Reviewerly/exHarmony huggingface dataset:
GSVD: Common Vulnerability Dataset for Smart Contracts on BSC and Polygon

The blockchain 2.0 age, marked by smart contract and Ethereum, has arrived couple years ago. Its technologies have expanded the application scenarios of blockchain technology and driven the boom of decentralized Finance. However, smart contract vulnerabilities and security issues are also emerging one after another. Hackers have exploited these vulnerabilities to cause huge economic losses. In recent years, a large amount of research on the analysis and detection of smart contract vulnerabilities has emerged, but there has been no common detection tool and corresponding test dataset. In this paper, we build GSVD dataset (Generalized Smart Contract Vulnerability Dataset) consisting four offline datasets using smart contracts on two chains, Polygon and BSC: two small Solidity datasets consisting of 153 labeled smart contract source codes, which can be used to test the performance of vulnerability mining tools; two large Solidity datasets consisting of 52,202 un labeled real smart contract source codes that can be used to verify the correctness of various theories and tools under a large number of real data conditions. At the same time, this paper integrates the scripting framework accompanying the GSVD dataset, which can execute a variety of popular automated vulnerability detection tools on top of these datasets and generate analysis results of contracts and potential vulnerabilities. We tested the Minor dataset under GSVD using three tools (Slither, Manticore, Mythril) that are kept up to date and found that the combined use of all tools detected 61.1% of labeled vulnerabilities, of which Mythril has the highest detection rate of 42.6%. It is not difficult to conclude that there`re still ample room for advancement for current smart contract vulnerability mining tools because of their underlying methods. Besides, our dataset can contribute to the ultimate target greatly by providing mining tools plenty real contracts information.
ABOUT THE ASSESSMENT OF PHYSICAL, MATHEMATICAL AND COMPUTATIONAL COMPLEXITY OF PHYSICS TASKS

The effective method of developing students' physical thinking consists in solving specially selected tasks presented by the teacher in accordance with the principle «from simple to complex». Therefore, the development of methods for assessing the didactic complexity of physical tasks is an urgent problem of didactics. Its resolution will optimize the learning process and knowledge control, quickly and correctly determine the didactic complexity of tasks for the Unified State Exam, physical Olympiads, various task books and test materials. It is established that the didactic complexity of the physics tasks consists of physical, mathematical and computational complexity. To evaluate the numerical components of complexity, it is necessary: 1) formulate the task condition so that it briefly and at the same time fully reflects the analyzed physical situ-ation; 2) write out 5 key terms – the most complex scientific concepts used in solving the physics task; 3) write down the formulas used and the result obtained in general form; 4) create a file in which physical formulas, mathematical reasoning, calculations are verbally encoded in the form of separate sentences; 5) analyze the text file using the special program, determine the overall semantic complexity and the coefficient of in-formation folding; 6) calculate the physical, mathematical and computational complexity by summing up the complexity of the corresponding terms and ordinary words. This will determine the didactic complexity of the task. The article presents examples of the proposed method use, analyzed the results.
Digital Literacy, Inclusivity and Sustainable Development in Africa

Sustainable development can only be achieved when no one is left behind. An enduring lesson from the COVID-19 crisis is how important the availability of digital infrastructure and skills for individuals and communities is for teaching, learning, employability or just being able to participate fully in society. Digital literacy has become critical for millions all over the world and the need has been keenly felt in Africa, where so many have had to quickly adapt and use online platforms for various purposes. The African library sector has been a key advocate for digital literacy across the nation. But what has been achieved and how? How has digital literacy assisted user communities? What remains to be done? &lt;br&gt;&lt;br&gt;This important book features contributions from libraries across Africa outlining how they have approached the shift towards a better and more widespread digital literacy. Coverage includes: &lt;ul&gt;&lt;li&gt;how in Kenya, in line with the country's national vision, libraries have been teaching their user communities, including deaf children, to ably operate in online spaces&lt;/li&gt; &lt;li&gt;the role of digital literacy in increasing employability in Tunisia&lt;/li&gt; &lt;li&gt;the efforts of the Ghana Library Authority, the National Library of Nigeria and the City of Johannesburg Library, South Africa in driving digital literacy through eLearning initiatives and other digital services&lt;/li&gt; &lt;li&gt;insights into the level of digital skills of students in Uganda and how tertiary institutions in Botswana have been moving to teaching and learning on digital platforms.&lt;/li&gt;&lt;/ul&gt;
Combined mass events at sci-tech libraries within the context of ecological literacy projects for students

