Reviewerly/exHarmony huggingface dataset:
AnimoSpace LMS Transcending Physical Learning Barriers

This chapter employed qualitative and case study approaches to elicit valuable insights from De La Salle University tertiary teachers' use of digital educational technology and AnimoSpace in addressing the challenges posed by online learning environments and the issues of equity and inclusion. Analysis of interviews and AnimoSpace statistical data was conducted through the lens of the task-technology fit perspective outlining the tasks and roles teachers perform in the online environment and the characteristics of AnimoSpace. Findings revealed that similar tasks and roles were performed when teaching and learning were shifted online. AnimoSpace and other digital technology fostered equity and inclusion in the online learning environment. However, additional roles/role changes were uncovered, specifically Technologist, Basic Troubleshooter, Instructional Designer, and Alternative Assessor. In addition, the lack of cost-effective or free proctoring tools compelled teachers to seek alternative forms of assessment to measure learning outcomes.
Student Digital Literacy Competence Study Language and Literature Education Program Indonesia

This study aims to determine the digital literacy competence of Indonesian Language and Literature Education Study Program students. This study used a descriptive qualitative method to process the data from the sixth-semester student at HKBP Nommensen University. The results of this study found that students had good basic skills, major skills, intermediate skills, advanced skills, and literacy skills. However, the technical capabilities are still relatively poor. In (1) Basic Competencies, some problems are resolved properly, there are also several topical problems that still exist but at least there is an overview of problem-solving. (2) Students' Basic Competencies have a fairly good introduction to the basics of computer equipment. (3) Students' Intermediate Competencies increase at this stage, namely understanding various features on laptops, using printers, and using Ms. applications. Word and use of e-mail applications. The mastery of this competency is due to the fact that informants often carry out these activities so that they are more accustomed to their use. (4) Advanced Competencies to access information using search engines and online databases are good. (5) Technical Competencies of the Indonesian Language and Literature Study Program students as research informants do not yet have technical expertise regarding hardware and software. Informants have not been able to repair damage to digital equipment even at a simple level. (6) Students' Digital Literacy Proficiency does not yet have sufficient literacy skills starting from formulating a search plan, establishing a search strategy, organizing, processing, applying and communicating information, and understanding standard citation formats, and do not have the effort to communicate information with technology.
Technology Risks and their Impact on the Quality of Banking Service: Field Research in a Sample of Iraqi Private Banks

This research aims to reach the impact of the relationship between technology risks and the quality of banking services in Iraqi private banks, and for the purpose of achieving this goal, the validity of the hypothesis was tested. There is a statistically significant relationship between technology risks and the quality of banking services provided to customers, through the descriptive and analytical method in determining the impact of the relationship among the search variables using the SPSS program .In addition to using the inductive method in interpreting the research variables and proving hypotheses, the research found a relationship with inverse statistical significance between the risks of technology and the quality of banking services provided. Users of electronic banking transactions, and this in turn reflects positively on the quality of banking service provided to customers.
On the strategy of emphasizing disruptive computer-based approaches when teaching astronomy to promote related careers

The knowledge attained in the last decades in space physics, and in particular in Astronomy, has attracted, in part due to its associated computational innovation components, the attention of young students all over the world. Thus, both teaching and the scientific dissemination of knowledge in these areas should seek to guide, based on this interdisciplinary characteristic, talented young people towards related careers. By introducing Space Physics and Astronomy as data-driven sciences, and with a high dependence on computer simulation and disruptive technologies, we make the research and development activities associated with them even more attractive. In this article we highlight the program called Decolar Astronomia (DA), an initiative carried out in the city of S˜aoJos´e dos Campos, SP, Brazil. In the DA 2022 edition, eight activities were formulated with the aim of emphasizing the relevance of computational knowledge in space sciences with an emphasis on 21st century Astronomy. In addition to describing the activities that were applied under DA-2022, we present a more general strategy that can be applied by any other program. This strategy emphasizes computational aspects (such as data-driven science and disruptive technologies) related to Astronomy, in motivating teaching programs for young students. The expected result, after applying this strategy, is that the interest in Astronomy and related careers becomes even greater.
Comprehensive Review of Deep learning Techniques in Electronic Medical Records

A digital collection of patient’s health care data like diagnosis history of patient, treatment details, medical prescriptions are stored electronically. This electronic patient health records (EPHR) model provides huge volume of real time data and used for clinical research. Natural Language processing (NLP) automatically retrieve the patient’s information based on decision support system. NLP performs traditional techniques of machine learning, deep learning algorithms and focussing on word embeddings, classification and prediction, extraction, knowledge graphs, phenotyping, etc. By using NLP technique, extract the information from clinical data and analysis it provides valuable patient medical information. NLP based on clinical systems are evaluated on document level annotations which contains document of patient report, health status of patient, document section types contain past medical history of patient, summary of discharge statement, etc. similarly the semantic properties contain severity of disease in the aspects of positivity, negativity. These documents are developed and implemented on word level or sentence level. In this survey article, we summarize the recent NLP techniques which are used in EPHR applications. This survey paper focuses on prediction, classification, extraction, embedding, phenotyping, multilingually etc techniques.
Hypergraph Embedding Based on Random Walk with Adjusted Transition Probabilities

