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Black Issues in Computing Education (BICE)

作者： Dale-Marie Wilson Marlon Mejias Felesia Stukes Gloria Washington Celeste Malone Department of Software and Information Systems University of North Carolina at Charlotte United States Department of Computer Science and Engineering Johnson C. Smith University United States Department of Electrical Engineering and Computer Science Howard University United States Department of Human Development & Psychoeducational Studies Howard University United States

ISBN: (数字)9798350372915

ISBN: (纸本)9798350372922

The number and diversity of researchers in the U.S. must increase significantly to support the demand for global leadership in research and computing. For more equitable representation in computing, there must be equitable access to graduate degrees and careers both academic and industrial in computing. However, there are barriers to the recruitment, retention, and persistence of racial/ethnic minoritized individuals in computer science due to racial inequities. With respect to Black students, these barriers are unique to their intersectional identities. HBCUs offer students a unique and rewarding experience including a familial community of support and understanding among faculty, students, and alumni. From 2015-2019, nearly 25% of Black STEM doctorate recipients earned a bachelor's degree from an HBCU and HBCUs produced 33% of the nation's Black computer scientists [1]. Thus, there are successful enculturation practices within STEM education at HBCUs [2] that lead to their students going onto terminal degrees. Through our funded NSF grants #2140867 & #210246, we proposed a knowledge transfer on Black student mentorship, research enculturation and social computing from two HBCUs (Howard University and Johnson C. Smith), to a PWI (University of North Carolina at Charlotte). Our goal is to democratize knowledge and the transfer of information by identifying and disseminating hidden curriculum that creates racial inequities that impede Black students' progress into graduate school. In this workshop we will disseminate our findings for developing Black undergraduate students' technical research ability but also holistically addressing the psychological and social challenges that they may face.

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Tensor Embedding: A Supervised Framework for human Behavioral Data Mining and Prediction 11

Tensor Embedding: A Supervised Framework for Human Behaviora...

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11th IEEE International Conference on Healthcare Informatics, ICHI 2023

作者： Hosseinmardi, Homa Ghasemian, Amir Lerman, Kristina Narayanan, Shrikanth Ferrara, Emilio University of Pennsylvania Department of Computer and Information Science PhiladelphiaPA United States Yale University Human Nature Lab New HavenCT United States University of Southern California Information Sciences Institute Marina Del ReyCA United States

ISBN: (纸本)9798350302639

Today's densely instrumented world offers tremendous opportunities for continuous acquisition and analysis of multimodal sensor data, providing temporal characterization of individual behaviors. Is it possible to efficiently couple such rich sensor data with predictive modeling techniques to provide contextual and insightful assessments of individual behavior? Such data is noisy, incomplete, and collected from multiple sources, each with a different temporal resolution and dimensionality. In addition, longitudinal studies tend to examine a number of aspects of human behavior, such as well being, performance, or personality, which poses a challenge for handcrafted features engineering. To address these challenges, we propose supervised tensor embedding (STE), an algorithm for the joint decomposition of input and target variables in high-dimensional multimodal data. Latent features obtained from STE can be fed into any regression model for the estimation of the target variable(s). Following the embedding of higher-order data, features from different sources can be coupled. Additionally, we demonstrate that feature selection on higher-order data can enhance performance. The efficiency of our method is tested on two real-world datasets consisting of 29 target variables. Compared to the state of the art, we find that (i) our method outperforms others in 21 prediction tasks, and (ii) there are a few aspects of human behavior that are unpredictable regardless of the method, which can be the limitation of multilinear methods or lack of proper independent variables. © 2023 IEEE.

关键词： Tensors

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On the Robustness of Editing Large Language Models

On the Robustness of Editing Large Language Models

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Ma, Xinbei Ju, Tianjie Qiu, Jiyang Zhang, Zhuosheng Zhao, Hai Liu, Lifeng Wang, Yulong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University China Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Baichuan Intelligent Technology China

ISBN: (纸本)9798891761643

Large language models (LLMs) have played a pivotal role in building communicative AI, yet they encounter the challenge of efficient updates. Model editing enables the manipulation of specific knowledge memories and the behavior of language generation without retraining. However, the robustness of model editing remains an open question. This work seeks to understand the strengths and limitations of editing methods, facilitating practical applications of communicative AI. We focus on three key research questions. RQ1: Can edited LLMs behave consistently resembling communicative AI in realistic situations? RQ2: To what extent does the rephrasing of prompts lead LLMs to deviate from the edited knowledge memory? RQ3: Which knowledge features are correlated with the performance and robustness of editing? Our empirical studies uncover a substantial disparity between existing editing methods and the practical application of LLMs. On rephrased prompts that are flexible but common in realistic applications, the performance of editing experiences a significant decline. Further analysis shows that more popular knowledge is memorized better, easier to recall, and more challenging to edit effectively. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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A great disturbance in the crypto:Understanding cryptocurrency returns under attacks