The author proposes non-traditional formats of mass events for sci-tech libraries in their efforts in educating high school students in ecological literacy as related to the ecologically oriented activities of the railways. The algorithm of designing combined event comprising traditional library services and interactive information support is discussed. The many-year experience of ecological activities of the Yudino Station Hub Library of Gorkovskaya Railway is reviewed. The events are to motivate students to develop their ecological minset and to support ecological principles. The concept envisages creativity combined with traditional functions, and is oriented toward industry’s needs and demands. The case study will be interesting to librarians, pedagogues, other professionals working in ecological education and support, in particular within the Russian Railways Ecological Strategy.
«Ecological education for sustainable development. The partnership trend» – The city scientific and practical conference. The experience of the Ecological Information Center at I. S. Nikitin Regional Universal Scientific Library

The experience of the Ecological Information Center in holding the city scientific and practical conference «Ecological education for sustainable development. The partnership trend» is discussed. The conference coincided with the All-Russia Days of Protection against Environmental Hazards. The conference participants delivered presentations on how the sustainable development ecological agenda was realized in Voronezh.
Building users’ ecological culture

The ecology-related activities of Sukhosolotino rural library is discussed. The role of playful elements of its efforts in stimulating interest toward ecology and environmental protection is emphasized.
Library as a significant institute to build ecological mindset in children and teenagers

The experience of consolidated ecological activities of Children’s Library No. 25 of Surgut Centralized Library System is discussed. Several vectors of library efforts are reviewed in detail; these are: motivating Surgut residents to manage waste, developing ecological mindset in young library users.
Organizing the conference «Innovations and green technologies»

The experience of Samara Regional Universal Scientific Library in organizing the conference «Innovations and green technologies» in partnership with the regional universities, is discussed.
Educational Challenges of University Students in the Pandemic: Analysis of the Perception of Students of the Accounting Sciences Course at Polo Petrolina-PE/Juazeiro-BA

This article aims to analyze the perception of students in accounting science courses about the educational challenges for higher education imposed by the coronavirus (covid-19) pandemic. To this end, the tools were initially analyzed as well as the adaptation of the online/remote teaching system used unexpectedly due to the need caused by the aforementioned pandemic at the beginning of 2020. The focus of the research revolves around obtaining information about the process of adapting students to the new tools used by teachers and the new teaching methodologies adopted for the performance of activities in remote mode. The methodology used was exploratory-descriptive, of a quali-quanti nature, having as a procedure the bibliographic research and case study. The sample is composed of 106 students from the accounting sciences courses in Petrolina-PE and Juazeiro-BA. The results indicate that, despite the difficulties imposed by the current scenario, a reasonable number of respondents demonstrate that they are managing to overcome barriers and are partially satisfied with the novelties used in the academic environment, however, with some reservations, such as the question of motivation for development of scientific research activities, something that can be improved in order to strengthen this new teaching model.
[Geochemical Characteristics of Cd in Different Parent Soils in Karst Area and Prediction of Cd Content in Maize].

The naturally high background value region of Cd derived from the weathering of carbonate has received wide attention. Due to the significant difference in soil physicochemical properties, soil Cd content, and bioavailability of different parent materials in the karst area, there are certain limitations in using the total soil Cd content to classify the environmental quality of cultivated land. In this study, surface soil and maize samples of eluvium and alluvial parent material in typical karst areas were collected systematically; the contents of maize Cd, soil Cd, pH, and oxides were analyzed, the Cd geochemical characteristics of different parent soils and the influencing factors of their bioavailability were revealed, and scientific and effective arable land use zoning suggestions based on the prediction model were suggested. The results showed that the physicochemical properties of different parent material soils in the karst area were obviously different. The alluvial parent material soil had low Cd content but high bioavailability, and the maize Cd exceeding rate was high. The maize Cd bioaccumulation factor was significantly negatively correlated with soil CaO, pH, Mn, and TC, and the correlation coefficients were -0.385, -0.620, -0.484, and -0.384, respectively. Compared with the multiple linear regression prediction model, using the random forest model to predict the maize Cd enrichment coefficient had higher accuracy and precision. Furthermore, a new scheme for the safe utilization of cultivated land at the plot scale based on soil Cd and predicted crop Cd content was proposed in this study, making full use of arable land resources to ensure crop safety.
Interval Methods in Knowledge Representation

International Journal of Uncertainty, Fuzziness and Knowledge-Based SystemsVol. 31, No. 02, pp. 351-352 (2023) No AccessInterval Methods in Knowledge RepresentationVladik KreinovichVladik KreinovichDepartment of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USAhttps://doi.org/10.1142/S0218488523970024Cited by:0 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Remember to check out the Most Cited Articles! Check out our titles on Fuzzy Logic & Z-Numbers With a wide range of areas, you're bound to find something you like. FiguresReferencesRelatedDetails Recommended Vol. 31, No. 02 Metrics History PDF download
An Approach for Mobile Application Design Using Figma

