Reviewerly/exHarmony huggingface dataset:
Bibliometric analysis of augmented reality in education and social science

Augmented reality (AR) overlays virtual elements on real-time images. Because to its ubiquitous use on desktop computers and handheld devices, it has become a major study topic. AR provides benefits like engagement, motivation, and learning result, but drawbacks like the technology tool and application and pedagogical challenges. After 2015, global Google searches for AR increased. This study analyzed “AR” literature released after 2015. Exclusion and inclusion criteria are studies were divided into social sciences and education and published in English. Research should be published during 2015-2022. 3,823 studies were analyzed. AR publications will increase after 2020. Annual citations averaged 2.49. Computer &amp;amp; Education leads the top ten journals in citations. National Taiwan Normal University and Beijing Normal University have the most publications when ordered by writers’ institutions. China leads global publications, whereas the US leads overall publications. China led in citations, followed by the US. Macedonia led the average number of article citations, followed by Israel. The strategic map shows the center and density of key issues. “Student learning,” “AR applications,” and “AR technology” are linked and vital terms. Based on the results of the bibliometric study, researchers who wish to work on AR may emphasize theme-based learning studies and AR. Researchers that are considering conducting bibliometric analysis on AR are urged to conduct research utilizing many databases.
A Secure Land Record Management System using Blockchain Technology

A land record (LR) contains very sensitive information related to land e.g. owner, buyer, etc. Currently, almost all over the world, the LR is maintained by different governmental offices and most of them maintain the LR with paper-based approach. Some of the works focus to digitalize the existing land record management system (LRMS) but with some security concerns. A blockchain-based LRMS can be effective enough to solve the existing issues. This paper proposes a blockchain-based LRMS that (i) digitalizes the existing paper-based system, (ii) ensures LR privacy using an asymmetric cryptosystem, (iii) preserves LR integrity, (iv) facilitates a platform for trading land through an advertising agency, and (v) accelerates the process of changing ownership that saves time significantly. Besides, this paper also proposes a new way of character to integer mapping named C2I table that reduces around 33% overhead of text to integer conversion compared to ASCII table. The experimental results, analyses, and comparisons indicate the effectiveness of the proposed LRMS over the state-of-the-art systems.
Deep learning-based virtual staining of histological tissues (Conference Presentation)

Histochemical staining is traditionally performed using chemical labeling, which can be time consuming and expensive, particularly when multiple stains are needed. We present a technique which can be used to virtually stain histological tissues using deep learning. As this technique is performed computationally, multiple stains can be performed on each tissue, allowing pathologists to get more information out of a single tissue section. These stains can be performed using autofluorescence images of unlabeled tissue sections, or with scans of stained H&E stained tissues, which fits into existing pathology workflows. These stains have been validated in blind studies by board-certified pathologists.
Efficient Persistent Fault Analysis with Small Number of Chosen Plaintexts

In 2018, Zhang et al. introduced the Persistent Fault Analysis (PFA) for the first time, which uses statistical features of ciphertexts caused by faulty Sbox to recover the key of block ciphers. However, for most of the variants of PFA, the prior knowledge of the fault (location and value) is required, where the corresponding analysis will get more difficult under the scenario of multiple faults. To bypass such perquisite and improve the analysis efficiency for multiple faults, we propose Chosen-Plaintext based Persistent Fault Analysis (CPPFA). CPPFA introduces chosen-plaintext to facilitate PFA and can reduce the key search space of AES-128 to extremely small. Our proposal requires 256 ciphertexts, while previous state-of-the-art work still requires 1509 and 1448 ciphertexts under 8 and 16 faults, respectively, at the only cost of requiring 256 chosen plaintexts. In particular, CPPFA can be applied to the multiple faults scenarios where all fault locations, values and quantity are unknown, and the worst time complexity of CPPFA is O(28+nf ) for AES-128, where nf represents the number of faults. The experimental results show that when nf &gt; 4, 256 pairs of plaintext-ciphertext can recover the master key of AES-128. As for LED-64, only 16 pairs of plaintext-ciphertext reduce the remaining key search space to 210.
The use of deep learning for smartphone-based human activity recognition

