Reviewerly/exHarmony huggingface dataset:
Quantum Graph Drawing

In this paper, we initiate the study of quantum algorithms in the Graph Drawing research area. We focus on two foundational drawing standards: 2-level drawings and book layouts. Concerning $2$-level drawings, we consider the problems of obtaining drawings with the minimum number of crossings, $k$-planar drawings, quasi-planar drawings, and the problem of removing the minimum number of edges to obtain a $2$-level planar graph. Concerning book layouts, we consider the problems of obtaining $1$-page book layouts with the minimum number of crossings, book embeddings with the minimum number of pages, and the problem of removing the minimum number of edges to obtain an outerplanar graph. We explore both the quantum circuit and the quantum annealing models of computation. In the quantum circuit model, we provide an algorithmic framework based on Grover's quantum search, which allows us to obtain, at least, a quadratic speedup on the best classical exact algorithms for all the considered problems. In the quantum annealing model, we perform experiments on the quantum processing unit provided by D-Wave, focusing on the classical $2$-level crossing minimization problem, demonstrating that quantum annealing is competitive with respect to classical algorithms.
Harmonizing the object recognition strategies of deep neural networks with humans.

The many successes of deep neural networks (DNNs) over the past decade have largely been driven by computational scale rather than insights from biological intelligence. Here, we explore if these trends have also carried concomitant improvements in explaining the visual strategies humans rely on for object recognition. We do this by comparing two related but distinct properties of visual strategies in humans and DNNs: where they believe important visual features are in images and how they use those features to categorize objects. Across 84 different DNNs trained on ImageNet and three independent datasets measuring the where and the how of human visual strategies for object recognition on those images, we find a systematic trade-off between DNN categorization accuracy and alignment with human visual strategies for object recognition. State-of-the-art DNNs are progressively becoming less aligned with humans as their accuracy improves. We rectify this growing issue with our neural harmonizer: a general-purpose training routine that both aligns DNN and human visual strategies and improves categorization accuracy. Our work represents the first demonstration that the scaling laws [1-3] that are guiding the design of DNNs today have also produced worse models of human vision. We release our code and data at https://serre-lab.github.io/Harmonization to help the field build more human-like DNNs.
High coherence among training exemplars promotes broad generalization of face families

How to best tailor learning experiences to promote conceptual knowledge that generalizes to new situations is a fundamental question across many domains. There have been conflicting findings on whether exposing a learner to highly variable exemplars during training improves generalization. In the present study, we used a face family category structure and manipulated the variability within the training set in terms of both the typicality (high vs. low training set coherence) and the number of unique examples (small vs. large training set size). Unlike some of prior work that trained participants to a performance criterion, the length of training was controlled across conditions. We found a clear benefit of high coherence training both in terms of category learning and broad generalization. Further, the category structure was designed such that participants could learn by picking up on shared features across category members and/or by memorizing individual category members and their unique features. The pattern of results from both categorization and an old/new recognition test indicated that high coherence training promoted the abstraction of shared features, whereas low coherence training resulted in difficulty both extracting commonalities among exemplars and memorizing unique exemplars. In contrast to the robust effects of training set coherence, there were minimal effects of training set size on generalization. Taken together, we show that training on typical examples can promote broad category knowledge by helping learners extract the central tendency of the category.
FedRAD: Heterogeneous Federated Learning via Relational Adaptive Distillation

As the development of the Internet of Things (IoT) continues, Federated Learning (FL) is gaining popularity as a distributed machine learning framework that does not compromise the data privacy of each participant. However, the data held by enterprises and factories in the IoT often have different distribution properties (Non-IID), leading to poor results in their federated learning. This problem causes clients to forget about global knowledge during their local training phase and then tends to slow convergence and degrades accuracy. In this work, we propose a method named FedRAD, which is based on relational knowledge distillation that further enhances the mining of high-quality global knowledge by local models from a higher-dimensional perspective during their local training phase to better retain global knowledge and avoid forgetting. At the same time, we devise an entropy-wise adaptive weights module (EWAW) to better regulate the proportion of loss in single-sample knowledge distillation versus relational knowledge distillation so that students can weigh losses based on predicted entropy and learn global knowledge more effectively. A series of experiments on CIFAR10 and CIFAR100 show that FedRAD has better performance in terms of convergence speed and classification accuracy compared to other advanced FL methods.
Sentiment overflow in the testing stack: Analyzing software testing posts on Stack Overflow

