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2021 International Conference on Smart Systems and Advanced Computing, SysCom 2021

作者： Gaurav, Akshat Zhou, Zhili Chui, Kwok Tai Colace, Francesco Chaurasia, Priyanka Hsu, Ching-Hsien Ronin Institute MontclairNJ07043 United States School Engineering Research Center of Digital Forensics Ministry of Education School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Hong Kong University of Salerno Italy Ulster University Magee campus Londonderry United Kingdom Department of Computer Science and Information Engineering Asia University Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Taiwan Department of Medical Research China Medical University Hospital China Medical University Taiwan

Due to the developemt in the latest digital technologies, internet service use has surged recently. In order for these online businesses to succeed, they must be able to consistently and effectively supply their services. As a result of the DDoS assault, online sources are impacted in terms of both their availability and their computational capacity. DDoS attacks are useful for cyber-attackers since there is no effective techniqque for the identification of them. In recent years, researchers have been experimenting with duffernet latest techniques like machine learning (ML) approaches to see whether they can build effective methods for detecting DDoS assaults. Machine learning and big data are used to identify DDoS assaults in this research paper. © 2021 Copyright for this paper by its authors.

关键词： Machine learning

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Exploiting Renewable Energy and UPS Systems to Reduce Power Consumption in Data Centers

Research Square

引用

Research Square 2021年

作者： Peng, Xiaopu Bhattacharya, Tathagata Cao, Ting Mao, Jianzhou Tekreeti, Taha Qin, Xiao Department of Computer Science and Software Engineering Auburn University AuburnAL United States

To develop environmental friendly and energy-efficient data centers, it is prudent to leverage on-site renewable sources like solar and wind. Data centers deploy distributed UPS systems to improve efficiency, scalability, and reliability of UPS systems, thereby handling the intermittent nature of renewable energy. We propose a renewable-energy manager called REDUX to (1) offer a smart way of managing energy supply of data centers powered by grid and renewable energy and (2) maintain a desirable balance between energy cost and system performance. To achieve this overarching objective, REDUX judiciously orchestrates distribute UPS devices (i.e., recharge or discharge) to allocate energy resources when (1) grid price is at low or high states or (2) renewable energy generation is at a low or fluctuate level. REDUX not only guarantees the stable operation of daily workload conditions, but also cuts back the energy cost of data centers by improving power resource utilization. Compared with the existing strategies, REDUX demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization. © 2021, CC BY.

关键词： Renewable energy resources

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Low-Power Random Access for Timely Status Update: Packet-based or Connection-based?

arXiv

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arXiv 2022年

作者： Chan, Tse-Tin Feng, Jian Pan, Haoyuan The Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

This paper studies low-power random access protocols for timely status update systems with information freshness requirements, measured by age of information (AoI). In an extensive network, a fundamental challenge is scheduling a large number of transmitters to access the wireless channel in a way that achieves low network-wide AoI while consuming minimal power. Conventional packet-based random access protocols involve transmitters contending for the channel by sending their entire data packets. When the packet duration is long, the time wasted due to packet collisions can be significant. In contrast, connection-based random access protocols establish connections with the receiver before transmitting data packets. From an information freshness perspective, there should be conditions that favor one approach over the other. We present a comparative study of the average AoI of packet-based and connection-based random access protocols. Specifically, we consider frame slotted Aloha (FSA) as a representative of packet-based random access and design a request-then-access (RTA) protocol for connection-based random access. Our analyses indicate that the choice between packet-based or connection-based protocols depends mainly on the payload size of update packets and the transmit power budget. In particular, RTA saves power and significantly reduces AoI, especially when the payload size is large. Overall, our investigation offers insights into the practical design of random access protocols for low-power timely status update systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Transmitters

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A Hybrid Stock optimization Approach for Inventory Management 3

A Hybrid Stock optimization Approach for Inventory Managemen...

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3rd International Congress on Human-computer Interaction, Optimization and Robotic Applications, HORA 2021

作者： Cimen, Egemen Berki Kurban, Ilknur Selmanoglu, Ozgur Sahin, Murat Kilinc, Deniz Data Science Eldor Corporation Izmir Turkey Software Development Eldor Corporation Izmir Turkey Technology Eldor Corporation Izmir Turkey Data Science Kalybe.AI Izmir Turkey Bakirçay University Computer Engineering Department of Faculty of Engineering and Architecture Izmir Turkey

