarial Transferability in Vision-Language Models

Authors: Atharv Mittal, Agam Pandey, Amritanshu Tiwari, Sukrit Jindal, Swadesh Swain

Abstract: Large Vision-Language Models (VLMs) have revolutionized computer vision, enabling tasks such as image classification, captioning, and visual question answering. However, they remain highly vulnerable to adversarial attacks, particularly in scenarios where both visual and textual modalities can be manipulated. In this study, we conduct a comprehensive reproducibility study of "An Image is Worth 100… ▽ More Large Vision-Language Models (VLMs) have revolutionized computer vision, enabling tasks such as image classification, captioning, and visual question answering. However, they remain highly vulnerable to adversarial attacks, particularly in scenarios where both visual and textual modalities can be manipulated. In this study, we conduct a comprehensive reproducibility study of "An Image is Worth 1000 Lies: Adversarial Transferability Across Prompts on Vision-Language Models" validating the Cross-Prompt Attack (CroPA) and confirming its superior cross-prompt transferability compared to existing baselines. Beyond replication we propose several key improvements: (1) A novel initialization strategy that significantly improves Attack Success Rate (ASR). (2) Investigate cross-image transferability by learning universal perturbations. (3) A novel loss function targeting vision encoder attention mechanisms to improve generalization. Our evaluation across prominent VLMs -- including Flamingo, BLIP-2, and InstructBLIP as well as extended experiments on LLaVA validates the original results and demonstrates that our improvements consistently boost adversarial effectiveness. Our work reinforces the importance of studying adversarial vulnerabilities in VLMs and provides a more robust framework for generating transferable adversarial examples, with significant implications for understanding the security of VLMs in real-world applications. △ Less

Submitted 28 June, 2025; originally announced June 2025.

Secure Resource Management in Cloud Computing: Challenges, Strategies and Meta-Analysis

Authors: Deepika Saxena, Smruti Rekha Swain, Jatinder Kumar, Sakshi Patni, Kishu Gupta, Ashutosh Kumar Singh, Volker Lindenstruth

Abstract: Secure resource management (SRM) within a cloud computing environment is a critical yet infrequently studied research topic. This paper provides a comprehensive survey and comparative performance evaluation of potential cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management. Cybersecurity is explored specifically in the conte… ▽ More Secure resource management (SRM) within a cloud computing environment is a critical yet infrequently studied research topic. This paper provides a comprehensive survey and comparative performance evaluation of potential cyber threat countermeasure strategies that address security challenges during cloud workload execution and resource management. Cybersecurity is explored specifically in the context of cloud resource management, with an emphasis on identifying the associated challenges. The cyber threat countermeasure methods are categorized into three classes: defensive strategies, mitigating strategies, and hybrid strategies. The existing countermeasure strategies belonging to each class are thoroughly discussed and compared. In addition to conceptual and theoretical analysis, the leading countermeasure strategies within these categories are implemented on a common platform and examined using two real-world virtual machine (VM) data traces. Based on this comprehensive study and performance evaluation, the paper discusses the trade-offs among these countermeasure strategies and their utility, providing imperative concluding remarks on the holistic study of cloud cyber threat countermeasures and secure resource management. Furthermore, the study suggests future methodologies that could effectively address the emerging challenges of secure cloud resource management. △ Less

Submitted 5 February, 2025; originally announced February 2025.

Riemann Sum Optimization for Accurate Integrated Gradients Computation

Authors: Swadesh Swain, Shree Singhi

Abstract: Integrated Gradients (IG) is a widely used algorithm for attributing the outputs of a deep neural network to its input features. Due to the absence of closed-form integrals for deep learning models, inaccurate Riemann Sum approximations are used to calculate IG. This often introduces undesirable errors in the form of high levels of noise, leading to false insights in the model's decision-making pr… ▽ More Integrated Gradients (IG) is a widely used algorithm for attributing the outputs of a deep neural network to its input features. Due to the absence of closed-form integrals for deep learning models, inaccurate Riemann Sum approximations are used to calculate IG. This often introduces undesirable errors in the form of high levels of noise, leading to false insights in the model's decision-making process. We introduce a framework, RiemannOpt, that minimizes these errors by optimizing the sample point selection for the Riemann Sum. Our algorithm is highly versatile and applicable to IG as well as its derivatives like Blur IG and Guided IG. RiemannOpt achieves up to 20% improvement in Insertion Scores. Additionally, it enables its users to curtail computational costs by up to four folds, thereby making it highly functional for constrained environments. △ Less

Submitted 5 January, 2025; v1 submitted 5 October, 2024; originally announced October 2024.

