Journal Articles, Association for Computing Machinery (ACM)

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2nd Workshop on Multimodal Motion Sickness Detection and Mitigation Methods for Car Journeys - Finding Consensus in the Field
Pöhlmann, Katharina; Al-Taie, Ammar; Li, Gang; Dam, Abhraneil; Wang, Yu-Kai; Wei, Chun-Shu; Papaioannou, Georgios (ACM, 2023-09-18)
The adoption of automated vehicles will be a positive step towards road safety and environmental benefits. However, one major challenge that still exist is motion sickness. The move from drivers to passengers who will engage in non-driving related tasks as well as the potential change in the layout of the car interior that will come with automated vehicles are expected to result in a worsened experience of motion sickness. The previous workshop [18] highlighted the need for consensus on guidelines regarding study design for motion sickness research. Hence, this workshop will develop a guide for motion sickness research through reflection and discussions on the current methodologies used by experts in the field. Further it will build on the knowledge collected from the previous workshop and will thereby facilitate not only new research ideas and fruitful collaborations but also find a consensus in the field in regard to study design and methodologies.
2nd Workshop on Uncertainty Reasoning and Quantification in Decision Making
Zhao, Xujiang; Zhao, Chen; Chen, Feng; Cho, Jin-Hee; Chen, Haifeng (ACM, 2023-08-06)
Uncertainty reasoning and quantification play a critical role in decision making across various domains, prompting increased attention from both academia and industry. As real-world applications become more complex and data-driven, effectively handling uncertainty becomes paramount for accurate and reliable decision making. This workshop focuses on the critical topics of uncertainty reasoning and quantification in decision making. It provides a platform for experts and researchers from diverse backgrounds to exchange ideas on cutting-edge techniques and challenges in this field. The interdisciplinary nature of uncertainty reasoning and quantification, spanning artificial intelligence, machine learning, statistics, risk analysis, and decision science, will be explored. The workshop aims to address the need for robust and interpretable methods for modeling and quantifying uncertainty, fostering reasoning decision-making in various domains. Participants will have the opportunity to share research findings and practical experiences, promoting collaboration and advancing decision-making practices under uncertainty.
Accepted Tutorials at The Web Conference 2022
Tommasini, Riccardo; Basu Roy, Senjuti; Wang, Xuan; Wang, Hongwei; Ji, Heng; Han, Jiawei; Nakov, Preslav; Da San Martino, Giovanni; Alam, Firoj; Schedl, Markus; Lex, Elisabeth; Bharadwaj, Akash; Cormode, Graham; Dojchinovski, Milan; Forberg, Jan; Frey, Johannes; Bonte, Pieter; Balduini, Marco; Belcao, Matteo; Della Valle, Emanuele; Yu, Junliang; Yin, Hongzhi; Chen, Tong; Liu, Haochen; Wang, Yiqi; Fan, Wenqi; Liu, Xiaorui; Dacon, Jamell; Lye, Lingjuan; Tang, Jiliang; Gionis, Aristides; Neumann, Stefan; Ordozgoiti, Bruno; Razniewski, Simon; Arnaout, Hiba; Ghosh, Shrestha; Suchanek, Fabian; Wu, Lingfei; Chen, Yu; Li, Yunyao; Liu, Bang; Ilievski, Filip; Garijo, Daniel; Chalupsky, Hans; Szekely, Pedro; Kanellos, Ilias; Sacharidis, Dimitris; Vergoulis, Thanasis; Choudhary, Nurendra; Rao, Nikhil; Subbian, Karthik; Sengamedu, Srinivasan; Reddy, Chandan; Victor, Friedhelm; Haslhofer, Bernhard; Katsogiannis- Meimarakis, George; Koutrika, Georgia; Jin, Shengmin; Koutra, Danai; Zafarani, Reza; Tsvetkov, Yulia; Balachandran, Vidhisha; Kumar, Sachin; Zhao, Xiangyu; Chen, Bo; Guo, Huifeng; Wang, Yejing; Tang, Ruiming; Zhang, Yang; Wang, Wenjie; Wu, Peng; Feng, Fuli; He, Xiangnan (ACM, 2022-04-25)
This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% of these are hands on.