ISBN: (纸本)9781665440585

Modern world is rapidly evolving around knowledge and business that know how to use knowledge shows superiority. Thus, smart decision making becomes vital for the modern business world to achieve sustainability in life and business. Especially, with a world of scarce resources, utilizing knowledge would play critical not only today but also for future. Moreover, using knowledge is inevitable in supply chain and inventory management must be supported with smart algorithms and modern heuristics to avoid excessive inventory while fighting with stockout. Therefore, this study explores the opportunity for inventory planning with heuristics and tailored techniques as well as how to hybridize modern heuristics and tailored techniques. In this study, inventory optimization experiments are proposed to model spare part inventory management and find the best way to determine reorder amount to deal with shortage and excessive inventory. Four heuristics which are i) basic golden ratio, ii) simulated annealing, iii) statistical rule-based heuristic and iv) hybrid algorithm of simulated annealing and statistical rule-based heuristic are evaluated on existing spare part dataset with an example from real-life part supplier named Eldor. In this test case, 400 products are analyzed, and best reorder points and amounts are selected with the help of heuristics. Heuristics' main structures and parameters are adjusted for the problem's need and improvement on quality of results. Parameters are determined according to trial and error with experts' guidance on heuristics. The best result suggests 8% improvement on cost and 37% improvement on inventory load could achieve with the help of heuristics. These solutions are usable against even hard constraints like shortage. © 2021 IEEE.

关键词： Simulated annealing

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The DEformer: An order-agnostic distribution estimating transformer

arXiv

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arXiv 2021年

作者： Alcorn, Michael A. Nguyen, Anh Department of Computer Science and Software Engineering Auburn University AuburnAL United States

Order-agnostic autoregressive distribution (density) estimation (OADE), i.e., autoregressive distribution estimation where the features can occur in an arbitrary order, is a challenging problem in generative machine learning. Prior work on OADE has encoded feature identity by assigning each feature to a distinct fixed position in an input vector. As a result, architectures built for these inputs must strategically mask either the input or model weights to learn the various conditional distributions necessary for inferring the full joint distribution of the dataset in an order-agnostic way. In this paper, we propose an alternative approach for encoding feature identities, where each feature's identity is included alongside its value in the input. This feature identity encoding strategy allows neural architectures designed for sequential data to be applied to the OADE task without modification. As a proof of concept, we show that a Transformer trained on this input (which we refer to as "the DEformer"2, i.e., the distribution estimating Transformer) can effectively model binarized-MNIST, approaching the performance of fixed-order autoregressive distribution estimating algorithms while still being entirely order-agnostic. Additionally, we find that the DEformer surpasses the performance of recent flow-based architectures when modeling a tabular dataset. Copyright © 2021, The Authors. All rights reserved.

关键词： Signal encoding

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Automatic Component-Based Synthesis of User-Configured Manufacturing Simulation Models

Automatic Component-Based Synthesis of User-Configured Manuf...

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Simulation Winter Conference

作者： Alexander Mages Carina Mieth Jens Hetzler Fadil Kallat Jakob Rehof Christian Riest Tristan Schäfer TRUMPF Werkzeugmaschinen SE & Co. KG Ditzingen GERMANY ITK Engineering GmbH GERMANY Department of Computer Science Chair for Software Engineering TU Dortmund University Dortmund GERMANY

ISBN: (纸本)9781665476621

Using simulation models for manufacturing facilities is a common approach for planning, optimizing, and testing different machine configurations and positioning before the actual construction. However, creating these models is time-consuming and costly. Consequently, only a few different simulation models are usually created based on best practices and experience, precluding any examination of the entire variety of possible solutions. To address these obstacles, we present a proof of concept to automate and hence reduce the cost of the process of simulation model creation, thereby allowing for the creation of a larger number of selectable solution variants. Based on a given master simulation model, which obtained all possible variations of a shop floor, we defined simulation building blocks as components. We used component-based synthesis using combinatory logic to synthesize a product line of varying simulation models for a given configuration to be executed and evaluated to find suitable solutions.

关键词： Costs Production facilities Planning Manufacturing Best practices Testing

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Cognitive Disentanglement for Referring Multi-Object Tracking

arXiv

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arXiv 2025年

作者： Liang, Shaofeng Guan, Runwei Lian, Wangwang Liu, Daizong Sun, Xiaolou Wu, Dongming Yue, Yutao Ding, Weiping Xiong, Hui Qingdao China Guangzhou China Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software Qingdao China Wangxuan Institute of Computer Technology Peking University Beijing China School of Automation Southeast University Nanjing China School of Computer Science Beijing Institute of Technology Beijing China School of Artificial Intelligence and Computer Science Nantong University Nantong China Guangzhou China Department of Electrical Engineering and Electronics University of Liverpool Liverpool United Kingdom

As a significant application of multi-source information fusion in intelligent transportation perception systems, Referring Multi-Object Tracking (RMOT) involves localizing and tracking specific objects in video sequences based on language references. However, existing RMOT approaches often treat language descriptions as holistic embeddings and struggle to effectively integrate the rich semantic information contained in language expressions with visual features. This limitation is especially apparent in complex scenes requiring comprehensive understanding of both static object attributes and spatial motion information. In this paper, we propose a Cognitive Disentanglement for Referring Multi-Object Tracking (CDRMT) framework that addresses these challenges. It adapts the "what" and "where" pathways from human visual processing system to RMOT tasks. Specifically, our framework comprises three collaborative components: (1)The Bidirectional Interactive Fusion module first establishes cross-modal connections while preserving modality-specific characteristics;(2) Building upon this foundation, the Progressive Semantic-Decoupled Query Learning mechanism hierarchically injects complementary information into object queries, progressively refining object understanding from coarse to fine-grained semantic levels;(3) Finally, the Structural Consensus Constraint enforces bidirectional semantic consistency between visual features and language descriptions, ensuring that tracked objects faithfully reflect the referring expression. Extensive experiments on different benchmark datasets demonstrate that CDRMT achieves substantial improvements over state-of-the-art methods, with average gains of 6.0% in HOTA score on Refer-KITTI and 3.2% on Refer-KITTI-V2. Our approach advances the state-of-the-art in RMOT while simultaneously providing new insights into multi-source information fusion. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantics

