Reviewerly/exHarmony huggingface dataset:
Metafore ed espressioni idiomatiche nel discorso economico e politico

The following paper tries to elaborate on aspects connected with Italian language of economics presented in newspapers published during pre-referendum period and after the results were communicated, i.e. between 4th June 2016 and 3rd June 2017.The article indicates at the beginning that the language of economics is a language for specific purposes.It is being noticed that metaphors are its distinctive feature, consequently it is them that become the central area of research.Throughout the analysis, metaphors studies conducted by George Lakoff and Mark Johnson will be the main point of reference.
Real-Time Waveform Matching with a Digitizer at 10 GS/s

Side-Channel Analysis (SCA) requires the detection of the specific time frame within which Cryptographic Operations (COs) take place in the side-channel signal. In laboratory conditions with full control over the Device under Test (DuT), dedicated trigger signals can be implemented to indicate the start and end of COs. For real-world scenarios, waveform-matching techniques have been established which compare the side-channel signal with a template of the CO's pattern in real time to detect the CO in the side channel. State-of-the-art approaches are implemented on Field-Programmable Gate Arrays (FPGAs). However, current waveform-matching designs process the samples from Analog-to-Digital Converters (ADCs) sequentially and can only work with low sampling rates due to the limited clock speed of FPGAs. This makes it increasingly difficult to apply existing techniques on modern DuTs that operate with clock speeds in the GHz range. In this paper, we present a parallel waveform-matching architecture that is capable of performing waveform matching at the speed of fast ADCs. We implement the proposed architecture in a high-end FPGA-based digitizer and deploy it to detect AES COs from the side channel of a single-board computer operating at 1 GHz. Our implementation allows for waveform matching at 10 GS/s with high accuracy, thus offering a speedup of 50× compared to the fastest state-of-the-art implementation known to us.
A Machine Learning Approach for the Simultaneous Detection of Preknowledge in Examinees and Items When Both Are Unknown

Abstract Pan and Wollack (PW) proposed a machine learning method to detect compromised items. We extend the work of PW to an approach detecting compromised items and examinees with item preknowledge simultaneously and draw on ideas in ensemble learning to relax several limitations in the work of PW. The suggested approach also provides a confidence score, which is based on an autoencoder to represent our confidence that the detection result truly corresponds to item preknowledge. Simulation studies indicate that the proposed approach performs well in the detection of item preknowledge, and the confidence score can provide helpful information for users.
Effective Twitter Sentiment Analysis Using Deep Belief Network with Enhanced Dragonfly Optimization Algorithm

In this research article, an effective model is implemented for predicting the outcome of the Indian general election 2019 and farmer's protest by utilizing the sentiment analysis of Twitter data.In the initial segment, the raw Twitter data are acquired from the Indian Political tweets 2019 and farmers protest tweets databases.Further, the denoising operations such as removal of unnecessary space, tabs, new-line, hashtag symbols, non-English characters, punctuations, numbers and special characters are used to enhance the quality of the acquired data.Then, the keyword trend analysis and topic modeling utilizing latent dirichlet allocations (LDAs) are performed for better data representation.Next, the extraction of the feature is carried out utilizing skip-gram and term frequency-document level frequency (TF-IDF) techniques, and further, the feature optimization is accomplished using enhanced dragonfly optimization algorithm (EDOA) for selecting the optimal feature vectors.In the EDOA, a Brownian motion is added for improving the probabilistic behaviors.Lastly, the selected features are given to the deep belief network (DBN) model to classify the people's sentiments into negative, neutral, and positive classes.The experimental evaluations demonstrated that the EDOA-DBN model has obtained 99.22% and 98.83% of accuracy on both Indian political tweets 2019 and farmers protest tweets databases, which are maximum related to other existing models.
Surface Artifact Scatters, Data Collection, and Significance

ABSTRACT The three authors research surface archaeological records dominated by low-density scatters and isolated artifacts, archaeological phenomena frequently encountered during cultural resource management (CRM) projects in areas of the United States and Australia. We each began researching surface artifact scatters for different reasons but converged on approaches that emphasize the formation of these forms of archaeological deposits. Through a variety of projects, we asked a common set of questions about the processes that both buried and exposed these materials, the methods needed to obtain a chronology in different regions, and the ways we might interpret artifacts found together in different densities. Answering these questions led to the collection and analyses of datasets in innovative ways and the questioning of a number of archaeological categories often thought of as fundamental for archaeological research. Here, we review examples of our respective research and consider the implications for CRM projects dealing with surface lithics.
ANALISIS KETERAMPILAN BERBICARA SISWA PADA TEMA 6 ENERGI DAN PERUBAHANNYA SUBTEMA 1 SUMBER ENERGI KELAS 3 SD NEGERI 2 BANGOAN