With the increased mobile usage throughout the world, there is an enormous demand for mobile applications that provide not only good functionality but also good experience to the user, which is why it is important to have a good user interface for a mobile application. In this chapter, various components of an app are designed and illustrated in Figma software to make the work easier for a developer to create them. It also demonstrates the usage of Fig-ma. Meanwhile it shows the design thinking behind adding the correct colour schemes, right font, using proper white space, proper placement of buttons, and other design principles.
THE CREATION AND DEVELOPMENT OF THE SCIENTIFIC DATABASE SYSTEM AT THE CHINESE ACADEMY OF SCIENCES

The Scientific Database System (SDBS) of the Chinese Academy of Sciences started in 1986 as the key project in the National Five Year Plan for 1986-1990 and followed the establishment of a chemistry database, one of the important projects placed in the National Programmer for Basic Sciences Development earlier in the period from 1978 to 1985. The growth of non-bibliographic and bibliographic databases implemented in the Academia Sinica has been fairly rapid over the past ten years. This paper describes briefly the project's current major activities, database products and their management system.
AlignGraph: A Group of Generative Models for Graphs

It is challenging for generative models to learn a distribution over graphs because of the lack of permutation invariance: nodes may be ordered arbitrarily across graphs, and standard graph alignment is combinatorial and notoriously expensive. We propose AlignGraph, a group of generative models that combine fast and efficient graph alignment methods with a family of deep generative models that are invariant to node permutations. Our experiments demonstrate that our framework successfully learns graph distributions, outperforming competitors by 25% — 560% in relevant performance scores.
Saucy physics

Colleagues at the University of Oxford have studied the physics behind why a smooth flow of liquid can suddenly spray everywhere.
Local-Global Semantic Fusion Single-shot Classification Method

In few-shot learning tasks, a series of semantic-based methods have shown excellent performance due to the modality fusion of both visual and semantic modalities. However, in single-shot learning tasks, the fused visual modality fails to comprehensively capture the class information since only one image is available. To address this issue, we propose a semantic-based single-shot method which considers from both local and global perspectives. Specifically, we fully exploit local visual features to replace the traditional image-level features in the modality fusion in those semantic-based methods. Moreover, a global classification loss is introduced to enlarge the encoding space for accurate and distinguishable local embeddings. Through a series of experiments, we show that by exploiting local features from a global classification perspective, our model boosts the performance of semantic-based approaches by a large margin on two different data sets and global classification loss is effective on both metrics.
Issues and tasks of the public history in Korea

In the first half of the 21st century, the commemoration and remembrance of 20th century history has overflowed into the public sphere. As different as the fields of public history are, so are their points of contention. The term “consuming history” does not capture the phenomenon. The older concept of “popularising history” prevents the understanding of the phenomenon. Understanding public history as “historiography by citizens” buries public history initiatives and interests from above, from politics and administration. Public history does not simply apply or express in public the historical truth that is the result of academic historical research, but it is a historical account in its own right. Issues and tasks continue to accumulate around it. The ethics of the practice of public history should also be revealed. Public history should be accepted as part of university history research and education. Public history is the “area of responsibility” of historians. ‘Public History Society’ should be established in Korea.
Adaptive RetinexNet and fusion strategy for low-intensity image enhancement

Aiming at the characteristics of low illumination image, such as low brightness, fuzzy detail information and obvious noise, we propose an adaptive RetinexNet low illumination image enhancement method with the fusion strategy. Use the mutual independence of each channel in the HSV color space model to enhance the brightness component; then use the correlation coefficient to make the saturation component adaptively adjust with the brightness component; on the basis of UNet, combine different areas of the illumination enhancement image to carry different levels of Noise, build a reflectivity noise reduction model; a coarse enhanced image is generated through the above three steps; The camera response model is obtained by combining the camera response function and the brightness conversion function, and the optimal exposure rate is found to obtain the exposure rate map; finally, the overexposed image and the coarsely enhanced image are combined with the original low-light image, and the image block decomposition is used for reconstruction and Fusion to get the final low-light enhanced image. The experimental results show that the algorithm we adopt can ensure the texture and details of the image while enhancing the contrast of the image.
THE PLAGIARISM CHECKER USING MACHINE LEARNING

