Journal Articles, Association for Computing Machinery (ACM)

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Orchard: Heterogeneous Parallelism and Fine-grained Fusion for Complex Tree Traversals
Singhal, Vidush; Sakka, Laith; Sundararajah, Kirshanthan; Newton, Ryan; Kulkarni, Milind (ACM, 2024)
Many applications are designed to perform traversals on tree-like data structures. Fusing and parallelizing these traversals enhance the performance of applications. Fusing multiple traversals improves the locality of the application. The runtime of an application can be significantly reduced by extracting parallelism and utilizing multi-threading. Prior frameworks have tried to fuse and parallelize tree traversals using coarse-grained approaches, leading to missed fine-grained opportunities for improving performance. Other frameworks have successfully supported fine-grained fusion on heterogeneous tree types but fall short regarding parallelization. We introduce a new framework Orchard built on top of Grafter. Orchard’s novelty lies in allowing the programmer to transform tree traversal applications by automatically applying fine-grained fusion and extract- ing heterogeneous parallelism.Orchard allows the programmer to write general tree traversal applications in a simple and elegant embedded Domain-Specific Language (eDSL). We show that the combination of fine-grained fusion and heterogeneous parallelism performs better than each alone when the conditions are met.
Bridging the Gap: Early Education on Robot and AI Ethics through the Robot Theater Platform in an Informal Learning Environment
Mitchell, Jennifer; Dong, Jiayuan; Yu, Shuqi; Harmon, Madison; Holstein, Alethia; Shim, Joon Hyun; Choi, Koeun; Zhu, Qin; Jeon, Myounghoon (ACM, 2024-03-11)
With the rapid advancement of robotics and AI, educating the next generation on ethical coexistence with these technologies is crucial. Our research explored the potential of a child-robot theater afterschool program in introducing and discussing robot and AI ethics with elementary school children. Conducted with 30 participants from a socioeconomically underprivileged school, the program blended STEM (Science, Technology, Engineering & Mathematics) with the arts, focusing on ethical issues in robotics and AI. Using interactive scenarios and a theatrical performance, the program aimed to enhance children’s understanding of major ethical issues in robotics and AI, such as bias, transparency, privacy, usage, and responsibility. Preliminary findings indicate the program’s success in engaging children in meaningful ethical discussions, demonstrating the potential of innovative, interactive educational methods in early education. This study contributes significantly to integrating ethical robotics and AI in early learning, preparing young minds for a technologically advanced and socially responsible future.
Learning Common Knowledge Networks Via Exponential Random Graph Models
Liu, Xueying; Hu, Zhihao; Deng, Xinwei; Kuhlman, Chris (ACM, 2023-11-06)
Common knowledge (CK) is a phenomenon where each individual within a group knows the same information and everyone knows that everyone knows the information, infinitely recursively. CK spreads information as a contagion through social networks in ways different from other models like susceptibleinfectious- recovered (SIR) model. In a model of CK on Facebook, the biclique serves as the characterizing graph substructure for generating CK, as all nodes within a biclique share CK through their walls. To understand the effects of network structure on CKbased contagion, it is necessary to control the numbers and sizes of bicliques in networks. Thus, learning how to generate these CK networks (CKNs) is important. Consequently, we develop an exponential random graph model (ERGM) that constructs networks while controlling for bicliques. Our method offers powerful prediction and inference, reduces computational costs significantly, and has proven its merit in contagion dynamics through numerical experiments.
ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
Wang, Shengkun; Bai, Yangxiao; Fu, Kaiqun; Wang, Linhan; Lu, Chang-Tien; Ji, Taoran (ACM, 2023-11-06)
For both investors and policymakers, forecasting the stock market is essential as it serves as an indicator of economic well-being. To this end, we harness the power of social media data, a rich source of public sentiment, to enhance the accuracy of stock market predictions. Diverging from conventional methods, we pioneer an approach that integrates sentiment analysis, macroeconomic indicators, search engine data, and historical prices within a multi-attention deep learning model, masterfully decoding the complex patterns inherent in the data. We showcase the state-of-the-art performance of our proposed model using a dataset, specifically curated by us, for predicting stock market movements and volatility.