Active Learning for Microarray based Leukemia Classification
Zhu, Kecheng (ACM, 2021-11-12)
In machine learning, data labeling is assumed to be easy and cheap. However, in real word cases especially clinical field, data sets are rare and expensive to obtain. Active learning is an approach that can query the most informative data for the training. This leads to an alternative to deal with the concern mentioned above. The Sampling method is one of the key parts in active learning because it minimizes the training cost of the classifier. By different query method, models with considerable difference could be produced. The difference in model could lead to significant difference in training cost and final accuracy outcome. The approaches that were used to in this experiment is uncertainty sampling, diversity sampling and query by committee. In the experiment, active learning is applied on the microarray data with improving results. The classification on two types leukemia (acute myeloid leukemia and acute lymophoblastic leukemia) indicates a boost in accuracy with the same number of samples compared to passive machine learning. The experiments leads to the conclusion that with small number of samples with randomness in the field of leukemia classification, active learning produce an more active model. Additionally, active learning with query by committee finds the most informative sample with fewest trials.
Adelie: Continuous Address Space Layout Re-randomization for Linux Drivers
Nikolaev, Ruslan; Nadeem, Hassan; Stone, Cathlyn; Ravindran, Binoy (ACM, 2022-02-28)
While address space layout randomization (ASLR) has been extensively studied for user-space programs, the corresponding OS kernel’s KASLR support remains very limited, making the kernel vulnerable to just-in-time (JIT) return-oriented programming (ROP) attacks. Furthermore, commodity OSs such as Linux restrict their KASLR range to 32 bits due to architectural constraints (e.g., x86-64 only supports 32-bit immediate operands for most instructions), which makes them vulnerable to even unsophisticated brute-force ROP attacks due to low entropy. Most in-kernel pointers remain static, exacerbating the problem when pointers are leaked. Adelie, our kernel defense mechanism, overcomes KASLR limitations, increases KASLR entropy, and makes successful ROP attacks on the Linux kernel much harder to achieve. First, Adelie enables the position-independent code (PIC) model so that the kernel and its modules can be placed anywhere in the 64-bit virtual address space, at any distance apart from each other. Second, Adelie implements stack re-randomization and address encryption on modules. Finally, Adelie enables efficient continuous KASLR for modules by using the PIC model to make it (almost) impossible to inject ROP gadgets through these modules regardless of gadget’s origin. Since device drivers (typically compiled as modules) are often developed by third parties and are typically less tested than core OS parts, they are also often more vulnerable. By fully re-randomizing device drivers, the last two contributions together prevent most JIT ROP attacks since vulnerable modules are very likely to be a starting point of an attack. Furthermore, some OS instances in virtualized environments are specifically designated to run device drivers, where drivers are the primary target of JIT ROP attacks. Using a GCC plugin that we developed, we automatically modify different kinds of kernel modules. Since the prior art tackles only user-space programs, we solve many challenges unique to the kernel code. Our evaluation shows high efficiency of Adelie’s approach: the overhead of the PIC model is completely negligible and re-randomization cost remains reasonable for typical use cases.
Aggregate VM: Why Reduce or Evict VM's Resources When You Can Borrow Them From Other Nodes?
Chuang, Ho-Ren; Manaouil, Karim; Xing, Tong; Barbalace, Antonio; Olivier, Pierre; Heerekar, Balvansh; Ravindran, Binoy (ACM, 2023-05-08)
Hardware resource fragmentation is a common issue in data centers. Traditional solutions based on migration or overcommitment are unacceptably slow, and modern commercial or research solutions like Spot VM may reduce or evict VM’s resources anytime.We propose an alternative solution that does not suffer from these drawbacks, the Aggregate VM.We introduce a new distributed hypervisor design, the resource-borrowing hypervisor, which creates Aggregate VMs: distributed VMs that temporarily aggregate fragmented resources belonging to different host machines, which require mobility of virtual CPUs, memory and IO devices.We implement a prototype, FragVisor, which runs guest software transparently.We also propose minimal modifications to the guest OS that can enable significant performance gains. We evaluate FragVisor over a set of microbenchmarks and IaaS-style real applications. Although Aggregate VMs are not a perfect fit for every type of applications, some workloads enjoy significant speedups compared to overcommitted scenarios (up to 3.9x with 4 distributed vCPUs).We further demonstrate that FragVisor is faster than a state-of-the-art competitor, GiantVM (up to 2.5x).
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.
Algorithm 1028: VTMOP: Solver for Blackbox Multiobjective Optimization Problems
Chang, Tyler; Watson, Layne T.; Larson, Jeffrey; Neveu, Nicole; Thacker, William; Deshpande, Shubhangi; Lux, Thomas (ACM, 2022-09-10)
VTMOP is a Fortran 2008 software package containing two Fortran modules for solving computationally expensive bound-constrained blackbox multiobjective optimization problems. VTMOP implements the algorithm of Deshpande et al. [2016], which handles two or more objectives, does not require any derivatives, and produces well-distributed points over the Pareto front. The first module contains a general framework for solving multiobjective optimization problems by combining response surface methodology, trust region methodology, and an adaptive weighting scheme. The second module features a driver subroutine that implements this framework when the objective functions can be wrapped as a Fortran subroutine. Support is provided for both serial and parallel execution paradigms, and VTMOP is demonstrated on several test problems as well as one real-world problem in the area of particle accelerator optimization.
