Reviewerly/exHarmony huggingface dataset:
Generating Safe Corridors Roadmap for Urban Air Mobility

Personal air transportation on short distances, so-called Urban Air Mobility (UAM), is a trend in modern aviation that raises new challenges as flying in urban areas at low altitudes induces an additional risk to people and properties on the ground. Risk-aware trajectory planning can mitigate the risk by detouring and flying over less populated and thus less risky areas. Existing risk-aware trajectory planning approaches are computationally demanding single-query methods that are impractical for online usage. Moreover, coordinated planning for multiple aircraft is prohibitively expensive. Therefore, we propose to reduce computational demands by determining low-risk areas called safe corridors and creating a roadmap of safe corridors based on multiple least risky trajectories. The created roadmap can be used in graph-based multi-agent planning methods for coordinated trajectory planning. The proposed method has been evaluated in a realistic urban scenario, suggesting a significant computational burden reduction and less risky trajectories than the current state-of-the-art methods.
PAGINATION AND ITS EFFICIENT METHODS FOR RESTFUL WEB SERVICES

In this paper on the basis of analyzed resources and accumulated IT-industry discuss the problem of the common approaches to pagination in RESTful web services, as well as the ways of overcoming it by using a method that fuses usual approaches while covering their weaknesses.
Fast QBE: Towards Real-Time Spoken Term Detection with Separable Model

State-of-the-art spoken term detection or query-by-example networks depend on recurrent neural network (RNN), which extract fixed-dimensional vectors (embedded vectors) from both spoken query and the search content, and then calculate cosine distances over the vectors. However, these methods depend on time sequence, so it is a computational cost task, can not meet the requirements of both the query accuracy and search speed. In this work, we introduce a fast Spoken term detection system based on a separable model—RepVGG. Because of the trick of reparameterization, it has a faster speed in inference. Secondly, we use non maximum suppression and norm in the step of inference to improve it performance. Thirdly, we use multilanguage training to improve both accuracy and robustness of the system. Corresponding experiments are designed to verify these ideas. It show that proposed methods can import the GPU real-time factor (RTF) from 150 to 2300, and outperforms the state-of-the art method.
DISTRIBUTION OF HEAVY METALS IN SOILS ON THE TERRITORY OF THE SMALL TOWN (ON THE EXAMPLE OF NESVIZH)

The results of geochemical studies of soils on the territory of Nesvizh are presented in the context of the distribution of heavy metals. Statistical data processing has been performed. For the purposes of the study, the regional and local geochemical background was calculated. The features of enrichment and pollution of urban soils by the studied components are revealed. The concentration coefficients and the total pollution coefficient are calculated. Monocomponent and polycomponent maps were constructed, reflecting the revealed regularities in the distribution of heavy metals. The results of correlation and factor analyses are presented. Associations of heavy metals, typical for the soils of the city, were revealed. Factor loadings are calculated. A graphical representation of the matrix of factor loadings is given. The factors responsible for the formation of the technogenic situation have been established, and their contribution has been determined. The map of factor loadings has been constructed. The interpretation of geochemical data both at the level of individual points and in the space of the entire city was carried out.
Deepfake in an Image Using Deep Learning

This paper provides a way to automatically and effectively detect facial interference in videos and focuses mainly on the latest two techniques used to produce fake videos. Deepfake and Face2Face describe a method for detecting face tampering in the video that is both automatic and efficient. Due to the compression that severely affects the data, traditional image forensics techniques are usually not well adapted to videos. As a result, this research takes a deep learning method and provides two networks, each with a small number of layers, that focus on image mesoscopic features. We test such rapid networks on a dataset that already exists as well as a dataset that we created from online videos to demonstrate that the proposed metric learning method can be very effective in making such a category. By using Deepfake and Face2Face, we have tried to solve this, and thus these networks provide us with the best possible results, giving us good accuracy. The model uses the MTCNN model to capture the images from videos and further proceeds with the process to detect whether the given video is real or fake. We have achieved an accuracy level of 80%.
Multi-view Fused Detection of Simulator Sickness Based on Deep Generalized Canonical Correlation Analysis

Simulator sickness is a general malaise induced by virtual reality equipment such as simulators. Its symptoms include visual fatigue, disorientation and nausea, which seriously affect the training effect of simulators and the experience of using virtual reality equipment. Current machine learning methods for assessing simulator sickness cannot reasonably utilize the potential correlation between data features. In this paper, we use deep generalized canonical correlation analysis (DGCCA) to fuse features from different physiological data, and classify the fused features using machine learning. It was validated on public datasets with good results.
A Smart Portable Goniometer-Based Knee Rehabilitation Device Using Arduino Microprocessor and Sensors

