Reviewerly/exHarmony huggingface dataset:
Grid cut and mix: flexible and efficient data augmentation

Data augmentation plays an indispensable role in expanding datasets and preventing neural network overfitting. In this paper, we analyzed the advantages and disadvantages of information dropping and information mixing methods in data augmentation. Then we combine the advantage of information dropping and information mixing, and propose Gridcut and Gridmix. Gridcut is based on the structured deletion of the input images like Gridmask. The size and shape of the deleted areas can be flexibly adjusted according to the Characteristics of the dataset. Based on this Gridcut, Gridmix interpolates pixel information of other images on the deleted areas. Comparative experiments demonstrate that our methods outperform the state-of-the-art augmentation strategies on CIFAR classification tasks. By adjusting the parameters, our method can also be flexibly expanded into Cutout, Cutmix and Gridmask.
Securing Genomics Data Using Blockchain Technology

Data dominates modern computation and research. Any representational or abstract entity in the world is addressed by the data associated with it. Genomics involves the study, sequencing, and analysis of genomes to analyze the structure and the features of genetic components. Thus, the data generated is extremely significant and sensitive to security. Blockchain being popular as a distributed database promises a revolution. The paper explores the requirements of securing such data and discusses the concept and implementation of scenarios where blockchain technology can be implemented in the form of smart contracts to secure and manage the information.
Mammogram density classification using deep convolutional neural network

Artificial intelligence plays an important role in the classification of medical images for computerized diagnosis of the disease. The computer-aided medical imaging analysis system is developed for breast tissue density classification in mammogram images. Mammogram density is considered as significant predictive markers for breast cancer detection, treatment and management. Recently, deep learning techniques achieved impressive results in computer-assisted disease diagnosis. The deep learning technique such as the convolution neural network (CNN) is used for automated classification of mammogram density as fatty, dense and glandular. This study investigates how computer-aided medical imaging analysis system provides a reliable classification of mammogram density. The proposed methodology is evaluated using a mini-MIAS (Mammogram Image Analysis Society) database. We obtained an average accuracy of 98.5%. So, the proposed CAD system aids the clinicians in the classification of mammogram density.
How can I choose an explainer? An Application-grounded Evaluation of Post-hoc Explanations.

There have been several research works proposing new Explainable AI (XAI) methods designed to generate model explanations having specific properties, or desiderata, such as fidelity, robustness, or human-interpretability. However, explanations are seldom evaluated based on their true practical impact on decision-making tasks. Without that assessment, explanations might be chosen that, in fact, hurt the overall performance of the combined system of ML model + end-users. This study aims to bridge this gap by proposing XAI Test, an application-grounded evaluation methodology tailored to isolate the impact of providing the end-user with different levels of information. We conducted an experiment following XAI Test to evaluate three popular post-hoc explanation methods -- LIME, SHAP, and TreeInterpreter -- on a real-world fraud detection task, with real data, a deployed ML model, and fraud analysts. During the experiment, we gradually increased the information provided to the fraud analysts in three stages: Data Only, i.e., just transaction data without access to model score nor explanations, Data + ML Model Score, and Data + ML Model Score + Explanations. Using strong statistical analysis, we show that, in general, these popular explainers have a worse impact than desired. Some of the conclusion highlights include: i) showing Data Only results in the highest decision accuracy and the slowest decision time among all variants tested, ii) all the explainers improve accuracy over the Data + ML Model Score variant but still result in lower accuracy when compared with Data Only; iii) LIME was the least preferred by users, probably due to its substantially lower variability of explanations from case to case.
Social Big Data: Case Studies

Based on the concepts and theory that we have introduced in another paper “Social Big Data: Concepts and Theory” in this issue, we will concretely explain hypothesis generation and integrated analysis through use cases in this paper.
An adaptive weak light image enhancement method

In this paper, an adaptive method is proposed to address the problems of over-enhancement of weak light image enhancing and adaptivity of parameter settings based on local gamma transform and illumination-reflection model. In which the source image is converted from RGB color space into the YUV color space firstly, and the illumination distribution of the scene is extracted by using a guided filtering function. Then, an adaptive local gamma transform is designed to perform enhancement on the illumination component and the dynamic range of gray-scale is expanded. Finally, the image is changed from YUV space to RGB space. Experimental results shows that the proposed algorithm can not only effectively improve the visual effect of the uneven light image but also reveal more detailed information in dark regions.
Evaluating the Impact of Semantic Technologies on Bibliographic Systems: A User-Centered and Comparative Approach