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Blockchain(Research and Applications) 2021年第3期2卷 68-78页

作者： Simona Ramos Fabio Pianese Thomas Leach Ester Oliveras Augmented Machine Interaction team AI Research LabNokia Bell LabsParis-SaclayNozay 91620France Department of Computer Science Universitat Pompeu FabraBarcelona 08002Spain Department of Economics Universita di PaviaPavia 27100Italy

A non-traditional type of financial asset,cryptocurrencies based on public blockchains are still little understood in their real-world *** events such as cyber-attacks and their media coverage can strongly affect their supply and demand,adoption and usage,efficiency,and infrastructural development,thus influencing their price stability and market *** the great technical complexity of blockchains,we believe that a pure economic analysis of risks associated with cryptocurrencies is simply not sufficient to convey the relationships between attacks and the disruptive effects that these can bring on the operation of *** these grounds,we survey the most common types of attacks for Proof-of-Work(PoW)cryptocurrencies and evaluate their impact on the returns of a number of real-world cryptocurrencies for which market data are *** to data availability,our event study analysis focuses on instances of 51% attacks,hard forks,and wallet *** main goal of our analysis is to understand the relationship between technical events(cyber-attacks and coordinated user/miner behavior)and the economic impacts surrounding *** aim to develop a deeper understanding of these systems that are objects of great research interest in separate disciplines,supporting policymakers in their regulatory decisions concerning crypto-assets and associated cyber-related financial risks.

关键词： Blockchain Cryptocurrencies Event studies Cyber attacks

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LaCour!: Enabling Research on Argumentation in Hearings of the European Court of human Rights

arXiv

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arXiv 2023年

作者： Held, Lena Habernal, Ivan Trustworthy Human Language Technologies Germany Department of Computer Science Technical University of Darmstadt Germany Research Center Trustworthy Data Science and Security of the University Alliance Ruhr Faculty of Computer Science Ruhr University Bochum Germany

Why does an argument end up in the final court decision? Was it deliberated or questioned during the oral hearings? Was there something in the hearings that triggered a particular judge to write a dissenting opinion? Despite the availability of the final judgments of the European Court of human Rights (ECHR), none of these legal research questions can currently be answered as the ECHR’s multilingual oral hearings are not transcribed, structured, or speaker-attributed. We address this fundamental gap by presenting LaCour!, the first corpus of textual oral arguments of the ECHR, consisting of 154 full hearings (2.1 million tokens from over 267 hours of video footage) in English, French, and other court languages, each linked to the corresponding final judgment documents. In addition to the transcribed and partially manually corrected text from the video, we provide sentence-level timestamps and manually annotated role and language labels. We also showcase LaCour! in a set of experiments that explore the interplay between questions and dissenting opinions. Apart from the use cases in legal NLP, we hope that law students or other interested parties will also use LaCour! as a learning resource, as it is freely available in various formats at https://***/datasets/TrustHLT/LaCour. © 2023, CC BY-SA.

关键词： Laws and legislation

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RJoules: An Energy Measurement Tool for R

RJoules: An Energy Measurement Tool for R

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Rajrupa Chattaraj Sridhar Chimalakonda Department of Computer Science and Engineering Research in Intelligent Systems and Human Analytics Lab Indian Institute of Technology Tirupati India

With the exponential growth of data, the demand for effective data analysis tools has increased significantly. R language, known for its statistical modeling and data analysis capabilities, has become one of the most popular programming languages among data scientists and researchers. As the importance of energy-aware software systems continues to rise, several studies investigate the impact of source code and different stages of machine learning model training on energy consumption. However, existing studies in this domain primarily focus on programming languages like Python and Java, resulting in a lack of energy measuring tools for other programming languages such as R. To address this gap, we propose “RJoules”, a tool designed to measure the energy consumption of R code snippets. We evaluate the correctness and performance of RJoules by applying it to four machine learning algorithms on three different systems. Our aim is to support developers and practitioners in building energy-aware systems in R. The demonstration of the tool is available at https://***/yMKFuvAM-DE and related artifacts at https://***/RJoules/.

