Reviewerly/exHarmony huggingface dataset:
Completeness for arbitrary finite dimensions of ZXW-calculus, a unifying calculus

The ZX-calculus is a universal graphical language for qubit quantum computation, meaning that every linear map between qubits can be expressed in the ZX-calculus. Furthermore, it is a complete graphical rewrite system: any equation involving linear maps that is derivable in the Hilbert space formalism for quantum theory can also be derived in the calculus by rewriting. It has widespread usage within quantum industry and academia for a variety of tasks such as quantum circuit optimisation, error-correction, and education.The ZW-calculus is an alternative universal graphical language that is also complete for qubit quantum computing. In fact, its completeness was used to prove that the ZX-calculus is universally complete. This calculus has advanced how quantum circuits are compiled into photonic hardware architectures in the industry.Recently, by combining these two calculi, a new calculus has emerged for qubit quantum computation, the ZXW-calculus. Using this calculus, graphical-differentiation, -integration, and -exponentiation were made possible, thus enabling the development of novel techniques in the domains of quantum machine learning and quantum chemistry.Here, we generalise the ZXW-calculus to arbitrary finite dimensions, that is, to qudits. Moreover, we prove that this graphical rewrite system is complete for any finite dimension. This is the first completeness result for any universal graphical language beyond qubits.
Fairify: Fairness Verification of Neural Networks

Fairness of machine learning (ML) software has become a major concern in the recent past. Although recent research on testing and improving fairness have demonstrated impact on real-world software, providing fairness guarantee in practice is still lacking. Certification of ML models is challenging because of the complex decision-making process of the models. In this paper, we proposed Fairify, an SMT-based approach to verify individual fairness property in neural network (NN) models. Individual fairness ensures that any two similar individuals get similar treatment irrespective of their protected attributes e.g., race, sex, age. Verifying this fairness property is hard because of the global checking and non-linear computation nodes in NN. We proposed sound approach to make individual fairness verification tractable for the developers. The key idea is that many neurons in the NN always remain inactive when a smaller part of the input domain is considered. So, Fairify leverages white-box access to the models in production and then apply formal analysis based pruning. Our approach adopts input partitioning and then prunes the NN for each partition to provide fairness certification or counterexample. We leveraged interval arithmetic and activation heuristic of the neurons to perform the pruning as necessary. We evaluated Fairify on 25 real-world neural networks collected from four different sources, and demonstrated the effectiveness, scalability and performance over baseline and closely related work. Fairify is also configurable based on the domain and size of the NN. Our novel formulation of the problem can answer targeted verification queries with relaxations and counterexamples, which have practical implications.
CHRONOS: Time-Aware Zero-Shot Identification of Libraries from Vulnerability Reports

Tools that alert developers about library vulnerabilities depend on accurate, up-to-date vulnerability databases which are maintained by security researchers. These databases record the libraries related to each vulnerability. However, the vulnerability reports may not explicitly list every library and human analysis is required to determine all the relevant libraries. Human analysis may be slow and expensive, which motivates the need for automated approaches. Researchers and practitioners have proposed to automatically identify libraries from vulnerability reports using extreme multi-label learning (XML). While state-of-the-art XML techniques showed promising performance, their experimental settings do not practically fit what happens in reality. Previous studies randomly split the vulnerability reports data for training and testing their models without considering the chronological order of the reports. This may unduly train the models on chronologically newer reports while testing the models on chronologically older ones. However, in practice, one often receives chronologically new reports, which may be related to previously unseen libraries. Under this practical setting, we observe that the performance of current XML techniques declines substantially, e.g., F1 decreased from 0.7 to 0.24 under experiments without and with consideration of chronological order of vulnerability reports. We propose a practical library identification approach, namely Chronos, based on zero-shot learning. The novelty of Chronos is three-fold. First, Chronos fits into the practical pipeline by considering the chronological order of vulnerability reports. Second, Chronos enriches the data of the vulnerability descriptions and labels using a carefully designed data enhancement step. Third, Chronos exploits the temporal ordering of the vulnerability reports using a cache to prioritize prediction of versions of libraries that recently had reports of vulnerabilities. In our experiments, Chronos achieves an average F1-score of 0.75, 3x better than the best XML-based approach. Data enhancement and the time-aware adjustment improve Chronos over the vanilla zero-shot learning model by 27% in average F1.
An Empirical Study of Deep Learning Models for Vulnerability Detection