All Use-After-Free Vulnerabilities Are Not Created Equal: An Empirical Study on Their Characteristics and Detectability
Chen, Zeyu; Liu, Daiping; Xiao, Jidong; Wang, Haining (ACM, 2023-10-16)
Over the past decade, use-after-free (UaF) has become one of the most exploited types of vulnerabilities. To address this increasing threat, we need to advance the defense in multiple directions, such as UaF vulnerability detection, UaF exploit defense, and UaF bug fix. Unfortunately, the intricacy rooted in the temporal nature of UaF vulnerabilities makes it quite challenging to develop effective and efficient defenses in these directions. This calls for an in-depth understanding of real-world UaF characteristics. This paper presents the first comprehensive empirical study of UaF vulnerabilities, with 150 cases randomly sampled from multiple representative software suites, such as Linux kernel, Python, and Mozilla Firefox. We aim to identify the commonalities, root causes, and patterns from realworld UaF bugs, so that the empirical results can provide operational guidance to avoid, detect, deter, and fix UaF vulnerabilities. Our main finding is that the root causes of UaF bugs are diverse, and they are not evenly or equally distributed among different software. This implies that a generic UaF detector/fuzzer is probably not an optimal solution. We further categorize the root causes into 11 patterns, several of which can be translated into simple static detection rules to cover a large portion of the 150 UaF vulnerabilities with high accuracy. Motivated by our findings, we implement 11 checkers in a static bug detector called Palfrey. Running Palfrey on the code of popular open source software, we detect 9 new UaF vulnerabilities. Compared with state-of-the-art static bug detectors, Palfrey outperforms in coverage and accuracy for UaF detection, as well as time and memory overhead.
Am I Really Angry? The Influence of Anger Intensities on Young Drivers' Behaviors
Wang, Manhua; Jeon, Myounghoon (ACM, 2023-09-18)
Anger can lead to aggressive driving and other negative behaviors. While previous studies treated anger as a single dimension, the present research proposed that anger has distinct intensities and aimed to understand the effects of different anger intensities on driver behaviors. After developing the anger induction materials, we conducted a driving simulator study with 30 participants and assigned them to low, medium, and high anger intensity groups. We found that drivers with low anger intensity were not able to recognize their emotions and exhibited speeding behaviors, while drivers with medium and high anger intensities might be aware of their anger along with its adverse effects and then adjusted their longitudinal control. However, angry drivers generally exhibited compromised lateral control indicated by steering and lane-keeping behaviors. Our findings shed light on the potentially different influences of anger intensities on young drivers’ behaviors, especially the importance of anger recognition for intervention solutions.
Amazon Alexa Skills as a Novel Modality for In-service Professional Micro-Development (WiP)
Robins, Andey; Hunt, Tiffany; Robertson, Dana A.; Carter, Richard (ACM, 2022-06-01)
Intelligent digital assistants, such as products like Amazon’s Alexa, are becoming more prolific and available as time progresses. This work reports on the process of developing applications, refered to by Alexa as ’skills,’ to support in-service professional development for teachers. The precedent of allowing Alexa to be utilized within the classroom, but not necessarily for teachers’ skill development, is explored to contextualize the application of the technology as a professional development modality. Across the entire content development spectrum – spanning script writing to deployment and release – lessons are presented from experience to assist future teams embarking on the process in the hopes they are able to avoid repeating the problems encountered by this research team. Finally, a general discussion relating to the future of intelligent digital assistants and their applications to professional development is presented with the intent of provoking discussion and setting the stage for more rigorous investigations into the usability, efficacy, and results of this technology.