Hypergraph Text Classification for Mental Health Misleading Advice
Alkulaib, Lulwah; Alhamadani, Abdulaziz; Sarkar, Shailik; Lu, Chang-Tien (ACM, 2023-11-06)
This paper introduces HyperMAD, a novel Hypergraph Convolutional Network model designed for the multiclass classification of mental health advice in Arabic tweets. The model distinguishes between misleading and valid advice, further categorizing each tweet into specific classes of advice. HyperMAD leverages high-order relations between words in short texts, captured through the definition of four types of hyperedges that represent local and global contexts as well as semantic similarity. Extensive experiments demonstrate the effectiveness of HyperMAD, with results outperforming those from existing baselines. The study also includes an ablation study to investigate the significance and contribution of each hyperedge type. The paper presents a case study analyzing the accuracy and types of Arabic mental health advice on Twitter, revealing that about 9% of the advice in response to mental health expressions on Twitter was accurate in general. The paper concludes with the hope that the application of HyperMAD can be utilized in flagging misleading responses on social media, providing the correct resources for those who choose to share their mental health struggles online.
From Guest to Family: An Innovative Framework for Enhancing Memorable Experiences in the Hotel Industry
Alhamadani, Abdulaziz; Althubiti, Khadija; Sarkar, Shailik; He, Jianfeng; Alkulaib, Lulwah; Behal, Srishti; Khan, Mahmood; Lu, Chang-Tien (ACM, 2023-11-06)
This paper presents an innovative framework developed to identify, analyze, and generate memorable experiences in the hotel industry. People prefer memorable experiences over traditional services or products in today’s ever-changing consumer world. As a result, the hospitality industry has shifted its focus toward creating unique and unforgettable experiences rather than just providing essential services. Despite the inherent subjectivity and difficulties in quantifying experiences, the quest to capture and understand these critical elements in the hospitality context has persisted. However, traditional methods have proven inadequate due to their reliance on objective surveys or limited social media data, resulting in a lack of diversity and potential bias. Our framework addresses these issues, offering a holistic solution that effectively identifies and extracts memorable experiences from online customer reviews, discerns trends on a monthly or yearly basis, and utilizes a local LLM to generate potential, unexplored experiences. As the first successfully deployed, fast, and accurate product of its kind in the industry, This framework significantly contributes to the hotel industry’s efforts to enhance services and create compelling, personalized experiences for its customers.
Towards Establishing a Training Program to Support Future CS Teaching-focused Faculty
Farghally, Mohammed; Seyam, Mohammed; Shaffer, Clifford A. (ACM, 2024-03-07)
Computer Science programs have seen high enrollments in recent years, which contributed to widening the capacity gap. One way to address this problem is to hire more teaching-focused faculty at both research and non-doctoral granting institutions. Although this kind of hiring has already been taking place in several institutions, PhD-granting CS departments have not been able to produce enough PhDs to meet the increasing demand, especially for PhD holders with interest in - and capacity for - teaching. In this paper, we describe our experience with the initial phase of building a training program within our (large, land grant, R1) institution, targeting graduate students interested in pursuing an academic teachingfocused career in CS. Through a semester-long set of meetings, conversations, and activities, we worked with participants on improving their teaching skills and applying effective pedagogies in the classroom. At the end of the semester, we surveyed participants about the value of those meetings to them, ideas for improvement, and perspectives for future directions. Most participants rated the meetings positively in terms of content relevance and usefulness, and the opportunity to connect and interact with other participants and invited faculty members. We also discuss the lessons learned and best practices, which can be widely applied by other departments looking to better prepare their graduate students for a CS teaching-focused faculty position.