Software testing is an integral part of modern software engineering practice. Past research has not only underlined its significance, but also revealed its multi-faceted nature. The practice of software testing and its adoption is influenced by many factors that go beyond tools or technology. This paper sets out to investigate the context of software testing from the practitioners’ point of view by mining and analyzing sentimental posts on the widely used question and answer website Stack Overflow. By qualitatively analyzing sentimental expressions of practitioners, which we extract from the Stack Overflow dataset using sentiment analysis tools, we discern factors that help us to better understand the lived experience of software engineers with regards to software testing. Grounded in the data that we have analyzed, we argue that sentiments like insecurity, despair and aspiration, have an impact on practitioners’ attitude towards testing. We suggest that they are connected to concrete factors like the level of complexity of projects in which software testing is practiced. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
Fine-Tashkeel: Fine-Tuning Byte-Level Models for Accurate Arabic Text Diacritization

Most of previous work on learning diacritization of the Arabic language relied on training models from scratch. In this paper, we investigate how to leverage pre-trained language models to learn diacritization. We fine-tune token-free pre-trained multilingual models (ByT5) to learn to predict and insert missing diacritics in Arabic text, a complex task that requires understanding the sentence semantics and the morphological structure of the tokens. We achieve state-of-the-art accuracy on the dia-critization task with minimal amount of training and no feature engineering, reducing WER (word error rate) by 40%. We release our fine-tuned models for the greater benefit of the researchers in the community.
Secure Internet of Things in Cloud Computing via Puncturable Attribute-Based Encryption with User Revocation

With significant achievements of Internet of Things (IoT) in cloud services, IoT devices are becoming primary targets of cyber attackers. To protect security of previous messages generated by IoT devices in cloud computing, puncturable attribute-based encryption (PABE) was introduced which achieves fine-grained access control and supports self-update to private keys to disable the decryption capability to existing cipheretxts. Users in PABE are identified by their attributes and authenticated via the attribute authority (AA) which issues them attribute-keys. But PABE does not consider any attribute revocation which should be managed by the AA. To address such a concern, this paper presents a primitive called puncturable attribute-based encryption with user revocation (PAER) which is the first light-weight PABE scheme with the user revocation achieved by the AA broadcasting the information for the key update. In addition to the light calculation of users in the decrypting process, another prominent advantage in PAER is that the AA does not need to communicate with users to revoke their attributes but broadcasts regular updates in the network. We present a concrete construction of PAER, and then implement the given scheme to evaluate its practicability in the real world.
Kernel Adversarial Learning for Real-world Image Super-resolution

Abstract Current deep image super-resolution (SR) approaches aim to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and additive noises. However, these techniques only assume crude approximations of the real-world image degradation process, which should involve complex kernels and noise patterns that are difficult to model using simple assumptions. In this paper, we propose a more realistic process to synthesise low-resolution images for real-world image SR by introducing a new Kernel Adversarial Learning Super-resolution (KASR) framework. In the proposed framework, degradation kernels and noises are adaptively modelled rather than explicitly specified. Moreover, we also propose a high-frequency selective objective and an iterative supervision process to further boost the model SR reconstruction accuracy. Extensive experiments validate the effectiveness of the proposed framework on real-world datasets.
External Search, National Knowledge Environments, and Innovation Performance

Prior research has documented the importance of the breadth and depth of external knowledge search for firms’ innovation performance. Still, most studies have focused on countries with abundant knowledge. We replicate and extend Laursen and Salter’s 2006 UK study using multiple EU multi-country datasets, which include countries with varying knowledge environments. Consistent with L&S, we find positive innovation performance effects of search breadth and depth, with diminishing returns. Adding to extant research, we find that the effect of search breadth is weaker in countries where knowledge is less abundant. We fail to find such systematic differences for search depth. Overall, by theoretically and empirically taking the firm’s national knowledge environment into account, we expand the environmental munificence perspective to the literature on open innovation.
Partitioning Quantum Chemistry Simulations with Clifford Circuits

Current quantum computing hardware is restricted by the availability of only few, noisy qubits which limits the investigation of larger, more complex molecules in quantum chemistry calculations on quantum computers in the near-term. In this work, we investigate the limits of their classical and near-classical treatment while staying within the framework of quantum circuits and the variational quantum eigensolver. To this end, we consider naive and physically motivated, classically efficient product ansatz for the parametrized wavefunction adapting the separable pair ansatz form. We combine it with post-treatment to account for interactions between subsystems originating from this ansatz. The classical treatment is given by another quantum circuit that has support between the enforced subsystems and is folded into the Hamiltonian. To avoid an exponential increase in the number of Hamiltonian terms, the entangling operations are constructed from purely Clifford or near-Clifford circuits. While Clifford circuits can be simulated efficiently classically, they are not universal. In order to account for missing expressibility, near-Clifford circuits with only few, selected non-Clifford gates are employed. The exact circuit structure to achieve this objective is molecule-dependent and is constructed using simulated annealing and genetic algorithms. We demonstrate our approach on a set of molecules of interest and investigate the extent of our methodology's reach. Empirical validation of our approach using numerical simulations shows a reduction of the qubit count of up to a 50\% at a similar accuracy as compared to the separable-pair ansatz.
A Secure and Effective Solution for Electronic Health Records with Hyperledger Fabric Blockchain