Semantic and linked-data technologies are currently used by several cultural heritage institutions to make their content available through the Web. Although these technologies are heavily oriented towards data reuse and integration, one clear benefit highlighted by recent literature is the enhancement of human cultural consumption and user experience through the development of novel cultural end-user applications like Online Public Access Catalogues (OPACs). However, to the best of our knowledge, studies into the impact of these technologies on end-user applications are scarce. In order to address this lack, we report the results of two within-group user-centred studies of two online bibliographic systems in a realistic setting – using a widely deployed OPAC and its counterpart linked-data based system, datos.bne.es. The results of our first within-group study show that users of the system based on linked data required significantly less time and visited fewer pages to complete a typical search and retrieval activity. Additionally, the results of our user satisfaction tests also provided significantly better results for this new system. These results are consistent with the hypothesis that semantic technologies applied to library catalogues provide an enhancement that helps satisfy users’ information needs.
Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks

We propose Multi-Level Local SGD, a distributed stochastic gradient method for learning a smooth, non-convex objective in a multi-level communication network with heterogeneous workers. Our network model consists of a set of disjoint sub-networks, with a single hub and multiple workers; further, workers may have different operating rates. The hubs exchange information with one another via a connected, but not necessarily complete communication network. In our algorithm, sub-networks execute a distributed SGD algorithm, using a hub-and-spoke paradigm, and the hubs periodically average their models with neighboring hubs. We first provide a unified mathematical framework that describes the Multi-Level Local SGD algorithm. We then present a theoretical analysis of the algorithm; our analysis shows the dependence of the convergence error on the worker node heterogeneity, hub network topology, and the number of local, sub-network, and global iterations. We illustrate the effectiveness of our algorithm in a multi-level network with slow workers via simulation-based experiments.
CRNN with 2D attention for word recognition of English exams

Word recognition is a basic task for intelligent K-12 education, which leads to further complex tasks including grammar checking, composition grading, etc. However, there is little study about recognition of students’ handwritten words. We propose a novel convolutional recurrent neural network architecture that combines attention mechanism with connectionist time classification loss for student handwritten words. And the method also performs excellently in handwritings of adults. With an ablation study, we show that our method is better than its counterpart without attention. The CRNN with attention model we proposed achieve superior performance on word recognition and has the potential to support applications of intelligent K-12 education.
Why Stake When You Can Borrow

As smart contract platforms autonomously manage billions of dollars of capital, quantifying the portfolio risk that investors engender in these systems is increasingly important. Recent work illustrates that Proof of Stake (PoS) is vulnerable to financial attacks arising from on-chain lending and has worse capital efficiency than Proof of Work (PoW) \cite{fanti_pos_econ}. Numerous methods for improving capital efficiency have been proposed that allow stakers to create fungible derivative claims on their staked assets. In this paper, we construct a unifying model for studying the security risks of these proposals. This model combines birth-death P\'olya processes and risk models adapted from the credit derivatives literature to assess token inequality and return profiles. We find that there is a sharp transition between 'safe' and 'unsafe' derivative usage. Surprisingly, we find that contrary to \cite{fanti2019compounding} there exist conditions where derivatives can \emph{reduce} concentration of wealth in these networks. This model also applies to Decentralized Finance (DeFi) protocols where staked assets are used as insurance. Our theoretical results are validated using agent-based simulation.
STUDY OF ELECTRIC MOTORS FOR AUTOMATED SOLAR PANEL CLEANING SYSTEMS

One of the main problems faced during solar panels operation is the contamination of their front surfaces. This paper presents the results of solving that problem by means of new equipment design development. The authors have proposed an autonomous and reliable cleaning method with low power consumption and no possibility of panel shading. In connection with the noted criteria, an automated system with a short payback period was created. The article presents a description of the developed device, the results of a study on the choice of an engine, an electric drive. The use of an automated system for cleaning solar panels will allow minimizing the share of human participation in the solar panels cleaning.
Institutional Quality and Economic Growth Correlation in Africa; An Augmented Mean Group and Common Correlated Effects Mean Group Approach

