Reviewerly/exHarmony huggingface dataset:
A proposal for organizing source code variability in the git version control system

Often, either to expand the target market or to satisfy specific new requirements, software systems inside a company are cloned, refactored, and customized, generating new derived software systems. Although this is a practical solution, it is not effective in the long-term because of the high maintenance costs when maintaining each of these derived software systems. Software product lines (SPLs) were proposed to reduce these costs; however, the lack of integration between variability realization mechanisms and version control systems reduces its attractiveness in the software development industry, especially in small and medium software companies. In this paper we propose an approach to integrate the conditional compilation mechanism used to implement the SPL variabilities and the Git version control system used to manage software versions in order to increase the attractiveness of the SPLs in the industry. The proposed solution also could be seen as a method to manage software system families' evolution in space and time.
The Fusion Of GPS And Gyroscope Based On Kalman Filter

In order to overcome the problems that the gyroscope will produce cumulative errors during long-term operation and the magnetometer is extremely susceptible to interference from the external environment. A new method of Kalman fusion model combining gyroscope and GPS is proposed in this paper. This method uses angle information which is read by GPS as the observation value, and heading angle which is obtained through the angular velocity integral model as the prior estimation value, performing Kalman fusion between them for obtaining the posterior estimate. The experimental results show that the proposed method can dynamically adjust the heading angle calculated by the integrator so that the accuracy of the estimated heading angle is improved. The dynamic response capability of the proposed model is excellent. The MSE of the average heading angle which is obtained by reading GPS in a period of time and the heading angle which is fused with GPS and gyroscope in different time periods is 6.87.
A Collaborative Recommendation Model of Agricultural Planting Technology Based on User Characteristics

In view of the lack of informatization in the field of agricultural planting production, the traditional collaborative filtering recommendation algorithm has problems such as cold start, sparse scoring matrix, and poor scalability, resulting in poor recommendation quality. A personalized recommendation model of collaborative filtering agricultural planting technology that integrates user characteristics is proposed. First, the user’s initial characteristics are constructed according to the user’s geographic location, planting occupation, and main crops, and user behavior information is used to update user characteristics. Then combine the user characteristic similarity model and the rating matrix similarity model, and reconcile the weighting factors to form the user’s comprehensive similarity. Finally, use Top-N for personalized recommendation. By integrating user characteristics, the recommendation model is more suitable for agricultural planting scenarios, and the recommendation results are more flexible and reasonable. Experimental results show that compared with user-based collaborative filtering recommendation and user-characteristic-based recommendation, the proposed algorithm improves precision by 2% and 7%, recall rate by 2% and 9%, and F1 value is 71%. The effectiveness of the proposed method is verified.
Text-Based Motion Synthesis with a Hierarchical Two-Stream RNN

We present a learning-based method for generating animated 3D pose sequences depicting multiple sequential or superimposed actions provided in long, compositional sentences. We propose a hierarchical two-stream sequential model to explore a finer joint-level mapping between natural language sentences and the corresponding 3D pose sequences of the motions. We learn two manifold representations of the motion –- one each for the upper body and the lower body movements. We evaluate our proposed model on the publicly available KIT Motion-Language Dataset containing 3D pose data with human-annotated sentences. Experimental results show that our model advances the state-of-the-art on text-based motion synthesis in objective evaluations by a margin of 50%.
Research on Sonar Image Denoising Method Based on Fixed Water Area Noise Model

Aiming at solving the problem that the imaging quality of underwater sonar is greatly affected by the noise of underwater environment, a sonar image denoising method based on the noise model of fixed water area is proposed. In this paper, the theoretical model of ambient noise in fixed water area is established, and the estimated parameters of the theoretical model are obtained through experimental tests. Then, these parameters are used as the key parameters of guided filtering based on frequency domain filtering pretreatment to denoise sonar image. The simulation results show that the proposed sonar image denoising method is superior to the traditional method in structural similarity(SSIM) and peak-signal-to-noise ratio(PSNR) compared with the traditional methods such as direct guided filtering, mean filtering and median filtering, at the same time, the image edge information and the clarity of the texture area are well maintained, which can effectively overcome the problem of texture loss and edge blurring caused by the traditional method. The research results of this paper have positive significance for improving the quality of sonar imaging in fixed waters.
Physiological Data Collection and Early Warning System for Elderly Living Alone Based on Internet of Things Technology