Blockchain is a decentralized database where you are allowed to add blocks and ready chains anytime. Blockchain technology has a great potential varying from the financial sector, including the IoT, e-commerce, accounting and auditing, electronic voting, asset and identity management, supply chain management, taxation, telecommunications, health care, and public services. Security and efficiency of these types of applications remain an issue. Blockchain can address all of these concerns in an efficient way, where it can provide benefits including easy deployment and maintenance, seamless authentication, privacy, and security. Blockchain serves as the basis for many applications in smart cities, whereas in this paper, we will put the focus, particularly on the sub-area of smart cities, namely the healthcare sector. We will make use of the experiences we had in smart cities, in order to suggest an efficient solution for electronic health records (EHRs). In this paper, we suggest a framework for EHRs that makes use of blockchain technology to store medical records more effectively, securely, and reliably while also facilitating quick access to them. In this regard, we are considering using Hyperledger, a permissioned blockchain platform.
Envisioning the future of virtual production in filmmaking: A remote co-design study

Abstract Virtual Production is a new process in the audiovisual industry, taking advantage of emerging technologies and attracting a significant degree of interest in academic and industrial research. This article documents a research process focusing on co-design of innovative solutions for Virtual Production relying on immersive technologies. Two remote collaborative workshops were organized involving audiovisual professionals covering different roles in different phases of the making process of audiovisual production. A range of innovative design concepts was generated as part of the research following group-based iterative discussion and evaluation. The study has contributed a set of innovative design solutions in relation to applications of immersive technologies in the audiovisual industry. The authors argue that the methods adopted have the potential to serve as a blueprint for design and implementation of future remote collaborative co-design processes in relation to audiovisual studies and, more generally, across disciplinary boundaries.
Evaluating Code Comment Generation With Summarized API Docs

Code comment generation is the task of generating a high-level natural language description for a given code snippet. API2Com is a comment generation model designed to leverage the Application Programming Interface Documentations (API Docs) as an external knowledge resource. Shahbazi et al. [1] showed that API Docs might help increase the model's performance. However, the model's performance in generating pertinent comments deteriorates due to the lengthy documentation used in the input as the number of APIs used in a method increases. In this paper, we propose to evaluate how summarizing the API Docs using an extractive text summarization technique, TextRank, will impact the overall performance of the API2Com. The results of our experiments using the same Java dataset confirm the inverse correlation between the number of APIs and the model's performance. As the number of APIs increases, the performance metrics tend to deteriorate for both configurations of the model, with or without API Docs summarization using TextRank. Experiments also show the impact of the number of APIs on TextRank algorithm capacity to improve the model per-formance. For example, with 8 APIs, TextRank summarization improved the model BLEU score by 18% on average, but the performance tends to decrease as the number of APIs increases. This demonstrates an open area of research to determine the winning combination in terms of the model configuration and the length of documentation used.
Iaso: Enhancing Syntax Error Correction using Deep Learning Techniques

Abstract Syntax errors can significantly impede students' progress and comprehension of programming concepts. AI-generated programs are also prone to syntax bugs, highlighting the importance of effectively addressing and resolving such errors. This paper presents Iaso, an automated program repair tool designed specifically to rectify syntax errors in student and AI-generated C programs. Iaso consists of three components: a syntax-based C program generator, an error localization model, and an ensemble code corrector. The program generator is trained on a seed dataset to generate complete and compilable C programs, which are then used to train the corrector. The corrector, trained on a combination of the seed dataset and a synthetic dataset, adopts an ensemble approach to gather fixes from multiple correctors trained on different datasets. During the repair process, Iaso's error localization model utilizes information from the deep learning model, raw code, and compiler messages to precisely identify the required repairs. To evaluate our method, we compare it against two automated program correctors: DrRepair and TransRepair. Iaso demonstrates notable performance, achieving a full repair rate of 78.8\% (+4.2\%) on the Deepfix test set and 91.0\% (+6.8\%) on the Tracer test set, generating over 1,300,000 erroneous C programs. Compared to the large language model PaLM, our strategy showcases a significant 6\% improvement in accuracy, considering the unlimited edit size used by PaLM. Furthermore, when compared to the state-of-the-art dialogue model ChatGPT, our method exhibits only a slight 1.4\% deviation while utilizing significantly fewer resources.
Joint polarization detection and degradation mechanisms for underwater image enhancement