The core drive of this study is to explore the correlation between institutional quality and economic growth in Africa for the period 1997 to 2017. We used institutional quality indicators from World Governance Indicators. We also used other control variables, which include; trade openness, gross fixed capital formation, government expenditure, foreign direct investment, and inflation; all the control variables were extracted from World Development Indicators as well as gross domestic growth per capita. Our sample size was 39 countries. Our methodology was correlation analysis, cross-sectional dependency test, panel unit root test, model estimation, and causality test. We used two different estimation techniques, Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCE). The main findings are; rule of law, political stability & absence of violence/terrorism, and voice & accountability have a positive impact on economic growth, while regulatory quality, control of corruption, and government effectiveness, has a negative impact on economic growth. Besides, there is a dual causality between economic growth and government effectiveness & rule of law influence. On the other hand, there is a single causality from economic growth to; voice & accountability, control of corruption, regulatory quality, and political stability & absence of violence/terrorism.
Extremely High Compression and Identification of Fingerprint Images Using SA4 Multiwavelet Transform

The aim of any fingerprint image compression technique is to achieve a maximum amount of compression with an adequate quality compressed image which is suitable for fingerprint recognition. Currently available techniques in the literature provide 100% recognition only up to a compression ratio of 180:1. The performance of any identification technique inherently depends on the techniques with which images are compressed. To improve the identification accuracy while the images are highly compressed, a multiwavelet-based identification approach is proposed in this paper. Both decimated and undecimated coefficients of SA4 (Symmetric Antisymmetric) multiwavelet are used as features for identification. A study is conducted on the identification performance of the multiwavelet transform with various sizes of images compressed using both wavelets and multiwavelets for fair comparison. It was noted that for images with size power of 2, the decimated multiwavelet-based compression and identification give a better performance compared to other combinations of compression/identification techniques whereas for images with size not a power of 2, the undecimated multiwavelet transform gives a better performance compared to other techniques. A 100% identification accuracy was achieved for the images from NIST-4, NITGEN, FVC2002DB3_B, FVC2004DB2_B and FVC2004DB1_B databases for compression ratios up to 520:1, 210:1, 445:1, 545:1 and 1995:1, respectively.
Towards an open conservation documentation service

This article proposes the implementation of a system which allows for sharing conservation data using Linked Data technologies. It begins with a review of the reasons why conservation data should be shared by default and highlights the value of sharing for practical tasks such as assessing environmental control based on condition surveys and environmental monitoring records. The article also makes the argument for public delivery of work which is completed with public funding and continues by examining the value of sharing data in other fields of research. It makes reference to important projects which were only possible because of sharing data, such as the Human Genome Project (HGP) and other projects in the fields of meteorology and biodiversity. A short introduction to ‘5 Star Linked Data’ is included alongside a description of key concepts for Linked Data, such as the production and maintenance of Uniform Resource Identifiers (URIs). An outline of the proposed work for making conservation data sharable is given which includes terminology harmonisation, reconciliation of records and mapping of local databases to a global schema (CIDOC-CRM). The article concludes with an attempt to identify ongoing costs for the community to maintain a data sharing system and refers to standard subscription models of conservation professional bodies and the requirement for synergy across them.
Cryptographic Keyed Hash Function: PARAŚU-256

Cryptography has distinct paramount significance in interconnected environment where whether we concern about ‘Computer-Security,’ ‘Information-Security,’ ‘Network-Security’ etc., doesn’t matters and, ingredients of this particular branch of computer science viz. ‘Encryption/Decryption’ systems, ‘Hash-Functions,’ ‘Digital-Signatures’ etc. also playing vital role from years in order to manage ‘Confidentiality,’ ‘Integrity’ and ‘Authenticity’ of resources. The Encryption/Decryption Cryptosystems managing the Confidentiality and Hash-Functions are fully devoted towards to look after the Integrity, Authenticity either alone or with Digital Signature schemes. There is a huge list of ‘Hash-Algorithms’ available with us to provide dedicated services (i.e., security) depends on how complex is to break it. Few renowned names are MD5, Blake, CRUSH, Grøstl, HAIFA, JH, Lake, SHA, Skein, Whirlpool etc., Keyed/Un-keyed constructions having its own justifiable capabilities. So in this direction we’re proposing a new secure keyed Hash-Function especially for military services where security is major concern apart from resource utilization which not only fulfilling basic properties but also providing justifiable services in order to manage integrity/authenticity of resources.
Automating Modular Verification of Secure Information Flow

