Reviewerly/exHarmony huggingface dataset:
Generic attacks on small-state stream cipher constructions in the multi-user setting

Abstract Small-state stream ciphers (SSCs), which violate the principle that the state size should exceed the key size by a factor of two, still demonstrate robust security properties while maintaining a lightweight design. These ciphers can be classified into several constructions and their basic security requirement is to resist generic attacks, i.e., the time–memory–data tradeoff (TMDTO) attack. In this paper, we investigate the security of small-state constructions in the multi-user setting. Based on it, the TMDTO distinguishing attack and the TMDTO key recovery attack are developed for such a setting. It is shown that SSCs which continuously use the key can not resist the TMDTO distinguishing attack. Moreover, SSCs based on the continuous-IV-key-use construction cannot withstand the TMDTO key recovery attack when the key length is shorter than the IV length, no matter whether the keystream length is limited or not. Finally, we apply these two generic attacks to TinyJAMBU and DRACO in the multi-user setting. The TMDTO distinguishing attack on TinyJAMBU with a 128-bit key can be mounted with time, memory, and data complexities of $$2^{64}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>64</mml:mn> </mml:msup> </mml:math> , $$2^{48}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>48</mml:mn> </mml:msup> </mml:math> , and $$2^{32}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>32</mml:mn> </mml:msup> </mml:math> , respectively. This attack is comparable with a recent work on ToSC 2022, where partial key bits of TinyJAMBU are recovered with more than $$2^{50}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>50</mml:mn> </mml:msup> </mml:math> users (or keys). As DRACO’s IV length is smaller than its key length, it is vulnerable to the TMDTO key recovery attack. The resulting attack has a time and memory complexity of both $$2^{112}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>2</mml:mn> <mml:mn>112</mml:mn> </mml:msup> </mml:math> , which means DRACO does not provide 128-bit security in the multi-user setting.
I Can’t Believe It’s Not Better: In-air Movement for Alzheimer Handwriting Synthetic Generation

During recent years, there here has been a boom in terms of deep learning use for handwriting analysis and recognition. One main application for handwriting analysis is early detection and diagnosis in the health field. Unfortunately, most real case problems still suffer a scarcity of data, which makes difficult the use of deep learning-based models. To alleviate this problem, some works resort to synthetic data generation. Lately, more works are directed towards guided data synthetic generation, a generation that uses the domain and data knowledge to generate realistic data that can be useful to train deep learning models. In this work, we combine the domain knowledge about the Alzheimer’s disease for handwriting and use it for a more guided data generation. Concretely, we have explored the use of in-air movements for synthetic data generation.
A Machine Learning Approach to Analyze the Effects of Alzheimer’s Disease on Handwriting Through Lognormal Features

Alzheimer’s disease is one of the most incisive illnesses among the neurodegenerative ones, and it causes a progressive decline in cognitive abilities that, in the worst cases, becomes severe enough to interfere with daily life. Currently, there is no cure, so an early diagnosis is strongly needed to try and slow its progression through medical treatments. Handwriting analysis is considered a potential tool for detecting and understanding certain neurological conditions, including Alzheimer’s disease. While handwriting analysis alone cannot provide a definitive diagnosis of Alzheimer’s, it may offer some insights and be used for a comprehensive assessment. The Sigma-lognormal model is conceived for movement analysis and can also be applied to handwriting. This model returns a set of lognormal parameters as output, which forms the basis for the computation of novel and significant features. This paper presents a machine learning approach applied to handwriting features extracted through the sigma-lognormal model. The aim is to develop a support system to help doctors in the diagnosis and study of Alzheimer, evaluate the effectiveness of the extracted features and finally study the relation among them.
A Brain-Controlled Spherical Robot Based on Augmented Reality (AR)

Spherical robots have broad application prospects due to their unique structure. This paper introduces a spherical robot that was controlled by an SSVEP-based BCI system. The stimuli of SSVEP were presented by AR, which enabled the subjects to gaze at the stimuli and the robot simultaneously. Offline experiments demonstrated the feasibility of the BCI system using AR stimuli. The average decoding accuracy reached 91.83 ± 9.83% for a 1.5-s decoding time window. An AR scene consisting of a rectangular racetrack with a circumference of 8m was designed to conduct online experiments. The average finishing laps in the brain-controlled group achieved 6.35 ± 1.45 laps in 5 min, with regard to 7.72 ± 1.62 laps in the controller-controlled one. The brain control method proposed in this paper achieved good results and verified the feasibility and stability of the BCI system through experiments. This study still had shortcomings in the decoding effect when subjects switched the gazing stimulus target. There is room for improvement, and it is worth further study.
The Neglected Role of GUI in Performance Evaluation of AI-Based Transcription Tools for Handwritten Documents