Light absorption and scattering exist in the underwater environment, which can lead to blurring, reduced brightness, and color distortion in underwater images. Polarized images have the advantages of eliminating underwater scattering interference, enhancing contrast, and detecting material information of the object in underwater detection. In this paper, from the perspective of polarization imaging, different concentrations (0.15 g/ml, 0.30 g/ml, and 0.50 g/ml), different wave bands (red, green, and blue), different materials (copper, wood, high-density PVC, aluminum, cloth, foam, cloth sheet, low-density PVC, rubber, and porcelain tile), and different depths (10 cm, 20 cm, 30 cm, and 40 cm) are set up in a chamber for the experimental environment. By combining the degradation mechanism of underwater images and the analysis of polarization detection results, it is proved that the degree of polarization images have greater advantages than degree of linear polarization images, degree of circular polarization images, S1, S2, and S3 images, and visible images underwater. Finally, a fusion algorithm of underwater visible images and polarization images based on compressed sensing is proposed to enhance underwater degraded images. To improve the quality of fused images, we introduce orthogonal matching pursuit (OMP) in the high-frequency part to improve image sparsity and consistency detection in the low-frequency part to improve the image mutation phenomenon. The fusion results show that the peak SNR values of the fusion result maps using OMP in this paper are improved by 32.19% and 22.14% on average over those using backpropagation and subspace pursuit methods. With different materials and concentrations, the underwater image enhancement algorithm proposed in this paper improves information entropy, average gradient, and standard deviation by 7.76%, 18.12%, and 40.8%, respectively, on average over previous algorithms. The image NIQE value shows that the image quality obtained by this paper’s algorithm is improved by about 69.26% over the original S0 image.
Evaluating Transferability in Multilingual Text Analyses

The validity of multilingual text analysis should be studied through the lens of transferability, the equivalence in applicability of the method across different language contexts. This study has three purposes, first, it formally conceptualizes transferability in multilingual text analysis and introduces the concepts of linguistic and contextual transferability. Second, it proposes a novel approach to evaluate the transferability using natural and synthetic data pairs and the Local Interpretable Model-Agnostic Explanations technique. Finally, it demonstrates how to properly conduct error analysis using the proposed approach and presents a suggested workflow and a practical guide. Using a multilingual BERT (mBERT) model fine-tuned with annotated manifestos and media texts from five Indo-European language speaking countries of the Comparative Agendas Project as an example, we demonstrate the linguistic and societal transferability using the testing material of parliamentary data from UK, Basque, Hong Kong, and Taiwan. This study sheds light on the transferability of mBERT for four scenarios of high and low linguistic and contextual similarity and illustrates the common causes that lead to prediction error in multilingual topic classification using mBERT.
Global Path Planning of UGVs in Large-Scale Off-Road Environment Based on Improved A-star Algorithm and Quadratic Programming

Global path planning is an essential component of intelligent vehicle study. This paper designs a two-layer global path planning method based on an improved A* algorithm and quadratic programming for UGVs in a large-scale off-road environment. In the first layer, we generate a global path from the current node to the target node via an improved A* algorithm, with a grid map containing the information of non-accessible areas and uncertainty of off-road terrains as input. The second layer smooths the entire path based on quadratic programming. We adopt efficiency improvement methods in both layers, which ensure the real-time performance of the algorithm. The planner has been verified by simulation and experiments, and the results validate the practicability and real-time performance of the designed method.
Ensuring wallet application security by resolving reentrancy attacks in blockchain smart contracts

With the advancements in technology, blockchain systems have seen widespread use and rapid growth in the field of data security and verification. Blockchain is used in a variety of applications, including financial transactions, healthcare, insurance, Internet of Things, education, and many more, with the promise of increased skills and resilience. This smart distributed peer-to-peer design drew interest from a variety of businesses and communities outside the financial sphere. The major focus of the proposed work is on security challenges and limitations of Ethereum-based smart contracts. Ethereum smart contract is vulnerable to reentrancy security attack. The proposed work analyze reentrancy attacks and assess countermeasures to dissuade vulnerabilities on the network.