Reviewerly/exHarmony huggingface dataset:
Analysis of Information Transfer from Heterogeneous Sources via Precise High-dimensional Asymptotics

We consider the problem of learning -- gaining knowledge from one source task and applying it to a different but related target task. A fundamental question in learning is whether combining the data of both tasks works better than using only the target task's data (equivalently, whether a information transfer happens). We study this question formally in a linear regression setting where a two-layer linear neural network estimator combines both tasks' data. The estimator uses a shared parameter vector for both tasks and exhibits positive or negative information by varying dataset characteristics. We characterize the precise asymptotic limit of the prediction risk of the above estimator when the sample sizes increase with the feature dimension proportionally at fixed ratios. We also show that the asymptotic limit is sufficiently accurate for finite dimensions. Then, we provide the exact condition to determine positive (and negative) information in a random-effect model, leading to several theoretical insights. For example, the risk curve is non-monotone under model shift, thus motivating a learning procedure that progressively adds data from the source task. We validate this procedure's efficiency on text classification tasks with a neural network that applies a shared feature space for both tasks, similar to the above estimator. The main ingredient of the analysis is finding the high-dimensional asymptotic limits of various functions involving the sum of two independent sample covariance matrices with different population covariance matrices, which may be of independent interest.
Heterogeneous Stitching of X-ray Images According to Homographic Evaluation

The C-arm X-ray system is a common intraoperative imaging modality used to observe the state of a fractured bone in orthopedic surgery. Using C-arm, the bone fragments are aligned during surgery, and their lengths and angles with respect to the entire bone are measured to verify the fracture reduction. Since the field-of-view of the C-arm is too narrow to visualize the entire bone, a panoramic X-ray image is utilized to enlarge it by stitching multiple images. To achieve X-ray image stitching with feature detection, the extraction of accurate and densely matched features within the overlap region between images is imperative. However, since the features are highly affected by the properties and sizes of the overlap regions in consecutive X-ray images, the accuracy and density of matched features cannot be guaranteed. To solve this problem, a heterogeneous stitching of X-ray images was proposed. This heterogeneous stitching was completed according to the overlap region based on homographic evaluation. To acquire sufficiently matched features within the limited overlap region, integrated feature detection was used to estimate a homography. The homography was then evaluated to confirm its accuracy. When the estimated homography was incorrect, local regions around the matched feature were derived from integrated feature detection and substituted to re-estimate the homography. Successful X-ray image stitching of the C-arm was achieved by estimating the optimal homography for each image. Based on phantom and ex-vivo experiments using the proposed method, we confirmed a panoramic X-ray image construction that was robust compared to the conventional methods.
Mgini - Improved Decision Tree using Minority Class Sensitive Splitting Criterion for Imbalanced Data of Covid-19

In the time of COVID-19, medical facilities struggling to fight against the pandemic. Most of the countries face a tough time fighting against this virus outbreak. Even developed countries are struggling to deal with this virus outbreak. Common problem countries face is a lack of medical staff and medical equipment. Machine learning has the potential to play an important role in a different area of medical facilities. With the help of the machine learning model, an effective diagnostic tool can be built which helps in the time of scarcity of medical staff. However medical data is imbalanced and this skew nature of data prevent machine learning algorithm from achieving high accuracy. To deal with this problem of imbalanced data, we proposed a modified decision tree algorithm that uses a minority sensitive Gini index called Mgini. In an imbalanced dataset of COVID-19, it is important to focus on the reduction of overall misclassification cost instead of trying improvement in accuracy value. Mgini is useful splitting criteria when the misclassification cost of the minority sample is huge as compared to the majority class. The use of this proposed new Gini index as a splitting criterion in the decision tree reduces the misclassification cost. Mgini based decision tree has higher accuracy and low misclassification cost as compare to the traditional Gini index based CART algorithm. Our proposed cost-sensitive approach improves imbalanced data classification without the use of data level sampling techniques.
An improved ACO-based decision tree algorithm for imbalanced datasets

Prediction of the minority class is challenging for classification of datasets with skewed class distribution. Bio-inspired classifier such as ant colony optimisation (ACO) decision tree could not ...
MPC-based Multi-UAV Path Planning for Convoy Protection in 3D

