Reviewerly/exHarmony huggingface dataset:
AdvRain: Adversarial Raindrops to Attack Camera-Based Smart Vision Systems

Vision-based perception modules are increasingly deployed in many applications, especially autonomous vehicles and intelligent robots. These modules are being used to acquire information about the surroundings and identify obstacles. Hence, accurate detection and classification are essential to reach appropriate decisions and take appropriate and safe actions at all times. Current studies have demonstrated that “printed adversarial attacks”, known as physical adversarial attacks, can successfully mislead perception models such as object detectors and image classifiers. However, most of these physical attacks are based on noticeable and eye-catching patterns for generated perturbations making them identifiable/detectable by the human eye, in-field tests, or in test drives. In this paper, we propose a camera-based inconspicuous adversarial attack (AdvRain) capable of fooling camera-based perception systems over all objects of the same class. Unlike mask-based FakeWeather attacks that require access to the underlying computing hardware or image memory, our attack is based on emulating the effects of a natural weather condition (i.e., Raindrops) that can be printed on a translucent sticker, which is externally placed over the lens of a camera whenever an adversary plans to trigger an attack. Note, such perturbations are still inconspicuous in real-world deployments and their presence goes unnoticed due to their association with a natural phenomenon. To accomplish this, we develop an iterative process based on performing a random search aiming to identify critical positions to make sure that the performed transformation is adversarial for a target classifier. Our transformation is based on blurring predefined parts of the captured image corresponding to the areas covered by the raindrop. We achieve a drop in average model accuracy of more than 45% and 40% on VGG19 for ImageNet dataset and Resnet34 for Caltech-101 dataset, respectively, using only 20 raindrops.
A LiDAR Multi-Object Detection Algorithm for Autonomous Driving

Three-dimensional object detection is the core of an autonomous driving perception system, which detects and analyzes targets around the vehicle to obtain their sizes, shapes, and categories to provide reliable operational decisions for achieving autonomous driving. To improve the detection and localization accuracy of multi-object targets such as surrounding vehicles and pedestrians in autonomous driving scenarios, based on PointPillars fast object detection network, a three-dimensional object detection algorithm based on the channel attention mechanism, ECA Modules-PointPillars, is proposed. Firstly, the improved algorithm uses point cloud columnarization features to convert a three-dimensional point cloud into a two-dimensional pseudo-image. Then, combining the 2D backbone network for feature extraction with the Efficient Channel Attention (ECA) modules to achieve the enhancement of the positional feature information in the pseudo-image and the weakening of the irrelevant feature information such as background noise. Finally, the single-shot multibox detector (SSD) algorithm was used to complete the 3D object detection task. The experimental results show that the improved algorithm improves the mAP by 3.84% and 4.04% in BEV mode and 3D mode, respectively, compared to PointPillars, which improves the mAP by 4.64% and 5.89% in BEV mode and 3D mode, respectively, compared to F-PointNet, improves the mAP by 11.78% and 14.19% in BEV mode and 3D mode, respectively, compared to VoxelNet, and improves the mAP by 9.47% and 6.55% in BEV mode and 3D mode, respectively, compared to SECOND, demonstrating the effectiveness and reliability of the improved algorithms in autonomous driving scenarios.
A comparison of virtual worlds based on the metaverse maturity model

The realization and adoption of the metaverse vision is still in its infancy. In order to answer the question of how close today's virtual worlds get to this metaverse vision, the metaverse maturity model has been applied to evaluate a sample of eight selected virtual worlds: Guild Wars 2, Fortnite, Second Life, Roblox, VRChat, Decentraland, Horizon Worlds, and The Sandbox. The model comprises eight core attributes with five maturity levels each. The results indicate that, on the one hand, technical preconditions for the metaverse, e.g.,sufficient computing power, network technology, and interoperability standards, are not yet met. On the other hand, core attributes which mainly depend on strategy, e.g., persistence and economic systems can yield high maturity levels, if the strategy is aligned accordingly. As the study shows the usefulness of the metaverse maturity model, future research should repeatedly evaluate metaverse maturity to track progress and identify the need for further development.
Personalized PV system recommendation for enhanced solar energy harvesting using deep learning and collaborative filtering