Transforming Grading Practices in the Computing Education Community
Decker, Adrienne; Edwards, Stephen H.; McSkimming, Brian; Edmison, Bob; Rorrer, Audrey; Pérez-Quiñones, Manuel A. (ACM, 2024-03-07)
It is often the case that computer science classrooms use traditional grading practices where points are allocated to assignments, mistakes result in point deductions, and assignment scores are combined using some form of weighted averaging to determine grades. Unfortunately, traditional grading practices have been shown to reduce achievement, discourage students, and suppress effort to such an extent that some common elements of traditional grading practices have been termed toxic. Using grades to reward or punish student behavior does not encourage learning and instead increases anxiety and stress. These toxic elements are present throughout computing education and computer science classrooms in the form of late penalties, lack of credit for code that doesn’t compile or pass certain unit tests, among others. These types of metrics, that evaluate behavior are often influenced by implicit bias, factors outside of the classrooms (e.g., part-time employment), and family life situations (e.g., students who are caregivers). Often, students in these situations are disproportionately from low-socioeconomic backgrounds and predominantly students of color. Through this paper, we will present a case for adoption of equitable grading practices and a call for additional support in classroom and teaching technologies as well as support from administrations both at the department and university level. By adopting a community of practice approach, we argue that we can support new faculty making these changes, which would be more equitable and inclusive. Further, these practices have been shown to better support student learning and can help increase student learning gains and retention.
Arabic Sentiment Analysis with Noisy Deep Explainable Model
Atabuzzaman, Md.; Shajalal, Md; Baby, Maksuda Bilkis; Boden, Alexander (ACM, 2023-12-15)
Sentiment Analysis (SA) is an essential task for numerous realworld applications. However, the majority of SA research focuses on high-resource languages such as English and Chinese, while limited-resource languages like Arabic and Bengali receive less attention. Additionally, existing Arabic sentiment analysis methods based on advanced artificial intelligence (AI) approaches tend to operate as black boxes, making it challenging to comprehend the reasoning behind their predictions. This paper proposes an explainable sentiment classification framework for the Arabic language. We introduce a noise layer to different deep learning (DL) models, including BiLSTM and CNN-BiLSTM, to address the issue of overfitting. The proposed framework enables the explanation of specific predictions by training a local surrogate explainable model, shedding light on the reasons behind each sentiment prediction (positive or negative). Experiments were conducted on publicly available benchmark Arabic SA datasets, and the results demonstrated that the inclusion of noise layers in the DL model improves performance for the Arabic language by mitigating overfitting. Our method also outperformed several state-of-the-art approaches. Moreover, the introduction of explainability with the noise layer enhances transparency and accountability, making the model suitable for practical adoption in AI-enabled systems.
A Logical Circuit Optimization in Balancing Delay and Energy Consumption
Shan, Qihang (ACM, 2023-11-03)
The fast-developing chip manufacturing technique and scaling of transistors allow us to fit more transistors on a small chip. The scaling down process, however, is facing a challenge. The smaller transistors are, the more influential quantum channeling and silicon atom size limit become. To improve efficiency, the solution of scaling down is no longer an option. Therefore, to further improve the efficiency of a chip without scaling down transistors, this paper presents a combinational circuit and focuses on an optimization approach where energy consumption is reduced in exchange for increasing delay. By adjusting the size of transistors, energy is saved while maintaining delay to an acceptable range. This approach manages to reduce energy consumption by about 56% while increasing delay by 50%. This paper represents one of many possible approaches that researchers had and has been working on and this tradeoff can benefit some circuit designs depending on the circuit’s purpose and hope to bring some insights on further optimization.
