Reviewerly/exHarmony huggingface dataset:
A novel approach to maneuvering target tracking based on random motion model using random Kalman filtering

Traditional maneuvering target tracking algorithms assume that the target motion model is one fixed or a limited number of them. For high-speed and strong maneuvering targets, when the model set cannot cover the maneuvering mode or the deviation is large, the performance of the tracker will drop rapidly. Therefore, this paper proposes a new maneuvering target tracking method – a random motion model based on Random Kalman Filtering (RKF). This algorithm uses a random model to describe the target maneuver, which is more widely used than traditional algorithms and is more stable when the target maneuver is not covered by the model set. Compared with the traditional single model, the classic related algorithm is Kalman Filtering (KF), the new method significantly improves the tracking effect when the target is maneuvering. At the same time, when the model set of the Interacting Multiple Model algorithm (IMM) does not match the real maneuvering state, the tracking error of the new method is smaller than IMM and there is no divergence trend.
Adoption of blockchain + Internet of Things in demand forecasting of farm supply chain

Abstract It aims to improve the application efficiency of modern science and technology in farm operation, enhance the prediction effect of machine learning and blockchain technology on farm supply and demand issues, and help enterprise managers to better manage and operate enterprise projects. First, the agricultural super business model under the blockchain + Internet of Things (IoT) is analysed, and the farm management system of the IoT + blockchain is constructed. Then, the demand forecasting model of agricultural products is established. Autoregressive Integrated Moving Average model (ARIMA) and Support Vector Machine (SVM) models are used to predict demand. Finally, the characteristics of the two models in prediction are discussed. The results show that the ARIMA model can reach 99.2% in the application efficiency evaluation of farm operation. Moreover, in the case of proper selection of model indicators, the prediction result is close to the actual value, the prediction error fluctuation is small, and the maximum error value is 0.4318. The local error of SVM model in predicting the demand of farm supply chain is slightly larger, and the maximum error value is 8.5430. By comparing the error results of the two models, the prediction accuracy of ARIMA model is higher than that of SVM model. The ARIMA model designed in this work can provide effective product demand prediction for farm operation and provide a strong technical reference for farm operation. The research not only provides technical support for machine learning and blockchain technology to predict farm supply and demand, but also contributes to the integrated management and sustainable development of enterprises.
Classical reasoning in the justification paradigm

Abstract Artemov, building upon a tradition beginning with Kolmogorov and Gödel, developed a paradigm for understanding Constructive Reasoning in terms of classical proofs. Kolmogorov–Gödel–Artemov constructivism flies in the face of the usual understanding of Constructive Reasoning as being distinguished from Classical Reasoning in terms of its theory of truth. Is there something that stands to traditional Classical Reasoning as Kolmogorov–Gödel–Artemov constructivism stands to Constructive Reasoning? In this paper, we develop an affirmative answer to this question by presenting a justification account of Classical Reasoning in terms of explicit justification. The traditional truth paradigm account of Classical Reasoning leads to the well-known paradoxes of material implication. We show that the justification account of Classical Reasoning avoids this problem.1
Barriers Affecting the Use of Blockchain by Freight Forwarders in Ho Chi Minh City

Blockchain technology can improve the efficiency and competitiveness of freight forwarding companies. Although blockchain technology is expected to be a solution to help companies build sustainable logistics networks, the adoption rate of this technology is still low. Therefore, this study identifies barriers to blockchain adoption in the freight forwarding industry. Instead of providing more reasons to adopt blockchain technology, the study explores the factors that prevent freight forwarders from adopting blockchain. Findings based on empirical data support the majority of research hypotheses. The study identified factors that impede the adoption of blockchain technology including Perceived complexity of the technology, Perceived risk of vendor lock-in, Perceived effort in collaboration and communication between firms, Perceived constraint on government support and Perceived constraint on existing regulations and legal framework within blockchain. On that basis, the study provides some implications related to the application of blockchain technology in freight forwarding companies in Ho Chi Minh City.
Assessing the contribution of the Adhoc crop health surveillance tool on the food security and livelihoods of smallholder farmers in Uganda