Here's a Free Lunch: Sanitizing Backdoored Models with Model Merge

Authors: Ansh Arora, Xuanli He, Maximilian Mozes, Srinibas Swain, Mark Dras, Qiongkai Xu

Abstract: The democratization of pre-trained language models through open-source initiatives has rapidly advanced innovation and expanded access to cutting-edge technologies. However, this openness also brings significant security risks, including backdoor attacks, where hidden malicious behaviors are triggered by specific inputs, compromising natural language processing (NLP) system integrity and reliabili… ▽ More The democratization of pre-trained language models through open-source initiatives has rapidly advanced innovation and expanded access to cutting-edge technologies. However, this openness also brings significant security risks, including backdoor attacks, where hidden malicious behaviors are triggered by specific inputs, compromising natural language processing (NLP) system integrity and reliability. This paper suggests that merging a backdoored model with other homogeneous models can significantly remediate backdoor vulnerabilities even if such models are not entirely secure. In our experiments, we verify our hypothesis on various models (BERT-Base, RoBERTa-Large, Llama2-7B, and Mistral-7B) and datasets (SST-2, OLID, AG News, and QNLI). Compared to multiple advanced defensive approaches, our method offers an effective and efficient inference-stage defense against backdoor attacks on classification and instruction-tuned tasks without additional resources or specific knowledge. Our approach consistently outperforms recent advanced baselines, leading to an average of about 75% reduction in the attack success rate. Since model merging has been an established approach for improving model performance, the extra advantage it provides regarding defense can be seen as a cost-free bonus. △ Less

Submitted 3 June, 2024; v1 submitted 29 February, 2024; originally announced February 2024.

BICM-compatible Rate Adaptive Geometric Constellation Shaping Using Optimized Many-to-one Labeling

Authors: Metodi Plamenov Yankov, Smaranika Swain, Ognjen Jovanovic, Darko Zibar, Francesco Da Ros

Abstract: In this paper, a rate adaptive geometric constellation shaping (GCS) scheme which is fully backward-compatible with existing state of the art bit-interleaved coded modulation (BICM) systems is proposed and experimentally demonstrated. The system relies on optimization of the positions of the quadrature amplitude modulation (QAM) points on the I/Q plane for maximized achievable information rate, wh… ▽ More In this paper, a rate adaptive geometric constellation shaping (GCS) scheme which is fully backward-compatible with existing state of the art bit-interleaved coded modulation (BICM) systems is proposed and experimentally demonstrated. The system relies on optimization of the positions of the quadrature amplitude modulation (QAM) points on the I/Q plane for maximized achievable information rate, while maintaining quantization and fiber nonlinear noise robustness. Furthermore, `dummy' bits are multiplexed with coded bits before mapping to symbols. Rate adaptivity is achieved by tuning the ratio of coded and `dummy' bits, while maintaining a fixed forward error-correction block and a fixed modulation format size. The points' positions and their labeling are optimized using automatic differentiation. The proposed GCS scheme is compared to a time-sharing hybrid (TH) QAM modulation and the now mainstream probabilistic amplitude shaping (PAS) scheme. The TH without shaping is outperformed for all studied data rates in a simulated linear channel by up to 0.7 dB. In a linear channel, PAS is shown to outperform the proposed GCS scheme, while similar performances are reported for PAS and the proposed GCS in a simulated nonlinear fiber channel. The GCS scheme is experimentally demonstrated in a multi-span recirculating loop coherent optical fiber transmission system with a total distance of up to 3000 km. Near-continuous zero-error flexible throughput is reported as a function of the transmission distance. Up to 1-2 spans of increased reach gains are achieved at the same net data rate w.r.t. conventional QAM. At a given distance, up to 0.79 bits/2D symbol of gain w.r.t. conventional QAM is achieved. In the experiment, similar performance to PAS is demonstrated. △ Less

Submitted 13 March, 2024; v1 submitted 10 November, 2023; originally announced December 2023.