The speaking skills intended in this study are the skills to express the articulation sounds or language sounds to express, express, or convey one's ideas, thoughts and feelings to be understood by others. The purpose of this study is to describe the data on how the analysis of students' speaking skills on the theme of energy 6 and the changes in sub-theme 1 energy source class III SD Negeri 2 Bangoan. The research approach used is a qualitative approach and the type of research used is descriptive. Related to data collection methods using interviews, tests and documentation. Interview and test methods are used to obtain information about students' speaking skills. Data analysis uses data reduction, data presentation, and drawing conclusions. The results of this study revealed that the sample taken by the researcher was one male student and one female student said male students were more skilled at speaking than female students
Examining public views on decentralised health data sharing

In recent years, researchers have begun to explore the use of Distributed Ledger Technologies (DLT), also known as blockchain, in health data sharing contexts. However, there is a significant lack of research that examines public attitudes towards the use of this technology. In this paper, we begin to address this issue and present results from a series of focus groups which explored public views and concerns about engaging with new models of personal health data sharing in the UK. We found that participants were broadly in favour of a shift towards new decentralised models of data sharing. Retaining 'proof' of health information stored about patients and the capacity to provide permanent audit trails, enabled by immutable and transparent properties of DLT, were regarded as particularly valuable for our participants and prospective data custodians. Participants also identified other potential benefits such as supporting people to become more health data literate and enabling patients to make informed decisions about how their data was shared and with whom. However, participants also voiced concerns about the potential to further exacerbate existing health and digital inequalities. Participants were also apprehensive about the removal of intermediaries in the design of personal health informatics systems.
Genuine Tripartite Non-Gaussian Entanglement

Triple-photon states generated by three-mode spontaneous parametric down-conversion are the paradigm of unconditional non-Gaussian states, essential assets for quantum advantage. How to fully characterize their non-Gaussian entanglement remains however elusive. We propose here a set of sufficient and necessary conditions for separability of the broad family of spontaneously generated three-mode non-Gaussian states. We further derive state-of-the-art conditions for genuine tripartite non-Gaussian entanglement, the strongest class of entanglement. We apply our criteria to triple-photon states revealing that they are fully inseparable and genuinely entangled in moments of order $3n$. Our results establish a systematic framework for characterizing the entanglement of triple-photon states and thus fostering their application in quantum information protocols.
Towards an Open Science Definition as a Political and Legal Framework: Sharing and Dissemination af Research Outputs

Our proposal of an Open Science definition as a political and legal framework where research outputs are shared and disseminated in order to be rendered visible, accessible, reusable is developed, standing over the concepts enhanced by the Budapest Open Science Initiative (BOAI), and by the Free/Open Source Software (FOSS) and Open data movements. We elaborate this proposal through a detailed analysis of some selected EC policies and laws as well as of the function of research evaluation practices. The legal aspects considered in our examination include, in particular, the study of the role of licences in the context of the dissemination of research outputs.
VulANalyzeR: Explainable Binary Vulnerability Detection with Multi-task Learning and Attentional Graph Convolution

Software vulnerabilities have been posing tremendous reliability threats to the general public as well as critical infrastructures, and there have been many studies aiming to detect and mitigate software defects at the binary level. Most of the standard practices leverage both static and dynamic analysis, which have several drawbacks like heavy manual workload and high complexity. Existing deep learning-based solutions not only suffer to capture the complex relationships among different variables from raw binary code but also lack the explainability required for humans to verify, evaluate, and patch the detected bugs. We propose VulANalyzeR, a deep learning-based model, for automated binary vulnerability detection, Common Weakness Enumeration-type classification, and root cause analysis to enhance safety and security. VulANalyzeR features sequential and topological learning through recurrent units and graph convolution to simulate how a program is executed. The attention mechanism is integrated throughout the model, which shows how different instructions and the corresponding states contribute to the final classification. It also classifies the specific vulnerability type through multi-task learning as this not only provides further explanation but also allows faster patching for zero-day vulnerabilities. We show that VulANalyzeR achieves better performance for vulnerability detection over the state-of-the-art baselines. Additionally, a Common Vulnerability Exposure dataset is used to evaluate real complex vulnerabilities. We conduct case studies to show that VulANalyzeR is able to accurately identify the instructions and basic blocks that cause the vulnerability even without given any prior knowledge related to the locations during the training phase.
Collaborative Motion Planning Based on the Improved Ant Colony Algorithm for Multiple Autonomous Vehicles