Abstract One of the perennial causes of food insecurity in Sub‐Saharan African countries is crop pests and diseases that affect agricultural production. To address this concern in Uganda, a mobile phone crowdsourcing Adhoc surveillance tool, Adsurv, was deployed and used by smallholder farmers. Adsurv is a crop health surveillance and monitoring mobile application that facilitates diagnosing and managing crop pests and diseases. Existing literature has examined the impacts of mobile technologies on food security or livelihoods in isolation, but has not conducted a holistic assessment of both. This study on the other hand aimed at assessing the contribution Adsurv made towards improved food security and livelihood assets of the smallholder farmers, which were not fully understood. The study contributes to applying the sustainable livelihood framework (SLF) as theory in evaluating Mobile for Development (M4D) initiatives. The Analytic Hierarchy Process (AHP) was further adopted to facilitate the development of a hierarchical assessment model of the multidimensional food security and livelihood aspects using data collected from 56 out of the targeted 70 participants. Results show that Adsurv mainly contributed towards food availability rather than utilization or access. These gains mostly enhanced the human assets of the smallholder farmers, empowering them with skills that positively impacted all other assets to support the attainment of sustainable livelihoods. The implication of these results in both research and practice is the need for further research trials, which will motivate farmers to recognize the importance of the nutritional value of food in their agronomic practices.
What can diagnostic algorithms do for primary care?

Diagnostic algorithms can help in decisionmaking, for example, in the first diagnosis, in complex or rare clinical pictures and for consistent monitoring.
Database of Damage Caused by Earthquakes in Puebla City, Mexico

Puebla City has been affected by earthquakes, producing damage to the infrastructure. Despite this recurring experience, to date there is no reliable record of damage caused by each earthquake. A damage database is a necessary tool to determine the most seismically vulnerable areas in Puebla City. It will help to evaluate and identify the zones historically more affected. Based on the fact that seismic activity is recurrent, soil ground conditions are relatively constant in the long term, and infrastructure has low level of change in the city between events, the damage database can be developed. By creating a simple model of empirical damage prediction, the intent is to determine the most vulnerable areas in a future seismic event. This paper seeks to show the patterns of damage in buildings on Puebla City and its most vulnerable zones using damage data information and tools like GIS and statistics.
Instant messaging and the facilitation of collaborative, student‐led learning and teacher‐support: the NZCEL EAP scenario

Abstract In the last couple of decades, the increased use of technology in the education sector has led to smartphone use becoming more prevalent in supporting students' learning. Apart from applications (apps) specifically designed for language educational purposes, such as Duolingo, there are also instant messaging apps that are not specifically designed for education purposes, but that can be useful in supporting learners. These include instant messaging apps such as, WhatsApp and WeChat. Instead of relegating phone app use to merely a source of entertainment and distraction, teachers increasingly use them as a vital form of communication to enhance education, including language learning. Apps have thus shifted from a tangential position to the forefront of the learning space. For the purposes of this research project, WeChat was used as the messaging app. This article focuses on the initial findings of a pilot study and concentrates on the perceived purposes for the participants' posts as they relate to the use of the app as a tool for collaboration, peer‐support, and knowledge sharing. Data was gathered through an online survey, semi‐structured interviews, and an analysis of the WeChat posts. Analysis of the posts and comments made by students during the interviews suggest that the tool formed a vital link between them, their classmates and teachers, and, at times, served as a social platform underlying the key educational purposes of the programme.
A high-performance Webshell detection model

Webshell exists as a command execution environment in the form of a web page file, which is often referred to as a backdoor. After hacking a website, hackers usually upload it to the web directory of the web server and mix it with the normal web files, and then access the backdoor program through the browser, which can achieve the purpose of controlling the browser. Since there are many kinds of web backdoors in the form of asp, php, jsp or cgi files, here we choose the more popular php file as the research object. In this paper, the Webshell dataset comes from common Webshell samples on the Internet, and the white samples mainly use common open source software developed based on PHP. We use bag-of-words and TF-IDF models for feature extraction, and construct Webshell detection models based on the LightGBM algorithm. The results show that our model is more than 98% accurate and has higher performance in space and time compared to the current popular classification models.
The Role of Geophysics in the Security of Critical Minerals

Securing mineral resources is important for a country's continuous development.As most shallow orebodies have already been mined, deep orebodies are targets for mineral exploration.It is difficult to predict the geometries of deep orebodies using geological and drilling data.Geophysical surveys provide three-dimensional (3D) physical models for the interpretation of deep underground structures.To improve interpretation accuracy, it is necessary to effectively integrate not only geophysical exploration but also various complex geological information.We propose a mineral exploration method based on a digital twin.Various types of complex geological information, including geophysical data, are integrated and built into the digital twin.Based on this, it is possible to enhance the reliability of interpretation and perform accurate simulations of exploration and drilling.With the use of a digital twin, we can expect that a stable integrated analysis can be performed to minimize the uncertainty of each exploration and increase the exploration success rate.
Towards Moment-Constrained Causal Modeling

