Reviewerly/exHarmony huggingface dataset:
The Case for Boosting Mobile Application QoE via Smart Band Switching in 5G/xG Networks

Authors:
raw_author_name: Anlan Zhang
raw_affiliation_string: University of Southern California, Los Angeles, United States of America
raw_author_name: Ruiyang Zhu
raw_affiliation_string: University of Michigan, Ann Arbor, USA
raw_author_name: Shuowei Jin
raw_affiliation_string: University of Michigan, Ann Arbor, USA
raw_author_name: Feng Qian
raw_affiliation_string: University of Southern California, Los Angeles, United States of America
raw_author_name: Z. Morley Mao
raw_affiliation_string: University of Michigan and Google, Ann Arbor, United States of America
raw_author_name: Zhi-Li Zhang
raw_affiliation_string: University of Minnesota - Twin Cities, Minneapolis, United States of America

Abstract: 5G and future 6G networks support diverse combinations of access technologies, architectures, and radio frequencies, with each combination termed as a "band" henceforth. Through comprehensive measurements in 12 cities across 5 countries, we experimentally show that operator-configured default bands are often highly sub-optimal, particularly under mobility. We then propose smart band switching, where a UE's band can be dynamically changed to improve the network performance and boost the application QoE. We discuss challenges, opportunities, and design choices for building a practical smart band switching system. We further develop preliminary UE-side band-switching logic on commodity smartphones, and evaluate it on commercial 5G networks.
Crowdotic: A Privacy-Preserving Hospital Waiting Room Crowd Density Estimation with Non-speech Audio

Authors:
raw_author_name: M Tanjid Hasan Tonmoy
raw_affiliation_string: Halıcıoğlu Data Science Institute, University of California San Diego, San Diego, CA, United States of America
raw_author_name: Andrew Lover
raw_affiliation_string: School of Public Health and Health Sciences, University of Massachusetts Amherst, Amherst, MA, USA
raw_author_name: George Corey
raw_affiliation_string: University Health Services, University of Massachusetts Amherst, Amherst, MA, USA
raw_author_name: Mohammad Arif Ul Alam
raw_affiliation_string: Miner School of Computer &amp; Information Sciences, University of Massachusetts Lowell, Lowell, MA, United States of America

Abstract: While passive audio recording of a crowd contains information about crowd density, such algorithm has limited applicability in hospital and clinical spaces with higher privacy expectations. In this preliminary work, we propose Crowdotic that uses a transformer-based model and exclusively non-speech audio segments for crowd density estimation in a hospital waiting room. To accomplish this, we deployed our sensor-based platform in the waiting room of a large hospital with IRB approval over a period of several months to capture non-speech audio and thermal images for the training and evaluation of our models. Our proposed approach shows that it is possible to achieve a reasonably high degree of accuracy with just a single microphone which can listen to non-speech based audio compared to other baselines. In addition to demonstrating superior performance without utilizing speech audio, we conduct further analysis using differential privacy techniques to provide additional privacy guarantees. Overall, our preliminary work demonstrates the viability of employing non-speech audio data for accurate occupancy estimation, while also ensuring the exclusion of speech-related content and providing robust privacy protections through differential privacy guarantees.
Challenges and opportunities in onboarding smart-home devices

Authors:
raw_author_name: Timothy J. Pierson
raw_affiliation_string: Dartmouth College, Hanover, NH, United States
raw_author_name: David Kotz
raw_affiliation_string: Dartmouth College, Hanover, NH, United States

