Reviewerly/exHarmony huggingface dataset:
Computational fuzzy extractor from LWE

Fuzzy extractors are able to derive uniform and stable strings from noisy sources. Traditional fuzzy extractors are defined as information-theoretical primitive. Unfortunately, the components of information-theoretical fuzzy extractors consume the entropy of the sources, which results in extracted strings being too short to be applied in practice. Fuller et al. introduced the notion of Computational Fuzzy Extractor (CFE) and proposed a CFE scheme, which extracts nearly all entropy from the sources, based on the Learning with Errors (LWE) assumption. However, their construction applies to a limited type of sources and only supports sub-linear error correctness rate. In this paper, we propose a generic construction of CFE from Computationally Private Secure Sketch (CPSS) and Lossy Computational Extractor (LCExt). By instantiating CPSS and LCExt based on the LWE assumption, we obtain specific CFE schemes from LWE. Compared with the CFE scheme by Fuller et al., our CFE scheme not only extracts almost all entropy from the source, but also supports linear error correctness rate, as long as the source has enough entropy.
Adaptively code-correlation robustness functions and its applications to private set intersection

In this paper, we propose a novel concept of adaptively code-correlation robustness functions (CCRFs) and construct this primitive from public random permutation. The security of our construction can be proved by H-Coefficient technique. Furthermore, the public random permutation can be instantiated by some lightweight designs like ChaCha20 512-bit permutation which is faster than SHA-512 and BLAKE2. We also give a one-sided malicious private set intersection (PSI) protocol in Universal Composability (UC) framework without random oracle model (ROM) assumption based on the adaptively CCRFs. In the high bandwidth setting, our implementation is nearly as fast as state-of-the-art semi-honest KKRT protocol, and sometimes even faster than it, owing to the efficiency of our instantiation of adaptively CCRFs, which may be of independent interest used in other secure protocols.
Using machine learning to model older adult inpatient trajectories from electronic health records data

Electronic Health Records (EHR) data can provide novel insights into inpatient trajectories. Blood tests and vital signs from de-identified patients' hospital admission episodes (AE) were represented as multivariate time-series (MVTS) to train unsupervised Hidden Markov Models (HMM) and represent each AE day as one of 17 states. All HMM states were clinically interpreted based on their patterns of MVTS variables and relationships with clinical information. Visualization differentiated patients progressing toward stable 'discharge-like' states versus those remaining at risk of inpatient mortality (IM). Chi-square tests confirmed these relationships (two states associated with IM; 12 states with ≥1 diagnosis). Logistic Regression and Random Forest (RF) models trained with MVTS data rather than states had higher prediction performances of IM, but results were comparable (best RF model AUC-ROC: MVTS data = 0.85; HMM states = 0.79). ML models extracted clinically interpretable signals from hospital data. The potential of ML to develop decision-support tools for EHR systems warrants investigation.
Do easy-to-read adaptations really facilitate sentence processing for adults with a lower level of education? An experimental eye-tracking study

The Easy-to-Read guidelines recommend visual support and lexical simplification to facilitate text processing, but few studies have empirically verified the efficacy of these guidelines. This study examined the influence of these recommendations on sentence processing by examining eye movements at the text- and word-level in adult readers. We tested 30 non-university adults (low education level) and 30 university adults (high education level). The experimental task consisted of 60 sentences. Half were accompanied by an image and half were not, and half contained a low-frequency word and half a high-frequency word. Results showed that visual support and lexical simplification facilitated processing in both groups of adults, and non-university adults were significantly slower than university adults at sentence processing. However, lexical simplification resulted in faster processing in the non-university adults’ group. Conclusions focus on the mechanisms in which both adaptations benefit readers, and practical implications for reading comprehension.
Design and implementation of a Decision Support Tool to assist the ranking of fossil-energy-free technologies and strategies for a given farm

The main objective of the AgroFossilFree Horizon2020 project is to create a framework under which all core stakeholders can cooperate to evaluate and promote the currently available fossil-energy-free technologies and strategies (FEFTS) in EU agriculture in a cost efficient manner. AgroFossilFree has implemented an online and interactive approach to communication, interaction and knowledge sharing and exchanging through the use of a specifically designed ICT tool, the "AgEnergy Platform", which deploys the collected information and knowledge on FEFTS in the form of easily accessible end-user material facilitating searching through the use of filters. Still, the question remains of how can you choose which one is the most appropriate one or the most cost-effective one for each farm. The answer to that in reality comes after a detailed study on a per farm basis. An intermediate level was developed to bridge these two extremes, that can provide a first insight on which FEFTS probably suits best to each farm using minimal user inputs. This Decision Support Tool (DST) based on Fuzzy Cognitive Maps, is integrated seamlessly within the AgroFossilFree platform and allows users to get a ranking of the technologies most suitable for their farm. Essentially, the DST mimics the consultation process of a number of experts if these experts were in the same room evaluating the input data provided by the end-user to propose the most effective action. This paper presents the theory, development steps undertaken and the final implementation of this DST in the AgroFossilFree website. The approach undertaken can be replicated to increase the functionality of other comparable platforms in the Agricultural sector and beyond.
Forensics for multi-stage cyber incidents: Survey and future directions

