Reviewerly/exHarmony huggingface dataset:
DeepRover: A Query-Efficient Blackbox Attack for Deep Neural Networks

Deep neural networks (DNNs) achieved a significant performance breakthrough over the past decade and have been widely adopted in various industrial domains. However, a fundamental problem regarding DNN robustness is still not adequately addressed, which can potentially lead to many quality issues after deployment, e.g., safety, security, and reliability. An adversarial attack is one of the most commonly investigated techniques to penetrate a DNN by misleading the DNN’s decision through the generation of minor perturbations in the original inputs. More importantly, the adversarial attack is a crucial way to assess, estimate, and understand the robustness boundary of a DNN. Intuitively, a stronger adversarial attack can help obtain a tighter robustness boundary, allowing us to understand the potential worst-case scenario when a DNN is deployed. To push this further, in this paper, we propose DeepRover, a fuzzing-based blackbox attack for deep neural networks used for image classification. We show that DeepRover is more effective and query-efficient in generating adversarial examples than state-of-the-art blackbox attacks. Moreover, DeepRover can find adversarial examples at a finer-grained level than other approaches.
On Using Information Retrieval to Recommend Machine Learning Good Practices for Software Engineers

Machine learning (ML) is nowadays widely used for different purposes and with several disciplines. From self-driving cars to automated medical diagnosis, machine learning models extensively support users’ daily activities, and software engineering tasks are no exception. Not embracing good ML practices may lead to pitfalls that hinder the performance of an ML system and potentially lead to unexpected results. Despite the existence of documentation and literature about ML best practices, many non-ML experts turn towards gray literature like blogs and Q&A systems when looking for help and guidance when implementing ML systems. To better aid users in distilling relevant knowledge from such sources, we propose a recommender system that recommends ML practices based on the user’s context. As a first step in creating a recommender system for machine learning practices, we implemented Idaka. A tool that provides two different approaches for retrieving/generating ML best practices: i) an information retrieval (IR) engine and ii) a large language model. The IR-engine uses BM25 as the algorithm for retrieving the practices, and a large language model, in our case Alpaca. The platform has been designed to allow comparative studies of best practices retrieval tools. Idaka is publicly available at GitHub: https://bit.ly/idaka. Video: https://youtu.be/cEb-AhIPxnM
Factors Affecting Information Systems Audit Conclusion

This paper examines the elements that significantly affect an information system audit's conclusion. The importance of guaranteeing the effectiveness, efficiency, and security of information systems has been underlined by the ongoing advancement of technology and the growing reliance on digital infrastructure. An information system audit's main goal is to assess the systems' overall integrity and to reassure stakeholders that they are trustworthy and sufficiently protected against any hazards. The paper identifies and examines the important elements that affect the outcome of information system audits by drawing on a thorough examination of the literature and empirical studies. Conducting a thorough and accurate information systems audit requires an understanding of these aspects and the proper handling of them. The results of this study give significant contributions to the field of information system auditing and provide a foundation for auditors, IT managers, and policymakers to create efficient plans for carrying out thorough and insightful audits. The ultimate goal of this article is to increase the resilience of information systems in a constantly evolving digital environment and to create a greater awareness of the elements that support audit conclusions.
Multilingual Code Co-evolution using Large Language Models

Many software projects implement APIs and algorithms in multiple programming languages. Maintaining such projects is tiresome, as developers have to ensure that any change (e.g., a bug fix or a new feature) is being propagated, timely and without errors, to implementations in other programming languages. In the world of ever-changing software, using rule-based translation tools (i.e., transpilers) or machine learning models for translating code from one language to another provides limited value. Translating each time the entire codebase from one language to another is not the way developers work. In this paper, we target a novel task: translating code changes from one programming language to another using large language models (LLMs). We design and implement the first LLM, dubbed Codeditor, to tackle this task. Codeditor explicitly models code changes as edit sequences and learns to correlate changes across programming languages. To evaluate Codeditor, we collect a corpus of 6,613 aligned code changes from 8 pairs of open-source software projects implementing similar functionalities in two programming languages (Java and C#). Results show that Codeditor outperforms the state-of-the-art approaches by a large margin on all commonly used automatic metrics. Our work also reveals that Codeditor is complementary to the existing generation-based models, and their combination ensures even greater performance.
Amiais@SL: A Metaverse Simulator to Support Playful Engagement in Learning About Cultural Heritage