In this paper, we propose a multi-UAV path planning strategy for fixed wing UAVs to provide convoy protection to a ground vehicle moving over a hilly terrain. As the ground target motion is in 3D, we have developed a Model-Predictive control based framework for tracking the convoy taking the terrain restriction, kinematic constraints of the UAV and the camera field-of-view into account. Due to these constraints, it may not be possible for a single UAV to track the convoy continuously and hence we develop a cooperative multi-UAV framework with two UAVs to ensure continuous tracking. The simulation results show that the proposed framework adequately tracks the target without losing the line-of-sight of the convoy.
Data Gathering and Justice in the Urban Informal Sector: Views from the Frontline

This case considers the challenges faced by civil society organisations (CSOs) in gathering and applying data in urban informal contexts. Given that one billion people are estimated to live in such contexts globally and that the number continues to grow it is clearly important to build understanding of these localities; understanding which CSOs have found difficult to acquire. The specific case is the development of participatory data gathering and knowledge creation programmes – ‘Views from the Frontline’ (VFL), ‘Action at the Frontline’ (AFL) and ‘Frontline’ – by the Global Network for Disaster Reduction and application of that knowledge to influence practice in informal urban contexts.It challenges tactical approaches which assume that knowledge will necessarily lead to effective action, highlighting the barriers faced in the VFL, AFL and Frontline programmes. It therefore investigates structural, distributive and instrumental aspects of data justice, highlighting the necessity for a nuanced and strategic understanding which takes account of power structures, if knowledge is to support effective social transformation. The discussion and findings contribute to the developing understanding of data justice and apply this specifically to the activity of CSOs in attempting to engage in informal urban contexts. They highlight the role of ‘small data’ gathering and application as having both process benefits through the engagement of participants, and impact benefits through increased visibility and voice for informal urban populations.
Deep Recommendation Model Based on BiLSTM and BERT

Recommendation models based on rating behavior often fail to properly deal with the problem of data sparsity, resulting in the cold-start phenomenon, which limits the recommendation effect. A model based on user behavior and semantics can better describe user preferences and item features to improve the performance of a recommender system, but is usually shallow and ignores deep features between the user and item. This paper proposes a deep neural network and self-attention mechanism (DSAM) model to solve these problems. The DSAM model introduces a two-way LSTM unit and a self-attention mechanism, combined with a large-scale pretrained BERT model to mine deep nonlinear features and hidden vectors in user comment information and perform score prediction. In comparative experiments carried out on the Amazon product dataset, the error of DSAM prediction results was lower than that of a reference group, and the average error was reduced by 4%.
Research on the blended Teaching Model of university English Based on Artificial Intelligence

At present, my country’s education system advocates the use of artificial intelligence technology in universities to strengthen the reform of ability training models and education methods, and to establish a new teaching system that includes knowledgeable learning and interactive learning.How to introduce artificial intelligence technology into college English education and efficiently integrate with college and university English “teaching” and “learning” is a key research issue in English teaching recently. This paper investigates the connotation of the mixed education mode, and proposes how to combine artificial intelligence-based “network teaching” with conventional teaching methods, and explains the advantages of the two teaching modes and explores a new model of college and university English education under the background of artificial intelligence.
Event Model Analysis

Event model analysis (EMA) is a narrative analysis technique that utilizes research on event cognition to reconstruct how people explain and interpret events. Event cognition research is premised on the idea that humans process the flow of information from their bodies and environment by simultaneously segmenting it into events and subevents (event segmentation) and generating working models of what is happening based on prior experience (event schemas) that are recalled only if stored in long-term memory as event models. Because we rely on event segmentation, models, and schemas – whether we are remembering past events, planning future events, telling a fictional story, or understanding a story told to us by others – historians can use research on event cognition to structure the way they extract and analyze data from narrative accounts. EMA can take two basic forms depending on the nature of the sources: attribution analysis and schema analysis. Attribution analysis, which is best suited to first- and third-person narratives, reveals the implicit attributions embedded in the narrative of an event and how attributions shift over time or between narrators and contexts. Schema analysis, which can be applied to second-person accounts, reveals how event schemas operate in models of specific events. We can use codes and charts to analyze relevant aspects of event models systematically.
BERT-based Text Simplification Approach to Reduce Linguistic Complexity of Bangla Language

The text simplification approach simplifies the linguistic complexity of a particular language so that the grammar and structure of a language are greatly simplified to read and understand while preserving the information and underlying meaning. Despite being spoken globally and having a rich history of Bangla literature, there is no work has been done in the Bangla language on this important topic. The work has been done to increase the number of Bangla literature readers and save Bangla historical writings from becoming extinct. We have also collected and used an extensive corpus consisting of 1,52,230 sentences along with a lexicon consisting of 22,580 complex-simple unique word pairs, which are mapped manually. This paper has presented two text simplification models based on Long Short Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). However, the proposed model based on BERT shows a satisfactory accuracy rate of 95.3%.
Formation of the Readiness of Classroom Teacher to Organization of Problem-Value Communication with Students in the Conditions of Implementation of the Working Program of Education