ANTHEM: Attentive Hyperbolic Entity Model for Product Search
Choudhary, Nurendra; Rao, Nikhil; Katariya, Sumeet; Subbian, Karthik; Reddy, Chandan K. (ACM, 2022-02-11)
Product search is a fundamentally challenging problem due to the large-size of product catalogues and the complexity of extracting semantic information from products. In addition to this, the blackbox nature of most search systems also hamper a smooth customer experience. Current approaches in this area utilize lexical and semantic product information to match user queries against products. However, these models lack (i) a hierarchical query representation, (ii) a mechanism to detect and capture inter-entity relationships within a query, and (iii) a query composition method specific to e-commerce domain. To address these challenges, in this paper, we propose an AtteNTive Hyperbolic Entity Model (ANTHEM), a novel attention-based product search framework that models query entities as two-vector hyperboloids, learns inter-entity intersections and utilizes attention to unionize individual entities and inter-entity intersections to predict product matches from the search space. ANTHEM utilizes the first and second vector of hyperboloids to determine the query’s semantic position and to tune its surrounding search volume, respectively. The attention networks capture the significance of intra-entity and inter-entity intersections to the final query space. Additionally, we provide a mechanism to comprehend ANTHEM and understand the significance of query entities towards the final resultant products. We evaluate the performance of our model on real data collected from popular e-commerce sites. Our experimental study on the offline data demonstrates compelling evidence of ANTHEM’s superior performance over state-of-the-art product search methods with an improvement of more than 10% on various metrics. We also demonstrate the quality of ANTHEM’s query encoder using a query matching task.
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.
ARCritique: Supporting Remote Design Critique of Physical Artifacts through Collaborative Augmented Reality
Li, Yuan; Lee, Sang Won; Bowman, Douglas A.; Hicks, David; Lages, Wallace S.; Sharma, Akshay (ACM, 2022-12-01)
Critique sessions are an essential educational activity at the center of many design disciplines, especially those involving the creation of physical mockups. Conventional approaches often require the students and the instructor to be in the same space to jointly view and discuss physical artifacts. However, in remote learning contexts, available tools (such as videoconferencing) are insufficient due to ineffective, inefficient spatial referencing. This paper presents ARCritique, a mobile Augmented Reality application that allows users to 1) scan physical artifacts, generate corresponding 3D models, and share them with distant instructors; 2) view the model simultaneously in a synchronized virtual environment with remote collaborators; and 3) point to and draw on the model synchronously to aid communication. We evaluated ARCritique with seven Industrial Design students and three faculty to use the app in a remote critique setting. The results suggest that direct support for spatial communication improves collaborative experiences.
Arguments for and Approaches to Computing Education in Undergraduate Computer Science Programmes
Cutts, Quintin; Kallia, Maria; Anderson, Ruth; Crick, Tom; Devlin, Marie; Farghally, Mohammed; Mirolo, Claudio; Runde, Ragnhild Kobro; Seppälä, Otto; Urquiza-Fuentes, Jaime; Vahrenhold, Jan (ACM, 2023-12-22)
Computing education (CE), the scientific foundation of the teaching and learning of subject matter specific to computing, has matured into a field with its own research journals and conferences as well as graduate programmes. Yet, and unlike other mature subfields of computer science (CS), it is rarely taught as part of undergraduate CS programmes. In this report, we present a gap analysis resulting from semi-structured interviews with various types of stakeholders and derive a set of arguments for teaching CE courses in undergraduate CS programmes. This analysis and the arguments highlight a number of opportunities for the discipline of CS at large, in academia, in industry, and in school education, that would be opened up with undergraduate CE courses, as well as potential barriers to implementation that will need to be overcome. We also report on the results of a Delphi process performed to elicit topics for such a course with various audiences in mind. The Delphi process yielded 19 high-level categories that encompass the subject matter CE courses should incorporate, tailored to the specific needs of their intended student audiences. This outcome underscores the extensive range of content that can be integrated into a comprehensive CE programme. Based on these two stakeholder interactions as well as a systematic literature review aiming to explore the current practices in teaching CE to undergraduate students, we develop two prototypical outlines of such a course, keeping in mind that departments may have different preferences and affordances resulting in different kinds of CE offerings. Overall, input from external stakeholders underscores the clear significance of undergraduate CE courses. We anticipate leveraging this valuable feedback to actively promote these courses on a broader scale.
Assessing enactment of content regulation policies: A post hoc crowd-sourced audit of election misinformation on YouTube
Juneja, Prerna; Bhuiyan, Md Momen; Mitra, Tanushree (ACM, 2023-04-19)
With the 2022 US midterm elections approaching, conspiratorial claims about the 2020 presidential elections continue to threaten users’ trust in the electoral process. To regulate election misinformation, YouTube introduced policies to remove such content from its searches and recommendations. In this paper, we conduct a 9-day crowd-sourced audit on YouTube to assess the extent of enactment of such policies. We recruited 99 users who installed a browser extension that enabled us to collect up-next recommendation trails and search results for 45 videos and 88 search queries about the 2020 elections. We find that YouTube’s search results, irrespective of search query bias, contain more videos that oppose rather than support election misinformation. However, watching misinformative election videos still lead users to a small number of misinformative videos in the up-next trails. Our results imply that while YouTube largely seems successful in regulating election misinformation, there is still room for improvement.