An AI-driven intelligent traffic management model for 6G cloud radio access networks

Authors: Smruti Rekha Swain, Deepika Saxena, Jatinder Kumar, Ashutosh Kumar Singh, Chung-Nan Lee

Abstract: This letter proposes a novel Cloud Radio Access Network (C-RAN) traffic analysis and management model that estimates probable RAN traffic congestion and mitigate its effect by adopting a suitable handling mechanism. A computation approach is introduced to classify heterogeneous RAN traffic into distinct traffic states based on bandwidth consumption and execution time of various job requests. Furth… ▽ More This letter proposes a novel Cloud Radio Access Network (C-RAN) traffic analysis and management model that estimates probable RAN traffic congestion and mitigate its effect by adopting a suitable handling mechanism. A computation approach is introduced to classify heterogeneous RAN traffic into distinct traffic states based on bandwidth consumption and execution time of various job requests. Further, a cloud-based traffic management is employed to schedule and allocate resources among user job requests according to the associated traffic states to minimize latency and maximize bandwidth utilization. The experimental evaluation and comparison of the proposed model with state-of-the-art methods reveal that it is effective in minimizing the worse effect of traffic congestion and improves bandwidth utilization and reduces job execution latency up to 17.07% and 18%, respectively. △ Less

Submitted 25 March, 2023; originally announced March 2023.

Efficient Resource Management in Cloud Environment

Authors: Smruti Rekha Swain, Ashutosh Kumar Singh, Chung Nan Lee

Abstract: In cloud computing resource management plays a significant role in data centres and it is directly dependent on the application workload. Various services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are offered by cloud computing to provide compute, network, and storage capabilities to the cloud users utilizing the pay-per-usage approa… ▽ More In cloud computing resource management plays a significant role in data centres and it is directly dependent on the application workload. Various services such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) are offered by cloud computing to provide compute, network, and storage capabilities to the cloud users utilizing the pay-per-usage approach. Resource allocation is a prior solution to address various demanding situations like the under/overload handling, resource wastage, load balancing, Quality-of-Services (QoS) violations, VM migration and many more. The primary aim of Virtual Machine Placement (VMP) is mapping of Virtual Machines (VMs) to physical machines (PMs), such that the PMs may be utilized to their maximum efficiency, where the already active VMs are not to be interrupted. It provides a list of live VM migrations that must be accomplished to get the optimum solution and reduces energy consumption to a larger extent. The inefficient VMP leads to wastage of resources, excessive energy consumption and also increase overall operational cost of the data center. On this context, this article provides an extensive survey of resource management schemes in cloud environment. A conceptual scheme for resource management, grouping of current machine learning based resource allocation strategies, and fundamental problems of ineffective distribution of physical resources are analyzed. Thereafter, a complete survey of existing techniques in machine learning based mechanisms in the field of cloud resource management are explained. Ultimately, the paper explores and concludes distinct approaching challenges and future research guidelines associated to resource management in cloud environment. △ Less

Submitted 24 June, 2022; originally announced July 2022.

Application of Top-hat Transformation for Enhanced Blood Vessel Extraction

Authors: Tithi Parna Das, Sheetal Praharaj, Sarita Swain, Sumanshu Agarwal, Kundan Kumar

Abstract: In the medical domain, different computer-aided diagnosis systems have been proposed to extract blood vessels from retinal fundus images for the clinical treatment of vascular diseases. Accurate extraction of blood vessels from the fundus images using a computer-generated method can help the clinician to produce timely and accurate reports for the patient suffering from these diseases. In this art… ▽ More In the medical domain, different computer-aided diagnosis systems have been proposed to extract blood vessels from retinal fundus images for the clinical treatment of vascular diseases. Accurate extraction of blood vessels from the fundus images using a computer-generated method can help the clinician to produce timely and accurate reports for the patient suffering from these diseases. In this article, we integrate top-hat based preprocessing approach with fine-tuned B-COSFIRE filter to achieve more accurate segregation of blood vessel pixels from the background. The use of top-hat transformation in the preprocessing stage enhances the efficacy of the algorithm to extract blood vessels in presence of structures like fovea, exudates, haemorrhages, etc. Furthermore, to reduce the false positives, small clusters of blood vessel pixels are removed in the postprocessing stage. Further, we find that the proposed algorithm is more efficient as compared to various modern algorithms reported in the literature. △ Less

Submitted 18 March, 2022; originally announced March 2022.

