Reviewerly/exHarmony huggingface dataset:
Beyond Static models and test sets: Benchmarking the potential of pre-trained models across tasks and languages

Although recent Massively Multilingual Language Models (MMLMs) like mBERT and XLMR support around 100 languages, most existing multilingual NLP benchmarks provide evaluation data in only a handful of these languages with little linguistic diversity.We argue that this makes the existing practices in multilingual evaluation unreliable and does not provide a full picture of the performance of MMLMs across the linguistic landscape.We propose that the recent work done in Performance Prediction for NLP tasks can serve as a potential solution in fixing benchmarking in Multilingual NLP by utilizing features related to data and language typology to estimate the performance of an MMLM on different languages.We compare performance prediction with translating test data with a case study on four different multilingual datasets, and observe that these methods can provide reliable estimates of the performance that are often onpar with the translation based approaches, without the need for any additional translation as well as evaluation costs.
Occlusion-Aware Motion Planning With Visibility Maximization via Active Lateral Position Adjustment

As the operational domain of autonomous vehicles expands, encountering occlusions during navigation becomes unavoidable. Most of the existing research on occlusion-aware motion planning focuses only on the longitudinal motion of the ego vehicle and neglects its lateral motion, resulting in output motion that can be overly conservative. This paper proposes a motion planner capable of actively adjusting the ego vehicle&#x2019;s lateral position to minimize occlusions. The proposed planner is applicable in various scenarios and can function under perception uncertainty. This work also extends our previously proposed 3D visibility estimation approach for addressing occlusions caused by objects which are not present in HD maps. The proposed planner first generates candidate trajectories. The current and future visibility of each trajectory is then estimated using live LiDAR data and HD maps. These estimated visibilities are converted into visibility costs, which are then used to determine the optimal output trajectory in conjunction with other planning costs. The proposed planner is tested in three scenarios using the CARLA simulator: an occluded T-junction crossing, turning at a low-visibility corner and preparing to pass a parked vehicle, using live localization and object detection results. The experimental results reveal that the proposed planner allows the ego vehicle to minimize occlusions by diverging from the center of the lane and, consequently, to discover occluded vehicles earlier than a baseline planner in most situations. Moreover, occlusions caused by a parked vehicle not present in the HD maps were estimated correctly using our extended visibility estimation method.
A Hybrid Metaheuristic for Transmission Tower Design Optimization

Jellyfish search optimizer (JSO) and Sine–cosine algorithm (SCA) are two metaheuristic methods having different search operators. The paper proposes a hybrid metaheuristic that combines the features of both the algorithms and develops a hybrid algorithm that outperforms both of its parents. Specifically, it first uses opposition-based learning in population initialization and then introduces a modified position update operator of SCA in the original structure of JSO. The proposed metaheuristic has been tested on a set of benchmark functions and to find the minimum weight design of the transmission tower. The proposed algorithm outperforms JSO and SCA for benchmark functions as well as the transmission tower problem.
An Extensive Empirical Comparison of <i>k</i>-means Initialization Algorithms

The k-means clustering algorithm, whilst widely popular, is not without its drawbacks. In this paper, we focus on the sensitivity of k-means to its initial set of centroids. Since the cluster recovery performance of k-means can be improved by better initialisation, numerous algorithms have been proposed aiming at producing good initial centroids. However, it is still unclear which algorithm should be used in any particular clustering scenario. With this in mind, we compare 17 such algorithms on 6,000 synthetic and 28 real-world data sets. The synthetic data sets were produced under different configurations, allowing us to show which algorithm excels in each scenario. Hence, the results of our experiments can be particularly useful for those considering k-means for a non-trivial clustering scenario.
Multiclass Image Classification Using OAA-SVM

Automatic image classification is a fundamental application of computer vision and machine learning. It employs feature extraction and classification. In this paper visible features like color and textures are investigated to determine the visual aspects of images. The color feature vectors are calculated from Hue (H), Saturation (S) and Value (V) color space. The texture features are extracted by using local binary pattern (LBP). The extracted features are fed to the most versatile support vector machine (SVM) classifier. Here the SVM classifier is utilized to categorize Wang images of ten different classes using one against all (OAA) approach. Finally, the effectiveness of the proposed model is evaluated using performance metrics like specificity, recall, precision and F1-score with individual and hybrid features.
Neuro-Symbolic Verification of Deep Neural Networks