Verifying secure information flow by reducing it to safety verification is a popular approach, based on constructing product programs or self-compositions of given programs. However, most such existing efforts are non-modular, i.e., they do not infer relational specifications for procedures in interprocedural programs. Such relational specifications can help to verify security properties in a modular fashion, e.g., for verifying clients of library APIs. They also provide security contracts at procedure boundaries to aid code understanding and maintenance. There has been recent interest in constructing modular product programs, but where users are required to provide procedure summaries and related annotations. In this work, we propose to automatically infer relational specifications for procedures in modular product programs. Our approach uses syntax-guided synthesis techniques and grammar templates that target verification of secure information flow properties. This enables automation of modular verification for such properties, thereby reducing the annotation burden. We have implemented our techniques on top of a solver for constrained Horn clauses (CHC). Our evaluation demonstrates that our tool is capable of inferring adequate relational specifications for procedures without requiring annotations. Furthermore, it outperforms an existing state-of-the-art hyperproperty verifier and a modular CHC-based verifier on benchmarks with loops or recursion.
Internet of Things Access Control System Based on Hyperledger

Abstract The current access control of IoT devices adopts the traditional method of centralized control, which leads to the shortcomings of single point of failure and low efficiency of the entire system. Due to the increasing amount of data generated by the Internet of Things network and the more complex data structure, the traditional access control system has exposed the problem of insufficient security when communicating. Based on the characteristics of blockchain decentralization, non-tampering, and programmable Hyperledger, an attribute-based access control mechanism combining blockchain with the Internet of Things is proposed to realize dynamic changes to the access control strategy and ensure the access control of the Internet of Things Safety and reliability.
Forecasting tuberculosis morbidity rate in Indonesia using autoregressive integrated moving average (ARIMA) method

Abstract Tuberculosis is a disease that can affect socio-economic development. Based on data from the World Health Organization, there were 810,918 tuberculosis cases in Indonesia, which is noted as the third-highest number of tuberculosis cases in Asia in 2016. Prevention and control of tuberculosis are of considerable importance, especially in the insurance field, to cover the cost of treatment, so an accurate model of tuberculosis morbidity is needed. The method used in forecasting the tuberculosis morbidity rate is Autoregressive Integrated Moving Average (ARIMA) method. The ARIMA method is a time series method that is widely used to predict morbidity rates in the future. The data used in this study is the number of incidence morbidity tuberculosis rates that occurred in Indonesia from 2000 to 2017, which is obtained from the World Bank. The results showed that ARIMA (1, 2, 0) is the best and very accurate model to forecast the morbidity rate in Indonesia from 2018 to 2027, with the mean absolute percentage error (MAPE) is 0.1682 % and Akaike Information Criterion (AIC) values is -181.0120. The results of forecasting tuberculosis morbidity rate are expected to help insurance companies in determining the amount of premium paid by customers who suffer tuberculosis diseases.
The Typed Graph Model

In recent years, the Graph Model has become increasingly popular, especially in the application domain of social networks. The model has been semantically augmented with properties and labels attached to the graph elements. It is difficult to ensure data quality for the properties and the data structure because the model does not need a schema. In this paper, we propose a schema bound Typed Graph Model with properties and labels. These enhancements improve not only data quality but also the quality of graph analysis. The power of this model is provided by using hyper-nodes and hyper-edges, which allows to present a data structure on different abstraction levels. We demonstrate by example the superiority of this model over the property graph data model of Hidders and other prevalent data models, namely the relational, object-oriented, and XML model.
The effects of pbl-based data science education program using app inventor on elementary students' computational thinking and creativity improvement

This study is to investigate the effect of PBL-based Data Science Education Program using App Inventor on Computational Thinking and Creativity of elementary students. Based on the results of the pre-requisite analysis by Rossetts demand analysis model, PBL-based Data Science Education program was designed according to the procedure of ADDIE model which is 42 hours of classroom instruction for elementary student. As a result of the Paired t-test, it was proved that the Computational Thinking was statistically significantly improved in the post-test. In addition, as a result of the Paired t-test and Wilcoxons signed rank test, it was found that the sub-factors of Creativity were Originality, Elavoration, Closure, Average, and Index. Therefore, it was confirmed that the PBL-based Data Science Education Program using App Inventor is effective in improving Computational Thinking and Creativity of elementary student.
Ineffability and Unexhaustification

Maximize Presupposition seems to apply globally in some cases, but locally in others. Singh’s (2011) account resolves this tension, but makes crucial use of dynamic semantics. We observe that an alternative static account is possible based on the principle AVOID  INEFFABILITY!. The principle licenses non-local application of maximize presupposition if and only if the structure is otherwise ineffable. After presenting our analysis, we move on to discuss cases where our account and Singh’s make different predictions.  Focusing on inferences associated with wh-questions in Spanish, we argue that our static account makes the correct predictions, while Singh’s dynamic account does not.