The increase in the complexity and sophistication of multi-stage cyber attacks, such as advanced persistent threats, paired with the large volume of data produced by modern systems and networks, have made forensic investigations more demanding in knowledge and resources. Thus, it is essential that cyber forensic investigators are supported to operate more efficiently, in terms of resources and evidence recovery, and cope with a wide range of cyber incidents. This paper presents a comprehensive survey of 49 works that aim to support cyber forensic investigations of modern multi-stage cyber incidents and highlights the need for decision support systems on the field. The works reviewed are compared using 11 criteria, such as their evaluation method, how they optimise the forensic process, or what stage of investigation they study. We also classify the surveyed papers using 8 categories that represent the overall aim of the proposed cyber investigation method or tool. We identify and discuss open issues, arising from this extensive survey, such as the need for realistic evaluation, as well as realistic and representative modelling to increase applicability and performance. Finally, we provide directions for future research on improving the state-of-the-art of cyber forensics.
NCT:noise-control multi-object tracking

Abstract Multi-Object Tracking (MOT) is an important topic in computer vision. Recent MOT methods based on the anchor-free paradigm trade complicated hierarchical structures for tracking performance. However, existing anchor-free MOT methods ignore the noise in detection, data association, and trajectory reconnection stages, which results in serious problems, such as missing detection of small objects, insufficient motion information, and trajectory drifting. To solve these problems, this paper proposes Noise-Control Tracker (NCT), which focuses on the noise-control design of detection, association, and reconnection. First, a prior depth denoise method is introduced to suppress the fusion feature redundant noise, which can recover the gradient information of the heatmap fusion features. Then, the Smoothing Gain Kalman filter is designed, which combines the Gaussian function with the adaptive observation coefficient matrix to stabilize the mutation noise of Kalman gain. Finally, to address the drift noise issue, the gradient boosting reconnection context mechanism is designed, which realizes adaptive trajectory reconnection to effectively fill the gaps in trajectories. With the assistance of the plug-and-play noise-control method, the experimental results on MOTChallenge 16 &amp;17 datasets indicate that the NCT can achieve better performance than other state-of-the-art trackers.
The Reality of Cyber Security in Bangladesh, Relevant Laws, Drawbacks and Challenges

Protection of data has become a matter of great concern as cyber-crime has now emerged as a major threat amid the rapid spread of the internet and information and communication technology across the globe. Despite being the most technologically advanced country, the United States is not out of the purview of the danger of cyber-crime. In such a situation, a country like Bangladesh which is still in the group of less developed countries is one of the most vulnerable countries in terms of cyber security. The Bangladesh government has gone through a huge digital transformation over the years and has been trying to connect the dots between the institutions digitally with the slogan of "Digital Bangladesh". Apart from that, national and multinational companies, operating in the country, are also offering online services to be part of the government's journey to the digital world. The distance between a consumer and a shop or a bank is now a click away, thanks to the wide access to the internet and the digital presence of the entities. Taking the advantage of easy excess and widespread use of the internet, opportunists and criminals have chosen the digital path to materialize their evil wishes. They are committing various crimes, including stealing money, and personal data, spreading rumors and cyber-attacks and other criminal activities. Amid the fragile security system, there is also a huge risk of targeted cyber-attacks by the hackers of any opponent country or frustrated group. Against this backdrop, it is evident that the state of cyber security in the country is very fragile and the existing laws and the measures of the state are very insignificant to tackle the growing threat. This study pinpoints the escalating cyber security concerns in the context of Bangladesh from a global perspective.
Evaluation of the Use of ICT Tools in Higher Education