Abstract: Smart-home devices have become integral to daily routines, but their onboarding procedures - setting up a newly acquired smart device into operational mode - remain understudied. The heterogeneity of smart-home devices and their onboarding procedure can easily overwhelm users when they scale up their smart-home system. While Matter, the new IoT standard, aims to unify the smart-home ecosystem, it is still evolving, resulting in mixed compliance among devices. In this paper, we study the complexity of device onboarding from users' perspectives. We thus performed cognitive walkthroughs on 12 commercially available smart-home devices, documenting the commonality and distinctions of the onboarding process across these devices. We found that onboarding smart home devices can often be tedious and confusing. Users must devote significant time to creating an account, searching for the target device, and providing Wi-Fi credentials for each device they install. Matter-compatible devices are supposedly easier to manage, as they can be registered through one single hub independent of the vendor. Unfortunately, we found such a statement is not always true. Some devices still need their own companion apps and accounts to fully function. Based on our observations, we give recommendations about how to support a more user-friendly onboarding process.
CodeQueries: A Dataset of Semantic Queries over Code

Authors:
raw_author_name: Surya Prakash Sahu
raw_affiliation_string: Indian Institute of Science, Bengaluru, India
raw_author_name: Shikhar Bharadwaj
raw_affiliation_string: Google Research, India
raw_author_name: Aditya Kanade
raw_affiliation_string: Microsoft Research, India, India
raw_author_name: Petros Maniatis
raw_affiliation_string: Google DeepMind, India
raw_author_name: Shirish Shevade
raw_affiliation_string: Indian Institute of Science, Bengaluru, India

Abstract: Developers often have questions about semantic aspects of code they are working on, e.g., “Is there a class whose parent classes declare a conflicting attribute?”. Answering them requires understanding code semantics such as attributes and inheritance relation of classes. An answer to such a question should identify code spans constituting the answer (e.g., the declaration of the subclass) as well as supporting facts (e.g., the definitions of the conflicting attributes). The existing work on question-answering over code has considered yes/no questions or method-level context. We contribute a labeled dataset, called CodeQueries, of semantic queries over Python code. Compared to the existing datasets, in CodeQueries, the queries are about code semantics, the context is file level and the answers are code spans. We curate the dataset based on queries supported by a widely-used static analysis tool, CodeQL, and include both positive and negative examples, and queries requiring single-hop and multi-hop reasoning.
An Approach for Providing Recommendation for Requirements Non-Conformant with Requirement Templates (RTs)

Authors:
raw_author_name: Shivani Balwani
raw_affiliation_string: Software Engineering Research Lab, DA-IICT, India
raw_author_name: Saurabh Tiwari
raw_affiliation_string: Software Engineering Research Lab, DA-IICT, India
raw_author_name: Sourish Dasgupta
raw_affiliation_string: Knowledge Discovery Lab, DA-IICT, India

Abstract: RTs generally possess a fixed syntactic structure and comprise pre-defined slots, and requirements written in the format of RTs must conform with the template structure. Suppose the requirements do not conform to the RT. In that case, manually verifying the conformity of requirements to RTs becomes a tedious task due to the large size of industry requirement documents and introduces the possibility of errors. Furthermore, rewriting requirements to conform to the template structure when they initially do not conform presents a significant challenge. This paper proposes a tool-based approach that automatically verifies whether Functional Requirements (FRs) conform to RTs. It recommends a Template Conformance (TC) requirement by generating a semantically identical requirement that Conforms to the template structure. Our study focused on two well-known RTs, Easy Approach to Requirements Syntax (EARS) and RUPPs, for checking conformance and making recommendations. We utilized Natural Language Processing (NLP) techniques and applied our approach to industrial and publicly available case studies. Our results demonstrate that the proposed tool-based approach facilitates requirement analysis and aids in recommending requirements based on their conformity with RTs. Our results show an accuracy of 83.9% for providing recommendations to non-conformant requirements with RTs.
Accelerating Software Development Using Generative AI: ChatGPT Case Study

Authors:
raw_author_name: Asha Rajbhoj
raw_affiliation_string: TCS Research, Tata Consultancy Services, India
raw_author_name: Piyush Kulkarni
raw_affiliation_string: TCS Research, Tata Consultancy Services, India
raw_author_name: Vinay Kulkarni
raw_affiliation_string: TCS Research, Tata Consultancy Services, India

