Reviewerly/exHarmony huggingface dataset:
Securing IoT Systems in a Post-Quantum Environment: Vulnerabilities, Attacks, and Possible Solutions

The Internet of Things (IoT) refers to the distributed systems environment connecting billions of devices to the Internet, and quantum computing is an emerging technology that has a positive impact on IoT security by speeding up data processing while also having a negative impact due to post-quantum security attacks. In this survey, we provide detailed information on the possible post-quantum security attacks that threaten the security of the layers of IoT systems in terms of their vulnerabilities. Also, we provide detailed information on the existing solutions against post-quantum security attacks and show how limitations in these solutions decrease the security performance. Furthermore, we develop classification models to allow the readers to choose the best security approach against post-quantum security attacks in terms of IoT layers. Finally, we show the open challenges of the surveyed quantum security solutions and propose a framework based on quantum machine learning that takes advantage of optical pulses of secure communication as a future solution for detecting post-quantum security attacks.
E-voting System using Cloud-based Hybrid Blockchain Technology

With the invention of Internet-enabled devices, cloud and blockchain-based technologies, an online voting system can smoothly carry out election processes. During pandemic situations, citizens tend to develop panic about mass gatherings, which may influence the decrease in the number of votes. This urges a reliable, flexible, transparent, secure, and cost-effective voting system. The proposed online voting system using cloud-based hybrid blockchain technology eradicates the flaws that persist in the existing voting system, and it is carried out in three phases: the registration phase, vote casting phase and vote counting phase. A timestamp-based authentication protocol with digital signature validates voters and candidates during the registration and vote casting phases. Using smart contracts, third-party interventions are eliminated, and the transactions are secured in the blockchain network. Finally, to provide accurate voting results, the practical Byzantine fault tolerance (PBFT) consensus mechanism is adopted to ensure that the vote has not been modified or corrupted. Hence, the overall performance of the proposed system is significantly better than that of the existing system. Further performance was analyzed based on authentication delay, vote alteration, response time, and latency.
A comprehensive evaluation of large Language models on benchmark biomedical text processing tasks

Recently, Large Language Models (LLMs) have demonstrated impressive capability to solve a wide range of tasks. However, despite their success across various tasks, no prior work has investigated their capability in the biomedical domain yet. To this end, this paper aims to evaluate the performance of LLMs on benchmark biomedical tasks. For this purpose, a comprehensive evaluation of 4 popular LLMs in 6 diverse biomedical tasks across 26 datasets has been conducted. To the best of our knowledge, this is the first work that conducts an extensive evaluation and comparison of various LLMs in the biomedical domain. Interestingly, we find based on our evaluation that in biomedical datasets that have smaller training sets, zero-shot LLMs even outperform the current state-of-the-art models when they were fine-tuned only on the training set of these datasets. This suggests that pre-training on large text corpora makes LLMs quite specialized even in the biomedical domain. We also find that not a single LLM can outperform other LLMs in all tasks, with the performance of different LLMs may vary depending on the task. While their performance is still quite poor in comparison to the biomedical models that were fine-tuned on large training sets, our findings demonstrate that LLMs have the potential to be a valuable tool for various biomedical tasks that lack large annotated data.
Routes to relevance: Philosophies of relevant logics

Abstract Relevant logics are a family of non‐classical logics characterized by the behavior of their implication connectives. Unlike some other non‐classical logics, such as intuitionistic logic, there are multiple philosophical views motivating relevant logics. Further, different views seem to motivate different logics. In this article, we survey five major views motivating the adoption of relevant logics: Use Criterion, sufficiency, meaning containment, theory construction, and truthmaking. We highlight the philosophical differences as well as the different logics they support. We end with some questions for future research.
Nonlinear causal network learning via Granger causality based on extreme support vector regression

For complex networked systems, based on the consideration of nonlinearity and causality, a novel general method of nonlinear causal network learning, termed extreme support vector regression Granger causality (ESVRGC), is proposed. The nonuniform time-delayed influence of the driving nodes on the target node is particularly considered. Then, the restricted model and the unrestricted model of Granger causality are, respectively, formulated based on extreme support vector regression, which uses the selected time-delayed components of system variables as the inputs of kernel functions. The nonlinear conditional Granger causality index is finally calculated to confirm the strength of a causal interaction. Generally, based on the simulation of a nonlinear vector autoregressive model and nonlinear discrete time-delayed dynamic systems, ESVRGC demonstrates better performance than other popular methods. Also, the validity and robustness of ESVRGC are also verified by the different cases of network types, sample sizes, noise intensities, and coupling strengths. Finally, the superiority of ESVRGC is successful verified by the experimental study on real benchmark datasets.
Exploiting negative correlation for unsupervised anomaly detection in contaminated time series

Anomaly detection in time series data is crucial for many fields such as healthcare, meteorology, and industrial fault detection. However, traditional unsupervised time series anomaly detection methods suffer from biased anomaly measurement under contaminated training data. Most of existing methods employ hard strategies for contamination calibration by assigning pseudo-label to training data. These hard strategies rely on threshold selection and result in suboptimal performance. To address this problem, in this paper, we propose a novel unsupervised anomaly detection framework for contaminated time series (NegCo), which builds an effective soft contamination calibration strategy by exploiting the observed negative correlation between semantic representation and anomaly detection inherent within the autoencoder framework. We innovatively redefine anomaly detection in data contamination scenarios as an optimization problem rooted in this negative correlation. To model this negative correlation, we introduce a dual construct: morphological similarity captures semantic distinctions relevant to normality, while reconstruction consistency quantifies deviations indicative of anomalies. Firstly, the morphological similarity is effectively measured based on the representative normal samples generated from the center of the learned Gaussian distribution. Then, an anomaly measurement calibration loss function is designed based on negative correlation between morphological similarity and reconstruction consistency, to calibrate the biased anomaly measurement caused by contaminated samples. Extensive experiments on various time series datasets show that the proposed NegCo outperforms state-of-the-art baselines, achieving an improvement of 6.2% to 26.8% in Area Under the Receiver Operating Characteristics (AUROC) scores, particularly in scenarios with heavily contaminated training data.
Diffusion model-based text-guided enhancement network for medical image segmentation

In recent years, denoising diffusion models have achieved remarkable success in generating pixel-level representations with semantic values for image generation modeling. In this study, we propose a novel end-to-end framework, called TGEDiff, focusing on medical image segmentation. TGEDiff fuses a textual attention mechanism with the diffusion model by introducing an additional auxiliary categorization task to guide the diffusion model with textual information to generate excellent pixel-level representations. To overcome the limitation of limited perceptual fields for independent feature encoders within the diffusion model, we introduce a multi-kernel excitation module to extend the model’s perceptual capability. Meanwhile, a guided feature enhancement module is introduced in Denoising-UNet to focus the model’s attention on important regions and attenuate the influence of noise and irrelevant background in medical images. We critically evaluated TGEDiff on three datasets (Kvasir-SEG, Kvasir-Sessile, and GLaS), and TGEDiff achieved significant improvements over the state-of-the-art approach on all three datasets, with F1 scores and mIoU improving by 0.88% and 1.09%, 3.21% and 3.43%, respectively, 1.29% and 2.34%. These data validate that TGEDiff has excellent performance in medical image segmentation. TGEDiff is expected to facilitate accurate diagnosis and treatment of medical diseases through more precise deconvolutional structural segmentation.
The Case for Boosting Mobile Application QoE via Smart Band Switching in 5G/xG Networks

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

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

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

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)

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

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

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

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

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

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

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

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

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.