Electricity is a crucial aspect of modern life, and with the increasing population and industrialization, energy demand has risen significantly. A swift transition to renewable energy sources such as wind and solar is essential for saving the planet. Solar energy is one of the most widely used renewable energy solutions, but choosing a PVSC poses a challenging problem that involves considering various factors, such as geographical location and energy consumption patterns. In this study, we investigate the effectiveness of using machine learning techniques to assist users in selecting the most suitable PVSC for their needs. We propose a new framework for PVSC recommendation, which encompasses a PV power forecasting model and a PV configuration recommendation system. We propose two forecasting models, one based on LSTM and the other based on CNN architecture. These two elements are responsible for extracting relevant features from time-series meteorological data, which are then passed to a feed-forward MLP layer that predicts the monthly energy production for different PVSCs. Subsequently, the prediction results are utilized to determine the optimal Residential scale PVSC for households, taking into account location and historical consumption data. The system was trained on simulated data and then tested on both a hold-out simulated test set and a real-world dataset. Our experiments show that the proposed framework is effective for long-term PV power forecasting and PVSC recommendation.
Offensive Language Detection in Under-Resourced Algerian Dialectal Arabic Language

This paper addresses the problem of detecting the offensive and abusive content in Facebook comments, where we focus on the Algerian dialectal Arabic which is one of the under-resourced languages. The latter has a variety of dialects mixed with different languages (i.e., Berber, French, and English). In addition, we deal with texts written in both Arabic and Roman scripts (i.e., Arabizi). Due to the scarcity of works on the same language, we have built a new corpus regrouping more than 8.7 k texts manually annotated as normal, abusive, and offensive. We have conducted a series of experiments using the state-of-the-art classifiers of text categorization, namely: BiLSTM, CNN, FastText, SVM, and NB. The results showed acceptable performances, but the problem requires further investigation on linguistic features to increase the identification accuracy.
Interactive Attention AI to Translate Low-Light Photos to Captions for Night Scene Understanding in Women Safety

There is amazing progress in deep learning-based models for image captioning and low-light image enhancement. For the first time in literature, this paper develops a deep learning model that translates night scenes to sentences, opening new possibilities for AI applications in the safety of visually impaired women. Inspired by image captioning and visual question answering, a novel 'Interactive Image Captioning' is developed. A user can make the AI focus on any chosen person of interest by influencing the attention scoring. Attention context vectors are computed from CNN feature vectors and user-provided start words. The encoder–attention–decoder neural network learns to produce captions from low-brightness images. This paper demonstrates how women safety can be enabled by researching a novel AI capability in the interactive vision–language model for perception of the environment in the night.
Toward AI grading of student problem solutions in introductory physics: A feasibility study

Solving problems is crucial for learning physics, and not only final solutions but also their derivations are important. Grading these derivations is labor intensive, as it generally involves human evaluation of handwritten work. AI tools have not been an alternative, since even for short answers, they needed specific training for each problem or set of problems. Extensively pretrained AI systems offer a potentially universal grading solution without this specific training. This feasibility study explores an AI-assisted workflow to grade handwritten physics derivations using MathPix and GPT-4. We were able to successfully scan handwritten solution paths and achieved an R-squared of 0.84 compared to human graders on a synthetic dataset. The proposed workflow appears promising for formative feedback, but for final evaluations, it would best be used to assist human graders.7 MoreReceived 26 April 2023Accepted 31 October 2023DOI:https://doi.org/10.1103/PhysRevPhysEducRes.19.020163Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasAssessmentScientific reasoning & problem solvingTechnologyPhysics Education Research
Chatbots in Foreign Language Teaching: Current Issues and Prospects for Forthcoming Research

The current stage is characterized by the dynamic development of artificial intelligence technologies and their introduction into foreign language teaching. Chatbots are one of such dialogical interactive teaching programs capable of developing foreign-language oral and written speech skills of a learner by maintaining a dialogue with them and imitating human speech on the basis of natural language and machine learning technologies and the algorithms of human speech embedded in the programme. The present paper is based on the analysis and synthesis of methodological studies devoted to the development of methods of teaching pupils and students foreign-language oral and written speech interaction based on chatbots. The most frequently discussed aspects in the problems of modern research are identified, and the prospects for further research related to the use of chatbots in teaching a foreign language are defined. The most commonly discussed aspects in methodological papers include: a) identifying learners’ attitudes towards the use of chatbots; b) identifying the possibility of developing learners’ written and oral speech skills based on chatbots; c) determining the level of learners’ proficiency in a foreign language for educational interaction with a chatbot. As prospective research, we identify studies aimed at a) developing step-by-step methods of teaching foreign language speech interaction based on chatbots; d) defining the nomenclature of foreign language speech interaction skills with a chatbot.
Reconnaissance of the 2022 Guanshan and Chihshang earthquakes in eastern Taiwan