Abstract: The Software Development Life Cycle (SDLC) comprises multiple phases, each requiring Subject Matter Experts (SMEs) with phase-specific skills. The efficacy and quality of deliverables of each phase are skill dependent. In recent times, Generative AI techniques, including Large-scale Language Models (LLMs) like GPT, have become significant players in software engineering. These models, trained on extensive text data, can offer valuable contributions to software development. Interacting with LLMs involves feeding prompts with the context information and guiding the generation of textual responses. The quality of the response is dependent on the quality of the prompt given. This paper proposes a systematic prompting approach based on meta-model concepts for SDLC phases. The approach is validated using ChatGPT for small but complex business application development. We share the approach and our experience, learnings, benefits obtained, and the challenges encountered while applying the approach using ChatGPT. Our experience indicates that Generative AI techniques, such as ChatGPT, have the potential to reduce the skills barrier and accelerate software development substantially.
Poster: Fusing Event and Depth Sensing for Dynamic Objects Localization and Tracking

Authors:
raw_author_name: Haoyang Wang
raw_affiliation_string: Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
raw_author_name: Xinlei Chen
raw_affiliation_string: Pengcheng Laboratory, Shenzhen, China; RISC-V International Open Source Laboratory, Shenzhen, China; Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China RISC-V International Open Source Laboratory, Shenzhen, China Pengcheng Laboratory, Shenzhen, China

Abstract: Background and motivation. The localization and tracking of high-speed dynamic objects find applications across various domains. For example, tracking unmanned aerial vehicles (UAVs) serves a crucial role for regulatory authorities, enabling the swift identification of illicitly operated drones.
A Contrastive Explanation Tool for Plans in Hybrid Domains

Authors:
raw_author_name: Mir Md Sajid Sarwar
raw_affiliation_string: School of Mathematical and Computational Sciences, Indian Association for the Cultivation of Science, Kolkata, India
raw_author_name: Rajarshi Ray
raw_affiliation_string: School of Mathematical and Computational Sciences, Indian Association for the Cultivation of Science, India
raw_author_name: Ansuman Banerjee
raw_affiliation_string: Advanced Computing and Microelectronics Unit, Indian Statistical Institute, India

Abstract: This paper presents a tool for generating contrastive explanations of plans in hybrid domains. The tool offers a collection of contrastive questions over a plan, such as "Why use action A?", for users to select. An explanation of the user question is produced by contrasting the original plan against an alternative that meets the user’s expectation implicit from the question. The tool has the provision to contrast with the best alternative that the underlying planner can generate in terms of plan makespan (duration of a plan) and length. The current version supports two planners in the hybrid domain, namely SMTPlan+, and ENHSP, which a tool user can select. The tool consists of (1) A web-based interactive GUI for selecting questions, viewing contrastive plans and the generated explanations, and (2) A back-end implementing an iterative re-modeling and re-planning algorithm. We demonstrate the working of the tool over two case studies.
A Codebert Based Empirical Framework for Evaluating Classification-Enabled Vulnerability Prediction Models

Authors:
raw_author_name: Vikram Singh
raw_affiliation_string: National Institute of Technology, Kurukshetra, India
raw_author_name: N L Bhanu Murthy
raw_affiliation_string: BITS Pilani Hyderabad, India, India
raw_author_name: Aneesh Krishna
raw_affiliation_string: Curtin University, Australia, Australia
raw_author_name: Lov Kumar
raw_affiliation_string: National Institute of Technology, Kurukshetra, India