To improve the online collaboration and planning capabilities between autonomous vehicles, this paper proposes a novel collaborative motion planning method. In this method, the ant colony algorithm was introduced and improved to achieve collaborative motion planning for multiple autonomous vehicles. First, independent subpopulations of the same size were generated according to the number of autonomous vehicles. Then, a multi-objective optimization function was established to optimize spatial collaboration and trajectory costs, and to update the pheromone in the ant colony algorithm. Meanwhile, the evaporation coefficient in the algorithm was adaptively adjusted to enhance the global search ability and improve the convergence speed of the algorithm. Finally, a feasible path was planned for each autonomous vehicle based on the path of each subpopulation. Simulation results show that the proposed method is effective and it can achieve stronger adaptability than the artificial potential field motion planning algorithm.
Efficient Location-Based Skyline Queries With Secure R-Tree Over Encrypted Data

Supporting efficient and secure location-based skyline queries on encrypted data, such as private data outsourced to cloud-based systems, remains an ongoing challenge for efficiency due to significant computational costs in the ciphertext domain. To accelerate privacy-preserving skyline queries, the secure index intuitively contributes to an increase in efficiency. However, designing such a secure index is a challenge while protecting the unlinkability of queries. Meanwhile, there exist little work that can commendably assure efficiency and security. In this paper, we demonstrate SecSky, an efficient solution for supporting secure location-based skyline queries through the secure index. To support SecSky, we devise a novel unified structure, named secure R-tree (SR-tree) index, without privacy leakage (especially indirect privacy). Subsequently, we propose a novel secure location-based dominance protocol, which is utilized to calculate the dominance relationship on the SR-tree. Using this protocol as the building block, our secure location-based skyline query protocol integrates SR-tree, permutation and perturbation techniques to facilitate query processing so as to dramatically reduce the computational overhead. Meanwhile, our proposed solution avoids compromising the privacy of datasets, queries, dominance relationship and skyline results. Finally, we analyze the complexity and security of SecSky. Findings from the experimental evaluation show that our proposed scheme outperforms several other protocols by at least 3 orders of magnitude in terms of query efficiency.
Development of a Common Data Model for a Multisite and Multiyear Study of Virtual Visit Implementation

Background/Objective: In multisite studies, a common data model (CDM) standardizes dataset organization, variable definitions, and variable code structures and can support distributed data processing. We describe the development of a CDM for a study of virtual visit implementation in 3 Kaiser Permanente (KP) regions. Methods: We conducted several scoping reviews to inform our study’s CDM design: (1) virtual visit mode, implementation timing, and scope (targeted clinical conditions and departments); and (2) extant sources of electronic health record data to specify study measures. Our study covered the period from 2017 through June 2021. Integrity of the CDM was assessed by a chart review of random samples of virtual and in-person visits, overall and by specific conditions of interest (neck or back pain, urinary tract infection, major depression). Results: The scoping reviews identified a need to address differences in virtual visit programs across the 3 KP regionsto harmonize measurement specifications for our research analyses. The final CDM contained patient-level, provider-level, and system-level measures on 7,476,604 person-years for KP members aged 19 years and above. Utilization included 2,966,112 virtual visits (synchronous chats, telephone visits, video visits) and 10,004,195 in-person visits. Chart review indicated the CDM correctly identified visit mode on&gt;96% (n=444) of visits, and presenting diagnosis on &gt;91% (n=482) of visits. Conclusions: Upfront design and implementation of CDMs may be resource intensive. Once implemented, CDMs, like the one we developed for our study, provide downstream programming and analytic efficiencies by harmonizing, in a consistent framework, otherwise idiosyncratic temporal and study site differences in source data.
Prognostics and Health Management of Rotating Machinery via Quantum Machine Learning

Prognostics and Health Management (PHM) concerns predicting machines’ behavior to support maintenance decisions through failure modes diagnosis and prognosis. Diagnosis is broadly applied in the context of rotating machines’ state classification using several traditional Machine Learning (ML) and Deep Learning (DL) methods. Recently, Quantum Computing (QC), a new and expanding research field, has contributed to different purposes and contexts, such as optimization, artificial intelligence, simulation, cybersecurity, pharmaceutics, and the energy sector. Despite the current limitations in terms of hardware, QC has been studied as an alternative for improving models’ speed and computational efficiency. Specifically, this paper proposes a Quantum Machine Learning (QML) approach to diagnose rolling bearings, which are essential components in rotating machinery, based on vibration signals. We apply hybrid models involving the encoding and construction of parameterized quantum circuits (PQC) connected to a classical neural network, the Multi-Layer Perceptron (MLP). We consider combinations of the Variational Quantum Eigensolver (VQE) framework with rotation gates and different entanglement (two-qubits) gates (CNOT, CZ and iSWAP). For each PQC configuration, we assess the impact of the number of layers (1, 5 and 10). We use two databases of different complexity levels not previously explored with QML, namely CWRU and JNU, with 10 and 12 failure modes, respectivel. For CWRU, all QML models presented higher accuracy than the classical MLP. For JNU, all QML models were superior to classical MLP as well. These results suggest that, despite the current limitations of quantum environments, QML models are promising tools to be further investigated in PHM.
Study on the adsorption properties of hypercrosslinked polymer fibrous membrane on dye sewage

