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7th Iranian Conference on Advances in Enterprise Architecture, ICAEA 2023

作者： Alimoradi, Reyhaneh Aliee, Fereidoon Shams Farahani, Bahar Shahid Beheshti University Soea Lab Computer Science and Engineering Department Tehran Iran Shahid Beheshti University Cyberspace Research Institute Tehran Iran

ISBN: (纸本)9798350308068

The use of robotic process automation (RPA) has been on the rise lately. However, more research needs to be done on the selection of the process and the factors affecting this choice. The key to the effectiveness of the outcome and the success of RPA hinge on selecting the appropriate processes and prioritizing them for automation. This study presents a novel method for selecting and prioritizing processes to enhance their efficiency within the organization. This approach enables us to prioritize processes for maximum automation advantage. It also identifies processes where RPA does not yield substantial advantages. The proposed approach was implemented in a real-world environment by conducting a case study at a logistics firm, demonstrating its benefits and increased efficiency. In addition, to validate the results, interviews with domain experts were conducted. During these interviews, the experts affirmed that the recommended approach successfully identified the most suitable processes and tasks for automation. © 2023 IEEE.

关键词： Robotics

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DashBot: Insight-Driven Dashboard Generation Based on Deep Reinforcement Learning

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IEEE Transactions on Visualization and computer Graphics 2023年第1期29卷 690-700页

作者： Deng, Dazhen Wu, Aoyu Qu, Huamin Wu, Yingcai Zhejiang University State Key Lab of CAD&CG China Hong Kong University of Science and Technology Department of Computer Science and Engineering Hong Kong Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies China

Analytical dashboards are popular in business intelligence to facilitate insight discovery with multiple charts. However, creating an effective dashboard is highly demanding, which requires users to have adequate data analysis background and be familiar with professional tools, such as Power BI. To create a dashboard, users have to configure charts by selecting data columns and exploring different chart combinations to optimize the communication of insights, which is trial-and-error. Recent research has started to use deep learning methods for dashboard generation to lower the burden of visualization creation. However, such efforts are greatly hindered by the lack of large-scale and high-quality datasets of dashboards. In this work, we propose using deep reinforcement learning to generate analytical dashboards that can use well-established visualization knowledge and the estimation capacity of reinforcement learning. Specifically, we use visualization knowledge to construct a training environment and rewards for agents to explore and imitate human exploration behavior with a well-designed agent network. The usefulness of the deep reinforcement learning model is demonstrated through ablation studies and user studies. In conclusion, our work opens up new opportunities to develop effective ML-based visualization recommenders without beforehand training datasets. © 2022 IEEE.

关键词： Reinforcement learning

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GoT: Effective Graph-of-Thought Reasoning in Language Models

GoT: Effective Graph-of-Thought Reasoning in Language Models

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2024 Findings of the Association for Computational Linguistics: NAACL 2024

作者： Yao, Yao Li, Zuchao Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China

ISBN: (纸本)9798891761193

With the widespread use of language models (LMs) in NLP tasks, researchers have discovered the potential of Chain-of-thought (CoT) to assist LMs in accomplishing complex reasoning tasks by generating intermediate steps. However, human thought processes are often non-linear, rather than simply sequential chains of thoughts. Therefore, we propose Graph-of-Thought (GoT) reasoning, which models human thought processes not only as a chain but also as a graph. By representing thought units as nodes and connections between them as edges, our approach captures the non-sequential nature of human thinking and allows for a more realistic modeling of thought processes. GoT adopts a two-stage framework with an additional GoT encoder for thought graph representation and fuses the graph representation with the original input representation through a gated fusion mechanism. We evaluate GoT's performance on a text-only reasoning task (AQUA-RAT) and a multimodal reasoning task (ScienceQA). Our model achieves significant improvement over the strong CoT baseline on the AQUA-RAT test set and boosts accuracy from 85.19% to 87.59% using the T5-base model over the state-of-the-art Multimodal-CoT (Zhang et al., 2023) on the ScienceQA test set. Our code is publicly available at https://***/Zoeyyao27/Graphof-Thought. © 2024 Association for Computational Linguistics.

关键词： Rats

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Collaborative image compression and classification with multi-task learning for visual Internet of Things

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Chinese Journal of Aeronautics 2022年第5期35卷 390-399页

作者： Bing DU Yiping DUAN Hang ZHANG Xiaoming TAO Yue WU Congchong RU School of Computer and Communication Engineering University of Science and Technology BeijingBeijing 100083China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China School of Computer Science and Technology Xidian UniversityXian 710071China Smart Network Computing Lab Cross-strait Tsinghua Research InstitutionBeijing 100084China

Widespread deployment of the Internet of Things(Io T)has changed the way that network services are developed,deployed,and *** onboard advanced Io T devices are equipped with visual sensors that form the so-called visual Io ***,the sender would compress images,and then through the communication network,the receiver would decode images,and then analyze the images for ***,image compression and semantic inference are generally conducted separately,and thus,current compression algorithms cannot be transplanted for the use of semantic inference directly.A collaborative image compression and classification framework for visual Io T applications is proposed,which combines image compression with semantic inference by using multi-task *** particular,the multi-task Generative Adversarial Networks(GANs)are described,which include encoder,quantizer,generator,discriminator,and classifier to conduct simultaneously image compression and *** key to the proposed framework is the quantized latent representation used for compression and *** with perceptual quality can achieve low bitrate compression and reduce the amount of data *** addition,the design in which two tasks share the same feature can greatly reduce computing resources,which is especially applicable for environments with extremely limited *** extensive experiments,the collaborative compression and classification framework is effective and useful for visual IoT applications.

