Reviewerly/exHarmony huggingface dataset:
Correctness-by-Construction

Correctness-by-Construction (CbC) is an incremental software development technique in the field of formal methods to create functionally correct programs guided by a specification. In contrast to post-hoc verification, where the specification and verification take part after implementing a program, with CbC the specification is defined first, and then the program is successively created using a small set of refinement rules that define side conditions preserving the correctness of the program. This specification-first, refinement-based approach as pursued by CbC has the advantage that errors are likely to be detected earlier in the design process and can be tracked more easily. Even though the idea of CbC emerged over 40 years ago, CbC is not widespread and is mostly used to create small algorithms.We believe in the idea of CbC and envision a scaled CbC approach that contributes to solving problems of modern software verification. In this short paper, we give an overview of our research regarding CbC in four different lines of research. For all of them, we provide tool support for building the CorC ecosystem that even further enables CbC-based development for different fields of application and size of software systems. Furthermore, we give an outlook on future work that extends on our concepts for CbC.
DAIS Project - Distributed Artificial Intelligence Systems

DAIS is a step forward in the area of artificial intelligence and edge computing. DAIS intends to create a complete framework for self-organizing, energy efficient and private-by-design distributed AI. DAIS is a European project with a consortium of 47 partners from 11 countries coordinated by RISE Research Institute of Sweden.
Modelling Stakeholders’ Knowledge and Perception in a Fuzzy Cognitive Map: The Case of the Extra-Virgin Olive Oil (EVOO) Value Chain in Central Italy

Italy is the second largest extra-virgin olive oil (EVOO) producer within the European Union. Despite its importance in preserving rural landscape and in supporting household economy, the EVOO sector faces several constraints due to high management costs, small farm size, lack of cooperation and investment, production vulnerability, and farmers’ ageing. Such a number of weak points suggests the need to identify and adopt innovative approaches, at both the farm and oil mill levels. In order to address these priorities, a fuzzy cognitive mapping (FCM) survey was carried out in Umbria region, central Italy, involving key local stakeholders of the EVOO value chain in the Orvieto district. Based on stakeholders’ perception and knowledge, this paper aims to identify and evaluate the most relevant components of the local olive oil value chain, and predict scenarios responding to hypothetical changes of the same components. These stakeholders were firstly invited to each build an individual fuzzy cognitive map and then, grouped all together, build a joint fuzzy cognitive map. Finally, the maps represented both the individual and the grouped stakeholders’ perceptions. The maps were translated into adjacency matrices in order to create an FCM model by applying the software “Mental Modeler”. In total, 24 participants, including practitioners, multipliers, researchers, suppliers and members of local administration, participated in the survey. The component analysis and the scenario analysis highlighted several priority issues: to preserve the ecosystem functioning, to implement cooperation, innovation and education, to adapt and mitigate climate change. The main novelty of this study is that all stakeholders’ categories in the EVOO sector recognize several challenges to sustain the EVOO value chain, in particular, climate change adaptation and mitigation.
A data-driven study of active meteorological stations and the factors motivating their establishment

Renewable energy development is closely related to natural meteorological properties. Hence, the identification of climate change and its underlying physical mechanisms across the globe is indispensable. Meteorological stations provide a wealth of high-quality data for observing global meteorological changes. However, there is no research focusing on the long-term development mechanisms and operation of meteorological stations. This study explores the operation and distribution of meteorological stations in 181 countries from 1800 to 2018 and analyzes the relationship between the number of active stations and development indicators by correlation analysis, least squares regression, and machine learning interpretable modeling based on station data from 25 countries. The results indicate that Gross Domestic Product (GDP) and government spending are the main factors influencing the number of active stations in each country, while GDP per capita and agricultural land area have weaker effects. Meanwhile, most of the meteorological stations are located in developed countries. In addition, machine learning models, including Multilayer Perceptron, Long Short-Term Memory (LSTM) neural network, Gated Recurrent Unit (GRU) neural network, and Broad Learning System, are developed to predict the number of active stations in a country. The experimental results show that GRU and LSTM models achieve better performance than other models.
Robust Multi-task Learning-based Korean POS Tagging to Overcome Word Spacing Errors