Deep learning (DL) models of code have recently reported great progress for vulnerability detection. In some cases, DL-based models have outperformed static analysis tools. Although many great models have been proposed, we do not yet have a good understanding of these models. This limits the further advancement of model robustness, debugging, and deployment for the vulnerability detection. In this paper, we surveyed and reproduced 9 state-of-the-art (SOTA) deep learning models on 2 widely used vulnerability detection datasets: Devign and MSR. We investigated 6 research questions in three areas, namely model capabilities, training data, and model interpretation. We experimentally demonstrated the variability between different runs of a model and the low agreement among different models' outputs. We investigated models trained for specific types of vulnerabilities compared to a model that is trained on all the vulnerabilities at once. We explored the types of programs DL may consider “hard” to handle. We investigated the relations of training data sizes and training data composition with model performance. Finally, we studied model interpretations and analyzed important features that the models used to make predictions. We believe that our findings can help better understand model results, provide guidance on preparing training data, and improve the robustness of the models. All of our datasets, code, and results are available at https://doi.org/10.6084/m9.figshare.20791240.
CoCoSoDa: Effective Contrastive Learning for Code Search

Code search aims to retrieve semantically relevant code snippets for a given natural language query. Recently, many approaches employing contrastive learning have shown promising results on code representation learning and greatly improved the performance of code search. However, there is still a lot of room for improvement in using contrastive learning for code search. In this paper, we propose CoCoSoDa to effectively utilize contrastive learning for code search via two key factors in contrastive learning: data augmentation and negative samples. Specifically, soft data augmentation is to dynamically masking or replacing some tokens with their types for input sequences to generate positive samples. Momentum mechanism is used to generate large and consistent representations of negative samples in a mini-batch through maintaining a queue and a momentum encoder. In addition, multimodal contrastive learning is used to pull together representations of code-query pairs and push apart the unpaired code snippets and queries. We conduct extensive experiments to evaluate the effectiveness of our approach on a large-scale dataset with six programming languages. Experimental results show that: (1) CoCoSoDa outperforms 18 baselines and especially exceeds CodeBERT, GraphCodeBERT, and UniXcoder by 13.3%, 10.5%, and 5.9% on average MRR scores, respectively. (2) The ablation studies show the effectiveness of each component of our approach. (3) We adapt our techniques to several different pre-trained models such as RoBERTa, CodeBERT, and GraphCodeBERT and observe a significant boost in their performance in code search. (4) Our model performs robustly under different hyper-parameters. Furthermore, we perform qualitative and quantitative analyses to explore reasons behind the good performance of our model.
Turn the Rudder: A Beacon of Reentrancy Detection for Smart Contracts on Ethereum

Smart contracts are programs deployed on a blockchain and are immutable once deployed. Reentrancy, one of the most important vulnerabilities in smart contracts, has caused millions of dollars in financial loss. Many reentrancy detection approaches have been proposed. It is necessary to investigate the performance of these approaches to provide useful guidelines for their application. In this work, we conduct a large-scale empirical study on the capability of five well-known or recent reentrancy detection tools such as Mythril and Sailfish. We collect 230,548 verified smart contracts from Etherscan and use detection tools to analyze 139,424 contracts after deduplication, which results in 21,212 contracts with reentrancy issues. Then, we manually examine the defective functions located by the tools in the contracts. From the examination results, we obtain 34 true positive contracts with reentrancy and 21,178 false positive contracts without reentrancy. We also analyze the causes of the true and false positives. Finally, we evaluate the tools based on the two kinds of contracts. The results show that more than 99.8% of the reentrant contracts detected by the tools are false positives with eight types of causes, and the tools can only detect the reentrancy issues caused by call.value(), 58.8% of which can be revealed by the Ethereum's official IDE, Remix. Furthermore, we collect real-world reentrancy attacks reported in the past two years and find that the tools fail to find any issues in the corresponding contracts. Based on the findings, existing works on reentrancy detection appear to have very limited capability, and researchers should turn the rudder to discover and detect new reentrancy patterns except those related to call.value().
Virtual reality assisted engineering education: A multimedia learning perspective

Virtual Reality (VR) is a powerful technology that can enhance engineering education by providing immersive and interactive learning experiences. However, many VR studies in engineering education lack a clear theoretical or pedagogical framework to guide their design and evaluation. This scoping review analyzes 51 studies on VR-assisted engineering education and reveals the trends and gaps in the current research. We argue that engineering VR studies should be informed by theories of learning and instruction that address the cognitive and socio-cognitive aspects of learning. We also suggest that VR-assisted engineering education research should incorporate multimedia design and pedagogical principles to optimize the effectiveness of VR applications. Our perspective aims to inspire engineering educators, practitioners, and instructional designers to develop VR contents and formulate VR research imperatives grounded in sound educational theories and multimedia learning principles.
Analysis of Stone Deterioration on the Facades of Hatuniye Madrasah