Student performance is influenced both by the effort made by the student to achieve their goals and by the strategies and tools used by the teacher to impart their knowledge. At the time of the pandemic, teachers had to make use of the different ICT tools that are available on the market, the preferred ones being those that were available free of charge. In the face-to-face university sector, the virtual treatment of students accustomed to face-to-face learning was difficult, in this sense, teachers concerned about the decrease in average student performance used tools that were adapted to the component and to the experience. The following research was proposed, which is part of a larger project, which seeks to relate the level of satisfaction with the use of technological tools with student performance. Three courses from different degrees of the Universidad Técnica Particular de Loja, in Ecuador, are taken as a sample, to which an instrument was applied to see their level of satisfaction. Using non-parametric statistics, specifically the Kruskal–Wallis Test contrast, it was detected that there is no difference between the groups in the level of satisfaction with the use of technological resources and the student performance. On the other hand, it was observed that the level of satisfaction with ICT tools depended on the difficulty of use.
A novel framework based on the multi-label classification for dynamic selection of classifiers

Multi-classifier systems (MCSs) are some kind of predictive models that classify instances by combining the output of an ensemble of classifiers given in a pool. With the aim of enhancing the performance of MCSs, dynamic selection (DS) techniques have been introduced and applied to MCSs. Dealing with each test sample classification, DS methods seek to perform the task of classifier selection so that only the most competent classifiers are selected. The principal subject regarding DS techniques is how the competence of classifiers corresponding to every new test sample classification task can be estimated. In traditional dynamic selection methods, for classifying an unknown test sample x, first, a local region of data that is similar to x is detected. Then, those classifiers that efficiently classify the data in the local region are also selected so as to perform the classification task for x. Therefore, the main effort of these methods is focused on one of the two following tasks: (i) to provide a measure for identifying a local region, or (ii) to provide a criterion for measuring the efficiency of classifiers in the local region (competence measure). This paper proposes a new version of dynamic selection techniques that does not follow the aforementioned approach. Our proposed method uses a multi-label classifier in the training phase to determine the appropriate set of classifiers directly (without applying any criterion such as a competence measure). In the generalization phase, the suggested method is employed efficiently so as to predict the appropriate set of classifiers for classifying the test sample x. It is remarkable that the suggested multi-label-based framework is the first method that uses multi-label classification concepts for dynamic classifier selection. Unlike the existing meta-learning methods for dynamic ensemble selection in the literature, our proposed method is very simple to implement and does not need meta-features. As the experimental results indicate, the suggested technique produces a good performance in terms of both classification accuracy and simplicity which is fairly comparable with that of the benchmark DS techniques. The results of conducting the Quade non-parametric statistical test corroborate the clear dominance of the proposed method over the other benchmark methods.
Multi-Model Fusion-Based Hierarchical Extraction for Chinese Epidemic Event

In recent years, Coronavirus disease 2019 (COVID-19) has become a global epidemic, and some efforts have been devoted to tracking and controlling its spread. Extracting structured knowledge from involved epidemic case reports can inform the surveillance system, which is important for controlling the spread of outbreaks. Therefore, in this paper, we focus on the task of Chinese epidemic event extraction (EE), which is defined as the detection of epidemic-related events and corresponding arguments in the texts of epidemic case reports. To facilitate the research of this task, we first define the epidemic-related event types and argument roles. Then we manually annotate a Chinese COVID-19 epidemic dataset, named COVID-19 Case Report (CCR). We also propose a novel hierarchical EE architecture, named multi-model fusion-based hierarchical event extraction (MFHEE). In MFHEE, we introduce a multi-model fusion strategy to tackle the issue of recognition bias of previous EE models. The experimental results on CCR dataset show that our method can effectively extract epidemic events and outperforms other baselines on this dataset. The comparative experiments results on other generic datasets show that our method has good scalability and portability. The ablation studies also show that the proposed hierarchical structure and multi-model fusion strategy contribute to the precision of our model.The online version contains supplementary material available at 10.1007/s41019-022-00203-6.
Assembly, Deployment and Extension of Blockchain Based Decentralized Autonomous Organizations: A Framework Point of View

Decentralised autonomous organisations, entities built on the blockchain based with no central leadership, are changing the landscape of organisations and governance. Operations governed by smart contracts where bylaws are embedded into code and the decentralised nature of their governance are offering a unique structure to organisations looking for transparency and community engagement. This paper will give an overview of blockchain technologies and DAOs, how computer programming is incorporated into DAOs through the use of smart contracts and how this can be used for governance, in addition to how this differs to centralised organisations. Various DAO frameworks will be explored, compared and analysed such as MolochDAO, MakerDAO and the LAO. From here, the current challenges of DAOs will be explored and recommendations will be proposed followed for DAO frameworks. Finally, having considered the above, the future of DAOs will be discussed, including recommendations, improvements and developments.
Training Routine for Electrical Power Station Operators Using Virtual Reality