The emerging field of digital phenotyping leverages the numerous sensors embedded in a smartphone to better understand its user's current psychological state and behavior, enabling improved health support systems for patients. As part of this work, a common task is to use the smartphone accelerometer to automatically recognize or classify the behavior of the user, known as human activity recognition (HAR). In this article, we present a deep learning method using the Resnet architecture to implement HAR using the popular UniMiB-SHAR public dataset, containing 11,771 measurement segments from 30 users ranging in age between 18 and 60 years. We present a unified deep learning approach based on a Resnet architecture that consistently exceeds the state-of-the-art accuracy and F1-score across all classification tasks and evaluation methods mentioned in the literature. The most notable increase we disclose regards the leave-one-subject-out evaluation, known as the most rigorous evaluation method, where we push the state-of-the-art accuracy from 78.24 to 80.09% and the F1-score from 78.40 to 79.36%. For such results, we resorted to deep learning techniques, such as hyper-parameter tuning, label smoothing, and dropout, which helped regularize the Resnet training and reduced overfitting. We discuss how our approach could easily be adapted to perform HAR in real-time and discuss future research directions.
Fiddling the Twiddle Constants - Fault Injection Analysis of the Number Theoretic Transform

In this work, we present the first fault injection analysis of the Number Theoretic Transform (NTT). The NTT is an integral computation unit, widely used for polynomial multiplication in several structured lattice-based key encapsulation mechanisms (KEMs) and digital signature schemes. We identify a critical single fault vulnerability in the NTT, which severely reduces the entropy of its output. This in turn enables us to perform a wide-range of attacks applicable to lattice-based KEMs as well as signature schemes. In particular, we demonstrate novel key recovery and message recovery attacks targeting the key generation and encryption procedure of Kyber KEM. We also propose novel existential forgery attacks targeting deterministic and probabilistic signing procedure of Dilithium, followed by a novel verification bypass attack targeting its verification procedure. All proposed exploits are demonstrated with high success rate using electromagnetic fault injection on optimized implementations of Kyber and Dilithium, from the open-source pqm4 library on the ARM Cortex-M4 microcontroller. We also demonstrate that our proposed attacks are capable of bypassing concrete countermeasures against existing fault attacks on lattice-based KEMs and signature schemes. We believe our work motivates the need for more research towards development of countermeasures for the NTT against fault injection attacks.
Investigating the Relationship between Technical Debt Management and Software Development Issues

Context: The presence of technical debt (TD) brings risks to software projects. Managers must continuously find a cost-benefit balance between the benefits of incurring in TD and the costs of its presence in a software project. Much attention has been given to TD related to coding issues, but other types of debt can also have impactful consequences on projects. Aims: This paper seeks to elaborate on the growing need to expand TD research to other areas of software development, by analyzing six elements related to TD management, namely: causes, effects, preventive practices, reasons for non-prevention, repayment practices, and reasons for non-repayment of TD. Method: We survey and analyze, quantitatively and qualitatively, the answers of 653 software industry practitioners on TD to investigate how the previously mentioned elements are related to coding and non-coding issues of the software development process. Results: Coding issues are commonly related to the investigated elements but, indeed, they are only part of the TD Management stage. Issues related to the project planning and management, human factors, knowledge, quality, process, requirements, verification, validation, and test, design, architecture, and the organization are also common sources of TD. We organize the results in a hump diagram and specialize it considering the point of view of practitioners that have used agile, hybrid, and traditional process models in their projects. Conclusion: The hump diagram, in combination with the detailed results, provides guidance on what to expect from the presence of TD and how to react to it considering several issues of software development. The results shed light on TD management of software elements, beyond source code related artifacts.
A Blockchain-Based Crowdsourcing Loan Platform for Funding Higher Education in Developing Countries

In developing countries, funding is a significant obstacle to receiving higher education.Brilliant but needy students cannot complete their studies since their parents are unemployed and their countries' economies are poor.As a result, the students' talents are not harnessed to their full potential.Blockchain technology is a growing platform for making and storing secure and trustworthy transactions on distributed ledgers.In this work, we employ blockchain technology coupled with crowdsourcing to provide a loan platform to offer financial assistance to students in tertiary institutions in developing countries.This platform will allow students to apply for loans through registered financial institutions from registered investors and pay them back with interest when they start working.We perform a thorough security analysis and back the efficiency of our work with numerical results.
Globalization, Governance, and Inclusive Human Development: An Asian Perspective