Asynchronous Technical Interviews: Reducing the Effect of Supervised Think-Aloud on Communication Ability
Behroozi, Mahnaz; Parnin, Chris; Brown, Chris (ACM, 2022-11-07)
Software engineers often face a critical test before landing a jobÐ passing a technical interview. During these sessions, candidates must write code while thinking aloud as they work toward a solution to a problem under the watchful eye of an interviewer. While thinking aloud during technical interviews gives interviewers a picture of candidates’ problem-solving ability, surprisingly, these types of interviews often prevent candidates from communicating their thought process effectively. To understand if poor performance related to interviewer presence can be reduced while preserving communication and technical skills, we introduce asynchronous technical interviewsÐwhere candidates submit recordings of think-aloud and coding.We compare this approach to traditional whiteboard interviews and find that, by eliminating interviewer supervision, asynchronicity significantly improved the clarity of think-aloud via increased informativeness and reduced stress. Moreover, we discovered asynchronous technical interviews preserved, and in some cases even enhanced, technical problem-solving strategies and code quality. This work offers insight into asynchronous technical interviews as a design for supporting communication during interviews, and discusses trade-offs and guidelines for implementing this approach in software engineering hiring practices.
Automated Urban Planning for Reimagining City Configuration via Adversarial Learning: Quantification, Generation, and Evaluation
Wang, Dongjie; Fu, Yanjie; Liu, Kunpeng; Chen, Fanglan; Wang, Pengyang; Lu, Chang-Tien (ACM, 2022)
Urban planning refers to the efforts of designing land-use configurations given a region. However, there is a time-consuming and labor-intensive process for designing effective configurations, which motivates us to ask: can AI accelerate the urban planning process, so that human planners only adjust generated configurations for specific needs? The recent advance of deep generative models inspires us to automate urban planning from an adversarial learning perspective. However, three major challenges arise: 1) how to define a quantitative land-use configuration? 2) how to automate configuration planning? 3) how to evaluate the quality of a generated configuration? In this paper, we systematically address the three challenges. Specifically, 1) We define a land-use configuration as a longitude-latitude-channel tensor. 2) We formulate the automated urban planning problem into a task of deep generative learning. The objective is to generate a configuration tensor given the surrounding contexts of a target region. In particular, we first construct spatial graphs using geographic and human mobility data to learn graph representations. We then combine each target area and its surrounding context representations as a tuple, and categorize all tuples into positive (well-planned areas) and negative samples (poorly-planned areas). Next, we develop an adversarial learning framework, in which a generator takes the surrounding context representations as input to generate a land-use configuration, and a discriminator learns to distinguish between positive and negative samples. 3) We provide quantitative evaluation metrics and conduct extensive experiments to demonstrate the effectiveness of our framework.
Benchmarking And Configuring Security Levels In Intermittent Computing
Santhana Krishnan, Archanaa; Schaumont, Patrick (ACM, 2022-09-05)
Intermittent computing derives its name from the intermittent character of the power source used to drive the computing, typically an energy harvester of ambient energy sources. Intermittent computing is characterized by frequent transitions between the powered and the non-powered state. To enable the processor to quickly recover from unexpected power loss, regular checkpoints store the run-time state of the program including variables, control information and machine state. In sensitive applications such as logged measurements, checkpoints must be secured against tamper and replay. We investigate the overhead of creating, securing and restoring checkpoints with respect to the application. We propose a configurable checkpoint security setting which leverages application properties to reduce overhead of checkpoint security and implement the same using a secure checkpointing protocol. We discuss a prototype implementation for an FRAM based micro-controller and we characterize the cost of adding and configuring security to traditional checkpointing using a suite of embedded benchmark applications.
Better Side-Channel Attacks Through Measurements
Singh, Alok K.; Gerdes, Ryan M. (ACM, 2023-11-30)
In recent years, there has been a growing focus on improving the efficiency of the power side-channel analysis (SCA) attack by using machine learning or artificial intelligence methods, however, they can only be as good as the data they are trained on. Previous work has not given much attention to improving the accuracy of measurements by optimizing the measurement setup and the parameters, and most new researchers rely on heuristics to make measurements. This paper proposes an effective methodology to launch power SCA and increase the efficiency of the attack by improving the measurements. We examine the heuristics related to measurement parameters, investigate ways to optimize the parameters, determine their effects empirically, and provide a theoretical analysis to support the findings. To demonstrate the shortcomings of commercial measurement devices, we present a low-cost measurement board design and its hardware realization. In doing so, we are able to improve the power measurements, by optimizing the measurement setup, which in turn improves the efficiency of the attack.

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