Every year, over 1 million emergency room visits and 1.9 million visits to primary care physicians are due to knee pain; knee pain results in a knee flexion contracture (functional loss of knee extension), decreased strength, and decreased function. Knee-injured patients can recover faster if they receive professional rehabilitation training. However, since professional rehabilitation training is expensive, tedious, and requires high professional knowledge, they are not accessible to many patients. Compared to the general population, athletes have a higher probability to suffer from knee injuries, and the inability to recover quickly or completely can easily affect their careers. By using a low-cost Arduino microprocessor and sensors, a new wearable non-invasive knee rehabilitation device is created, which can provide rehabilitation training tutorials through the accompanying software and prompt the user in real-time whether the posture is correct, thus helping the injured person to perform rehabilitation training in a more standard and safe manner. Compared with the large and expensive devices available on the market, this device has the advantages of being miniature, portable, and low-cost, and aims to provide inexpensive and convenient rehabilitation training guidance for athletes with knee injuries to help them recover quickly and return to the field of play.
Special Topic: An Introduction to Kalman Filter

The Kalman filter is often heralded as among the most impactful mathematical concepts and algorithms of applied mathematics of the twentieth century. The basic mathematical theory is presented in this chapter, together with an often cited example to illustrate the nature of the computations required for estimation and prediction with the Kalman filter.
Combining UML Profiles to Design Serious Games Dedicated to Trace Information in Decision Processes

An expert assessment consists of an ordered series of decisions that have to respond to time-evolving information contexts. Improving decisions made in a risk context requires better knowledge of reasoning mechanisms. The authors think that serious games can constitute a rich observatory for reasoning and decisions. However, the design of these games is not trivial and is rarely scalable or reusable. This paper proposes a UML profile library for generically modeling expert reasoning in situations using serious games that involve risks. Two main UML profiles are dedicated to both serious games and gamer decisions traceability modeling. Complementary profiles address risk expert reasoning modeling and data quality modeling. The authors illustrate the approach using the design of a serious game about avalanche risk analysis.
The Concept of Client-Server Architecture for Remote Control of a Distributed Knowledge Base

The concept of a client-server architecture for storing and managing data of a distributed knowledge base is proposed. The client-server organization uses the principle of the “readers-writers” task with the priority of writers. The advantages of the proposed concept in comparison with analogues are presented. The server part consists of a read server, a write server, a verification server, a cache server. The client part consists of an IO module, a reader module, and a writer module. The paper shows an example of the implementation of a simplified concept. Smart Grid ontology was chosen as the knowledge base used. Apache Jena Fuseki is used as a tool for organizing the server part. This server supports the organization of requests through the user interface and through the Rest protocol. To organize the execution of requests via the Rest protocol to the server, the Insomnia software tool was used. Examples of read and write requests are shown. All queries for examples are made in SPARQL.
Crowd Violence Detection in Videos Using Deep Learning Architecture

Violence is considered as a serious and sensory issue and visualizing the violence is therefore subjected to ethical implications. Surveillance cameras may be placed in several locations to monitor and capture people’s activities. From the massive data collected through surveillance camera, detecting violence is a tedious task. Recently computer vision-based methods are used to identify violent activities automatically. Researchers were using key frame extraction technique to delete duplicate frames and detect violence from the input video. In the recent day’s, they use a pretrained network for selecting visually important frames and perform violence detection. In a constrained environment these networks perform well, but the accuracy drops for inputs acquired in unconstrained environment. In this work, a keyframe extraction technique is initially performed to remove duplicate consecutive frames followed by a pretrained VGG 16 deep learning architecture on Hockey fight dataset for detecting and recognizing violence. By minimizing duplicate frames, the cost of training data and thus the computational cost is reduced. Previously proposed sequential CNN architectures used a single kernel size for feature selection and classification tasks, whereas the proposed VGG16 architecture uses 3 kernels. The proposed VGG 16 network is found to be improving on following aspects: model size is small, speed is faster, faster convergence is done by residual learning, generalization is better and degradation problem is solved. Experimental methodology tested on Hockey fight dataset has shown that the suggested architecture is able to perform well even in an unconstrained environment.
General Framework for Reversible Data Hiding in Texts Based on Masked Language Modeling