This paper aims to inspect the often neglected role of Graphical User Interfaces (GUI) in AI-based tools designed to assist in the transcription of handwritten documents. While the precision and recall of the handwritten word recognition have traditionally been the primary focus, we argue that the time parameter associated with the GUI, specifically in terms of validation and correction, plays an equally crucial role. By investigating the influence of GUI design on the validation and correction aspects of transcription we want to highlight how the time that the user must take to interact with the interface must be taken into account to evaluate the performance of the transcription process. Through comprehensive analysis and experimentation, we illustrate the profound impact that GUI design can have on the overall efficiency of transcription tools. We demonstrate how the time saved through the utilization of an assistant tool is heavily dependent on the operations performed within the interface and the diverse features it offers. By recognizing GUI design as an essential component of transcription tools, we can unlock their full potential and significantly improve their effectiveness.
A Feature Extraction Algorithm for Exoskeleton Speech Control System Based on Noisy Environment

Exoskeleton devices based on speech systems are often affected by noise in the actual working environment, which reduces the recognition rate of speech systems. In order to reduce the influence of noise on the speech system, this paper proposes using joint features of Mel Frequency Cepstrum Coefficients and Gammatone Frequency Cepstrum Coefficients. Then, an improved Fast Correlation-Based Filtering algorithm is used to perform dimensionality reduction and remove irrelevant and redundant features in the joint features to obtain the optimal feature subset. Finally, the experiments show that the feature recognition effect is greatly improved after dimensionality reduction is performed under a low signal-to-noise ratio.
Estimating the Optimal Training Set Size of Keyword Spotting for Historical Handwritten Document Transcription

We address the problem of estimating the tradeoff between the size of the training set and the performance of a KWS when used to assist the transcription of small collections of historical handwritten documents. As this application domain is characterized by a lack of data, and techniques such as transfer learning and data augmentation require more resources than those that are commonly available in the organizations holding the collections, we address the problem of getting the best out of the available data. For this purpose, we reformulate the problem as that of finding the size of the training set leading to a KWS whose performance, when used to support the transcription, allows to obtain the largest reduction of the human efforts to achieve the complete transcription of the collection. The results of a large set of experiments on three publicly available datasets largely adopted as a benchmark for performance evaluation show that a training set made of 5 to 8 pages is enough for achieving the largest reduction, independently of the actual pages included in the training set and the corresponding keyword lists. They also show that the actual time reduction depends much more on the keyword list than on the KWS performance.
IntuiSketch, a Pen-Based Tutoring System for Anatomy Sketch Learning

This paper presents the first works on IntuiSketch, a pen-based intelligent tutoring system for anatomy courses in higher education. Pen-based tablets offer the possibility to have pen and touch interaction, which mimics the traditional pen and paper setting. The objective here is to combine online recognition techniques, that enable to interpret the sketches drawn by the students, with tutoring techniques, that model the domain knowledge. IntuiSketch is able to analyze the student drawings relatively to a problem defined by the teacher, and generate corrective feedback. The online recognition is based on the bi-dimensional grammar CD-CMG (Context Driven Constraint Multiset Grammar) which models the document structure, coupled with a fuzzy incremental classifier, which is able to learn from few examples. The tutoring system is based on constraint modeling, which enables to define domain and problem knowledge, and to analyse the student production relatively to the constraints that have to be satisfied to solve the problem. In this paper, we present a new architecture for anatomy sketch targeted intelligent tutoring system that combines different techniques. We also present a qualitative study of the feedback that our first system version is able to generate on a case study.
An Evaluation of Preschool Children’s Books with Respect to the Principle of Appropriateness for Children