SARI: Shared Autonomy across Repeated Interaction
Jonnavittula, Ananth; Mehta, Shaunak; Losey, Dylan (ACM, 2024)
Assistive robot arms try to help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy. Within shared autonomy, both the human and robot maintain control over the robot’s motion: as the robot becomes conident it understands what the human wants, it intervenes to automate the task. But how does the robot know these tasks in the irst place? State-of-the-art approaches to shared autonomy often rely on prior knowledge. For instance, the robot may need to know the human’s potential goals beforehand. During long-term interaction these methods will inevitably break down Ð sooner or later the human will attempt to perform a task that the robot does not expect. Accordingly, in this paper we formulate an alternate approach to shared autonomy that learns assistance from scratch. Our insight is that operators repeat important tasks on a daily basis (e.g., opening the fridge, making cofee). Instead of relying on prior knowledge, we therefore take advantage of these repeated interactions to learn assistive policies. We introduce SARI, an algorithm that recognizes the human’s task, replicates similar demonstrations, and returns control when unsure. We then combine learning with control to demonstrate that the error of our approach is uniformly ultimately bounded. We perform simulations to support this error bound, compare our approach to imitation learning baselines, and explore its capacity to assist for an increasing number of tasks. Finally, we conduct three user studies with industry-standard methods and shared autonomy baselines, including a pilot test with a disabled user. Our results indicate that learning shared autonomy across repeated interactions matches existing approaches for known tasks and outperforms baselines on new tasks. See videos of our user studies here: https://youtu.be/3vE4omSvLvc
Rebuttal How-to: Strategies, Tactics, and the Big Picture in Research
Yao, Danfeng (Daphne) (ACM, 2023-12-21)
Rebuttals are not published, thus, it is difficult for junior researchers to read successful rebuttals and improve. This article demystifies rebuttal writing by showing the arm-the-champion strategy and a few key tactics. More importantly, we also discuss the conformity nature of conference reviewing and why researchers should not be defeated by paper rejections.
StructCoder: Structure-Aware Transformer for Code Generation
Tipirneni, Sindhu; Zhu, Ming; Reddy, Chandan (ACM, 2024)
There has been a recent surge of interest in automating software engineering tasks using deep learning. This paper addresses the problem of code generation, where the goal is to generate target code given source code in a different language or a natural language description. Most state-of-the-art deep learning models for code generation use training strategies primarily designed for natural language. However, understanding and generating code requires a more rigorous comprehension of the code syntax and semantics. With this motivation, we develop an encoder-decoder Transformer model where both the encoder and decoder are explicitly trained to recognize the syntax and data flow in the source and target codes, respectively. We not only make the encoder structure-aware by leveraging the source code?s syntax tree and data flow graph, but we also support the decoder in preserving the syntax and data flow of the target code by introducing two novel auxiliary tasks: AST (Abstract Syntax Tree) paths prediction and data flow prediction. To the best of our knowledge, this is the first work to introduce a structure-aware Transformer decoder that models both syntax and data flow to enhance the quality of generated code. The proposed StructCoder model achieves state-of-the-art performance on code translation and text-to-code generation tasks in the CodeXGLUE benchmark, and improves over baselines of similar size on the APPS code generation benchmark. Our code is publicly available at https://github.com/reddy-lab-code-research/StructCoder/.
Graph Time-series Modeling in Deep Learning: A Survey
Chen, Hongjie; Eldardiry, Hoda (ACM, 2024)
Time-series and graphs have been extensively studied for their ubiquitous existence in numerous domains. Both topics have been separately explored in the field of deep learning. For time-series modeling, recurrent neural networks or convolutional neural networks model the relations between values across time steps, while for graph modeling, graph neural networks model the inter-relations between nodes. Recent research in deep learning requires simultaneous modeling for time-series and graphs when both representations are present. For example, both types of modeling are necessary for time-series classification, regression, and anomaly detection in graphs. This paper aims to provide a comprehensive summary of these models, which we call graph time-series models. To the best of our knowledge, this is the first survey paper that provides a picture of related models from the perspective of deep graph time-series modeling to address a range of time-series tasks, including regression, classification, and anomaly detection. Graph time-series models are split into two categories, a) graph recurrent/convolutional neural networks and b) graph attention neural networks. Under each category, we further categorize models based on their properties. Additionally, we compare representative models and discuss how distinctive model characteristics are utilized with respect to various model components and data challenges. Pointers to commonly used datasets and code are included to facilitate access for further research. In the end, we discuss potential directions for future research.