Abstract: Software vulnerabilities are discovered quite frequently and cause substantial damage and security breaches. In the realm of cybersecurity, the detection of vulnerabilities plays a pivotal role in safeguarding critical systems and data. In this paper, we evaluate the efficacy of the source code embeddings obtained from the pre-trained CodeBERT model in detecting vulnerable code snippets and perform a comparative analysis with different data pre-processing and ML classification algorithms to recommend the best techniques for vulnerability detection. Precisely, we investigated the predictive ability of 540 different models developed using the CodeBERT embeddings, five feature selection techniques, a class balancing technique, and fifteen classification algorithms. Unlike regular deep learning-based models, these models are quick to implement and require minimal computational resources. The models are evaluated on robustness in detecting codes with known and previously unseen vulnerabilities. The developed models display high average accuracy and AUC, proving the efficiency of the CodeBERT embeddings in capturing vulnerabilities. We find that ensembles of decision trees (RF and EXTR) perform the best for vulnerability detection, which can be attributed to the advantages of ensembling. Furthermore, the effectiveness of class balancing and feature selection techniques as data pre-processing techniques is evaluated using box plots and the Friedman test. The significance of this research lies in its contribution to the field of cybersecurity by introducing reliable and efficient vulnerability detection methods.
A Hierarchical Attention Networks based Model for Bug Report Prioritization

Authors:
raw_author_name: Anurag Yadav
raw_affiliation_string: ABV-IIITM Gwalior, India, India
raw_author_name: Santosh Singh Rathore
raw_affiliation_string: ABV-IIITM Gwalior, India, India

Abstract: Software is becoming increasingly complex, leading to a surge in bugs. Bug reports typically contain essential details for bug reproduction and resolution. Currently, a triage engineer manually reads bug reports, classifies the bugs, and assigns priorities to the critical bugs. This task is time-consuming. While machine learning and deep learning models have been developed to automate this task, some have shown subpar performance, and others, although promising, have demonstrated inconsistency when applied to new bug reports. This paper proposes a Hierarchical Attention Network (HAN) model for prioritizing software bug reports. The bug reports are preprocessed through text normalization, tokenization using the DistilBERT tokenizer, and attention mechanisms to capture hierarchical relationships within the text. The model architecture incorporates bidirectional GRU layers and attention mechanisms at both word and sentence levels. The proposed model is evaluated on four software projects using precision, recall, and F1-score measures. Next, the proposed model is compared with convolution neural networks (CNN), support vector machines (SVM), random forests, and logistic regression techniques. The results showed that the proposed HAN model performed better than the other techniques.
SecSEC: Securing Smart Ethereum Contracts

Authors:
raw_author_name: Chavhan Sujeet Yashavant
raw_affiliation_string: Indian Institute of Technology, Kanpur, India

Abstract: Smart contracts are a driving force for the Ethereum blockchain. A smart contract is a code that resides on blockchain and executes when certain predetermined conditions are satisfied. Ethereum smart contracts handle ether (a cryptocurrency) equivalent to millions of dollars and other essential assets. A bug in the smart contract can cause financial loss and damage to essential assets. The Ethereum community has developed ample tools to detect bugs in smart contracts. However, the tools generate false reports. We plan to integrate existing tools and detect smart contract bugs by combining the best of each tool. We use the Logistic Regression model to combine the tools. We are working on a training dataset of around 40K real-world Ethereum smart contracts labelled with vulnerabilities for the Logistic Regression model. We also plan to improve individual tools by finding the drawbacks of each tool. Finally, we aim to develop a new tool that will be better than existing tools regarding precision and recall.
Modeling matrix variate time series via hidden Markov models with skewed emissions

Authors:
raw_author_name: Michael P. B. Gallaugher
raw_affiliation_string: Department of Statistical Science Baylor University  Waco Texas USA
raw_author_name: Xuwen Zhu
raw_affiliation_string: Culverhouse College of Business University of Alabama  Tuscaloosa Alabama USA

Abstract: Abstract Data collected today have increasingly become more complex and cannot be analyzed using regular statistical methods. Matrix variate time series data is one such example where the observations in the time series are matrices. Herein, we introduce a set of three hidden Markov models using skewed matrix variate emission distributions for modeling matrix variate time series data. Compared to the hidden Markov model with matrix variate normal emissions, the proposed models present greater flexibility and are capable of modeling skewness in time series data. Parameter estimation is performed using an expectation maximization algorithm. We then look at both simulated data and salary data for public Texas universities.
A Non-Convex Fractional-Order Differential Equation for Medical Image Restoration