Formal verification has emerged as a powerful approach to ensure the safety and reliability of deep neural networks. However, current verification tools are limited to only a handful of properties that can be expressed as first-order constraints over the inputs and output of a network. While adversarial robustness and fairness fall under this category, many real-world properties (e.g., "an autonomous vehicle has to stop in front of a stop sign") remain outside the scope of existing verification technology. To mitigate this severe practical restriction, we introduce a novel framework for verifying neural networks, named neuro-symbolic verification. The key idea is to use neural networks as part of the otherwise logical specification, enabling the verification of a wide variety of complex, real-world properties, including the one above. A defining feature of our framework is that it can be implemented on top of existing verification infrastructure for neural networks, making it easily accessible to researchers and practitioners.
Factors Impacting the Performance of Data Transferred Via VPN

As a cost effective measure to attain security and confidentiality of data, Virtual Private Network (VPN) is used to interconnect two networks. The research shows that the protocols and algorithms of VPNs adds the overhead and in turn affect the network performance. The two end point hardware appliances are configured with standard configuration to establish site to site VPN. There are different data formats transferred via these tunnels. A research was conducted in a simulation environment of open source technology to identify the various factors impacting the performance of data transfer via VPN tunnels. Empirical measurement shows that performance depends critically on nature of data and compressibility in different internet bandwidth conditions. This was also noticed that nested VPN architecture adds complexity in security at the cost of multifold transmission delays. VPN provide security at the cost of performance; hence application specific cost benefit analysis is essential to choose the optimal architecture.
RCRN: Real-world Character Image Restoration Network via Skeleton Extraction

Constructing high-quality character image datasets is challenging because real-world images are often affected by image degradation. There are limitations when applying current image restoration methods to such real-world character images, since (i) the categories of noise in character images are different from those in general images; (ii) real-world character images usually contain more complex image degradation, e.g., mixed noise at different noise levels. To address these problems, we propose a real-world character restoration network (RCRN) to effectively restore degraded character images, where character skeleton information and scale-ensemble feature extraction are utilized to obtain better restoration performance. The proposed method consists of a skeleton extractor (SENet) and a character image restorer (CiRNet). SENet aims to preserve the structural consistency of the character and normalize complex noise. Then, CiRNet reconstructs clean images from degraded character images and their skeletons. Due to the lack of benchmarks for real-world character image restoration, we constructed a dataset containing 1,606 character images with real-world degradation to evaluate the validity of the proposed method. The experimental results demonstrate that RCRN outperforms state-of-the-art methods quantitatively and qualitatively.
I Know What You Are Searching for: Code Snippet Recommendation from Stack Overflow Posts

Stack Overflow has been heavily used by software developers to seek programming-related information. More and more developers use Community Question and Answer forums, such as Stack Overflow, to search for code examples of how to accomplish a certain coding task. This is often considered to be more efficient than working from source documentation, tutorials, or full worked examples. However, due to the complexity of these online Question and Answer forums and the very large volume of information they contain, developers can be overwhelmed by the sheer volume of available information. This makes it hard to find and/or even be aware of the most relevant code examples to meet their needs. To alleviate this issue, in this work, we present a query-driven code recommendation tool, named Que2Code , that identifies the best code snippets for a user query from Stack Overflow posts. Our approach has two main stages: (i) semantically equivalent question retrieval and (ii) best code snippet recommendation. During the first stage, for a given query question formulated by a developer, we first generate paraphrase questions for the input query as a way of query boosting and then retrieve the relevant Stack Overflow posted questions based on these generated questions. In the second stage, we collect all of the code snippets within questions retrieved in the first stage and develop a novel scheme to rank code snippet candidates from Stack Overflow posts via pairwise comparisons. To evaluate the performance of our proposed model, we conduct a large-scale experiment to evaluate the effectiveness of the semantically equivalent question retrieval task and best code snippet recommendation task separately on Python and Java datasets in Stack Overflow. We also perform a human study to measure how real-world developers perceive the results generated by our model. Both the automatic and human evaluation results demonstrate the promising performance of our model, and we have released our code and data to assist other researchers.
Systematic Review of Factors that Influence the Cybersecurity Culture

There is a need to shift from a purely technological approach in addressing cybersecurity threats to a more human inclusive method. As a result, cybersecurity culture is gaining momentum in research as an approach in addressing cybersecurity challenges due to human related issues. To develop a better understanding of cybersecurity culture, this paper presents a comprehensive view of cybersecurity culture (CSC) factors. These holistic cybersecurity culture factors have been developed by conducting a detailed review of literature. A total of 539 records were initially identified from seven different databases and via other sources, from which 58 records were finally selected using focused inclusion and exclusion criteria. The review identified a total of 29 cybersecurity culture factors, with security education, training, and awareness (SETA), and top management or leadership support appearing among the 10 dominant factors. The researchers produced a consolidated list of factors for CSC that can guide future researchers in this research area.
Quantumness beyond entanglement: The case of symmetric states