Whiteboarding: A Tool to Improve CS1 Student Self-Efficacy
Chapin, John; Bowen, Bradley (ACM, 2023-12-05)
Many students struggle in Introductory Computer Science (CS1) and fail or drop out of the class. A lack of CS self-efficacy - the belief that the individual can complete a task - is frequently the cause of this failure to succeed in CS1. Solutions have been proposed to improve student self-efficacy in CS1. Unfortunately, a lack of self-efficacy in CS1 classes is still a problem. This study examines a pedagogical tool, whiteboarding, and its effect on student perception of self-efficacy during the programming problem-solving process for novice programmers. Whiteboarding refers to students using whiteboards during the CS problem solving process. Focus group sessions, researcher notes, and memos were used to collect qualitative data. The whiteboarding intervention was conducted in two AP CS A classes during the first four weeks of the year. Seventeen 10th-grade students participated in the focus groups. Three focus groups of four students and one focus group of five students were conducted at the end of the intervention. These findings indicate that whiteboarding can be a vital tool that increases student self-efficacy by improving their success at programming activities, increasing collaboration and feedback, and providing an active, positive learning environment that holds students accountable for their work. The themes that emerged from the focus group sessions were: Engagement with the Problem, Engagement with Others, and Engagement with the Environment. Teaching success in the CS1 classroom requires student self-efficacy. This study highlights a teaching pedagogy that CS1 educators can implement to increase student self-efficacy.
Self-Correlation and Cross-Correlation Learning for Few-Shot Remote Sensing Image Semantic Segmentation
Wang, Linhan; Lei, Shuo; He, Jianfeng; Wang, Shengkun; Zhang, Min; Lu, Chang-Tien (ACM, 2023-11-13)
Remote sensing image semantic segmentation is an important problem for remote sensing image interpretation. Although remarkable progress has been achieved, existing deep neural network methods suffer from the reliance on massive training data. Few-shot remote sensing semantic segmentation aims at learning to segment target objects from a query image using only a few annotated support images of the target class. Most existing few-shot learning methods stem primarily from their sole focus on extracting information from support images, thereby failing to effectively address the large variance in appearance and scales of geographic objects. To tackle these challenges, we propose a Self-Correlation and Cross-Correlation Learning Network for the few-shot remote sensing image semantic segmentation. Our model enhances the generalization by considering both self-correlation and cross-correlation between support and query images to make segmentation predictions. To further explore the self-correlation with the query image, we propose to adopt a classical spectral method to produce a class-agnostic segmentation mask based on the basic visual information of the image. Extensive experiments on two remote sensing image datasets demonstrate the effectiveness and superiority of our model in few-shot remote sensing image semantic segmentation. The code is available at https://github.com/linhanwang/SCCNet.
Spatial Temporal Graph Neural Networks for Decentralized Control of Robot Swarms
Chen, Siji; Sun, Yanshen; Li, Peihan; Zhou, Lifeng; Lu, Chang-Tien (ACM, 2023-11-13)
Recent research has explored the use of graph neural networks (GNNs) for decentralized control in swarm robotics. However, it has been observed that relying solely on local states is insufficient to imitate a centralized control policy. To address this limitation, previous studies proposed incorporating 𝐾-hop delayed states into the computation. While this approach shows promise, it can lead to a lack of consensus among distant flock members and the formation of small localized groups, ultimately resulting in task failure. Our approach is to include the delayed states to build a spatiotemporal GNN model (ST-GNN) by two levels of expansion: spatial expansion and temporal expansion. The spatial expansion utilizes 𝐾-hop delayed states to broaden the network while temporal expansion, can effectively predict the trend of swarm behavior, making it more robust against local noise. To validate the effectiveness of our approach, we conducted simulations in two distinct scenarios: free flocking and flocking with a leader. In both scenarios, the simulation results demonstrated that our decentralized ST-GNN approach successfully overcomes the limitations of local controllers. We performed a comprehensive analysis on the effectiveness of spatial expansions and temporal expansions independently. The results clearly demonstrate that both significantly improve overall performance. Furthermore, when combined, they achieve the best performance compared to global solution and delayed states solutions. The performance of ST-GNN underscores its potential as an effective and reliable approach for achieving cohesive flocking behavior while ensuring safety and maintaining desired swarm characteristics.