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A Multi-Dimensional Analysis of Top Frequent Pattern Mining Algorithms

SSRN

引用

SSRN 2022年

作者： Ishfaq, Umar Khan, Hikmat Ullah Iqbal, Saqib Department of Computer Science COMSATS University Islamabad Wah Campus Wah Cantt470040 Pakistan Department of Software Engineering Al-Ain University of Science and Technology Al-Ain15258 United Arab Emirates

Itemset mining (IM) is a relatively new and an important subdomain of data mining that aims to discover interesting as well as useful patterns (also called itemsets) which appear frequently together in a database having transactions from few thousand to billions. Originally, IM was designed for market-basket analysis, however, vast applications of IM has transformed it into a more general and a popular research area. Many techniques have been designed for mining the frequent itemsets or frequent patterns and current literature presents the performance analysis of several of such algorithms. However, most of the studies includes classical performance measures such as time and space complexity. In this paper, we aim present a comparative analysis of widely used frequent patterns mining algorithms on various benchmark datasets including classical as well as a novel performance measure using different values of support. The study can provide valuable insights, particularly to young researchers in proposing new model as well as choosing appropriate baseline technique. © 2022, The Authors. All rights reserved.

关键词： Data mining

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The Impact of Forced Working-From-Home on Code Technical Debt: An Industrial Case Study

The Impact of Forced Working-From-Home on Code Technical Deb...

引用

Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Ehsan Zabardast Javier Gonzalez-Huerta Francis Palma Software Engineering Research Lab SERL Sweden Blekinge Institute of Technology Karlskrona Sweden Department of Computer Science and Media Technology Linnaeus University Kalmar Sweden

Background: The COVID-19 outbreak interrupted regular activities for over a year in many countries and resulted in a radical change in ways of working for software development companies, i.e., most software development companies switched to a forced Working-From-Home (WFH) mode. Aim: Although several studies have analysed different aspects of forced WFH mode, it is unknown whether and to what extent WFH impacted the accumulation of technical debt (TD) when developers have different ways to coordinate and communicate with peers. Method: Using the year 2019 as a baseline, we carried out an industrial case study to analyse the evolution of TD in five components that are part of a large project while WFH. As part of the data collection, we carried out a focus group with developers to explain the different patterns observed from the quantitative data analysis. Results: TD accumulated at a slower pace during WFH as compared with the working-from-office period in four components out of five. These differences were found to be statistically significant. Through a focus group, we have identified different factors that might explain the changes in TD accumulation. One of these factors is responsibility diffusion which seems to explain why TD grows faster during the WFH period in one of the components. Conclusion: The results suggest that when the ways of working change, the change between working from office and working from home does not result in an increased accumulation of TD.

关键词： COVID-19 Employee welfare Data analysis Codes Pandemics Companies Switches

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RENFIS-MA: A Recurrent Neuro-Fuzzy Inference System for Time Series Water Level Prediction

RENFIS-MA: A Recurrent Neuro-Fuzzy Inference System for Time...

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2021 International Conference on Information science and Communications Technologies, ICISCT 2021

作者： Mahmud, Mohammad Sultan Sadatdiynov, Kuanishbay Fayzullaev, Bayram Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Nukus Branch of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Telecommunication Technologies Nukus Uzbekistan

ISBN: (纸本)9781665432580

It is a well-known fact that improving time series prediction accuracy is an important and challenging issue. Exten-sive research has been conducted using soft-computing techniques to improve their prediction accuracy. In this paper, we propose a new methodology to predict time-series water-level, namely, recurrent error-based neuro-fuzzy inference system with moving average (RENFIS-MA). In particular, we used the adaptive network-based fuzzy inference system to build a prediction model. RENFIS-MA realizes that with the lagged variables of a time series feeding back network output-error to the input layer and moving-average of auto-regressive input making accurate predictions. It can identify the system's characteristics quite well and provides a new way to make time-series predictions. A comparative analysis of RENFIS-MA with four soft-computing models is conducted using the prediction of the water level of the river of Jamuna in Bangladesh. The trained networks are used for predicting both single-step and multiple-step ahead. In the simulation, two performance measures [root-mean-square error (RMSE) and prediction accuracy (Acc.)] demonstrate that the proposed method is more effective and accurate for prediction among all the compared models. © 2021 IEEE.

关键词： Errors

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