The fundamental problem with causal inference involves discovering causal relations between variables used to describe observational data. We address this problem within the formalism of information field theory (IFT). Specifically, we focus on the problems of bivariate causal discovery (X &rarr; Y, Y &rarr; X) from an observational dataset (X,Y). The bivariate case is especially interesting because the methods of statistical independence testing are not applicable here. For this class of problems, we propose the moment-constrained causal model (MCM). The MCM goes beyond the additive noise model by exploiting Bayesian hierarchical modeling to provide non-parametric reconstructions of the observational distributions. In order to identify the correct causal direction, we compare the performance of our newly-developed Bayesian inference algorithm for different causal directions (X&rarr;Y, Y &rarr; X) by calculating the evidence lower bound (ELBO). To this end, we developed a new method for the ELBO estimation that takes advantage of the adopted variational inference scheme for parameter inference.
Predictive Data Analysis Using Linear Regression and Random Forest

A statistical technique called predictive analysis (or analytics) makes use of machine learning and computers to find patterns in data and forecasts future actions. It is now preferred to go beyond descriptive analytics in order to learn whether training initiatives are effective and how they may be enhanced. Data from the past as well as the present can be used in predictive analysis to make predictions about what might occur in the future. Businesses can improve upcoming learning projects by taking actionable action after identifying the potential risks or possibilities. This chapter compares two predictive analysis models used in the predictive analysis of data: the Generalized Linear Model with Linear Regression (LR) and the Decision Trees with Random Forest (RF). With an RMSE (Root Mean Square Error) of 0.0264965 and an arithmetic mean for all errors of 0.016056967, Linear Regression did better in this analysis than Random Forest, which had an RMSE of 0.117875 and an arithmetic mean for all errors of 0.07062315. Through the hyper-parameter tuning procedure, these percentage errors can still be decreased. The combined strategy of combining LR and RF predictions, by averaging, nevertheless produced even more accurate predictions and will overcome the danger of over-fitting and producing incorrect predictions by individual algorithms, depending on the quality of data used for the training.
Comparing different machine‐learning techniques to date Nile Delta sediments based on portable X‐ray fluorescence data

Abstract Geomorphology generally aims to describe and investigate the processes that lead to the formation of landscapes, while geochronology is needed to detect their timing and duration. Due to restrictions on exporting geological samples from Egypt, modern geoscientific studies in the Nile Delta lack the possibility of dating the investigated sediments and geological features by standard techniques such as OSL or AMS 14 C; therefore, this study aims to validate a new approach using machine‐learning algorithms on portable X‐ray fluorescence (pXRF) data. Archaeologically dated sediments from the archaeological excavations of Buto (Tell el‐Fara'in; on‐site) that pXRF analyses have geochemically characterized serve as training data for running and comparing Neural Nets, Random Forests, and single‐decision trees. The established pXRF fingerprints are transferred via machine‐learning algorithms to set up a chronology for undated sediments from sediment cores (i.e., the test data) of the nearby surroundings (off‐site). Neural Nets and Random Forests work fine in dating sediments and deliver the best classification results compared with single‐decision trees, which struggle with outliers and tend to overfit the training data. Furthermore, Random Forests can be modeled faster and are easier to understand than the complex, less transparent Neural Nets. Therefore, Random Forests provide the best algorithm for studies like this. Furthermore, river features east of Kom el‐Gir are dated to pre‐Ptolemaic times (before 332 B.C.) when Kom el‐Gir had possibly not yet been settled. The research in this paper shows the success of close interactions from various scientific disciplines (Geoinformatics, Physical Geography, Archaeology, Ancient History) to decipher landscape evolution in the long‐term‐settled Nile Delta's environs using machine learning. With the approach's design and the possibility of integrating many other geographical/sedimentological methods, this study demonstrates the potential of the methodological approach to be applied in other geoscientific fields.
Sarcasm Detection on Tweets: Ensemble Approach