Plagiarism is one of the most increasing problems in many fields; the academic field is one of them. It comes in various forms, from replacing a word with its synonym to sentence modification, transformation, and many more. Though humans are known for their efficient working ability, they may not be able to detect plagiarism in all scenarios accurately. They can’t see similarities against more than one million online documents in seconds. So, concluding that plagiarism detection is tedious and time-consuming work for a human, it would be nice to have a plagiarism checker to do plagiarism detection for us. In this project, a plagiarism checker application is developed based on machine learning as its core that searches a vast database for plagiarized content. Using the vector embeddings concept, a plagiarism checker application is developed, which converts data, for example, text data, into a list of numbers, thus allowing various operations to be performed on the converted data. Vectors are helpful because when we present real-world entities like audio, images, text, etc., as vector embeddings, the semantic similarity between these entities can be quantified by how close they’re to each other as points in vectors. Models are trained to translate entities into vectors; NLP is commonly used for such training. These International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 04 | April - 2023 Impact Factor: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM18949 | Page 2 vector embeddings will be added to the pre-processed database, which will then be ready to be used for our similarity check. The machine learning-based application will take text as input from the user, check the text against the database, and return all the articles from which the input text could be plagiarized, along with the match score. Keywords: Plagiarism detection, machine learning, vector embeddings, NLP.
Thermodynamics of bidirectional associative memories

Abstract In this paper we investigate the equilibrium properties of bidirectional associative memories (BAMs). Introduced by Kosko in 1988 as a generalization of the Hopfield model to a bipartite structure, the simplest architecture is defined by two layers of neurons, with synaptic connections only between units of different layers: even without internal connections within each layer, information storage and retrieval are still possible through the reverberation of neural activities passing from one layer to another. We characterize the computational capabilities of a stochastic extension of this model in the thermodynamic limit, by applying rigorous techniques from statistical physics. A detailed picture of the phase diagram at the replica symmetric level is provided, both at finite temperature and in the noiseless regimes. Also for the latter, the critical load is further investigated up to one step of replica symmetry breaking. An analytical and numerical inspection of the transition curves (namely critical lines splitting the various modes of operation of the machine) is carried out as the control parameters—noise, load and asymmetry between the two layer sizes—are tuned. In particular, with a finite asymmetry between the two layers, it is shown how the BAM can store information more efficiently than the Hopfield model by requiring less parameters to encode a fixed number of patterns. Comparisons are made with numerical simulations of neural dynamics. Finally, a low-load analysis is carried out to explain the retrieval mechanism in the BAM by analogy with two interacting Hopfield models. A potential equivalence with two coupled Restricted Boltmzann Machines is also discussed.
Image Captioning with Local-Global Visual Interaction Network

Existing attention based image captioning approaches treat local feature and global feature in the image individually, neglecting the intrinsic interaction between them that provides important guidance for generating caption. To alleviate above issue, in this paper we propose a novel Local-Global Visual Interaction Network (LGVIN) that novelly explores the interactions between local feature and global feature. Specifically, we devise a new visual interaction graph network that mainly consists of visual interaction encoding module and visual interaction fusion module. The former implicitly encodes the visual relationships between local feature and global feature to obtain an enhanced visual representation containing rich local-global feature relationship. The latter fuses the previously obtained multiple relationship features to further enrich different-level relationship attribute information. In addition, we introduce a new relationship attention based LSTM module to guide the word generation by dynamically focusing on the previously output fusion relationship information. Extensive experimental results show that the superiority of our LGVIN approach, and our model obviously outperforms the current similar relationship based image captioning methods.
Multi-level 3DCNN with Min-Max Ranking Loss for Weakly-Supervised Video Anomaly Detection

Video anomaly detection in real-world surveillance systems is challenging due to the unavailability of large annotated data, visual challenges like partial occlusion and illumination change, and the untrimmed nature of videos. In this paper, we propose a method that mitigates the above challenges. The proposed method adopts a weakly-supervised learning paradigm to address the scarcity of temporally annotated data. In this, only video-level supervision is required for learning, but precise temporal locations of anomalies are detected during testing. To effectively learn from weak supervision, a Min-Max ranking loss is proposed with the objective to maximize the margin of separation between anomaly and normal instances and to minimize the separation among the normal instances simultaneously. Further, to handle the visual challenges in real-world scenarios, a multi-level feature combination strategy from 3DCNN is proposed to extract the fine lower-level representation of the input video sequences. An efficient temporal dependency encoding is utilized further to capture the sharp change in untrimmed surveillance videos. The proposed method is evaluated on a widely used benchmark anomaly detection dataset, UCF-Crime. The results demonstrate that the proposed method achieves competitive performance compared to recently reported anomaly detection methods.