Reviewerly/exHarmony huggingface dataset:
Using capabilities for strict runtime invariant checking

In this paper we use pre-existing language support for both reference and object capabilities to enable sound runtime verification of representation invariants. Our invariant protocol is stricter than the other protocols, since it guarantees that invariants hold for all objects involved in execution. Any language already offering appropriate support for reference and object capabilities can support our invariant protocol with minimal added complexity. In our protocol, invariants are simply specified as methods whose execution is statically guaranteed to be deterministic and to not access any externally mutable state. We formalise our approach and prove that our protocol is sound, in the context of a language supporting mutation, dynamic dispatch, exceptions, and non-deterministic I/O. We present case studies showing that our system requires a lighter annotation burden compared to Spec#, and performs orders of magnitude less runtime invariant checks compared to the ‘visible state semantics’ protocols of D and Eiffel.
Assessing Readability Formulas in the Wild

Recent advances have facilitated major improvements in developing intelligent and purpose-oriented readability formulas to predict the overall difficulty of a text in terms of text comprehension and processing. Such readability formulas are mediating technologies that help match appropriate reading texts with students, thus enabling the development of smart learning environments that adapt learning resources to learner skills. Newer readability formulas include linguistic features that are more predictive of human judgments of text readability than traditional readability formulas, such as Flesch-Kincaid Grade Level. However, in many cases, these formulas have not been tested beyond their ability to predict reading scores. The purpose of this study is to examine the validity of newer readability models along with more traditional readability formulas using behavioral data and text comprehension scores. The results indicate that readability models employing linguistic features more theoretically related to text processing and comprehension outperform readability models that do not employ similar features. The findings support the long-term growth of readability formulas that are continuously improved to increase the wellbeing of learners.
Key Technologies of Data Security and Privacy Protection in the Internetof- Things Group Intelligence Perception

Aim: To study the key technology of privacy protection and data security in the Internet of Things group intelligence perception, a cryptographic-based technology was proposed. Background: First, the background of group intelligence perception and three existing problems were briefly introduced, and the inherent contradictions among the three problems, which led to some challenges in this study, were reported. Objective: A network model, a security model, and design goals were constructed, and the solution based on cryptography-related technology was proposed. Method: The security and cost complexity of the proposed solution was theoretically analysed. The calculation cost of mobile phone measurement verified the practical feasibility of the scheme. Results: When ɛ was considerably small, both false positive (FP) and false negative (FN) were small (close to 0). With an increase in ɛ, the FP rate increased, and the FN rate became 0. When ɛ = 0.5, the FP rate was only 0.1, and the equivalent FN rate was 0 after the user completed 50 tasks. The trust model used was highly accurate. Conclusions: This study has great significance to promote the safe and reliable application development to ensure the genuineness and trustworthiness of data in the aggregation of intermediate data and back-end data services.
Formation of Students’ Creativity by Means of Electronic Environment Moodle

The authors investigate the problem of the formation of creativity of students of economic university. The importance of solving this problem in the context of improvement of the quality of training of graduates of higher education from the standpoint of professional orientation of training is emphasized. The article highlights the role of e-learning environment Moodle (modular object-oriented dynamic learning environment) as a means of formation of the creativity of future bachelors of economics. The possibilities of electronic environment Moodle are demonstrated for creating of training courses, filling them in the form of electronic resources (lectures, practical tasks, questionnaires, tests, forums, etc.), providing interactive interaction of subjects of the educational process. In the course of the study, a table of indicators of the level of formation of the creative-activity component of students’ mathematical competence was built to identify creativity. Based on the results of the pedagogical experiment, the assessment of the formation of the creative-activity component of mathematical competence of future bachelors was carried out. The article presents the results of generalized indicators of the dynamics of the formation of the creative activity component at the subject, interdisciplinary and professional stages of multilevel mathematical training in economic university.
Domain organisation emerges in cross-modal but not within-modal semantic feature integration