The article is devoted to the problem of the class teacher’s readiness for problem-value communication with pupils as one of the factors of the effectiveness of educational activities. High demands for the moral and ethical component of the modern young generation require the teacher to pay attention to the transmission of socially significant knowledge and the formation of value attitudes towards oneself, others and the world. One of the tools for understanding and forming value orientations is problem-value communication. Such communication can be organized, first of all, in the class team. The authors of the article believe that the readiness of the class teacher to organize problem-value communication with pupils the requires special training using potential of formal and non-formal education. The purpose of the article is to substantiate and describe the essential characteristics of the formation of the readiness of the class teacher to organize problem-value communication with pupils. Methodology. For methodological substantiation, the authors rely on axiological, systemic and event-driven approaches. Results. The research results are in the characteristics of the components of readiness and the conditions for its formation by means of formal and non-formal education. Also described are subjective factors that negatively affect the formation of readiness to organize problem-value communication with pupils.
Adaguc-Server: Interactive Access to Heterogeneous Meteorological and Climatological Datasets Using Open Standards

Adaguc-server is an open source geographical information system to visualize, combine, compare and share real-time meteorological, climatological and remote sensing data via OGC standards. During implementation of the server, we ran into the limits of what is possible with OGC standards. To overcome the found limits, extensions to the OGC standards were developed to achieve the required functionality and interoperability. The presented solution is used as a building block in a web based meteorological working station.
Comparison Study on Improved Movie Recommender Systems

Recommendation system is a crucial part of offering items especially in services that offer streaming. For streaming movie services on OTT, RS are a helping hand for users in finding new movies for leisure. In this paper, we propose a machine learning an approach based on auto encoders to produce a CF system which outputs movie rating for a user based on a huge DB of ratings from other users. Utilising Movie Lens dataset, we explore the use of deep learning neural network based Stacked Auto encoders to predict user s ratings on new movies, thereby enabling movie recommendations. We consequently implement Singular Value Decomposition (SVD) to recommend movies to users. The experimental result showcase that our R S out performs a user-based neighbourhood baseline in terms of MSE on predicted ratings and in a survey in which user judge between recommendation s from both systems.
A Construction Method of Final Exponentiation for a Specific Cyclotomic Family of Pairing-Friendly Elliptic Curves with Prime Embedding Degrees

Pairings on elliptic curves which are carried out by the Miller loop and final exponentiation are used for innovative protocols such as ID-based encryption and group signature authentication. As the recent progress of attacks for finite fields in which pairings are defined, the importance of the use of the curves with prime embedding degrees <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> has been increased. In this manuscript, the authors provide a method for providing efficient final exponentiation algorithms for a specific cyclotomic family of curves with arbitrary prime <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k\equiv 1(\text{mod}\ 6)$</tex> . Applying the proposed method for several curves such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k=7$</tex> , 13, and 19, it is found that the proposed method gives rise to the same algorithms as the previous state-of-the-art ones by the lattice-based method.
Navigation

Robot navigation is the problem of guiding a robot towards a goal. The human approach to navigation is to make maps and erect signposts, and at first glance it seems obvious that robots should operate the same way.
COVID-19 Information on Youtube: Analysis of Quality and Reliability of Videos in Eleven Widely Spoken Languages Across Africa