An enhanced focus on inclusive economic growth has recently led to a paradigm shift in framing development policies.Inclusive human development cannot be achieved if principles of participation and equity are disregarded.The current study investigates the implications of globalization and governance from the perspective of inclusiveness by using IHDI (Inequality-Adjusted Human Development Index) for a set of Asian countries from 2010 to 2018.The Feasible Generalized Least Square (FGLS) technique is used to assess this relation, and the results show that governance and globalization aid in making human development more inclusive.Population growth has a negative relation with inclusive human development, and higher population growth in Asia puts pressure on the already fragile institutions.The study also entails reducing corrupt practices, ensuring accountability, and transparency in political and regulatory processes is vital for broad-based human development.Higher liberalization, higher levels of social globalization, and effective governance can also be helpful in realizing the goal of more inclusive human development in Asia.
Kegunaan Model CIPP dalam Evaluasi Pendidikan Inklusi

Inclusion..education..in..the..modern..era..as..it..is..today becomes a very important thing in the world of Education. Therefore,..this study was conducted to find out the usefulness of the CIPP model in the expansion of inclusion education programs. In the process of evaluation of inclusion education, this CIPP model consists of 4 steps, namely: 1) context evaluation, conducted to set curriculum objectives, 2) input evaluation, determining the selection of strategies and teaching methods to be used, 3) evaluation of the process, conducting the selection of the learning process and solving existing problems, 4) product evaluation, is the result of the achievement of a program. In this study, researchers used the research method of literature studies in finding data related to the problem topic.
Timely Business Record Management Keeps a Business Safe An Opinion from Small and Medium Enterprises

The delayed recording of transactions creates risk for small and medium entrepreneurial businesses. This practice may become the cause of errors, and fraud in business records may suffer in the future. The study aims to create awareness among entrepreneurial businesses. The data was collected from 120 respondents by convenience sampling method. Used a well-developed and verified scale closed-ended questionnaire for primary data collection. Used SPSS and descriptive statistics for data analysis. The results showed that many irregularities occurred in small and medium entrepreneurial businesses due to the delayed recording of business transactions. Pointing out the delayed recordings of business transactions practice in small and medium entrepreneurial businesses is a grey area and is a novelty for this research. The guidelines may be followed by other small and medium enterprises for keeping them safe from irregularities.
Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data

The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model's transfer learning skills for various tumor-related medical events. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological medical events.
She adapts to her student: An expert pragmatic speaker tailoring her referring expressions to the Layman listener

Communication is a dynamic process through which interlocutors adapt to each other. In the development of conversational agents, this core aspect has been put aside for several years since the main challenge was to obtain conversational neural models able to produce utterances and dialogues that at least at the surface level are human-like. Now that this milestone has been achieved, the importance of paying attention to the dynamic and adaptive interactive aspects of language has been advocated in several position papers. In this paper, we focus on how a Speaker adapts to an interlocutor with different background knowledge. Our models undergo a pre-training phase, through which they acquire grounded knowledge by learning to describe an image, and an adaptive phase through which a Speaker and a Listener play a repeated reference game. Using a similar setting, previous studies focus on how conversational models create new conventions; we are interested, instead, in studying whether the Speaker learns from the Listener's mistakes to adapt to his background knowledge. We evaluate models based on Rational Speech Act (RSA), a likelihood loss, and a combination of the two. We show that RSA could indeed work as a backbone to drive the Speaker toward the Listener: in the combined model, apart from the improved Listener's accuracy, the language generated by the Speaker features the changes that signal adaptation to the Listener's background knowledge. Specifically, captions to unknown object categories contain more adjectives and less direct reference to the unknown objects.
A Comparison of SVM Against Pre-trained Language Models (PLMs) for Text Classification Tasks

The emergence of pre-trained language models (PLMs) has shown great success in many Natural Language Processing (NLP) tasks including text classification. Due to the minimal to no feature engineering required when using these models, PLMs are becoming the de facto choice for any NLP task. However, for domain-specific corpora (e.g., financial, legal, and industrial), fine-tuning a pre-trained model for a specific task has shown to provide a performance improvement. In this paper, we compare the performance of four different PLMs on three public domain-free datasets and a real-world dataset containing domain-specific words, against a simple SVM linear classifier with TFIDF vectorized text. The experimental results on the four datasets show that using PLMs, even fine-tuned, do not provide significant gain over the linear SVM classifier. Hence, we recommend that for text classification tasks, traditional SVM along with careful feature engineering can provide a cheaper and superior performance than PLMs.
EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python