End-to-end neural network-based approaches have recently demonstrated significant improvements in natural language processing (NLP). However, in the NLP application such as assistant systems, NLP components are still processed to extract results using a pipeline paradigm. The pipeline-based concept has issues with error propagation. In Korean, morphological analysis and part-of-speech (POS) tagging step, incorrectly analyzing POS tags for a sentence containing spacing errors negatively affects other modules behind the POS module. Hence, we present a multi-task learning-based POS tagging neural model for Korean with word spacing challenges. When we apply this model to the Korean morphological analysis and POS tagging, we get findings that are robust to word spacing errors. We adopt syllable-level input and output formats, as well as a simple structure for ELECTRA and RNN-CRF models for multi-task learning, and we achieve a good performance 98.30 of F1, better than previous studies on the Sejong corpus test set.
Patient–Robot Co-Navigation of Crowded Hospital Environments

Intelligent multi-purpose robotic assistants have the potential to assist nurses with a variety of non-critical tasks, such as object fetching, disinfecting areas, or supporting patient care. This paper focuses on enabling a multi-purpose robot to guide patients while walking. The proposed robotic framework aims at enabling a robot to learn how to navigate a crowded hospital environment while maintaining contact with the patient. Two deep reinforcement learning models are developed; the first model considers only dynamic obstacles (e.g., humans), while the second model considers static and dynamic obstacles in the environment. The models output the robot’s velocity based on the following inputs; the patient’s gait velocity, which is computed based on a leg detection method, spatial and temporal information from the environment, the humans in the scene, and the robot. The proposed models demonstrate promising results. Finally, the model that considers both static and dynamic obstacles is successfully deployed in the Gazebo simulation environment.
NEW PROJECTS OF DEVELOPING EDUCATION

The article presents the existing practices of the developing education, which are imple-mented in educational institutions of the Russian Federation in the period from 1998 to the present day, taking into account modern trends and challenges to the world educa-tion: individualization and personalization; digitalization, networking, collaboration (coop-eration, team work); interdisciplinarity in the content; meta competence as a result of edu-cation; smart learning in the era of the information society with the availability of commu-nication technologies and team work; gamification as an introduction to learning educa-tional games, etc. To all these challenges and trends of the modern reality, the educa-tional system of D.B. Elkonin- V.V. Davydov has its own "answers" through certain prac-tices that are realized in the network of classes and schools of the developing education of the Russian Federation. In addition, new projects that can be viewed as a resource of the development of the domestic educational system are described
The Challenges of Cloud Technology Implementation in Oil Companies

Abstract Given that data and information have become the most valuable assets of companies that can be efficiently and economically stored in the cloud infrastructure, there is great pressure on business entities to capitalize on this business and technological opportunity. There is a particular pressure on oil companies to find new ways to create a competitive advantage by using the opportunities offered by cloud-based businesses. This is primarily a result of the fact that the reorientation of business activities to this type of storage and integration of data and applications represents a major change in the business world, and the growth trend continues in all aspects of the business. This paper deals with the possibilities and challenges of applying cloud technology in oil companies operating in turbulent conditions caused by fluctuations in oil prices in the global market..
An Efficient Dynamic Multi-Sources To Single-Destination (DMS-SD) Algorithm In Smart City Navigation Using Adjacent Matrix

Dijkstra's algorithm is one of the most popular clas-sic path-planning algorithms, achieving optimal solutions across a wide range of challenging tasks. However, it only calculates the shortest distance from one vertex to another, which is hard to directly apply to the Dynamic Multi-Sources to Single-Destination (DMS-SD) problem. This paper proposes a modified Dijkstra algorithm to address the DMS-SD problem, where the destination can be dynamically changed. Our method deploys the concept of the Adjacent Matrix from Floyd's algorithm and achieves the goal with mathematical calculations. We formally show that all-pairs shortest distance information in Floyd's algorithm is not required in our algorithm. Extensive experiments verify the scalability and optimality of the proposed method.
On the benchmarking of ResNet forgery image model using different datasets

This paper presents the benchmarking and improve-ment of the ResNet image forgery model using three different datasets (CASIA, Columbia, and LSBU). The model is based on classification, where forgery images have been edited using cut-paste modification technique. The images are categorized to check if the algorithm can successfully identify the difference between the original and the forgery image. All images have been pre-processed with Gray-Edge detectors to obtain get better classification results. Experimental results have shown that the Gray-edge technique has improved the accuracy across all image datasets.
Conditional Metadata Embedding Data Preprocessing Method for Semantic Segmentation