This study aims to determine the suitability of the books to the principle of appropriateness for children. The research database, modelled in the form of case study, one of the qualitative methods, consists of a selection of 60 books that were randomly selected among the most read preschool children’s books, targeting an audience aged 0 to 6 years. The data, which were organized using descriptive analysis, underwent a thorough examination through content analysis. Educational messages in the books presented explicitly and implicitly were also identified using the document analysis method, analyzed through the descriptive analysis method, and evaluated in terms of children’s education and development. The cases in which the texts of pre-school children’s books can be characterized as conflicting with the principle of appropriateness for children can be categorized as follows: errors in language and expression (spelling and punctuation mistakes; non-adherence to textuality criteria; inadequate attention to consistency; selection of words unsuited to children’s cognitive development; and translation errors), errors related to images (image-text mismatch, or inconsistency between images), and errors concerning children’s universe of meaning (explicit and implicit messages that diverge from values-oriented education). The results of the study were discussed in detail, supported by the relevant literature. As a result, to mitigate the potential adverse effects arising from errors in children’s books, it becomes essential to follow the basic principles of children’s literature as to the communication of explicit and implicit messages, to review books in detail before they are presented to children, and to organize the content accordingly.
SISTEM PAKAR UNTUK DIAGNOSA KERUSAKAN KOMPUTER MENGGUNAKAN METODE FORWARD CHAINING DAN CERTAINTY FACTOR BERBASIS WEB

Damage to the computer is caused by several problematic components such as problematic RAM, hard disk or VGA. This condition is a condition of minor damage which sometimes users consider this to be fatal damage due to a lack of knowledge about computer components, which is why an expert system is needed to help users understand. damage to the computer. In this research, the Forward chaining method will be used in the system that will be used in the program. This research will focus on a system that produces and adopts knowledge from an expert in the field and to be able to diagnose computer damage using a web-based method. An expert system is an application that uses the expertise or knowledge of an expert in a particular field and this expertise is implemented in a system.
Management of medical record data with multi-level security on Amazon Web Services

Abstract Blockchain technology has gained significant attention in the most recent years due to its potential to address related issues to privacy, transparency, and security. In the healthcare industry, it is considered a promising solution to challenges related to data management, medical data privacy, and security. This paper proposes a novel framework for managing electronic medical healthcare records that uses Amazon Web Services (AWS) to create a secure and cost-effective solution. The proposed solution uses a serverless approach to reduce costs and only pay for the compute resources when needed. The framework focuses on improving the security of access to the blockchain network by implementing a multi-level security approach, starting from protecting the Hyperledger and implementing role-based access for end-users who require access to the stored data. The effectiveness of the proposed framework is demonstrated by creating a role-based access matrix for three different user types and performing multiple experiments to validate access for each user type.
IMPACT OF THE AGRICULTURAL CREDIT GUARANTEE SCHEME FUND ON AGRICULTURAL PRODUCTIVITY IN NIGERIA 1981-2020

This study ascertains the impact of the Agricultural Credit Guarantee Scheme Fund on Agricultural Productivity in Nigeria 1981-2020. The ex-post facto research design was adopted for the study. Secondary data obtained from the Central Bank of Nigeria Annual Statistical Bulletin was used for the study. Jarque Bera test statistics was used to check for data normality while the Augmented Dickey Fuller test (ADF) was used to check the stationarity of the data. Johansen co-integration and error correction model was used to estimate the nexus that exists between the variables of the study. Findings from the study revealed that in the longrun, volume of funding to crop sector (FCP) has a positive effect on the output of crops and the effect is statistically significant. Volume of funding to livestock (FCP) is positively signed and in line with a priori expectation. Volume of funding to fishery (FFS) has positive effect on output of fishery within the period under study and this effect is in line with a priori expectation. Finally, volume of funding to forestry (FFT) is positively signed and in line with a priori expectation. The researcher concluded that quality credit to the agricultural sector is very important in both long and shortrun as agriculture plays a vital role in the life of people especially in developing countries such as Nigeria. It was recommended among others that more funding be dedicated by government to improve productivity while policy geared towards improving funding in the shortrun should be aggressively pursued by the government so as to achieve an equilibrium of positive effect of funding in both the long and shortrun period for the four sectors examined.
The long history of shortening: a diachronic analysis of abbreviation practices from the fifteenth to the twenty-first century