Nowadays, it is accepted that truly quantum correlations can exist even in the absence of entanglement. For the case of symmetric states, a physically trivial unitary transformation can alter a state from entangled to separable, and vice versa. We propose to certify the presence of quantumness via an average of a state's bipartite entanglement properties over all physically relevant modal decompositions. We investigate extremal states for such a measure: SU(2)-coherent states possess the least quantumness, whereas the opposite extreme is inhabited by states with maximally spread Majorana constellations.
Salient Phrase Aware Dense Retrieval: Can a Dense Retriever Imitate a Sparse One?

Xilun Chen, Kushal Lakhotia, Barlas Oguz, Anchit Gupta, Patrick Lewis, Stan Peshterliev, Yashar Mehdad, Sonal Gupta, Wen-tau Yih. Findings of the Association for Computational Linguistics: EMNLP 2022. 2022.
Product pricing solutions using hybrid machine learning algorithm

E-commerce platforms have been around for over two decades now, and their popularity among buyers and sellers alike has been increasing. With the COVID-19 pandemic, there has been a boom in online shopping, with many sellers moving their businesses towards e-commerce platforms. Product pricing is quite difficult at this increased scale of online shopping, considering the number of products being sold online. For instance, the strong seasonal pricing trends in clothes—where Brand names seem to sway the prices heavily. Electronics, on the other hand, have product specification-based pricing, which keeps fluctuating. This work aims to help business owners price their products competitively based on similar products being sold on e-commerce platforms based on the reviews, statistical and categorical features. A hybrid algorithm X-NGBoost combining extreme gradient boost (XGBoost) with natural gradient boost (NGBoost) is proposed to predict the price. The proposed model is compared with the ensemble models like XGBoost, LightBoost and CatBoost. The proposed model outperforms the existing ensemble boosting algorithms.
Human activity recognition utilizing SVM algorithm with gridsearch

Human Activity Recognition (HAR) has become one of the most debatable research topics due to the availability of sensors and accelerometers, the low cost and less power consumption, the live data streaming, and the advances in computer vision, machine learning, as well as in artificial intelligence. This study proposes a HAR system utilizing Support Vector Machine (SVM) algorithms to navigate hyperplane which could separate the two datasets from two different classes. Hyperplane is defined as a function separating between classes. Regardless of its capability for data classification, such an application enables this research to be distinct from previous research by adding the application of Tuning Hyperparameter to SVM. Further, the Dataset which initially contains the combined data, is separated into a static activity dataset and a dynamic activity dataset. In this study, the accuracy of applying SVM+Hyperparameter has raised up to 96.26%. Hence, this selection becomes the novelty of this research, which could improve the accuracy from previous research without applying Hyperparameter.
Boosted Dense Retriever

Patrick Lewis, Barlas Oguz, Wenhan Xiong, Fabio Petroni, Scott Yih, Sebastian Riedel. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.
Introduction of an Assistance System to Support Domain Experts in Programming Low-Code to Leverage Industry 5.0

The rapid technological leaps of Industry 4.0 increase the pressure and demands on humans working in automation, which is one of the main motivators of Industry 5.0.In particular, automation software development for mechatronic systems becomes increasingly challenging, as both domain knowledge and programming skills are required for high-quality, maintainable software.Especially for small companies from automation and robotics without dedicated software engineering departments, domain-specific low-code platforms become indispensable that enable domain experts to develop code intuitively using visual programming languages, e.g., for tasks such as retrofitting mobile machines.However, for extensive functionalities, visual programs may become overwhelming due to the scaling-up problem.In addition, the ever-shortening timeto-market increases the time pressure on programmers.Thus, an assistance system concept is introduced that can be implemented by low-code platform suppliers based on combining data mining and static code analysis.Domain experts are supported in developing low-code by targeted recommendations, metric-based complexity measurement, and reducing complexity by encapsulating functionalities.The concept is implemented for the industrial low-code platform HAWE eDesign to program hydraulic components in mobile machines, and its benefits are confirmed in a user study and an industrial expert workshop.
Global Progress in Oil and Gas Well Research Using Bibliometric Analysis Based on VOSviewer and CiteSpace