A large body of literature supports theories positing a distributed, perceptually grounded semantic memory system. Prominent models have assumed distributed features are integrated into networks using either shallow or deep hierarchies. Previous behavioural tests of modality effects in shallow and deep hierarchies inspired by, but not implemented in, connectionist models support deep hierarchy architectures. We behaviourally replicate and model speeded dual feature verification in a sample of general-purpose modality-specific computational models of semantic memory trained on feature production norms for 541 concepts. The cross-modal advantage in semantic processing shown behaviourally and in simulations supports hierarchically organised distributed models of semantic memory and provides novel insight into the division of labour in these models. Analyses of the emergent model structure suggest animacy distinctions arise from the self-organisation of statistical co-occurrences among multisensory features but weakly among unisensory features. These findings suggest a privileged role of the multisensory convergence area for category representation.
Exact Solution of a Fully General Non-Local-Thermodynamic-Equilibrium Two-Level Atom

We describe an algorithm for the solution of a statistical/average atom non-local-thermodynamic-equilibrium atomic kinetics model of steady-state plasmas in which all one-and two-electron processes are included in full generality.
Private and Secure Blockchain-Based Mechanism for an Online Voting System

Current electronic voting systems are prone to a range of security threats, including distributed denial of service attacks, polling booth arrangement, vote modification and manipulation, malware attacks, and so on, and they also incur huge organizational resources and time. Existing systems become distrustful as a result. This paper proposes a private and secure blockchain-based mechanism that improves the overall resilience of e-voting. A decentralized and distributed ledger was developed to provide a certain amount of privacy and security for an election by removing any type of tampering. Furthermore, cryptographic principles and a consensus protocol were used to ensure the legitimacy and legality of each transaction, making it safe and thereby strengthening the election process. Every vote cast by a voter through our website is added to the blockchain as a transaction, and the voter is represented by a single unique address allocated to him or her for voting. The proposed mechanism’s performance in terms of security, privacy, and scalability reveals that it reduces interference that could taint the integrity of a fair election.
An Automated Cervical Cancer Detection Mechanism Using Pap Smear Images

Cervical cancer is one predominant type of cancer and ranks as the second most common type of cancer that often affects women over 30 years. Among all cancers in women, 6–29% constitutes cervical cancer. The test used for detecting the tumor is called the Pap smear test, which requires expert pathologists to classify the abnormal cells in the precancerous earlier stages. In rural villages, such experts are not easily accessible, and they need emerging technologies for the early detection of precancerous lesions. This proposed model is constructed using a fully connected 24 × 1 CNN model in the Herlev dataset of Pap smear cell images. This automated pap smear image classification model works through the learning and analysis of fully connected CNN with data augmentation process. Experimental results of the proposed scheme claim that this model is classifying the abnormal cervical cancer cells with an accuracy of 90.2%.
A study of the roles of school administrators in increasing the quality of school life through social responsibility projects in primary schools

The structure and expectations of societies are constantly changing, developing, and advancing as time demands. Accordingly, the vision, mission, purpose, and objectives of educational institutions are changing and are shaped according to the expectations of the society. School Directors, teachers, and families, briefly the community, should know that schools are institutions that best fulfill children's learning, and make them feel happy and safe. A cheerful and peaceful school environment always brings academic success. Children who have a quality school life are aware of the responsibility for their behaviors as well as the social responsibilities for their environment. Thus, students who are closely interested in social responsibility projects are not just academically developed; at the same time, their social, emotional, and mental development increases, and their social cohesion and awareness develop. This research aims to examine ways to improve the quality of social life in schools through social responsibility projects to be started as well as opinions by School Directors. In this study, the qualitative method and case study design were used. The study group of this research consisted of 15 teachers working in primary schools affiliated with the Department of Primary Education of the Ministry of National Education. The participants were interviewed through live connections. The findings revealed that students are very willing to take part in social responsibility projects, and significant improvements have been made in their academic achievement; they attended more willingly, and there have been noticeable changes in the quality of the school.
Technology of problem-based teaching of academic English for chemists in the system of additional education

The aim of the article is to consider the features of additional education for adults, to research the main problems that arise in the training of adult chemists, and to substantiate the choice for further study and modernization of the problem-based learning technology.&#x0D; The article discusses the features of adult education, the specifics of the system of additional education, and substantiates the choice of a system of additional education for adult education.&#x0D; The authors substantiate the choice of problem-based learning technology for subsequent modernization and implementation in the system of distance learning of adults in academic English.&#x0D; The results are the following: the problem-based learning technology to effective use in adult chemists teaching in the system of additional education was substantiated; the problem-based learning technology can be successfully upgraded for these conditions
Identifying the source of gold geochemical anomalies in Jiaodong, eastern China: Tracking the occurrence of gold nanoparticles in a metallogenic system