Authors:
raw_author_name: Donghong Zhao
raw_affiliation_string: School of Mathematics and Physics, University of Science and Technology Beijing, Beijing 100083, China

Abstract: We propose a new non-convex fractional-order Weber multiplicative denoising variational generalized function, which leads to a new fractional-order differential equation, and prove the existence of a unique solution to this equation. Furthermore, the model is solved using the partial differential equation (PDE) method and the alternating direction multiplier method (ADMM) to verify the theoretical results. The proposed model is tested on some symmetric and asymmetric medical computerized tomography (CT) images, and the experimental results show that the combination of the fractional-order differential equation and the Weber function has better performance in medical image restoration than the traditional model.
Design and assessment of a virtual reality learning environment for firefighters

Authors:
raw_author_name: Steven G. Wheeler
raw_author_name: Simon Hoermann
raw_author_name: Stephan Lukosch
raw_author_name: Robert W. Lindeman

Abstract: The use of virtual reality (VR) in firefighter training is promising because it provides cost-effective, safe environments that arouse similar behavioral responses to real-life scenarios. However, the pedagogical potential of VR and its impact on learning outcomes compared to traditional methods is currently an under-explored area. This research investigates how well VR can support learning compared to traditional methods in the context of training firefighters in combating vegetation fires. A VR learning environment was developed, informed by a “design for learning” framework providing a pedagogical underpinning. A between-subjects experiment was conducted with 40 participants to measure the knowledge transfer of the VR learning environment against the official textbook. In addition, VR's theorized learning benefits of intrinsic motivation, situational interest, and self-efficacy were compared with textbook-based learning. Lastly, the design quality of the learning environment was assessed based on its learning and user experience. We employed a primarily quantitative approach to data collection and analysis, using a combination of knowledge test results and questionnaires, with supporting qualitative data from semi-structured interviews and observation notes to answer our hypotheses. The results found a significant difference between the knowledge transfer of both conditions, with textbook-based learning more effectively transferring factual and conceptual knowledge than VR. No significant difference was found in reported self-efficacy between the two conditions but was found in reported levels of intrinsic motivation and situational interest, which were higher in the VR condition. The design was found to have facilitated a good user and learning experience, assessed via questionnaire responses. During interviews, VR participants reported high levels of satisfaction with the experience, praising the hands-on learning approach and interactivity, while reporting frustration with the lack of knowledge reinforcement and initial difficulties with the controls. A key finding was that presence was found to be negatively associated with knowledge transfer, which we theorize to be caused by the novelty of the realistic VR environment distracting participants from the more familiar lesson content. This research contributes to the body of work related to knowledge transfer within VR in this domain while highlighting key pedagogical and design considerations that can be used to inform future design implementations.
Addressing durability in collaborative organising: Event atmospheres and polyrhythmic affectivity

Authors:
raw_author_name: David Rozas
raw_affiliation_string: Complutense University of Madrid, Spain,

Abstract: Collaborative organising is known to burn like a rocket: it thrives on intense passion, relationality and creativity but quickly falls into pieces. This article explores the underestimated role of events and their affective atmospheres to sustain collaborative work. Drawing insights from two ethnographic field studies within an open-source software community and a network of impact entrepreneurs, we introduce the notion of ‘polyrhythmic affectivity’ at the core of polycentric governance. It encapsulates how frictional reverberances between three atmospherically experienced affective intensities – togetherness, dissonance and mutuality – are able to maintain emergent yet enduring order. We argue that the collective motivational force of collaborative organising, can be stabilised through a process of ‘affective commoning’ to sustain collaborative atmospheres as shared creative resources.