Single-Image 3D Human Digitization with Shape-guided Diffusion
Albahar, Badour; Saito, Shunsuke; Tseng, Hung-Yu; Kim, Changil; Kopf, Johannes; Huang, Jia-Bin (ACM, 2023-12-10)
We present an approach to generate a 360-degree view of a person with a consistent, high-resolution appearance from a single input image. NeRF and its variants typically require videos or images from different viewpoints. Most existing approaches taking monocular input either rely on ground-truth 3D scans for supervision or lack 3D consistency. While recent 3D generative models show promise of 3D consistent human digitization, these approaches do not generalize well to diverse clothing appearances, and the results lack photorealism. Unlike existing work, we utilize high-capacity 2D diffusion models pretrained for general image synthesis tasks as an appearance prior of clothed humans. To achieve better 3D consistency while retaining the input identity, we progressively synthesize multiple views of the human in the input image by inpainting missing regions with shape-guided diffusion conditioned on silhouette and surface normal. We then fuse these synthesized multi-view images via inverse rendering to obtain a fully textured high-resolution 3D mesh of the given person. Experiments show that our approach outperforms prior methods and achieves photorealistic 360-degree synthesis of a wide range of clothed humans with complex textures from a single image.
Triggering Modes in Spectrum-Based Multi-location Fault Localization
Dao, Tung; Meng, Na; Nguyen, ThanhVu (ACM, 2023-11-30)
Spectrum-based fault localization (SBFL) techniques can aid in debugging, but their practicality in industrial settings has been limited due to the large number of tests needed to execute before applying SBFL. Previous research has explored different trigger modes for SBFL and found that applying it immediately after the first test failure is also effective. However, this study only considered single-location bugs, while multi-location bugs are prevalent in real-world scenarios and especially at our company Cvent, which is interested in integrating SBFL to its CI/CD workflow. In this work, we investigate the effectiveness of SBFL on multilocation bugs and propose a framework called Instant Fault Localization for Multi-location Bugs (IFLM). We compare and evaluate four trigger modes of IFLM using open-source (Defects4J) and close-source (Cvent) bug datasets. Our study showed that it is not necessary to execute all test cases before applying SBFL. However, we also found that that applying SBFL right after the first failed test is less effective than applying it after executing all tests for multi-location bugs, which is contrary to the single-location bug study. We also observe differences in performance between real and artificial bugs. Our contributions include the development of IFLM and CVent bug datasets, analysis of SBFL effectiveness for multi-location bugs, and practical implications for integrating SBFL in industrial environments.
Co-dependence Aware Fuzzing for Dataflow-Based Big Data Analytics
Humayun, Ahmad; Kim, Miryung; Gulzar, Muhammad Ali (ACM, 2023-11-30)
Data-intensive scalable computing has become popular due to the increasing demands of analyzing big data. For example, Apache Spark and Hadoop allow developers to write dataflow-based applications with user-defined functions to process data with custom logic. Testing such applications is difficult. (1) These applications often take multiple datasets as input. (2) Unlike in SQL, there is no explicit schema for these datasets and each unstructured (or semi-structured) dataset is segmented and parsed at runtime. (3) Dataflow operators (e.g., join) create implicit co-dependence constraints between the fields of multiple datasets. An efficient and effective testing technique must analyze co-dependence among different regions of multiple datasets at the level of rows and columns and orchestrate input mutations jointly on co-dependent regions. We propose DepFuzz to increase the effectiveness and efficiency of fuzz testing dataflow-based big data applications. The key insight behind DepFuzz is twofold. It keeps track of which code segments operate on which datasets, which rows, and which columns. By analyzing the use of dataflow operators (e.g., join and groupByKey) in tandem with the semantics of UDFs, DepFuzz generates test data that subsequently reach hard-to-reach regions of the application code. In real-world big data applications, DepFuzz finds 3.4× more faults, achieving 29% more statement coverage in half the time as Jazzer’s, a state-of-the-art commercial fuzzer for Java bytecode. It outperforms prior DISC testing by exposing deeper semantic faults beyond simpler input formatting errors, especially when multiple datasets have complex interactions through dataflow operators.

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