Semantic segmentation is one of the key research areas in computer vision, which has very important applications in areas such as autonomous driving and medical image diagnosis. In recent years, the technology has advanced rapidly, where current models have been able to achieve high accuracy and efficient speed on some widely used datasets. However, the semantic segmentation task still suffers from the inability to generate accurate boundaries in the case of insufficient feature information. Especially in the field of medical image segmentation, most of the medical image datasets usually have class imbalance issues and there are always variations in factors such as shape and color between different datasets and cell types. Therefore, it is difficult to establish general algorithms across different classes and robust algorithms that differ across different datasets. In this paper, we propose a conditional data preprocessing strategy, i.e., Conditional Metadata Embedding (CME) data preprocessing strategy. The CME data preprocessing method will embed conditional information to the training data, which can assist the model to better overcome the differences in the datasets and extract useful feature information in the images. The experimental results show that the CME data preprocessing method can help different models achieve higher segmentation performance on different datasets, which shows the high practicality and robustness of this method.
Blockchain-based Multi-layer Data Security Sharing Model

Facing the era of big data, the traditional data sharing model has many problems such as low execution efficiency, opaque data sharing process, and inflexible processing, which can no longer meet the demand for secure data sharing. So we present a blockchain-based multi-layer data security sharing model (ML-DSM). The model not only combines blockchain and attribute-based access control technology, but also introduces malicious access behavior judgment and user rating mechanism. Judging malicious access behavior helps detect malicious users in time at the early stage of access control, and effectively prevents malicious attacks such as DoS. In the user rating mechanism, the smart contract evaluates the user's reputation level based on the user's current and historical reputation scores and judges whether the reputation level is higher than the level of the requested data. The rating mechanism not only dynamically adjusts the access rights of the requestor but also effectively reduces the average judgment time of access control. The final experimental analysis results show that the data sharing model in this paper can improve the execution efficiency of access control while ensuring the security of the shared data. Thus ML-DSM achieves more secure and efficient data sharing.
Research on Intelligent Condition Monitoring of Power System Based on Beidou Technology

BeiDou Navigation Satellite System (BDS) has effectively improved management efficiency and significantly reduced operating costs in the intelligent agricultural production, smart traffic management and epidemic prevention. Based on the research of BeiDou technology, an intelligent condition monitoring mode of power system is proposed for the demand of environmental condition monitoring of power transmission lines and substation equipment, combined with big data and artificial intelligence technology. The monitoring effectiveness of transmission and substation application scenario monitoring mode is elaborated to provide useful reference for condition monitoring.
Dynamic real-time research hotspot data track and visualization platform for Think Tank Information

Nowadays, with the rapid development of global science and technology, a large number of talents are emerging in various fields. Think tank is a comprehensive industry elite talent, cutting-edge scientific and technological achievements, innovative research projects and professional papers and works as one of the platform. In order to better assist leaders, an objective, fair and high-value dynamic data storage and display system should be established. This paper takes the think tank provided in the think tank report as the data source, surveys and statistics the talents and experts in popular fields in the Beijing-Tianjin-Hebei region, as well as their papers and works, major scientific research projects and achievements, and uses this as the label to crawl and store the data. Crawlers written using the webmagic framework and RuoYi based systems can be deployed on the server for a long time, and they will crawl all the data from the data source every certain time to achieve real-time updates.
Comprehensive defense scheme against container escape related to container management procedure

Container technology has become a widely used virtualization technology in cloud platform because of its lightweight virtualization characteristics. However, compared with traditional virtual machine technology, the security and isolation of the container are poor and it may lead to container escape, because container technology shares the kernel with the host. This attack will pose a serious threat to the host and other containers on the same host. We studied the container escape attack caused by container management vulnerabilities, and propose a comprehensive container security protection scheme by using AppArmor and Seccomp. Through the simulation of vulnerability environment, the structure proves that the scheme is indeed effective.
An Efficient Grid-based Privacy-preserving Framework Against User-Server Collusion in Spatial Crowdsourcing

With the popularity of spatial crowdsourcing services, the issue of privacy leakage has attracted widespread attention from institutions and researchers, especially the location privacy of participants. To address this issue, several location privacy-preserving frameworks have been proposed. However, these frameworks have limited performance in terms of task assignment efficiency and lack consideration of the threat of brute force attacks under user-server collusion. Facing these challenges, in this paper, we propose an efficient grid-based privacy-preserving framework to achieve a two-sided improvement in assignment efficiency and security level. For the first challenge, we utilize a recoding algorithm to construct a grid-index Quadtree to reduce the time cost of finding suitable workers, and finally achieve faster-than-linear task assignment complexity. For the other challenge, we introduce a two cloud server model, including a Re-Server and a SC-Server, and propose a Two-Step Re-Encryption scheme based on secure Knn computation. The task assignment request is first sent to the Re-Server and then forwarded to the SC-Server after re-encryption. SC server returns the assignment result and the worker is finally notified by the Re-Server. To break the way of user-server collusion, we also design two parameters, hash signature and valid timestamp, to achieve unlinkability between data and user identities. The extensive experiments based on real-world datasets show that our framework is more efficient in task assignment than state-of-the-art works and more suitable for big data scenarios.
Improved Algorithm for Intra-frame Prediction Based on AVS3