The cultural heritage of rural communities is at risk as their populations age and their social and economic structures deteriorate. That is the case of Amiais, a small rural Portuguese village, in which only 15 inhabitants remain, mostly older adults. Therefore, the preservation of both tangible and intangible cultural heritage of these rural regions, such as Amiais, has become an urgent concern. ICT, and particularly virtual 3D worlds or metaverses, such as Second Life® (SL), have been playing an important role in this matter, offering new possibilities for enhancing and preserving culture and allowing people to learn about and experience culture in new and exciting ways. This paper describes the design and development of a simulation of Amiais village in SL (Amiais@SL), in which visitors can engage in gamified immersive cultural experiences, learn about the village and its cultural heritage, interact with virtual agents, and participate in cultural activities through different virtual tours and challenges.
Experiencing Design Fieldwork: Walking Interviews on Madeira Island

This article invites reflections on the design fieldwork practice as a process for connecting, relating and understanding complex social, natural, cultural and political systems, non-linear and interdependent visions about change at the root of many ecological issues. Through walking and talking with natural scientists in Madeira Island, we conducted a place-based inquiry that aimed to understand site-specific problems and opportunities to recount biological and cultural aspects of its peculiar forests. We used a fieldwork approach as embodied contextual analysis for amplifying the unique aspects, connecting ecological controversies, and bridging local efforts that cross both natural science and design. This contribution wants to present the place-based research project we conducted and specifically the walking interview process method we used to outline collaborative visions of development.
Improving Software Design Teaching, a Comparative Study

With the constant development of software technologies, it has become increasingly difficult for universities to design curricula that provide students with technical skills that stay relevant by the time they join the workforce; this is particularly true for design. This paper proposes an approach for training students in design software. We aim to understand whether alternative methods for teaching Adobe Photoshop, Illustrator, and InDesign to undergraduate students could effectively replace traditional approaches. We conducted a comparative study with first-semester students: two groups underwent training following traditional methods (lectures followed by in-class exercises), whereas two groups learned using the eduScrum methodology. For the latter approach, students were given 15 assignments to be developed during the semester, with complete freedom on where and how to research the necessary knowledge to achieve the required results. During this process, the instructor acted as a facilitator rather than an instructor, as the Scrum methodology advocates. As we foresaw, some of the tools studied during the semester were surpassed by software updates Adobe introduced, thus adding to our initial argument for developing the methodology in the first place. While we firmly believe that the eduScrum methodology can become a successful approach for teaching design software, further studies are necessary to investigate whether a hybrid methodology might be a better solution to help students transition to a more independent way of learning.
Invited Paper: Machine Learning Based Blind Side-Channel Attacks on PQC-Based KEMs - A Case Study of Kyber KEM

Kyber KEM, the NIST selected PQC standard for Public Key Encryption and Key Encapsulation Mechanisms (KEMs) has been subjected to a variety of side-channel attacks, through the course of the NIST PQC standardization process. However, all these attacks targeting the decapsulation procedure of Kyber KEM either require knowledge of the ciphertexts or require to control the value of ciphertexts for key recovery. However, there are no known attacks in a blind setting, where the attacker does not have access to the ciphertexts. While blind side-channel attacks are known for symmetric key cryptographic schemes, we are not aware of such attacks for Kyber KEM. In this paper, we fill this gap by proposing the first blind side-channel attack on Kyber KEM. We target leakage of the pointwise multiplication operation in the decryption procedure to carry out practical blind side-channel attacks resulting in full key recovery. We perform practical validation of our attack using power side-channel from the reference implementation of Kyber KEM taken from the pqm4 library, implemented on the ARM Cortex-M4 microcontroller. Our experiments clearly indicate the feasibility of our proposed attack in recovering the full key in only a few hundred to few thousand traces, in the presence of a suitably accurate Hamming Weight (HW) classifier.
Effective and Efficient Qubit Mapper

Quantum computing has been accumulating tremendous attention in recent years. In current superconducting quantum processors, each qubit can only be connected with a limited number of neighbors. Therefore, the original quantum circuit should be converted to a hardware-dependent circuit, and this process is called qubit mapping and routing, in which typically extra SWAP gates need to be inserted. Due to a limited qubit lifetime, one of the main objectives of qubit mapping and routing is to minimize the circuit depth, which is a time-consuming process. By studying several existing greedy mappers, we extract and analyze two patterns that significantly impact the mapping and routing performance. Then, we propose a sliding window method named SWin, which dramatically reduces the computational cost with negligible performance degradation. Compared with the state-of-the-art greedy methods, SWin can find an effective result by up to 39% depth decrease, on average of 16% for large-scale circuits. Moreover, SWin can be easily modified to be noise-aware, while the depth reduction will yield better performance for real execution. Furthermore, SWin still performs well for various chip couplings.
NearUni: Near-Unitary Training for Efficient Optical Neural Networks