关键词： Deep learning Generative Adversarial Network(GAN) Image classification Image compression Internet of Things

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UML and NFR-framework based method for the analysis of the requirements of an information system

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International Journal of Information Technology (Singapore) 2023年第1期15卷 411-422页

作者： Arif, Mohd. Mohammad, Chaudhary Wali Sadiq, Mohd. Computer Science and Technology Research Group Department of Applied Sciences and Humanities Faculty of Engineering and Technology Jamia Millia Islamia A Central University New Delhi 110025 India Software Engineering Lab Computer Engineering Section UPFET Jamia Millia Islamia A Central University New Delhi 110025 India

Software requirements are the expectation of stakeholders which are identified and modeled by various requirements elicitation and modeling techniques like traditional methods, goal oriented methods, and unified modeling language (UML), etc. The identified software requirements are broadly classified into functional requirements (FRs) and non-functional requirements (NFRs). Based on our literature review, we found that most of the focus in the area of software engineering is on the analysis of FRs as compared to NFRs. For the successful development of any information system both FRs and NFRs should be considered equally during the elicitation and modeling process;and ignoring NFRs may lead to failure of software. Therefore, to address this issue, we developed a method for the analysis of both FRs and NFRs so that a successful software product can be developed by a software company. In the proposed method, FRs are modeled by UML use-case diagram, class-diagram, and activity diagram. The NFRs are analyzed using NFR-framework;and NFR propagation rules have been derived from the requirements of an information system. In this framework, fuzzy based approach has been used to analyze the contribution links of softgoal interdependency graph. The applicability of the proposed method is discussed by using FRs and NFRs of library information system. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Functional requirements Library information system Non-functional requirements Software requirements UML

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LayerMerge: Neural Network Depth Compression through Layer Pruning and Merging 41

LayerMerge: Neural Network Depth Compression through Layer P...

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41st International Conference on Machine Learning, ICML 2024

作者： Kim, Jinuk El Halabi, Marwa Ji, Mingi Song, Hyun Oh Department of Computer Science and Engineering Seoul National University Korea Republic of Neural Processing Research Center Korea Republic of Samsung - SAIT AI Lab Montreal Canada Google United States

Recent works show that reducing the number of layers in a convolutional neural network can enhance efficiency while maintaining the performance of the network. Existing depth compression methods remove redundant non-linear activation functions and merge the consecutive convolution layers into a single layer. However, these methods suffer from a critical drawback;the kernel size of the merged layers becomes larger, significantly undermining the latency reduction gained from reducing the depth of the network. We show that this problem can be addressed by jointly pruning convolution layers and activation functions. To this end, we propose LayerMerge, a novel depth compression method that selects which activation layers and convolution layers to remove, to achieve a desired inference speedup while minimizing performance loss. Since the corresponding selection problem involves an exponential search space, we formulate a novel surrogate optimization problem and efficiently solve it via dynamic programming. Empirical results demonstrate that our method consistently outperforms existing depth compression and layer pruning methods on various network architectures, both on image classification and generation tasks. We release the code at https://***/snu-mllab/LayerMerge. Copyright 2024 by the author(s)

关键词： Dynamic programming

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Misinformation with Legal Consequences (MisLC): A New Task Towards Harnessing Societal Harm of Misinformation

Misinformation with Legal Consequences (MisLC): A New Task T...

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2024 Findings of the Association for Computational Linguistics, EMNLP 2024

作者： Luo, Chu Fei Shayanfar, Radin Bhambhoria, Rohan Dahan, Samuel Zhu, Xiaodan Department of Electrical and Computer Engineering Ingenuity Labs Research Institute Queen's University Canada Conflict Analytics Lab Queen's University Canada Cornell Law School United States

ISBN: (纸本)9798891761681

Misinformation, defined as false or inaccurate information, can result in significant societal harm when it is spread with malicious or even innocuous intent. The rapid online information exchange necessitates advanced detection mechanisms to mitigate misinformation-induced harm. Existing research, however, has predominantly focused on assessing veracity, overlooking the legal implications and social consequences of misinformation. In this work, we take a novel angle to consolidate the definition of misinformation detection using legal issues as a measurement of societal ramifications, aiming to bring interdisciplinary efforts to tackle misinformation and its consequence. We introduce a new task: Misinformation with Legal Consequence (MisLC), which leverages definitions from a wide range of legal domains covering 4 broader legal topics and 11 fine-grained legal issues, including hate speech, election laws, and privacy regulations. For this task, we advocate a two-step dataset curation approach that utilizes crowd-sourced checkworthiness and expert evaluations of misinformation. We provide insights about the MisLC task through empirical evidence, from the problem definition to experiments and expert involvement. While the latest large language models and retrieval-augmented generation are effective baselines for the task, we find they are still far from replicating expert performance. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Data collection of wireless sensor network based on trajectory optimization of laser-charged UAV