This paper presents the use of Virtual Reality (VR) to improve the content learning and practice of electrical power stations power station operators, where a training routine was created for them using a tool of the RVCEMIG software, called Routine Editor. With this training routine an operator will go through a self-guided visit, with the objective of identify electrical equipment of power station. using instructions will be given to him and at the end of the visit, a learning assessment is carried out with questions about the observed equipment. The final purpose of the method is to use to improve the traditional training method. Where, using a model for creating feedback reports, called Kirkpatrick model, it will be possible to carry out these comparisons properly in order to measure the changes achieved by the use of the tool within the company, being able to demonstrate the benefits of this new type of training.
Digital Competencies in Teachers of Regular Basic Education in Tacna-Peru

For some years, teachers’ digital skills have been critical to the educational process; nevertheless, with the health catastrophe released by Covid-19, they have become critical. So, the purpose of this study was to examine and quantify the degree of digital literacy among instructors of Regular Basic Education at UGEL Tacna. The study used a hybrid quantitative-qualitative methodology and was mostly descriptive in nature. There were 1,329 teachers in the population and sample. The questionnaire and the interview were employed as instruments. The findings suggest that 49.89% teachers had a high degree of digital competence, while 12.19% have a basic level, which includes instructors at the elementary, primary, and secondary levels. Instructors get knowledge about web 2.0 technologies and their utility and application via interviews; it is advised that teachers enhance cognitive digital abilities. That is, the investigative skills geared toward the virtual environment and their development in pupils. It concludes by identifying the most critical training needs associated with the cognitive digital domain's medium-low level, establishing the necessity of developing a strengthening plan for cognitive digital skills that have not been developed satisfactorily, particularly in the creation of digital educational materials and related educational projects.
Sentinel-2 Satellite Image Enhancement and Compression Based on DWT and Vector Quantization

Satellite images are created by transmitting a microwave signal to a target region from a spacecraft or aviation and then analyzing the returned signal. It is a radar image obtained by active sensors that can acquire all weather information regardless of the time of day or weather conditions, unlike typical optical sensors. The noise in the satellite image differs from that found in general photos captured with traditional optical sensors. Many satellite photos struggle from a common problem known as noise. To remove the noise on remotely sensed images, various forms of noise require different approaches. Identifying and denoising noise in remote sensing photographs is a tough task. A characteristic of satellite images is the presence of noise, where this noise often requires preprocessing of the images by a denoising method adapted prior to their compression, storage and transmission. To achieve a considerably higher-grade denoising image of remotely sensed images, wavelet is required and it is reviewed. A novel approach of satellite image enhancement and picture compression technology has been used in this research. When an image is contaminated by noise, it is filtered with a variety of filters before being treated using the DWT and compressed by 2D level space vector quantization, lossy compression technique, which removes noisy bits from the satellite image. Consequently, the picture will arrive ground station in less time.
Segmentation of Liver in CT images using 3D-Res-UNet

Medical segmentation is a method for locating the borders of internal organs on images taken using the two modern imaging modalities that are most frequently used: MRI and 3D computer tomography. The U-net illustrates a deep neural network architecture with the ability to segment medical pictures. Its basic structure is made up of two components: encoders and decoders. To construct the Modified U-Net architecture that is utilized in this project, we have used the free-source Monai Framework.
Non-invasive Haemoglobin Estimation by Observing Nail Color: A PCA based Approach

Although conventional laboratory based haemoglobin estimation provides accurate estimation, however, it is time-consuming, painful, inconvenient in cases of chronic anaemic patients for regular diagnoses. Moreover, it needs trained professionals with specialized laboratory equipment. This paper presents a haemoglobin prediction model non-invasively by analysing the videophotography of color changes in finger nail-bed caused due to certain amount of pressure application and sudden release. To estimate the haemoglobin level, three most relevant frames are considered that comprise significant changes in intensity values due to blood occlusion and release. A reduced dimensional feature set is introduced by applying Principal Component Analysis (PCA) to enhance the coordination between the feature values. The reduced dimensional features are correlated with the clinically validated haemoglobin level through an ensemble prediction module comprising of a set of regression model and Multilayer Perceptron (MLP) architecture for predicting the haemoglobin level of unlabeled samples. The proposed system proves its efficiency with respect to the Root Mean Square Error (RMSE) value, prediction accuracy, classification accuracy, specificity and sensitivity.