Optical neural networks with Mach-Zender interferometers (MZIs) have demonstrated advantages over their electronic counterparts in computing efficiency and power consumption. However, implementing the computation with a weight matrix in DNNs using this technique requires the decomposition of the weight matrix into two unitary matrices, because an optical network can only realize a single unitary matrix due to its structural property. Accordingly, a direct implementation of DNNs onto optical networks suffer from a low area efficiency. To address this challenge, in this paper, a near-unitary training framework is proposed. In this framework, a weight matrix in DNNs is first partitioned into square submatrices to reduce the number of MZIs in the optical networks. Afterwards, training is adjusted to make the partitioned submatrices as close to unitary as possible. Such a matrix is then represented further by the sum of a unitary matrix and a sparse matrix. The latter implements the difference between the unitary matrix and the near-unitary matrix after training. In this way, only one optical network is needed to implement this unitary matrix and the low computation load in the sparse matrix can be implemented with area-efficient microring resonators (MRRs). Experimental results show that the area footprint can be reduced by 81.81%, 85.51%, 48.6% for ResNet34, VGG16, and fully connected neural networks, respectively, while the inference accuracy is still maintained on CIFAR100 and MNIST datasets.
RNA-ViT: Reduced-Dimension Approximate Normalized Attention Vision Transformers for Latency Efficient Private Inference

The concern over data and model privacy in machine learning inference as a service (MLaaS) has led to the development of private inference (PI) techniques. However, existing PI frameworks, especially those designed for large models such as vision transformers (ViT), suffer from high computational and communication overheads caused by the expensive multi-party computation (MPC) protocols. The encrypted attention module that involves the softmax operation contributes significantly to this overhead. In this work, we present a family of models dubbed RNA-ViT, that leverage a novel attention module called reduced-dimension approximate normalized attention and a latency efficient GeLU-alternative layer. In particular, RNA-ViT uses two novel techniques to improve PI efficiency in ViTs: a reduced-dimension normalized attention (RNA) architecture and a high order polynomial (HOP) softmax approximation for latency efficient normalization. We also propose a novel metric, accuracy-to-latency ratio (A2L), to evaluate modules in terms of their accuracy and PI latency. Based on this metric, we perform an analysis to identify a nonlinearity module with improved PI efficiency. Our extensive experiments show that RNA-ViT can achieve average 3.53×, 3.54×, 1.66× lower PI latency with an average accuracy improvement of 0.93%, 2.04%, and 2.73% compared to the state-of-the-art scheme MPCViT [1], on CIFAR-10, CIFAR-100, and Tiny-ImageNet, respectively.
Design Methodology for a Shared Mobility App

Nowadays, mobile applications (app) have become allies and indispensable in people’s everyday life. Problems that previously required some time to be solved are now solved with a few taps on, i.e., a smartphone screen. The work presented in this paper is part of a master’s project that consists of developing a user-centered procedure for a mobile application. Thus, this article focuses on studying a methodology for the user-centered interface design of an app, with the aim of making the process based on interaction design principles, considering usability aspects in order to favor the user’s work. Based on the perceived needs of the Polytechnic Institute of Viana do Castelo, and with the aim to develop a broader Academic Mobility as a Service (AMaaS), this project consists of studying a work methodology for the development of a prototype of an app with the aim of encouraging sustainable and healthy mobility within the academic community and campuses.
Usability Analysis of Augmented Reality-Based Learning Applications for Kids: Insights from SUS and Heuristic Evaluation

In today’s fast-paced world, augmented reality (AR) has emerged as a widely accepted and profitable approach for engaging children in interactive learning experiences. However, the rapid development of this technology has left many individuals struggling with even basic interactions due to a lack of prior expertise and usability. While various studies have explored the use of AR functionalities in learning applications, few have addressed the associated usability problems. To integrate AR-based applications into children’s learning platforms effectively, ex- tensive research on user adaptability is needed. The primary goal of this study is to provide a set of heuristics for evaluating the usability of AR-based learning applications for kids and to compare heuristic evaluation and System Usability Scale (SUS) evaluation results to assess their applicability. Seventeen heuristics, including Nielsen’s 10 Usability Heuristics, were employed to evaluate four AR applications designed for children’s learning. The findings revealed significant heuristic problems in all applications, with SUS scores falling below the acceptance thresh- old. Moreover, the study demonstrated that both heuristic evaluation and SUS evaluation methods yield comparable results, confirming the efficacy of the proposed heuristics in assessing the usability of AR-based learning applications for children.
Toward Consistent High-Fidelity Quantum Learning on Unstable Devices via Efficient In-Situ Calibration