In this paper, we consider embedding hypergraphs using random walks. By executing a random walk on a hypergraph and inputting the resulting node sequence into a skip-gram used in natural language processing, a vector representation that captures the graph structure can be obtained. We propose a random walk method with adjustable transition probabilities for hypergraphs. As a result, we argue that it is possible to embed graph features more appropriately. Experimental results show that by tuning the parameters of the proposed method appropriately, highly accurate results can be obtained even for large hypergraphs for machine learning tasks such as node label classification.
Aerial assessment of solar panel surfaces using drone

The concept of image processing has come a long way in the recent past. There are multiple softwares based on image processing with better accuracy and efficiency even when compared to the human eye. With open-source libraries like Python growing rapidly in development, there are many image processing libraries like OpenCV, Mahotas, Pillow / PIL, NumPy are being used widely and in most image processing softwares. This project is a demonstration of how image processing and machine learning is used to identify and differentiate different contaminants on the surface of a solar panel. A solar panel is surrounded by lots of empty areas like on rooftops of buildings and solar farms. Due to this, it is very easy for contaminants like dirt and bird droppings to make the solar panel dirty. When this happens and with more accumulation of contaminants, the solar panel efficiency exponentially decreases. Hence it is important to keep such surfaces clean as much as possible. Although, cleaning the surfaces is a tedious and moreover, a dangerous job. It is better to be prepared with the nature of the contaminants on the surface before actually going to clean it so that the cleaners will not have to take anything more than the necessary equipment.
Aerial images object detection method based on cross-scale multi-feature fusion

Aerial image target detection technology has essential application value in navigation security, traffic control and environmental monitoring. Compared with natural scene images, the background of aerial images is more complex, and there are more small targets, which puts higher requirements on the detection accuracy and real-time performance of the algorithm. To further improve the detection accuracy of lightweight networks for small targets in aerial images, we propose a cross-scale multi-feature fusion target detection method (CMF-YOLOv5s) for aerial images. Based on the original YOLOv5s, a bidirectional cross-scale feature fusion sub-network (BsNet) is constructed, using a newly designed multi-scale fusion module (MFF) and cross-scale feature fusion strategy to enhance the algorithm's ability, that fuses multi-scale feature information and reduces the loss of small target feature information. To improve the problem of the high leakage detection rate of small targets in aerial images, we constructed a multi-scale detection head containing four outputs to improve the network's ability to perceive small targets. To enhance the network's recognition rate of small target samples, we improve the K-means algorithm by introducing a genetic algorithm to optimize the prediction frame size to generate anchor boxes more suitable for aerial images. The experimental results show that on the aerial image small target dataset VisDrone-2019, the proposed method can detect more small targets in aerial images with complex backgrounds. With a detection speed of 116 FPS, compared with the original algorithm, the detection accuracy metrics mAP0.5 and mAP0.5:0.95 for small targets are improved by 5.5% and 3.6%, respectively. Meanwhile, compared with eight advanced lightweight networks such as YOLOv7-Tiny and PP-PicoDet-s, mAP0.5 improves by more than 3.3%, and mAP0.5:0.95 improves by more than 1.9%.
An Extension of Overbeck’s Attack with an Application to Cryptanalysis of Twisted Gabidulin-Based Schemes

In this article, we discuss the decoding of Gabidulin and related codes from a cryptographic point of view, and we observe that these codes can be decoded solely from the knowledge of a generator matrix. We then extend and revisit Gibson and Overbeck attacks on the generalized GPT encryption scheme (instantiated with the Gabidulin code) for different ranks of the distortion matrix. We apply our attack to the case of an instantiation with twisted Gabidulin codes.
New NTRU Records with Improved Lattice Bases