Two significant earthquakes struck eastern Taiwan on consecutive days in mid-September of 2022. The first, the Guanshan earthquake, occurred on the night of September 17 (local time) with a local magnitude (M L ) of 6.4, and the second, the Chihshang earthquake, occurred on the afternoon of September 18 with a mainshock of M L of 6.8. The strong motion of these two events resulted in a series of ground surface ruptures, 2 collapsed buildings, two collapsed bridges, and more than 100 partially damaged structures along the Chihshang and Yuli faults around the Longitudinal Valley in eastern Taiwan. The observed response spectra at the damage sites of the Chihshang earthquake were significantly larger than the design response spectra of the 1997 Taiwan building code. Near-fault velocity pulses with a maximum value of 132 cm/s were observed along the faults, destructively impacting the damaged structures. The details and causes of the structural damage are presented in this article according to the findings of the on-site reconnaissance and ground motion analysis. Finally, the behavior of selected structures that have a structural health monitoring system at the time of these two destructive earthquakes is also evaluated.
QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model

Security has always been a critical issue in machine learning (ML) applications. Due to the high cost of model training – such as collecting relevant samples, labeling data, and consuming computing power – model-stealing attack is one of the most fundamental but vitally important issues. When it comes to quantum computing, such a quantum machine learning (QML) model-stealing attack also exists and is even more severe because the traditional encryption method, such as homomorphic encryption can hardly be directly applied to quantum computation. On the other hand, due to the limited quantum computing resources, the monetary cost of training QML model can be even higher than classical ones in the near term. Therefore, a well-tuned QML model developed by a third-party company can be delegated to a quantum cloud provider as a service to be used by ordinary users. In this case, the QML model is likely to be leaked if the cloud provider is under attack. To address such a problem, we propose a novel framework, namely QuMoS, to preserve model security. Instead of applying encryption algorithms, we propose to divide the complete QML model into multiple parts and distribute them to multiple physically isolated quantum cloud providers for execution. As such, even if the adversary in a single provider can obtain a partial model, it does not have sufficient information to retrieve the complete model. Although promising, we observed that an arbitrary model design under distributed settings cannot provide model security. We further developed a reinforcement learning-based security engine, which can automatically optimize the model design under the distributed setting, such that a good trade-off between model performance and security can be made. Experimental results on four datasets show that the model design proposed by QuMoS can achieve a close accuracy to the model designed with neural architecture search under centralized settings while providing the highest security than the baselines.
High-Round QAOA for MAX $k$-SAT on Trapped Ion NISQ Devices

The Quantum Alternating Operator Ansatz (QAOA) is a hybrid classical-quantum algorithm that aims to sample the optimal solution(s) of discrete combinatorial optimization problems. We present optimized QAOA circuit constructions for sampling MAX <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> -SAT problems, specifically for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=3$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=4$</tex> . The novel 4-SAT QAOA circuit construction we present uses measurement based uncomputation, followed by classical feed forward conditional operations. The QAOA circuit parameters for 3-SAT are optimized via exact classical (noise-free) simulation, using HPC resources to simulate up to 20 rounds on 10 qubits. In order to explore the limits of current NISQ devices we execute these optimized QAOA circuits for random 3-SAT test instances with clause-to-variable ratio 4 on four trapped ion quantum computers: Quantinuum H1-1 (20 qubits), IonQ Harmony (11 qubits), IonQ Aria 1 (25 qubits), and IonQ Forte (30 qubits). The QAOA circuits that are executed include <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=10$</tex> up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p=20$</tex> , and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n=22$</tex> for <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p=1$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p=2$</tex> . The high round circuits use upwards of 9,000 individual gate instructions, making these some of the largest QAOA circuits executed on NISQ devices. Our main finding is that current NISQ devices perform best at low round counts (i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$p=1, \ldots, 5$</tex> ) and then – as expected due to noise – gradually start returning satisfiability truth assignments that are no better than randomly picked solutions as the number of QAOA rounds are further increased.
A Novel Spatial-Temporal Variational Quantum Circuit to Enable Deep Learning on NISQ Devices