The durability of natural stone in traditional buildings is important for the structures to survive today. External environmental factors have negative effects on natural stone materials. When the material is exposed to negative factors, deterioration occurs in the stone. It is important to determine this deterioration and its causes correctly and to offer solutions for the transfer of buildings to future generations. Failure to identify the factors that cause deterioration leads to the growth of damages and the formation of new damages. Determining the deterioration on the surfaces of Hatuniye Madrasah and its causes will be useful in terms of preventing problems and making the right interventions. As a result of the analyses, the types of deterioration that occurred on the facades of the building and the changes in the chemical properties of the stone according to the results of the XRF chemical analysis were determined.
SPAN: An open-source plugin for photovoltaic potential estimation of individual roof segments using point cloud data

Decentralized solar PhotoVoltaic (PV) is one of the most promising energy sources for cities and individuals pursuing energy self-sufficiency. Especially, the already available rooftop surfaces are a major contributor to push for rooftop mounted PV systems. However, accurate PV potential estimation of individual buildings is still a challenging task since many parameters must be considered such as meteorological factors, panel technology, geographical location, available roof surface area, surface azimuth and tilt angle. In this study, we created an efficient approach that can be used for roof surface's PV potential estimation based on point cloud data and capable of processing various scales from single building to city scale. In the proposed approach, each roof surface's features were utilized for PV potential estimation by employing the PVGIS database. PV potential estimation was carried out on daily, monthly, and annual periods to provide a better estimation. Also, we developed a flexible and easy to use open-source plugin based on the QGIS software for rooftop mounted PV potential estimation capable of estimating every roof surface's PV potential. The method was tested on 80 buildings selected from ROOFN3D dataset. The proposed approach achieved an overall accuracy of 84% and an F1 score of 0.92.
QNMF: A quantum neural network based multimodal fusion system for intelligent diagnosis

The Internet of Medical Things (IoMT) has emerged as a significant research area in the medical field, enabling the transmission of various types of data to the cloud for analysis and diagnosis. Fusing data from multiple modalities can enhance accuracy but requires substantial computing power. Theoretically, quantum computers can rapidly process large volumes of high-dimensional medical data. Despite accelerated developments in quantum computing, research on quantum machine learning (QML) for multimodal data processing remains limited. Considering these factors, this paper presents a quantum neural network-based multimodal fusion system for intelligent diagnosis (QNMF) that can process multimodal medical data transmitted by IoMT devices, fuse data from different modalities, and improve the performance of intelligent diagnosis. This system employs a quantum convolutional neural network (QCNN) to efficiently extract features from medical images. These QCNN-based features are then fused with other modality features (such as blood test results or breast cell slices), and used to train an effective variational quantum classifier (VQC) for intelligent diagnosis. The experimental results demonstrate that a QCNN can effectively extract image data features. Furthermore, QNMF achieved an accuracy of 97.07% and 97.61% on breast cancer diagnosis and Covid-19 diagnosis experiments, respectively. In addition, the QNMF exhibits strong quantum noise robustness.
Privacy-Preserving Federated Learning With Malicious Clients and Honest-but-Curious Servers

Federated learning (FL) enables multiple clients to jointly train a global learning model while keeping their training data locally, thereby protecting clients’ privacy. However, there still exist some security issues in FL, e.g., the honest-but-curious servers may mine privacy from clients’ model updates, and the malicious clients may launch poisoning attacks to disturb or break global model training. Moreover, most previous works focus on the security issues of FL in the presence of only honest-but-curious servers or only malicious clients. In this paper, we consider a stronger and more practical threat model in FL, where the honest-but-curious servers and malicious clients coexist, named as the non-fully trusted model. In the non-fully trusted FL, privacy protection schemes for honest-but-curious servers are executed to ensure that all model updates are indistinguishable, which makes malicious model updates difficult to detect. Toward this end, we present an Adaptive Privacy-Preserving FL (Ada-PPFL) scheme with Differential Privacy (DP) as the underlying technology, to simultaneously protect clients’ privacy and eliminate the adverse effects of malicious clients on model training. Specifically, we propose an adaptive DP strategy to achieve strong client-level privacy protection while minimizing the impact on the prediction accuracy of the global model. In addition, we introduce DPAD, an algorithm specifically designed to precisely detect malicious model updates, even in cases where the updates are protected by DP measures. Finally, the theoretical analysis and experimental results further illustrate that the proposed Ada-PPFL enables client-level privacy protection with 35% DP-noise savings, and maintains similar prediction accuracy to models without malicious attacks.
On the overhead of 'business models'