Novel ways of enumerating restrained dominating sets of cycles

Authors: Sushmita Paul, Ratanjeet Pratap Chauhan, Srinibas Swain

Abstract: Let $G = (V, E)$ be a graph. A set $S \subseteq V$ is a restrained dominating set (RDS) if every vertex not in $S$ is adjacent to a vertex in $S$ and to a vertex in $V - S$. The restrained domination number of $G$, denoted by $γ_r(G)$, is the smallest cardinality of a restrained dominating set of $G$. Finding the restrained domination number is NP-hard for bipartite and chordal graphs. Let… ▽ More Let $G = (V, E)$ be a graph. A set $S \subseteq V$ is a restrained dominating set (RDS) if every vertex not in $S$ is adjacent to a vertex in $S$ and to a vertex in $V - S$. The restrained domination number of $G$, denoted by $γ_r(G)$, is the smallest cardinality of a restrained dominating set of $G$. Finding the restrained domination number is NP-hard for bipartite and chordal graphs. Let $G_n^i$ be the family of restrained dominating sets of a graph $G$ of order $n$ with cardinality $i$, and let $d_r(G_n, i)=|G_n^i|$. The restrained domination polynomial (RDP) of $G_n$, $D_r(G_n, x)$ is defined as $D_r(G_n, x) = \sum_{i=γ_r(G_n)}^{n} d_r(G_n,i)x^i$. In this paper, we focus on the RDP of cycles and have, thus, introduced several novel ways to compute $d_r(C_n, i)$, where $C_n$ is a cycle of order $n$. In the first approach, we use a recursive formula for $d_r(C_n,i)$; while in the other approach, we construct a generating function to compute $d_r(C_n,i)$. △ Less

Submitted 21 November, 2022; v1 submitted 22 November, 2021; originally announced November 2021.

Medium Access Strategies for Integrated Access and Backhaul at mmWaves Unlicensed Spectrum

Authors: Biswa P. S. Sahoo, Styabrata Swain, Hung-Yu Wei, Mahasweta Sarkar

Abstract: The unlicensed spectrum is recently considered one of the defining solutions to meet the steadily growing traffic demand. This, in turn, has led to the enhancement for LTE in Release-13 to enable Licensed-Assisted Access (LAA) operations. The design of the medium access control (MAC) protocol for the LAA system to harmonically coexist with the incumbent WLAN system operating in an unlicensed band… ▽ More The unlicensed spectrum is recently considered one of the defining solutions to meet the steadily growing traffic demand. This, in turn, has led to the enhancement for LTE in Release-13 to enable Licensed-Assisted Access (LAA) operations. The design of the medium access control (MAC) protocol for the LAA system to harmonically coexist with the incumbent WLAN system operating in an unlicensed band is critical and challenging. In this paper, we consider an Integrated Access and Backhaul (IAB) system coexisting with a Wi-Fi network operating at millimeter-wave (mmWave) unlicensed spectrum, for which a listen-before-talk-based (LBT) based medium access mechanism is carefully designed. Additionally, we have considered an in-band system that supports both access and backhaul in a single node where the small-cell or the IAB nodes compete with the WiGig for medium access. We present comprehensive experimental results and give design insights based on the simulation results. △ Less

Submitted 22 March, 2021; originally announced April 2021.

Results on Competitiveness of Online Shortest Remaining Processing Time(SRPT) Scheduling with Special Classes of Inputs

Authors: Sheetal Swain, Rakesh Mohanty, Debasis Dwibedy

Abstract: Shortest Remaining Processing Time (SRPT) is a well known preemptive scheduling algorithm for uniprocessor and multiprocessor systems. SRPT finds applications in the emerging areas such as scheduling of client's requests that are submitted to a web server for accessing static web pages, managing the access requests to files in multiuser database systems and routing of packets across several links… ▽ More Shortest Remaining Processing Time (SRPT) is a well known preemptive scheduling algorithm for uniprocessor and multiprocessor systems. SRPT finds applications in the emerging areas such as scheduling of client's requests that are submitted to a web server for accessing static web pages, managing the access requests to files in multiuser database systems and routing of packets across several links as per bandwidth availability in data communications. SRPT has been proved to be optimal for the settings, where the objective is to minimize the mean response time of a list of jobs. According to our knowledge, there is less attention on the study of online SRPT with respect to the minimization of makespan as a performance criterion. In this paper, we study the SRPT algorithm for online scheduling in multiprocessor systems with makespan minimization as an objective. We obtain improved constant competitiveness results for algorithm SRPT for special classes of online job sequences based on practical real life applications. △ Less

Submitted 18 December, 2020; originally announced December 2020.

A survey of repositories in graph theory

Authors: Srinibas Swain, C. Paul Bonnington, Graham Farr, Kerri Morgan

Abstract: Since the pioneering work of R. M. Foster in the 1930s, many graph repositories have been created to support research in graph theory. This survey reviews many of these graph repositories and summarises the scope and contents of each repository. We identify opportunities for the development of repositories that can be queried in more flexible ways. Since the pioneering work of R. M. Foster in the 1930s, many graph repositories have been created to support research in graph theory. This survey reviews many of these graph repositories and summarises the scope and contents of each repository. We identify opportunities for the development of repositories that can be queried in more flexible ways. △ Less

Submitted 23 June, 2020; originally announced June 2020.