This article investigates continuities and changes in abbreviation practices from late Middle English to twenty-first-century digital platforms. Adopting a diachronic perspective and lexicological framework, it quantitatively analyses frequency patterns across fifteenth-century memoranda, letters and administrative receipts, seventeenth-century letters and depositions, late nineteenth-century letters, early twentieth-century letters and a subcorpus of WhatsApp instant messages dating from 2018–19. It then presents analyses of the frequencies of various abbreviation forms, such as clippings, and abbreviated lexemes, such as their use for names, over time. The article then provides a qualitative analysis of these lexeme categories over the centuries, with a focus on specific examples. Major changes to overall abbreviation density across time are identified. The forms of abbreviation also go through major change, but the types of lexemes that are abbreviated stay more consistent over time. For example, abbreviations being used for closed-class function words such as the and that are dominant from the earliest data we have looked at to the present day. Overall, the study demonstrates how situating new media abbreviation practices within a historical continuum can enhance our understanding of them.
Changing trend of temperature in nanjing and its correlation with some human activities in recent 41 years

Nowadays, climate change and its following impacts have influence on human life, society, and natural ecosystems in many regions on a global scale. Based on the datasets of temperature and human activities, this research analyses the temperature variation trend in Nanjing from 1980 to 2020 by linear regression and time series decomposition. In addition, by comparing correlation coefficients and multiple linear regression, this research explores the correlation between changing temperatures and human activities in the past 41 years. The results show that the temperature of Nanjing has had an increasing trend in the past 41 years, and the warming amplitude is the largest in the spring. Also, temperature change has a correlation with some human activities, and the correlation between total energy consumption and temperature is the biggest. Thus, the influence of human activities on climate is really important.
A quantitative security evaluation and analysis model for web applications based on OWASP application security verification standard

In today's digital world, web applications are popular tools used by businesses. As more and more applications are deployed on the web, they are seen as increasingly attractive targets by malicious actors eager to exploit any security gaps present. Organizations are always at risk for potential vulnerabilities in their web-based software systems, which can lead to data loss, service interruption, and lack of trust. Therefore, organizations need to have an effective and efficient method for assessing and analyzing the security of acquired web-based software to ensure adequate confidence in its use. Quantitative security evaluation employs mathematical and computational techniques to express the security level that a system reaches. This research focuses on improving the quantitative analysis of web application security evaluation. We strive to unite the Open Web Application Security Project's (OWASP) Application Security Verification Standard (ASVS) into a structural and analyzable model, which aims to efficiently evaluate web application security levels while providing meaningful insights into their strengths and weaknesses.
Blockchain PoS and PoW Consensus Algorithms for Airspace Management Application to the UAS-S4 Ehécatl

This paper introduces an innovative consensus algorithm for managing Unmanned Aircraft System Traffic (UTM) through blockchain technology, a highly secure consensus protocol, to allocate airspace. A smart contract was developed on the Ethereum blockchain for allocating airspace. This technique enables the division of the swarm flight zone into smaller sectors to decrease the computational complexity of the algorithm. A decentralized voting system was established within these segmented flight zones, utilizing two primary methodologies: Proof of Work (PoW) and Proof of Stake (PoS). By employing 1000 UAS-S4s across various locations and heading angles, a swarm flight zone was generated. The efficiency of the devised decentralized consensus system was assessed based on error rate and validation time. Despite PoS displaying greater efficiency in cumulative probability for block execution, the comparative analysis indicated PoW outperformed PoS concerning the potential for conflicts among UASs.
Leveraging Taxonomical Engineering for Security Baseline Compliance in International Regulatory Frameworks

A surge in successful Information Security (IS) breaches targeting Research and Education (R&amp;E) institutions highlights a pressing need for enhanced protection. Addressing this, a consortium of European National Research and Education Network (NREN) organizations has developed a unified IS framework. This paper aims to introduce the Security Baseline for NRENs and a security maturity model tailored for R&amp;E entities, derived from established security best practices to meet the specific needs of NRENs, universities, and various research institutions. The models currently in existence do not possess a system to smoothly correlate varying requirement tiers with distinct user groups or scenarios, baseline standards, and existing legislative actions. This segmentation poses a significant hurdle to the community’s capacity to guarantee consistency, congruency, and thorough compliance with a cohesive array of security standards and regulations. By employing taxonomical engineering principles, a mapping of baseline requirements to other security frameworks and regulations has been established. This reveals a correlation across most regulations impacting R&amp;E institutions and uncovers an overlap in the high-level requirements, which is beneficial for the implementation of multiple standards. Consequently, organizations can systematically compare diverse security requirements, pinpoint gaps in their strategy, and formulate a roadmap to bolster their security initiatives.