The original NTRU cryptosystem from 1998 can be considered the starting point of the great success story of lattice-based cryptography. Modern NTRU versions like NTRU-HPS and NTRU-HRSS are round-3 finalists in NIST’s selection process, and also Crystals-Kyber and especially Falcon are heavily influenced by NTRU. Coppersmith and Shamir proposed to attack NTRU via lattice basis reduction, and variations of the Coppersmith-Shamir lattice have been successfully applied to solve official NTRU challenges by Security Innovations, Inc. up to dimension $$n=173$$ . In our work, we provide the tools to attack modern NTRU versions, both by the design of a proper lattice basis, as well as by tuning the modern BKZ with lattice sieving algorithm from the G6K library to NTRU needs. Let n be prime, $$\varPhi _n := (X^n-1)/(X-1)$$ , and let $$\mathbb {Z}_q[X]/(\varPhi _n)$$ be the cyclotomic ring. As opposed to the common belief, we show that switching from the Coppersmith-Shamir lattice to a basis for the cyclotomic ring provides benefits. To this end, we slightly enhance the LWE with Hints framework by Dachman-Soled, Ducas, Gong, Rossi with the concept of projections against almost-parallel hints. Using our new lattice bases, we set the first cryptanalysis landmarks for NTRU-HPS with $$n \in [101,171]$$ and for NTRU-HRSS with $$n \in [101,211]$$ . As a numerical example, we break our largest HPS-171 instance using the cyclotomic ring basis within 83 core days, whereas the Coppersmith-Shamir basis requires 172 core days. We also break one more official NTRU challenges by Security Innovation, Inc., originally worth 1000$, in dimension $$n=181$$ in 20 core years. Our experiments run up to BKZ blocksizes beyond 100, a regime that has not been reached in analyzing cryptosystems so far.
A High-Performance Hardware Implementation of the LESS Digital Signature Scheme

In 2022, NIST selected the first set of four post-quantum cryptography schemes for near-term standardization. Three of them - CRYSTALS-Kyber, CRYSTALS-Dilithium, and FALCON - belong to the lattice-based family and one - SPHINCS $$^+$$ - to the hash-based family. NIST has also announced an “on-ramp” for new digital signature candidates to add greater diversity to the suite of new standards. One promising set of schemes - a subfamily of code-based cryptography - is based on the linear code equivalence problem. This well-studied problem can be used to design flexible and efficient digital signature schemes. One of these schemes, LESS, was submitted to the NIST standardization process in June 2023. In this work, we present a high-performance hardware implementation of LESS targeting Xilinx FPGAs. The obtained results are compared with those for the state-of-the-art hardware implementations of CRYSTALS-Dilithium, SPHINCS $$^+$$ , and FALCON.
Time and Query Complexity Tradeoffs for the Dihedral Coset Problem

The Dihedral Coset Problem ( $$\textsf{DCP}$$ ) in $$\mathbb {Z}_N$$ has been extensively studied in quantum computing and post-quantum cryptography, as for instance, the Learning with Errors problem reduces to it. While the Ettinger-Høyer algorithm is known to solve the $$\textsf{DCP}$$ in $$O\left( \log {N} \right) $$ queries, it runs inefficiently in time $$O\left( N \right) $$ . The first time-efficient algorithm was introduced (and later improved) by Kuperberg (SIAM J. Comput. 2005). These algorithms run in a subexponential amount of time and queries $$\tilde{O}\left( 2^{\sqrt{c_{\textsf{DCP}}\log {N}}} \right) $$ , for some constant $$c_{\textsf{DCP}}$$ . The sieving algorithms Kuperberg admit many trade-offs between quantum and classical time, memory and queries. Some of these trade-offs allow the attacker to reduce the number of queries if they are particularly costly, which is notably the case in the post-quantum key-exchange CSIDH. Such optimizations have already been studied, but they typically fall into two categories: the resulting algorithm is either based on Regev’s approach of reducing the $$\textsf{DCP}$$ with quadratic queries to a subset-sum instance, or on a re-optimization of Kuperberg’s sieve where the time and queries are both subexponential. In this paper, we introduce the first algorithm to improve in the linear queries regime over the Ettinger-Høyer algorithm. We then show that we can in fact interpolate between this algorithm and Kuperberg’s sieve, by using the latter in a pre-processing step to create several quantum states, and solving a quantum subset-sum instance to recover the full secret in one pass from the obtained states. This allows to interpolate smoothly between the linear queries-exponential time complexity case and the subexponential query and time complexity case, thus allowing a fine tuning of the complexity taking into account the query cost. We also give on our way a precise study of quantum subset-sum algorithms in the non-asymptotic regime.
Markov Decision Processes: A Gentle Tutorial

Markov Decision Processes (MDPs) form the cornerstone of reinforcement learning (RL) and serve as a fundamental modeling tool for making sequential decisions. In this note, we present a comprehensive definition of MDPs and provide a detailed derivation of the Bellman equations, along with the optimality results. Our approach aims to ensure a thorough understanding by avoiding the omission of any steps in the mathematical proofs. The primary goal is to facilitate reading classic textbooks on (approximate) dynamic programming, optimal control, and reinforcement learning, where proofs and derivations can sometimes obscure crucial details, making them less accessible to readers from diverse scientific and engineering backgrounds.
Towards an Ontology of Pre-20th Century Scientific Instrument Types