In the near-term noisy intermediate-scale quantum (NISQ) era, high noise will significantly reduce the fidelity of quantum computing. What's worse, recent works reveal that the noise on quantum devices is not stable, that is, the noise is dynamically changing over time. This leads to an imminent challenging problem: At run-time, is there a way to efficiently achieve a consistent high-fidelity quantum system on unstable devices? To study this problem, we take quantum learning (a.k.a., variational quantum algorithm) as a vehicle, which has a wide range of applications, such as combinatorial optimization and machine learning. A straightforward approach is to optimize a variational quantum circuit (VQC) with a parameter-shift approach on the target quantum device before using it; however, the optimization has an extremely high time cost, which is not practical at run-time. To address the pressing issue, in this paper, we proposed a novel quantum pulse-based noise adaptation framework, namely QuPAD. In the proposed framework, first, we identify that the CNOT gate is the fidelity bottleneck of the conventional VQC, and we employ a more robust parameterized multi-qubit gate (i.e., Rzx gate) to replace CNOT gate. Second, by benchmarking Rzx gate with different parameters, we build a fitting function for each coupling qubit pair, such that the deviation between the theoretic output of Rzx gate and its on-device output under a given pulse amplitude and duration can be efficiently predicted. On top of this, an evolutionary algorithm is devised to identify the pulse amplitude and duration of each Rzx gate (i.e., calibration) and find the quantum circuits with high fidelity. Experiments show that the runtime on quantum devices of QuPAD with 8–10 qubits is less than 15 minutes, which is up to 270 x faster than the parameter-shift approach. In addition, compared to the vanilla VQC as a baseline, QuPAD can achieve 59.33% accuracy gain on a classification task, and average 66.34% closer to ground state energy for molecular simulation.
Tensor Network-Based Continuous Variable Quantum Circuit Optimization for Preparation of GKP State

Tensor networks are highly promising methods for efficiently simulating quantum systems using classical computers. In recent years, the efficiency of tensor networks has been eagerly harnessed to simulate NISQ devices in the era of quantum supremacy. However, as of now, quantum advantage with NISQ devices has not been clearly demonstrated in practical applications. This provide impetus for us to intensify our effort in fault tolerant quantum computation (FTQC). Various physical systems and schemes for FTQC have been proposed, with GKP encoding in bosonic systems being one such example. The preparation of GKP states serves as an important building block for realizing FTQC with the GKP code. We introduce tensor networks for simulating and optimizing the GKP state preparation circuit. It is known that the generation of approximated GKP states is achievable through photon counting and post-selection applied to properly prepared multimode Gaussian states. However, it requires optimization of numerous circuit parameters, which is typically computationally challenging on classical computers due to the involvement of the photon counting measurement process. We attempt to utilize tensor networks for more efficient parameter optimization. Additionally, we explore further efficient approach in terms of tensor network structure. We propose a multi-cutoff dimension approach combined with a tree tensor network structure.
Design of Quantum Machine Learning Course for a Computer Science Program

In this work, we present the design and plan of Quantum machine learning (QML) course in a computer science (CS) University program at senior undergraduate level / first year graduate level. Based on our survey, there is a lack of detailed design and assessment plan for the delivery of QML course. In this paper we have presented the QML course design with week by week details of QML concepts and hands on activities that are covered in the course. We also present how this QML course can be assessed from CS program learning outcomes perspective.
Sum-of-Squares Bounds for Quantum Optimal Control

The precise control of devices on the quantum scale is crucial for all quantum computing applications. The limitations of quantum control are therefore of great interest to those developing quantum hardware or researching quantum information protocols. We show how moment-sum-of-squares techniques can be combined with quantum dynamics and filtering theory to construct a hierarchy of convex optimization problems that furnish hard, computable bounds on the best achievable performance in quantum control tasks. These bounds can serve as witnesses of fundamental limitations, certificates of optimality, or performance targets and thus complement existing controller design strategies. In particular, our work allows us to compute bounds for common quantum control tasks, such as maximizing the fidelity of the final quantum state relative to a target state given a finite time budget or finding the minimum time required to reach the target state with near-unit fidelity. Our work also naturally applies to feedback-controlled quantum systems subjected to continuous observations, e.g., homodyne detection or photon counting, which are of emerging interest for the next generation of quantum devices. We provide an open-source package, MarkovBounds.jl, that is built upon the optimization ecosystem in the Julia programming language and exposes a high-level interface to the proposed bounding framework.