OLH, obviously, has a business model for its open-access publishing. We operate due to a membership model in which approximately 300 libraries pay an annual fee so that we can exist and publish all our work openly. It works pretty well and is able to sustain our activities – so long as the pandemic doesn’t truly scupper us. Please do join! But the overheads of running that business model are not trivial.
Topic modeling and sentiment analysis of service experience in patient complaint information of a hospital

ObjectiveTo explore the valuable thematic information and sentiment distribution in patients′ medical service complaint texts based on topic modeling and sentiment analysis, and investigate the main driving factors affecting patients′ service experience and satisfaction.MethodsTopic mining was carried out on the offline patient complaint text set of a tertiary hospital in South China from 2013 to 2017. The seed word set extracted from 1 000 sampled texts was used to guide semi-supervised Latent Dirichlet Allocation training of texts. Relevant subject categories were extracted and subject characteristics were graded emotionally.Resultsin the end, 30 subject categories were extracted from the 8 000 complaint texts, and the sentiment score of the subject characteristics was consistent with the sentiment tendency of the actual data set. However, the satisfaction was relatively low in  toilet ,  ward ,  hygiene and other subjects, and the main complaint subjects included  attitude ,  examination ,  ward among others.ConclusionsBased on the theme distribution, combined with the results of emotional analysis and the specific clinical environment, strengthening management in the medical service sector with a large negative emotional score can guide the hospital management practice and service improvement process, and help to improve the patients′ perception experience and emotional experience.Key words: Medical service experience; Satisfaction; Negative text; Topic modeling
Proveniência e Intemperismo da Formação São Carlos, Cretáceo Superior, e significado tectônico na borda leste da Bacia Bauru, SE Brasil

Geochemistry of major and trace elements has been used as an important tool for the study of provenance and tectonic and climatic evolution of sedimentary basins. The Sao Carlos Formation is an Upper Cretaceous unit that lies on the eastern border of the Bauru Basin. Despite the paleontological and paleodepositional studies performed in this unit in the last years, little is known about the correspondence between tectonic and climatic conditions acting during the first stages of sedimentation. The hypothesis of this paper is to evaluate Sao Carlos and Aracatuba formations and understand the evolution of the eastern border of the basin. Thus, were conducted geochemical studies using X-ray fluorescence on sandstones, siltstones, and shales from the Sao Carlos Formation. According to the chemical weathering index, which presented values ranging from 57.12 to 71.58%, the oxides of major elements indicate that moderate weathering processes affected the source area, possibly associated with the arid-semiarid climate. Alkaline rocks, granites, gneisses, and metasediments were the main lithotypes of the source area. Ternary diagrams show that the tectonic environment was equivalent to the passive continental margin, coinciding with the Serra do Mar and, secondarily, Alto Paranaiba Uplift regions. Based on major and trace elements, their ratios, and published data on the basin, was elaborated a paleogeographic model of the eastern border of the Bauru Basin, concluding that the source area of the sediments was constituted by intermediate and felsic rocks, sometimes recycled by sedimentary processes.
Compliance and Security

A typical DevOps pipeline finishes as soon as the package has been deployed to a secure environment. DevSecOps introduces extra steps to your pipeline to verify and support the compliance of your product in international markets. The topic of compliance takes more than just a license into account. International markets introduce their own set of legal requirements for a solution provider. European countries, for example, have GDPR (the General Data Protection Regulation). This requires the solution vendors and ISVs to apply a set of rules across their organization (changes such as recruiting a Data Protection Officer) as well as the solution (such as user “consent” for data collection and applying a data removal policy). This compliance rule not only applies to solutions being used from within Europe, but also to the solutions that provide services to Europeans, even from outside Europe.
Artificial Intelligence and 5G Will Force Journalism Schools to Accentuate the Basics in Instruction

This reflection considers the need for journalism schools to more intensely teach skills that cannot be replicated by AI or 5G. Critical thinking and writing are examples of Non-curation skills and they may need to be taught more because they cannot be mimicked by AI. Curation skills involve borrowing from other sources, which include human and digital platforms. It might be prudent to teach curation skills as a tool that is meant to strictly support non-curation journalism skills - which are commanded only by a human. The most secure journalism jobs will be those which cannot be replicated by AI.
ENABLING MLEARNING TECHNOLOGIES INTO INTEGRATING OFF-CAMPUS INTERNSHIP AND CAPSTONE COURSES FOR TECHNOLOGICAL UNIVERSITY IN TAIWAN

The purpose of this study is to develop a concept model of integrating off-campus internships and capstone coursesthrough mLearning technologies. There are five steps of this concept model. The first step is to develop the teachingobjectives and