Studies related to oil and gas wells have attracted worldwide interest due to the increasing energy shortfall and the requirement of sustainable development and environmental protection. However, the state of oil and gas wells in terms of research characteristics, technological megatrends, article-produced patterns, leading study items, hot topics, and frontiers is unclear. This work is aimed at filling the research gaps by performing a comprehensive bibliometric analysis of 6197 articles related to oil and gas wells published between 1900 and 2021. VOSviewer and CiteSpace software were used as the main data analysis and visualization tools. The analysis shows that the annual variation of article numbers, interdisciplinary numbers, and cumulative citations followed exponential growth. Oil and gas well research has promoted the expansion of research fields such as engineering, energy and fuels, geology, environmental sciences and ecology, materials science, and chemistry. The top 10 influential studies mainly focused on shale gas extraction and its impact on the environment. More studies were produced by larger author teams and inter-institution collaborations. Elkatatny and Guo have greatly contributed to the application of artificial intelligence in oil and gas wells. The two most contributing institutions were the Southwest Petr Univ and China Univ Petr from China. The People’s Republic of China, the US, and Canada were the countries with the most contributions to the development of oil and gas wells. The authoritative journal in engineering technology was J Petrol Sci Eng, in environment technology was Environ Sci Technol, in geology was Aapg Bull, and in materials was Cement Concrete Res. The keyword co-occurrence network cluster analysis indicated that oil well cement, new energy development, machine learning, hydraulic fracturing, and natural gas and oil wells are the predominant research topics. The research frontiers were oil extraction and its harmful components (1992–2016), oil and gas wells (1997–2016), porous media (2007–2016), and hydrogen and shale gas (2012–2021). This paper comprehensively and quantitatively analyzes all aspects of oil and gas well research for the first time and presents valuable information about active and authoritative research entities, cooperation patterns, technology trends, hotspots, and frontiers. Therefore, it can help governments, policymakers, related companies, and the scientific community understand the global progress in oil and gas well research and provide a reference for technology development and application.
Community Targeted Phishing

Looking at today phishing panorama, we are able to identify two diametrically opposed approaches. On the one hand, massive phishing targets as many people as possible with generic and preformed texts. On the other hand, spear phishing targets high-value victims with hand-crafted emails. While nowadays these two worlds partially intersect, we envision a future where Natural Language Generation (NLG) techniques will enable attackers to target populous communities with machine-tailored emails. In this paper, we introduce what we call Community Targeted Phishing (CTP), alongside with some workflows that exhibit how NLG techniques can craft such emails. Furthermore, we show how Advanced NLG techniques could provide phishers new powerful tools to bring up to the surface new information from complex data-sets, and use such information to threaten victims' private data.
Development and usage of an anesthesia data warehouse: lessons learnt from a 10-year project

Abstract This paper describes the development and implementation of an anesthesia data warehouse in the Lille University Hospital. We share the lessons learned from a ten-year project and provide guidance for the implementation of such a project. Our clinical data warehouse is mainly fed with data collected by the anesthesia information management system and hospital discharge reports. The data warehouse stores historical and accurate data with an accuracy level of the day for administrative data, and of the second for monitoring data. Datamarts complete the architecture and provide secondary computed data and indicators, in order to execute queries faster and easily. Between 2010 and 2021, 636 784 anesthesia records were integrated for 353 152 patients. We reported the main concerns and barriers during the development of this project and we provided 8 tips to handle them. We have implemented our data warehouse into the OMOP common data model as a complementary downstream data model. The next step of the project will be to disseminate the use of the OMOP data model for anesthesia and critical care, and drive the trend towards federated learning to enhance collaborations and multicenter studies.
Assets in Software Engineering: What are they after all?

During the development and maintenance of software-intensive products or services, we depend on various artefacts. Some of those artefacts, we deem central to the feasibility of a project and the product’s final quality. Typically, these central artefacts are referred to as assets. However, despite their central role in the software development process, little thought is yet invested into what eventually characterises as an asset, often resulting in many terms and underlying concepts being mixed and used inconsistently. A precise terminology of assets and related concepts, such as asset degradation, are crucial for setting up a new generation of cost-effective software engineering practices. In this position paper, we critically reflect upon the notion of assets in software engineering. As a starting point, we define the terminology and concepts of assets and extend the reasoning behind them. We explore assets’ characteristics and discuss what asset degradation is as well as its various types and the implications that asset degradation might bring for the planning, realisation, and evolution of software-intensive products and services over time. We aspire to contribute to a more standardised definition of assets in software engineering and foster research endeavours and their practical dissemination in a common, more unified direction.