This paper introduces the project Tools of Knowledge: Modelling the Scientific Instrument Trade, 1550–1914, and focuses on our ongoing development of an ontology to represent knowledge about scientific instrument types produced during this period. After a brief introduction, we discuss the background to the project and a summary of previous work related to knowledge representation of scientific instrument types, particularly in the area of Linked Open Data. We continue with an explanation of the methods used to normalize and structure our data, as well as the vocabularies used to describe relationships between terms. Our interim findings comprise our reflections on the nature of these complex relationships and their semantic representation, alongside examples of analysis that has been facilitated through application of the ontology-in-progress to the project database. To conclude, we summarize our findings so far and provide an indication of how we might expand and enhance the ontology, as well as its contribution to the wider Linked Open Data ecosystem.
Epigraphical Heritage Documentation via CIDOC CRM and CRMtex

As the need for solid and interoperable heritage documentation models and systems becomes bigger, addressing aspects of representing archaeological and textual/epigraphical information adds special importance and value. Towards this end, this paper explores the use of CIDOC CRM and CRMtex to represent epigraphical heritage information. This paper aims to (1) describe, study and explain how ancient funerary inscriptions can be represented through CIDOC CRM and its extensions, (2) examine these archaeological and textual objects at two levels: (a) physical description, including size, shape, material, text and symbols, and b) interpretations, including chronology, text content, other archaeological interpretation. Our work consists of a case study based on a funerary inscription with a Greek inscription of the 5th century AD discovered in Ghor as-Safi (Byzantine Zoora or Zoara) in modern Jordan. This inscription is a typical example of funerary inscriptions in the area and the world, as it contains the basic information frequently found on similar object: name of the deceased, age, date of death, symbols. CIDOC CRM and CRMtex has been used to represent the production and the physical characteristics of a funerary inscription, its textual information (including ancient writing, transcription and translation) and cultural information content, its date, and its current condition and location. This case study initiates the work towards a model and an automated system for archaeological/epigraphical documentation and data integration.
Practical Application and Material Documentation of CIDOC – CRM (Conceptual Reference Model): A Scoping Review

This paper aims to collect, critical study and present the research literature on practical applications and material documentation of CIDOC – CRM, from 2009 to 2019, in English language.The main objective of this study is the observation of semantic standard progress over the ten years of its using. The present scoping review used the methodological framework of Arksey and O'Malley (2005) and has followed by the rules of PRISMA – ScR. To conduct the research, Scopus/Elsevier, ScienceDirect, Springer, Semantic Scholar, CORE Aggregator, Google Scholar and MDPI Open Access Journals were searched to identify papers that were published in conference proceedings or in scientific journals, as well as through technical report papers that were developed by official bodies of CIDOC – CRM. From the 73 articles that we retrieved, 20 related to the topic of practical applications and material documentation with CIDOC – CRM semantic standard were selected for analysis. The majority of the included articles were published after 2010, from 2013 to 2019, equally distributed. Concluding this short scoping review indicates the importance of semantic ontology CIDOC – CRM for: a) solving semantic interoperability problems and ensuring information exchange and integration between heterogeneous sources of cultural heritage information, b) documenting the intangible cultural heritage, c) optimizing the documentation methods of digitizing products and representing provenance, and d) bringing spatiotemporal concepts and cultural heritage data.
Process-Level Integration for Linked Open Data Development Workflows: A Case Study

Dataset maintenance and development is a complex endeavor that necessitates the use of different systems through the dataset lifecycle. The semantic web succeeded in setting common standards that enable data interchange between these systems. Still, there is often a lack of interoperability and integration at the process level, indispensable to use different systems in a single, combined workflow. In this regard, we considered use cases concerning the interaction of dataset editors with content publication/fruition systems, which provide a linked data interface, and dataset catalogs, which meet the FAIR principle of findability and persistency. As a case study, we considered ShowVoc and OntoPortal as examples of these two classes of systems. Having committed to the collaborative knowledge editor VocBench 3, we contributed extensions for it that address the defined use cases. Evaluating our contributions, we identified some aspects of future improvement in the relevant subset of VocBench 3.
Unwavering Mathematical Equation Deciphering from the Paperwork Imagery

Abstract: The paper proposes a new approach for identifying equations in document images, with an emphasis on handwritten equations. The proposed strategy makes use of Python-based machine learning algorithms, and for the user interface, HTML, CSS, and JS. The document image is transformed into a machine-readable format during the pre-processing stage utilizing optical character recognition (OCR). Then, image-improving methods like smoothing, thresholding, and morphological procedures are used. A deep neural network architecture is used to accurately identify math expressions in the pre-processed photos. Convolutional and recurrent layers make up this layout, which was developed utilizing machine learning techniques