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Refined Statistical Bounds for Classification Error Mismatches with Constrained Bayes Error

arXiv

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arXiv 2024年

作者： Yang, Zijian Eminyan, Vahe Schlüter, Ralf Ney, Hermann Machine Learning and Human Language Technology Group Lehrstuhl Informatik 6 Computer Science Department RWTH Aachen University Germany AppTek GmbH Germany

In statistical classification/multiple hypothesis testing and machine learning, a model distribution estimated from the training data is usually applied to replace the unknown true distribution in the Bayes decision rule, which introduces a mismatch between the Bayes error and the model-based classification error. In this work, we derive the classification error bound to study the relationship between the Kullback-Leibler divergence and the classification error mismatch. We first reconsider the statistical bounds based on classification error mismatch derived in previous works, employing a different method of derivation. Then, motivated by the observation that the Bayes error is typically low in machine learning tasks like speech recognition and pattern recognition, we derive a refined Kullback-Leibler-divergence-based bound on the error mismatch with the constraint that the Bayes error is lower than a threshold. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Refined Statistical Bounds for Classification Error Mismatches with Constrained Bayes Error

Refined Statistical Bounds for Classification Error Mismatch...

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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Zijian Yang Vahe Eminyan Ralf Schlüter Hermann Ney Computer Science Department Machine Learning and Human Language Technology Group Lehrstuhl Informatik 6 RWTH Aachen University Germany AppTek GmbH Germany

ISBN: (数字)9798350348934

ISBN: (纸本)9798350348941

关键词： Analytical models Conferences Training data Machine learning Speech recognition Data models Bayes methods Testing Information theory

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DBJoules: An Energy Measurement Tool for Database Management Systems

arXiv

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arXiv 2023年

作者： Lella, Hemasri Sai Chattaraj, Rajrupa Manasa, Kurra Chimalakonda, Sridhar Research in Intelligent Systems and Human Analytics Lab Department of Computer Science and Engineering Indian Institute of Technology Tirupati India

In the rapidly evolving landscape of modern data-driven technologies, software relies on large datasets and constant data center operations using various database systems to support computation-intensive tasks. As energy consumption in software systems becomes a growing concern, selecting the right database from energy-efficiency perspective is also critical. To address this, we introduce DBJoules, a tool that measures the energy consumption of activities in database systems. DBJoules supports energy measurement of CRUD operations for four popular databases. Through evaluations on two widely-used datasets, we identify disparities of 7% to 38% in the energy consumption of these databases. Hence, the goal is to raise developer awareness about the effect of running queries in different databases from an energy consumption perspective, enabling them to select appropriate database for sustainable usage. The tool’s demonstration is available at https://***/D1MTZum0jok and related artifacts at https://***/DBJoules/. Copyright © 2023, The Authors. All rights reserved.

关键词： Energy utilization

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5G Channel Estimation Kernels on RISC-V Vector Digital Signal Processors

5G Channel Estimation Kernels on RISC-V Vector Digital Signa...

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International Conference on Microelectronics, ICM

作者： Javier Acevedo Frank H. P. Fitzek Patrick Seeling Deutsche Telekom Chair of Communication Networks Dresden TU Centre for Tactile Internet with Human-in-the-Loop (CeTI) Department of Computer Science Central Michigan University

ISBN: (数字)9798350379396

ISBN: (纸本)9798350379402

Each generation of the wireless communication networks has contiguous demands for higher computing capabilities and lower the power consumption in the integrated circuitry. Thus, the optimization of the base-band signal processing kernels and the hardware architectures plays a pivotal role in the performance, latency, and energy efficiency of the Physical Layer (PHY). Channel Estimation (CE), a computationally intensive task with hard real-time requirements, involves significant amounts of matrix multiplications and inversions, when data flows between transmitter (TX) and receiver (RX). In this work, we present a software implementation of CE for the 5th Generation Cellular Networks (5G) New Radio (NR) in the Reduced Instruction Set computer (RISC)-V architecture, showcasing how its vector processing capabilities can satisfy the throughput and delay requirements of 5G use cases. Specifically, we ported two CE kernels, the Least-Squares (LSE) and Minimum Mean Square Error (MMSE), onto a state-of-the-art single core vector processor. By exploiting data vectorization, the ARA core computes matrix arithmetic operations on multiple data sets simultaneously. Our single vector core serial optimization demonstrates that with a $16\times 16$ matrix window size and 16 vector lanes, the application's performance achieves a speedup of 78.27 when computing the LSE algorithm. When the number of operations per cycle are compared, the vector processor outperforms its scalar counterpart by 181.91 for the MMSE kernel. Our implementation orders the nested loops in a latency-reducing fashion to compute matrix arithmetic operations, resulting in LSE and MMSE estimator computing times of $8.44 \mu\iota \mathrm{s}$ and $12.31\ \mu\mathrm{s}$ , respectively.

关键词： Channel estimation Signal processing algorithms computer architecture Vectors Hardware Vector processors Matrix decomposition Kernel Optimization Clocks

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