With the fast development of natural language processing, recent advances in information hiding focus on covertly embedding secret information into texts. These algorithms either modify a given cover text or directly generate a text containing secret information, which, however, are not reversible, meaning that the original text not carrying secret information cannot be perfectly recovered unless much side information are shared in advance. To tackle with this problem, in this paper, we propose a general framework to embed secret information into a given cover text, for which the embedded information and the original cover text can be perfectly retrieved from the marked text. The main idea of the proposed method is to use a masked language model to generate such a marked text that the cover text can be reconstructed by collecting the words of some positions and the words of the other positions can be processed to extract the secret information. Our results show that the original cover text and the secret information can be successfully embedded and extracted. Meanwhile, the marked text carrying secret information has good fluency and semantic quality, indicating that the proposed method has satisfactory security, which has been verified by experimental results. Furthermore, there is no need for the data hider and data receiver to share the language model, which significantly reduces the side information and thus has good potential in applications.
Phonological analysis of Kĩtigania borrowed words from the Maa Language: An optimality approach

The notion of loanwords in languages that are in a contact situation is a phenomenon that is very common and unavoidable. For languages of the world, language change is remorseless, inevitable and ceaseless. This is because every day there are new innovations, change of ideas and emerging technologies that force language to be dynamic so as to keep up with the changes. Even as languages borrow, linguistic borrowing which entails loan word adaptation is an overwhelmingly phonological process. This phonological analysis has not been adequately examined in Kĩtigania borrowing from the Maa language whereby both are in contact situation. That is why this article did a phonological analysis of Kĩtigania borrowed words from the Maa language using the Optimality Theory’s markedness and faithfulness constraints. Data used in this article was collected using descriptive research design. Purposive sampling was done to select ten Maa and ten Kĩtigania speakers who were neither too old nor too young. These respondents had control over their articulators. Then interviews were conducted with all the respondents to elicit words borrowed to Kĩtigania and their meanings. The findings revealed that when borrowed segments are adapted to Kĩtigania, they undergo sound modification so that those adapted are faithful to those in the source language (Maa Language) and they are present in the Kĩtigania inventory. However, the loans undergo structure modification so as to fit the Kĩtigania syllabic structure. Nevertheless, markedness dominated faithfulness in Kĩtigania borrowing. Therefore, this article concludes that similarity of sounds between two languages brought by shared features as well as a language’s inventory and syllabic structure play a big role in Kĩtigania borrowing. In addition, different repair strategies which result to phonological processes are employed in Kĩtigania borrowing. They include; deletion, substitution and voicing or weakening.
Spatially Invariant Unsupervised 3D Object-Centric Learning and Scene Decomposition

We tackle the problem of object-centric learning on point clouds, which is crucial for high-level relational reasoning and scalable machine intelligence. In particular, we introduce a framework, SPAIR3D, to factorize a 3D point cloud into a spatial mixture model where each component corresponds to one object. To model the spatial mixture model on point clouds, we derive the Chamfer Mixture Loss, which fits naturally into our variational training pipeline. Moreover, we adopt an object-specification scheme that describes each object’s location relative to its local voxel grid cell. Such a scheme allows SPAIR3D to model scenes with an arbitrary number of objects. We evaluate our method on the task of unsupervised scene decomposition. Experimental results demonstrate that SPAIR3D has strong scalability and is capable of detecting and segmenting an unknown number of objects from a point cloud in an unsupervised manner.
A Machine Learning-Based Missing Data Imputation with FHIR Interoperability Approach in Sepsis Prediction

Sepsis is a dangerous infection that can affect different parts of the body. It is caused by the body’s immune system overreacting to an infection. Therefore, hospitalized patients can benefit from having an Artificial Intelligence (AI)-based approach to alert healthcare professionals when the patient is at risk of sepsis. However, automating and diagnosing sepsis in hospitalized patients is difficult, as sepsis can present differently in each individual. However, although deep learning (DL) algorithms have demonstrated high accuracy and exemplary performance in different clinical categories, very few have been integrated into medical systems. In this paper, we introduce an interoperability platform to integrate electronic healthcare records (EHRs) and clinical modeling of mortality risk in patients with sepsis to support patient monitoring and early detection. Furthermore, we assess the training and evaluation of machine learning algorithms to classify the risk of a hospitalized patient getting sepsis using the EHRs from two hospitals released in the cardiology challenge 2019 for early sepsis prediction from clinical data. Additionally, we focus on structuring the data in units and building a clinical document architecture using the standard Fast Healthcare Interoperability Resources (FHIR) standard to enable the interoperability to train and deploy the model from different hospitals. We also present a dashboard for patient monitoring and support of early detection. We evaluated GradientBoosting, and LightGradientBoostingMachine (LightGBM) classifiers on two test sets, one by each hospital EHRs, using the F1-score represents the perfect accuracy and recall of the model and the area under the receiver operating characteristic curve (AUC). When trained on data from hospital A, the LightGBM yields an F1-score of 0.87 and 0.26 for the two test sets. The LightGBM trained from hospital B yields an F1-score of 0.20 and 0.88 for the two test sets, while using the data training combination of two hospitals improves the prediction accuracy with an F1-score of 0.93 and 0.94 for the two test sets. The accuracy of sepsis classification in hospitalized patients relies on the diversity of the training data since we identified variations in the values of the clinical characteristics used to represent each patient’s condition. Therefore, we highlight that reliably composing a diverse and structured dataset improves the results. Also, adding a model for imputation of missing values improves the classification performance and deals with the missing data that often appear in hospitals.
Disruptive Monetary Phenomenon, Challenges and Complexities (Cryptocurrencies)