This paper presents an open-source Python toolbox called Ensemble Feature Importance (EFI) to provide machine learning (ML) researchers, domain experts, and decision makers with robust and accurate feature importance quantification and more reliable mechanistic interpretation of feature importance for prediction problems using fuzzy sets. The toolkit was developed to address uncertainties in feature importance quantification and lack of trustworthy feature importance interpretation due to the diverse availability of machine learning algorithms, feature importance calculation methods, and dataset dependencies. EFI merges results from multiple machine learning models with different feature importance calculation approaches using data bootstrapping and decision fusion techniques, such as mean, majority voting and fuzzy logic. The main attributes of the EFI toolbox are: (i) automatic optimisation of ML algorithms, (ii) automatic computation of a set of feature importance coefficients from optimised ML algorithms and feature importance calculation techniques, (iii) automatic aggregation of importance coefficients using multiple decision fusion techniques, and (iv) fuzzy membership functions that show the importance of each feature to the prediction task. The key modules and functions of the toolbox are described, and a simple example of their application is presented using the popular Iris dataset.
Deterministic-Probabilistic image model in technologies of aerospace monitoring

The problem of increasing the effectiveness of aerospace monitoring and automatic processing of video data is analysed. For evaluating the informative features of image it has been suggested to use deterministic-probabilistic model, which ensures allocating and recognizing the image data (clusters) under observation based on calculations, firstly, of image's evaluating functionality through variation of function and, secondly, of momentary and cumulant (invariant) functions. Usage of a two-level model of image for evaluation of informative features, provides the possibility of implementing a stream processing of video data and recognizing the observable image data in real-time mode and in conditions of minimum level of prior awareness about signal and jamming environment. Article describes the computing algorithm of video data processing in challenges of aerospace monitoring and recognizing the objects of the earth surface.
Robust Audio Watermarking Based on Empirical Mode Decomposition and Group Differential Relations

W. Lai, T. Chou, M. Chou, and BJ. W.. Schuller, "Robust Audio Watermarking Based on Empirical Mode Decomposition and Group Differential Relations,"
Evaluation Model of Comprehensive Ability of Accounting Talents in Higher Vocational Colleges that Uses Big Data Calculation

The rapid development of new technologies such as big data, artificial intelligence, mobile Internet, cloud computing, and blockchain has exerted a multi-dimensional impact on the accounting industry, and the needs of enterprises have changed accordingly.The demand for compound talents with a solid financial foundation and big data application ability is surging, but there is no corresponding standard for evaluating the comprehensive ability of those talents.Based on the needs of enterprises, we construct the comprehensive ability evaluation framework for higher vocational accounting talents, including four middle-level indicators such as professional quality, professional ability, management communication, and data processing with 20 specific indicators.The AHP method is used to determine the weight, and the fuzzy comprehensive evaluation method is used to empirically evaluate the accounting talents in higher vocational colleges.The result is used to realize the accurate and effective evaluation of the comprehensive ability of accounting talents in higher vocational colleges under the background of big data.
Guidelines for choosing hyperparameters of echo state networks for system identification: two case studies

Echo state networks (ESN) can be used to model dynamical systems in the context of reservoir computing using standard regression algorithms, which is one of their main advantages. However, there are some hyperparameters that need to be carefully chosen and there is no general recommendation on how to perform this important step. After setting the ESN paradigm for system identification, this paper describes the choice of hyperparameters in the context of two case studies: one using experimental data of a pilot heater and the other using the Duffing-Ueda oscillator with chaotic dynamics. The main findings are: 1) ESNs can reproduce the chaotic regime of the Duffing-Ueda oscillator for a specific region on the hyperparameter space; 2) some hyperparameters may not be critical from a statistical perspective but can still drastically affect the dynamical regime; 3) the ESN initialisation is not critical when the hyperparameters are adequately chosen.
A Security Study about Electron Applications and a Programming Methodology to Tame DOM Functionalities

The Electron platform represents a paradigm to develop modern desktop apps using HTML and JavaScript.Microsoft Teams, Visual Studio Code and other flagship products are examples of Electron apps.This new paradigm inherits the security challenges in web programming into the desktop-app realm, thus opens a new way for local-machine exploitation.We conducted a security study about real-world Electron apps, and discovered many vulnerabilities that are now confirmed by the app vendors.The conventional wisdom is to view these bugs as sanitization errors.Accordingly, secure programming requires programmers to explicitly enumerate all kinds of unexpected inputs to sanitize.We believe that secure programming should focus on specifying programmers' intentions as opposed to their non-intentions.We introduce a concept called DOM-tree type, which expresses the set of DOM trees that an app expects to see during execution, so an exploit will be caught as a type violation.With insights into the HTML standard and the Chromium engine, we build the DOM-tree type mechanism into the Electron platform.The evaluations show that the methodology is practical, and it secures all vulnerable apps that we found in the study.