In the era of web 2.0 social media data is getting generated at huge amount of value to different organizations. Twitter is one of the platforms where different communities express their opinion on social media platform. These different opinion leads to tremendous amount information to work on such as opinion mining, sentiment analysis, sarcasm detection on various social media platform. Sentiment analysis is a natural language processing where author find contextual meaning of text to identify the sentiment of people on digital platform. Sentiment analysis is study of people’s behaviour. Now a day’s people use Sarcasm in many forms to convey their feelings on many social media platforms. Therefore, Sarcasm detection which is sub branch of sentiment analysis becomes a challenging job. Our research is broadly focusing on sarcasm detection of tweets using different machine learning model and neural network model. To enhance the result of classification author have used ensemble model such as staking. Proposed model is inputted with training dataset of tweeter which approximately divided into 70% into training and 30 % into testing dataset. Ensemble model is trained on cleaned dataset and our model is generating an acceptable accuracy which can classify the sarcastic tweets accurately.
An improved AODV routing algorithm based on energy consumption for Ad Hoc networks

The Ad Hoc networks solves the problem of quickly establishing a temporary Ad Hoc network without the need for fixed facilities. However, problems such as network performance degradation, delay increase, and link interruption are endless. Aiming at the problem of shortening the survival time of Ad Hoc networks nodes, this paper proposes an improved AODV routing algorithm EC-MAODV (Energy Consumption-Modification AODV) based on energy consumption. EC-MAODV takes energy as the starting point, determines the time extension based on the residual energy of the current node, balance the power consumption of each node, and extends the networks lifetime. The effectiveness of EC-MAODV routing algorithm is proved by network simulator NS-3. The simulation results show that compared with the AODV routing protocol, the algorithm in this paper has more than 7% improvement and 19% increase in average delay and node lifetime. It can effectively prolong the life of the network, reduce the number of dead nodes, effectively solve the energy consumption problem of the wireless Ad Hoc network, and improve the overall performance of the network.
Implementation of the Posyandu Information System in Tirtorahayu Village Kapanewon Galur Kulon Progo Regency

Kalurahan Tirtorahayu is one of the villages that is highly receptive to the growth of information technology. No Posyandu Information System (SIP) has been implemented in the village to date; so, the management of maternal and child health data has encountered considerable obstacles. This activity was intended to develop and implement SIP in Tirtortahayu Village. The activities consist of four phases: 1) situation analysis; 2) application development; 3) system implementation; and 4) program evaluation. The situation analysis was conducted via FGDs and direct observation of Posyandu activity implementation. SIP development was done with direct user participation. Posyandu cadres who became pilots, village midwives, and personnel who handled health data received training in two phases. The first phase consists of a web-based SIP training, while the second phase focuses on maternal and child health data entry via an Android-based application. The results of the evaluation proved that the participants viewed this SIP as important and as needed immediate implementation. The successful implementation of this SIP were the support of the village government, the usability of the application, and the experience of participants in using information technology-based applications.
Экономическая грамотность студентов российских вузов как условие успеха выпускников на рынке труда и профилактики безработицы

Today it is especially important to consider education, in particular higher education, in the context of the investment sphere of the state, since it belongs to the largest industries of any country in the world. Young specialists&nbsp;–&nbsp;graduates of Russian universities for a number of geopolitical, economic, social, cultural and other reasons in the coming years will massively face the problems of decent employment after graduation, since they choose the areas of education before the large-scale change of economic and political priorities of the state. Consequently, economic literacy in a broad context and financial literacy in a narrow subject today has become an acute problem area for students of Russian universities, especially non-economic profile. It acts as a primary tool for creating means of adaptation of young people in the labor market and prevention of unemployment. Teaching at the university, discussing problem areas with students, including students with disabilities, we identified their concerns about adaptation to society from the position of employment, financial well-being, which is reflected in our previous research. This prompted the search for tools and methodology for solving an urgent topic.
A Client-based User Authentication Scheme for the Cloud of Things Environment

The limited capabilities of IoT devices have resulted in some of the tasks of IoT applications being distributed to a cloud server, which witnessed the arisen of the cloud of things (COT). It enables IoT applications' development and deployment as a service, providing additional data storage, enhanced processing performance, and fast communication between devices. As COT involves communication between IoT devices, a remote server, and users, remote user authentication is crucial to meeting security demands. Therefore, this study designs a client-based user authentication scheme utilizing smartphone fingerprint recognition technology to fill the gap. The scheme comprises six phases, namely (i) configuration phase, (ii) enrolment phase, (iii) authentication phase, (iv) password update phase, (v) fingerprint revocation phase, and (vi) smartphone revocation phase. The security analysis and automated verification using ProVerif suggested that the scheme is resistant to user impersonating attacks, replay attacks, and man-in-the-middle attacks.