The wastewater contaminated by dyes contains many organic compounds, which have carcinogenic, mutagenic, and other effects, endangering human health. The traditional wastewater treatment methods have problems such as high cost, secondary pollution, difficulty in large-scale production, and complicated operation and management, which has restricted their wide use. In recent years, the research of adsorption technology on the treatment of dyeing wastewater is getting deeper. Membrane adsorption is an economical, convenient, and easy-to-operate technique, highly efficient for dye removal, and has been widely used for the treatment of wastewater.This research combined the protocol of electrospinning, crosslinking, and sulfonation modification to prepare hydrophilic polystyrene nanofibers, which improved the specific surface area of polymers, enhanced the stability and mechanical strength of nanofibers, and improved the hydrophilicity and adsorption capacity of nanofibers. Secondly, the morphologies, chemical structure, and wetting properties of the fibrous membranes were characterized; The adsorption effect of fiber membranes on methylene blue (MB) was also investigated; Finally, it was concluded that the fitted Langmuir model with a higher determination coefficient was more suitable to describe the adsorption behavior, which indicated that the adsorption of MB by fiber membranes was in the form of monolayer adsorption. The nanofibrous membranes prepared by this method can efficiently adsorb dye molecules, offer an effective scheme in the field of water treatment materials, and have great research value.
The Performance Analysis of Machine Learning Algorithms for Credit Card Fraud Detection

This paper studies the performance analysis of machine learning (ML) and data mining techniques for anomaly detection in credit cards. As the usage of digital money or plastic money grows in developing nations, so does the risk of fraud. To counter these scams, we need a sophisticated fraud detection method that not only identifies the fraud but also detects it before it occurs efficiently. We have introduced the notion of credit card fraud and its many variants in this research. Numerous ML fraud detection approaches are studied in this paper including Principal Component Analysis (PCA) data mining and the Fuzzy C-Means methodologies, as well as the Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB) algorithms. The existing and proposed models for credit card fraud detection have been thoroughly reviewed, and these strategies have been compared using quantitative metrics including accuracy rate and characteristics curves. This paper discusses the shortcomings of existing models and proposes an efficient technique to analyze the fraud detection.
Verification-Preserving Inlining in Automatic Separation Logic Verifiers

Bounded verification has proved useful to detect bugs and to increase confidence in the correctness of a program. In contrast to unbounded verification, reasoning about calls via (bounded) inlining and about loops via (bounded) unrolling does not require method specifications and loop invariants and, therefore, reduces the annotation overhead to the bare minimum, namely specifications of the properties to be verified. For verifiers based on traditional program logics, verification is preserved by inlining (and unrolling): successful unbounded verification of a program w.r.t. some annotation implies successful verification of the inlined program. That is, any error detected in the inlined program reveals a true error in the original program. However, this essential property might not hold for automatic separation logic verifiers such as Caper, GRASShopper, RefinedC, Steel, VeriFast, and verifiers based on Viper. In this setting, inlining generally changes the resources owned by method executions, which may affect automatic proof search algorithms and introduce spurious errors. In this paper, we present the first technique for verification-preserving inlining in automatic separation logic verifiers. We identify a semantic condition on programs and prove in Isabelle/HOL that it ensures verification-preserving inlining for state-of-the-art automatic separation logic verifiers. We also prove a dual result: successful verification of the inlined program ensures that there are method and loop annotations that enable the verification of the original program for bounded executions. To check our semantic condition automatically, we present two approximations that can be checked syntactically and with a program verifier, respectively. We implement these checks in Viper and demonstrate that they are effective for non-trivial examples from different verifiers.
Generative Artificial Intelligence (GAI) Software – Assessment on Biased Behavior

With the introduction of Chat GPT, which is a public tool developed by Open GAI and its underlying technology Generative Pretrained Transformer (GPT)., generative AI is forecasted to grow dramatically in the next years. Generative AI however also raises a number of ethical considerations, particularly when it comes to issues such as privacy, intellectual property rights, and potential biases in the generated content. One major concern is the possibility of using generative AI to create deepfake videos or images that could be used for malicious purposes, such as political manipulation or the spread of misinformation. A lot of literature sources also conclude that Chat GPT is not suitable, as misinformation is generated without any true and accurate content, which why this study compares five existing Generated artificial intelligence software to identify whether those issues can be generally concluded or if they depend upon the specific applied technology and rule-set.