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High-Confidence Computing 2024年第2期4卷 116-128页

作者： Chuanwen Luo Jian Zhang Jin Qian Yi Hong Zhibo Chen Yunan Hou Xiujuan Zhang Yuqing Zhu School of Information Science and Technology Beijing Forestry UniversityBeijing 100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing 100083China College of Information Engineering Taizhou UniversityTaizhou 225300China School of Computer Science Qufu Normal UniversityRizhao 276826China Research Center of Applied Mathematics and Machine Intelligence Zhejiang LabHangzhou 311121China Department of Computer Science California State UniversityCA 90032USA

Unmanned Aerial Vehicle(UAV)can be used as wireless aerial mobile base station for collecting data from sensors in UAV-based Wireless Sensor Networks(WSNs),which is crucial for providing seamless services and improving the performance in the next generation wireless ***,since the UAV are powered by batteries with limited energy capacity,the UAV cannot complete data collection tasks of all sensors without energy replenishment when a large number of sensors are deployed over large monitoring *** overcome this problem,we study the Real-time Data Collection with Lasercharging UAV(RDCL)problem,where the UAV is utilized to collect data from a specified WSN and is recharged using Laser Beam Directors(LBDs).This problem aims to collect all sensory data from the WSN and transport it to the base station by optimizing the flight trajectory of UAV such that realtime data performance is ensured It has been proven that the RDCL problem is *** address this,we initially focus on studying two sub-problems,the Trajectory Optimization of UAV for Data Collection(TODC)problem and the Charging Trajectory Optimization of UAV(CTO)problem,whose objectives are to find the optimal flight plans of UAV in the data collection areas and charging areas,*** we propose an approximation algorithm to solve each of them with the constant ***,we present an approximation algorithm that utilizes the solutions obtained from TODC and CTO problems to address the RDCL ***,the proposed algorithm is verified by extensive simulations.

关键词： Wireless sensor networks Laser-charged UAV Trajectory optimization Data collection

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SEMFI: A Software-Based and Real-Time Energy Monitoring Platform for WiFi IoT Devices 12

SEMFI: A Software-Based and Real-Time Energy Monitoring Plat...

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12th Annual IEEE Global Humanitarian Technology Conference, GHTC 2022

作者： Chawla, Aastha Kannan, Nidusha Goyalia, Sreya Ramanna, Vikram Sheth, Jaykumar Dezfouli, Behnam Santa Clara University Internet of Things Research Lab Department of Computer Science and Engineering United States

ISBN: (数字)9781665450973

ISBN: (纸本)9781665450973

Many WiFi-based Internet of Things (IoT) devices rely on limited energy resources such as a battery. Although monitoring and studying the energy consumption of these devices is essential, the use of external, hardware-based energy measurement tools is costly, non-scalable, and introduces many challenges regarding the connectivity of such tools with devices. In this paper, we propose Software-based Energy Management Tool for WiFi (SEMFI), a novel tool to collect, analyze, and monitor the power cycles of IoT devices without the need for any external tools. The basic idea is to modify the WiFi Access Point (AP)'s software to keep track of the power status of devices reported in packets. SEMFI also includes back-end and front-end components for data storage, analysis, and visualization. We demonstrate the effectiveness and features of SEMFI via empirical evaluations. © 2022 IEEE.

关键词： Energy efficiency

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Nepali Encoder Transformers: An Analysis of Auto Encoding Transformer Language Models for Nepali Text Classification 1

Nepali Encoder Transformers: An Analysis of Auto Encoding Tr...

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1st Annual Meeting of the ELRA/ISCA Special Interest Group on Under-Resourced Languages, SIGUL 2022

作者： Maskey, Utsav Bhatta, Manish Bhatta, Shiva Raj Dhungel, Sanket Bal, Bal Krishna Information and Language Processing Research Lab Department of Computer Science & Engineering Kathmandu University Dhulikhel Nepal

ISBN: (纸本)9791095546917

Language model pre-training has significantly impacted NLP and resulted in performance gains on many NLP-related tasks, but comparative study of different approaches on many low-resource languages seems to be missing. This paper attempts to investigate appropriate methods for pretraining a Transformer-based model for the Nepali language. We focus on the language-specific aspects that need to be considered for modeling. Although some language models have been trained for Nepali, the study is far from sufficient. We train three distinct Transformer-based masked language models for Nepali text sequences: distilbert-base (Sanh et al., 2019) for its efficiency and minuteness, deberta-base (P. He et al., 2020) for its capability of modeling the dependency of nearby token pairs and XLM-ROBERTa (Conneau et al., 2020) for its capabilities to handle multilingual downstream tasks. We evaluate and compare these models with other Transformer-based models on a downstream classification task with an aim to suggest an effective strategy for training low-resource language models and their fine-tuning. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

关键词： Natural language processing systems

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