Introduction: Whilst the Coronavirus disease 2019 (COVID-19) vaccination rollout is well underway, there is concern in Africa where less than 2% of global vaccinations have occurred. In absence of herd immunity, health promotion remains essential. YouTube has been widely utilised as a source of medical information in previous outbreaks and pandemics. There is limited data on COVID-19 information on YouTube videos, especially in languages widely spoken in Africa. This study investigated the quality and reliability of such videos.Methods: Medical information related to COVID-19 was analysed in 11 languages (English, isiZulu, isiXhosa, Afrikaans, Nigerian Pidgin, Hausa, Twi, Arabic, Amharic, French and Swahili). Cohen’s Kappa was used to measure inter-rater reliability. A total of 562 videos were analysed. Viewer interaction metrics and video characteristics, source and content type were collected. Quality was evaluated using the Medical Information Content Index (MICI) scale and reliability by modified DISCERN tool.Results: Kappa coefficient of agreement for all languages was p<0.01. Informative videos (471/ 562, 83.8%) accounted for the majority whilst misleading videos (12/562, 2.13%) were minimal. Independent users (246/562, 43.8%) were the predominant source type. Transmission information (477/562 videos, 84.9%) was most prevalent whilst content covering screening or testing was reported in less than a third of all videos. Mean total MICI score was 5.75/ 5 (SD 4.25) and mean Total DISCERN score was 3.01 / 5 (SD 1.11).Conclusion: YouTube is an invaluable, easily accessible resource for information dissemination during health emergencies. Misleading videos are often a concern, however, our study found a negligible proportion. Whilst most videos were fairly reliable, the quality of videos was poor, especially noting a dearth of information covering screening or testing. Governments, academic institutions and healthcare workers must harness the capability of digital platforms such as YouTube to contain the spread of misinformation.Funding Information: No funding was provided for this study. Declaration of Interests: The authors declare that they have no conflict of interest.
Ensemble representation

An important strategy of our visual system to cope with limited memory capacity is to summarize and organize complex information as a concise yet informative representation. Ensemble representations are the statistical summaries (e.g., average, variability, and the shape of distribution) of visual scenes ranging from low-level visual features to higher-order abstract information. Ensemble representations organize visual memory efficiently in three different ways. First, they improve the quality of stored information in memory by canceling out noise or reducing redundant information. Second, they provide a hierarchical structure of a scene representation to organize it efficiently. Finally, they are useful sources for the visual system to anticipate forthcoming inputs, guide attention, and maintain visual stability.
Trends in online consumer fraud:

Following the advent of the Internet, the interaction between sellers and consumers is increasingly shifting from a face-to-face towards an online environment. This chapter examines what online consumer fraud is, revisiting the definition and common fraud schemes. It reviews some of the current approaches non-governmental and commercial institutions take to detect and prevent online consumer fraud. The chapter discusses darknet markets – internet platforms that, amongst other things, sell both legal and illicit goods while providing anonymity – and how they facilitating online consumer fraud. It closes with a discussion of how methods from data science can be applied to support the detection and prevention of online consumer fraud. The dark web represents a small portion of the deep web, on which users and hosts are anonymized.
Outdoor Displays for Industrial Applications

Abstract As display technologies and production methods evolve, new developments for luminance, cooling, bonding, and readability enhance performance and functionality.
Quality Assurance of Micro-Services - When to trust your micro-service test results?

Micro-service architecture has become a standard software architecture style, with loosely coupled, specified, and implemented services owned by small teams and independently deployable. In particular, with the emergence of managed services, deployment aspects have to be addressed explicitly. While tools and frameworks support micro-service developers in developing and unit-testing their services, less attention has been given to higher testing levels, particularly to the integration testing phase. This paper identifies aspects that limit the expressiveness and therefore the trust of integration testing and test results in the context of managed micros-services and function as a service. We propose the introduction of instance identification to overcome these limitations and illustrate how instance identification can be used to enhance integration testing's expressiveness and trust into integration test results.
HUBUNGAN KURIKULUM 2013 DENGAN MOTIVASI BELAJAR PESERTA DIDIK DI SMK PELITA CIAMPEA

The purpose of this study was to find out the perceptions of students about the 2013 curriculum, to find out the motivation of students in learning, to determine the relationship between learners' perceptions of the 2013 curriculum and learning motivation of Pelita Ciampea Vocational School students, and research lecturers at STKIP Muhammadiyah Bogor. From the results of the study with a total sample of 57 people obtained results (1) Perception of students about the 2013 curriculum at Pelita Ciampea Vocational School has a positive phenomenon, as evidenced by the maximum value that can be obtained by students is 185, the highest score is 175, the lowest 111, the average 147.74. (2) The learning motivation of students at Ciampea Vocational School has a positive phenomenon, as evidenced by the results of the questionnaire with the maximum value that can be obtained at 135, the highest score of 126, the lowest 90, while the average is 105.72. (3) From the results of the moment product correlation test the relationship between learners 'perceptions of the 2013 curriculum and students' learning motivation has a value of 0.969 while the r-table value is 0.266, and 0.969&amp;gt; 0.266 or r-count&amp;gt; r-table. Thus it can be concluded that H0 is rejected and H1 is accepted or in other words, there is a positive relationship between variables X (students' perceptions of the 2013 curriculum) with variable Y (student motivation). The level of relationship between variable X and variable Y lies in the interval 0.80-1.00 which means that the relationship between the two is very strong.