uavs3e is a video encoder based on the AVS3 coding standard. This paper focuses on improving the process of traversing multiple modes at each depth based on the cumbersome block partitioning of uavs3e, first counts the division depths on each LCU (Largest Coding Unit) boundary, and then calculates the image texture change information to derive the threshold value of the texture change degree with high correlation between the division depths on the intersection boundary of two LCUs. And a fast algorithm is proposed to terminate the division depth in advance according to the image information. Experimental results show that using the above optimisation algorithm can save an average 16.24% of coding time while paying only an average 0.53% of performance loss (PSNR-Y) (peak signal to noise ratio-Luminance component).
Research on Term Extraction of the Belt and Road Initiative Texts Based on BERT Model

Domain term extraction plays an important role in natural language processing. Term extraction of the Belt and Road Initiative (BRI) texts based on BERT model is helpful to follow the trend in this field and appreciate the value of “The Belt and Road Initiative” development. It is an inevitable trend of technological development to use natural language processing technology to term extraction. In order to improve the efficiency of domain term extraction, a term extraction model based on BERT-BiLSTM-CRF (Bidirectional Long Short-Term Memory-Conditional Random Field) is applied to obtain annotation sequence of the BRI terms after input, then the extracted term results are analyzed. Compared with other term extraction models, the BRI term extraction model based on BERT-BiLSTM-CRF achieved good results in terms of precision, recall and F1-score, showing that the model is feasible and effective.
Machine Learning-based Gesture Recognition Using Wearable Devices

Traditional gesture recognition solutions are based on touch screens or vision, limited by environmental conditions and not portable. The accelerometer-based gesture recognition technology can be integrated into small wearable smart devices, such as smart bracelets, smartwatches or smart rings. The portability and reliability of this technology make it a broad market and application space. This project is based on a smartwatch accelerometer dataset from TensorFlow Datasets. By experimenting with two different pre-processing algorithms: Kalman Filter and Savitzky-Golay Filter, feature extraction algorithms and machine learning algorithms (random forests, k-nearest neighbours, support vector machine), the relatively optimal algorithm for each part to combine to obtain a good accelerometer-based gesture recognition model were filtered out, including gravity reduction, Fourier transforms, a normal exception elimination algorithm, Savitzky-Golay Filter and Support Vector Machine (SVM). The best accuracy rate of this model is over 97%, with a similar degree of precision, recall rate and f1 score.
Design and Implementation of Personalized MOOC Recommendation System Based on Spark

With the popularity of MOOCs, the number of MOOC platforms and courses has increased rapidly. However, in the face of massive and scattered course information, learners usually need to access different platforms, search for related topics, read course introductions and course syllabuses, and finally, choose one course that suits them. Learners are now facing Serious problems of information trek and information overload. Therefore, aggregating course information from various platforms and using machine learning technology to provide learners with accurate course recommendations can greatly improve the learning efficiency and enthusiasm of learners. This paper first builds a distributed course and user data collection tool. Basing on the collected the collected course and user information, the course attributes and user learning behavior are modeled. Using generated knowledge about users and courses, a course recommender system is designed, which is based on Spark framework and can provide users with scientific and personalized course recommendations.
Opinion Mining of Politics and Inflation using Roman Urdu Dataset

In today's world, data volume is immense and is expanding at an exponential rate. A leading factor of this is the data shared over social media by millions of people daily in the form of text, audio, video, and images. Opinion mining is a branch of text mining that seeks to extract public opinion about an event or particular topic on any online platform like review sites, Twitter, Facebook, etc. Most of the work in opinion mining is done in the English language and very little is done in Roman Urdu, which is a dominant social media language in Pakistan and India. Inflation and politics are amongst the most talked-about and discussed topics on social media in Pakistan. In this study, an Inflation and Politics-based Roman Urdu dataset is prepared that is extracted from the Roman Urdu dataset available at Kaggle. Various operations (with text processing, without text preprocessing, with attribute selection, without attribute selection) were performed on the data over machine learning algorithms of Naive Bayes, Bayes Net, KStar, Decision Tree, and Random Forest and determined which algorithm gave the best accuracy on the training data. WEKA (Version 3.8.4) was used to generate opinion mining regarding politics and inflation in Pakistan using the Roman Urdu dataset.