The gold particles induced geochemical anomaly shows good potential for the prospecting of gold deposits all around world. Most of the discovered gold resources are located at geochemical related anomaly area in Jiaodong, which are associated with Micro-to nanoscale particle matter. However, it has been known little about the relationship between the occurrence of gold nanoparticles and their geochemical anomaly in multimedia accommodating to the detailed process of gold mineralization system and geochemical exploration. Micro-to nanoscale gold as nanoparticles are related to the hydrothermal fluid flows and precipitation among elemental migration. This paper presents gold nanoparticles in ore, constructed the fundamental link to geochemical anomaly distribution maps of gold in drainage sediments, wall rocks, and ore, aimed to identify the source of primary and secondary geochemical anomalies according to careful observation at nanoscale gold and revealed the genesis of gold mineralization and their potential in Jiaodong. A potential model of migration pathway of gold nanoparticle was built to understand the process of massive gold accumulation and the further prospecting in the Jiaodong Peninsula.
Application of Blockchain Innovative Technology in Logistics and Supply Chain Management: A New Paradigm for Future Logistics

The paper provides an overview of the practice of using blockchain technology in the digitalization process of logistics and supply chain management of leading companies in different countries. The key global issues are observed to the essence of blockchain technology, practice, and prospects of its application in various industries and fields of activity. This study illustrates the examples of using blockchain technology by leading companies, particularly, in retail. It is indicated that the integration of key business processes in supply chains today needs adequate information support that can be provided through technology of blockchain, which also provides transparency and traceability of up to date. The study indicates that blockchain technology has the potential to improve supply chain sustainability performance in retail sector and it is expected blockchain technology to rise in popularity in supply chain management information.
Boolean Searchable Encryption Scheme Supporting Forward Security

Abstract To address the problem that most existing searchable encryption schemes do not support Boolean search for multiple keywords and cannot satisfy the forward security of indexes, a Boolean searchable encryption scheme supporting forward security (BSFS) is proposed in this paper. Two problems are solved by using techniques related to set theory and cryptographic knowledge: (1) The ability to implement Boolean search for multiple keywords based on traditional searchable encryption schemes; (2) The forward security of the index is satisfied by the clever construction of the index. The security analysis shows that the correctness, confidentiality and forward security of the scheme are guaranteed and that it has a high degree of practical applicability.
An End-To-End NER Model with Explicit Boundary and Type Information

Abstract Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP). The purpose is to locate and classify named entities with specific meanings, including the person and location. However, there are two problems with the existing methods: Firstly, the model cannot significantly distinguish boundaries and types of entities, especially if there are many types of entities. Secondly, the labels do not provide information on the differences in the boundaries and types of model entities. Therefore, the Explicit Boundary and Type Information (EBTI) models are used to address both issues to improve NER performance. Specifically, the boundary detection module uses for pure boundary recognition, and the type module uses to determine entity type information and then combine them to improve the performance of NER. Experiments are conducted on the widely used dataset CoNLL2003 to demonstrate the advantages of our method, with results comparable to the current state-of-the-art (SOTA).
A security scheme for distributing analysis codes supporting CDM-based research in a multi-center environment

Although the common data model (CDM) has achieved a standardization of medical data and a de-identification of personal patient information, hospitals still store CDM data in an on-premises environment, making it difficult for researchers to access medical data.In this study, for easy access to CDM data in a multi-institutional participatory CDM research environment and to encourage data-driven research, researchers outside hospital networks securely access and analyze CDM data in the target medical center, analyze it, and respond to the results through a public network. We propose an automated security framework that operates on a public network, such as the Internet.The proposed scheme allows authenticated researchers to securely deliver CDM data analysis codes to a medical institution distributed on the network. The institutional servers automatically execute authenticated codes and return the results to the researcher safely. For this purpose, we designed a scheme based on cryptography. The scheme operates on a group of servers consisting of an authentication process, a signing process, a ticket-granting process, a relaying process, and a data analysis process located at the hospital providing medical CDM data. The scheme consists of four phases for a secure medical data analysis in a distributed environment: authentication, code signing, ticket issuing, and distribution and return.Although the CDM has de-identified patient privacy, the issue still needs to be carefully addressed. Therefore, we established four security objectives to verify that the proposed scheme can be operated safely and formally proved them using BAN logic.As a result of the proof using BAN logic, the proposed scheme was verified to achieve the proposed security goal. Although this scheme was designed solely for CDM, it can be applied to systems with similar environments and functional goals.
Dynamic Resource-Efficient Scheduling in Data Stream Management Systems Deployed on Computing Clouds