The present document develops on the presence of cryptocurrencies on Latin America. It begins with the explanation of the world scenario that incentive the born of the Bitcoin as an alternative to fiat currencies, as well as the main reasons the financial institutions over the world have attacked it over the years. The main functionality is summarized as the main argument of the supporters as a secure, transparent, and decentralized asset. Later the general conditions of under-development in Latin America are presented: corruption in the governments, increasing socioeconomic gap and migration. Under this stage, the possibility to incorporate cryptocurrencies is explored by pointing the aid to the previously discussed conditions. In this aspect, the advances of the acceptance of cryptocurrencies as money among some countries is briefly explored. Finally, a discussion on the risk and benefits of cryptocurrencies is presented to explain the volatility with a lack of international regulation.
A knowledge tracing model based on attention mechanism

For the past few years, with the gradual promotion and application of personalized online education system, researchers began to consider using online education system to trace and analyze students' knowledge status, so as to teach students according to their aptitude and improve their learning ability. At present, researchers use the deep knowledge tracing method to research students' academic performance based on the interaction records of students' answers and build deep knowledge tracing model, which has shown good performance. However, the deep knowledge tracing model does not carry out in-depth analysis of knowledge interaction, leading to its poor interpretability. Therefore, in this study, we regard attention mechanism as an effective interpretability module to construct a new knowledge tracing model, which effectively improves the interpretability and predictive ability. The experimental results also indicate that the expression of this model is improved to a certain extent.
Learning the Image Prior by Unrolling an Optimization Method

Nowadays neural networks are omnipresent thanks to the amazing adaptability they possess, despite their poor interpretability and the difficulties they give when manipulating the parameters. On the other side, we have the classical variational approach, where the restoration is obtained as the solution of a given optimization problem. The bilevel approach is connected to both approaches and consists first in devising a parametric formulation of the variational problem, then in optimizing these parameters with respect to a given dataset of training data. In this work we analyze the classical bilevel approach in combination with unrolling techniques, where the parameters of the variational problem are trained with respect to the results obtained after a fixed number of iterations of an optimization method applied to it. This results in a large scale optimization problem which can be solved by means of stochastic methods; as we observed in our numerical experiments, the stochastic approach can produce medium accuracy results in very few epochs. Moreover, our experiments also show that the unrolling approach leads to results which are comparable with those of the original bilevel method in terms of accuracy.
A Span Information Fusion-Based End-to-End Relation Extraction Model for Power Knowledge Graph

Entity relation extraction is a crucial step towards constructing knowledge graph. Most of the current researches on entity relationship extraction focus on joint extraction in terms of entities and relationships. However, due to that the context information respectively from entities and relationships is very different, existing joint extraction approaches often suffer from the information noise caused by both them, which may significantly affect the performance of the whole model and bring about a lower efficacy in extracting relationships. In this paper, we proposed an end-to-end relation extraction method, which uses a text representation enhanced pretraining model and fuses span information for entity relation extraction. We use the BERT model to pre-train the datasets from power project management. In order that the output of BERT contains as much contextual knowledge as possible, the external knowledge is embedded into a vector representation and further spliced into the token embeddings in the BERT input for downstream tasks. Then, we respectively use the Span-level NER method to extract all possible fragment arrangements, and SoftMax to judge the entity type of each Span. At last, when performing relation extraction, the entity boundary and types are taken as identifiers and further added into the front and end of entity span, which is used as the input of the relational model for predicting the possible relationship between the pair of spans. Experiments were made on the power project management dataset, and the results show that entity types can provide very important information for relation extraction, and the performance of the proposed method is also competitive against the state-of-the-art entity-relationship joint extraction method.