Quantum computing presents a promising approach for machine learning with its capability for extremely parallel computation in high-dimension through superposition and entanglement. Despite its potential, existing quantum learning algorithms, such as Variational Quantum Circuits (VQCs), face challenges in handling more complex datasets, particularly those that are not linearly separable. What's more, it encounters the deployability issue, making the learning models suffer a drastic accuracy drop after deploying them to the actual quantum devices. To overcome these limitations, this paper proposes a novel spatial-temporal design, namely “ST-VQC”, to integrate non-linearity in quantum learning and improve the robustness of the learning model to noise. Specifically, ST-VQC can extract spatial features via a novel block-based encoding quantum sub-circuit coupled with a layer-wise computation quantum sub-circuit to enable temporal-wise deep learning. Additionally, a SWAP-Free physical circuit design is devised to improve robustness. These designs bring a number of hyperparameters. After a systematic analysis of the design space for each design component, an automated optimization framework is proposed to generate the ST-VQC quantum circuit. The proposed ST-VQC has been evaluated on two IBM quantum processors, ibm_cairo with 27 qubits and ibmq_lima with 7 qubits to assess its effectiveness. The results of the evaluation on the standard dataset for binary classification show that ST-VQC can achieve over 30% accuracy improvement compared with existing VQCs on actual quantum computers. Moreover, on a non-linear synthetic dataset, the ST-VQC outperforms a linear classifier by 27.9%, while the linear classifier using classical computing outperforms the existing VQC by 15.58%.
Numerical Evidence for Exponential Speed-Up of QAOA over Unstructured Search for Approximate Constrained Optimization

Despite much recent work, the true promise and limitations of the Quantum Alternating Operator Ansatz (QAOA) [30] are unclear. A critical question regarding QAOA is to what extent its performance scales with the input size of the problem instance, in particular the necessary growth in the number of QAOA rounds to reach a high approximation ratio. We present numerical evidence for an exponential speed-up of QAOA over Grover-style unstructured search [28] in finding approximate solutions to constrained optimization problems. Our result provides a strong hint that QAOA is able to exploit the structure of an optimization problem and thus overcome the lower bound for unstructured search [11]. To this end, we conduct a comprehensive numerical study on several Hamming-weight constrained optimization problems for which we include combinations of all standardly studied mixer and phase separator Hamiltonians [18] (Ring mixer, Clique mixer, Objective Value phase separator) as well as quantum minimum-finding [20] inspired Hamiltonians [16], [27] (Grover mixer, Threshold-based phase separator). We identify Clique-Obj-QAOA with an exponential speed-up over Grover-Th-QAOA and tie the latter's scaling to that of unstructured search, with all other QAOA combinations coming in at a distant third. Our result suggests that maximizing QAOA performance requires a judicious choice of mixer and phase separator, and should trigger further research into other QAOA variations.
Augmenting interpretable models with large language models during training

Recent large language models (LLMs), such as ChatGPT, have demonstrated remarkable prediction performance for a growing array of tasks. However, their proliferation into high-stakes domains and compute-limited settings has created a burgeoning need for interpretability and efficiency. We address this need by proposing Aug-imodels, a framework for leveraging the knowledge learned by LLMs to build extremely efficient and interpretable prediction models. Aug-imodels use LLMs during fitting but not during inference, allowing complete transparency and often a speed/memory improvement of greater than 1000x for inference compared to LLMs. We explore two instantiations of Aug-imodels in natural-language processing: Aug-Linear, which augments a linear model with decoupled embeddings from an LLM and Aug-Tree, which augments a decision tree with LLM feature expansions. Across a variety of text-classification datasets, both outperform their non-augmented, interpretable counterparts. Aug-Linear can even outperform much larger models, e.g. a 6-billion parameter GPT-J model, despite having 10,000x fewer parameters and being fully transparent. We further explore Aug-imodels in a natural-language fMRI study, where they generate interesting interpretations from scientific data.
Quantum Software Engineering Challenges from Developers' Perspective: Mapping Research Challenges to the Proposed Workflow Model

Despite the increasing interest in quantum computing, the aspect of development to achieve cost-effective and reliable quantum software applications has been slow. One barrier is the software engineering of quantum programs, which can be approached from two directions. On the one hand, many software engineering practices, debugging in particular, are bound to classical computing. On the other hand, quantum programming is closely associated with the phenomena of quantum physics, and consequently, the way we express programs resembles the early days of programming. Moreover, much of the software engineering research today focuses on agile development, where computing cycles are cheap and new software can be rapidly deployed and tested, whereas in the quantum context, executions may consume lots of energy, and test runs may require lots of work to interpret. In this paper, we aim at bridging this gap by starting with the quantum computing workflow and by mapping existing software engineering research to this workflow. Based on the mapping, we then identify directions for software engineering research for quantum computing.