Scheduling streaming applications in Data Stream Management Systems (DSMS) have been investigated for years. As the deployment platform of DSMS migrates from on-premise clusters to elastic computing clouds, new requirements have emerged for the scheduling process to tackle workload fluctuations with heterogeneous cloud resources. Resource-efficient scheduling is to improve cost efficiency at runtime by dynamically matching the resource demands of streaming applications with the resource availability of computing nodes. In this chapter, we model the scheduling problem as a bin-packing variant and propose a heuristic-based algorithm to solve it with minimised inter-node communication. We also present a prototype scheduler named D-Storm, which extends the original Apache Storm framework into a self-adaptive MAPE-K (Monitoring, Analysis, Planning, Execution, Knowledge) architecture and validates the efficacy and efficiency of our scheduling algorithm. The evaluation carried out on real-world applications such as Twitter Sentiment Analysis proves that D-Storm outperforms the existing resource-aware scheduler and the default Storm scheduler in terms of reducing inter-node traffic and application latency, as well as yielding resource savings through task consolidation.
Defects Maintainability Prediction of the Software

The accurate estimation of the software maintainability is a fundamental parameter for the software quality analysis or computation. The availability of defect-related historical data leads to the motivation of determining impact of defects in maintainability prediction of the software. This chapter evaluates the mentioned scenario by using maintainability index and defect data. The maintainability index is computed using the object-oriented metrics of the software. In the two experiments conducted, the index is predicted with and without using the software defect attribute, respectively. Random forest is used as the learning model, and the performance measure is evaluated using accuracy and F-score. The outcomes infer to the accuracy of existing applications on the defect data. K fold cross validation approach adopts for performance evaluation, especially for the training accuracy of the model.
Weighted Average-Based Complexity Calculation in Block Selection Oriented Reversible Data Hiding

Complexity of blocks is one of the key factors to influence the performance of reversible data hiding. By embedding data into blocks with smaller complexity, the invalid shifting in pixel value ordering (PVO) based reversible data hiding is largely reduced, thus increasing the imperceptibility. However, the existing techniques are not always accurate in complexity calculation, since context pixels are usually treated equally. As a result, a great number of invalid pixels are assigned too large a weight in complexity calculation, which inevitably affects the precision. In this paper, we propose a weighted average based novel strategy in complexity calculation. In this scheme, we calculate the weight of a certain context pixel with the help of its adjacent neighbors, which ensures the one in a smooth area is assigned a larger weight, and otherwise the opposite. Experimental results show that invalid shifting is largely reduced, thus our scheme is better in imperceptibility compared with the state of the art and has potential application in a lot of fields such as medical imaging.
Analysis of the Occlusion Interference Problem in Target Tracking

As an indispensable part in the field of computer vision, target tracking has been widely used in intelligent transportation, missile guidance, unmanned aerial vehicle (UAV) tracking, and many other fields. It has become one of the hot directions in computer vision in recent years, while occlusion problem has always been a great difficulty and challenge in the process of target tracking. In this article, the problem of occlusion interference in target tracking is described, and the solution of occlusion problem is proposed based on different occlusion conditions. Due to the disadvantages of feature point center weighting, multiparticle template matching, and Kalman filter trajectory prediction algorithms in different cases, some algorithms with higher robustness and stability are developed to solve the occlusion problem. In the analysis of the anti-occlusion model, it is found that some tracking errors caused by occlusion can be solved by improving the quality of negative training samples and enriching the diversity of positive sample sets. According to the different training characteristics of online and offline tracking algorithms, the anti-occlusion model suitable for an active learning algorithm under different tracking conditions is found, and the tracking algorithm and characteristics of the active learning algorithm are listed, which is helpful to select the suitable tracking model in different scenarios. Finally, the future development of occlusion problem in target tracking is prospected.