This paper provides a solution of physiological data collection and early warning system for the elderly living alone based on Internet of Things technology. The system adopts STC15F2K60S2 chip microcomputer, various sensors, display screen, buzzer and other hardware for system development. The system realizes the real-time collection and display of human physiological data such as blood pressure, heart rate, step counting, body temperature, indoor temperature and humidity information. When the data of blood pressure, heart rate and temperature exceeded the preset value, the server would warn with flashing lights and buzzers. The system experimented the functions of information acquisition, display and early warning of mobile phone and PC. In addition, the server can synthesize data collected over a period of time to predict the value of future data. The system can be used for the nursing of the elderly living alone or the monitoring and management of the special population in the community by community workers, effectively preventing the physiological accidents and psychological hidden dangers of the elderly and those with mobility difficulties.
Trajectory Tracking Control of an Amphibious Spherical Robot Using MPC Approach

Trajectory tracking control is a basic problem in rescue, detection and obstacle avoidance tasks. The trajectory tracking process has its own constraints, and the core of model predictive control is to solve the quadratic programming problem with constraints, so MPC is used to solve the trajectory tracking problem of amphibious spherical robot in this paper. Firstly, based on the 3-DOF dynamic state space equation of the robot, the model is approximately linearized and discretized to facilitate the design of controller. To solve the difficulty of adjusting the weight matrix of MPC, an adaptive parameter adjustment method based on output error is designed in this paper. The linear trajectory and square trajectory are simulated on MATLAB and Gazebo simulation platform respectively. The simulation results verify the applicability and stability of the designed controller.
Statistical Interpretation and Modeling Analysis of Multidimensional Complicated Computer Data

Statistics is a science about the collection, collation, analysis and interpretation of statistical data. It is a science of methodological nature. Its purpose is to explore the inherent quantitative regularity of data in order to achieve a scientific understanding of objective things. Statistical data modeling, discriminant analysis and algorithm research is one of the basic problems in the application of statistics. In recent years, it is being widely valued by scholars in many fields. With the continuous advancement of science and technology, the informationization has become the mainstream. The society as a whole has huge demand for it and faces the huge data collection needs. At the same time, we must face the requirements of elaborate analysis. After that, it is the call of the value information, finally forming a scientific decision.
A cloud-based framework for verifiable privacy-preserving spectrum auction

Spectrum auction is one of the most effective ways to achieve dynamic spectrum allocation in cognitive radio networks, and it provides one effective way to manage the spectrum demands of IoT devices with limited resources. Most spectrum auctions focus on protecting bidder privacy and achieving excellent social efficiency, but few tackles the verification of auction results that are controlled by the auctioneer. In this paper, we propose a cloud-based framework for verifiable privacy-preserving spectrum auctions. Our framework adopts a modified AFGH re-encryption algorithm that achieves both bid privacy protection and auction results verification at the same time. The cloud server helps to compute auction results based on homomorphic encryption, and an auctioneer decrypts the encrypted data from the server to obtain auction results. Meanwhile, the property of re-encryption makes it possible for any bidder to verify the auction results without compromising other bidders’ privacy.
The Novel Sequence Distance Measuring Algorithm Based on Optimal Transport and Cross-Attention Mechanism

In this paper, we propose a novel sequence distance measuring algorithm based on optimal transport (OT) and cross-attention mechanism. Given a source sequence and a target sequence, we first calculate the ground distance between each pair of source and target terms of the two sequences. The ground distance is calculated over the subsequences around the two terms. We firstly pay attention from each the source terms to each target terms with attention weights, so that we have a representative source subsequence vector regarding each term in the target subsequence. Then, we pay attention from each representative vector of the term of the target subsequence to the entire source subsequence. In this way, we construct the cross-attention weights and use them to calculate the pairwise ground distances. With the ground distances, we derive the OT distance between the two sequences and train the attention parameters and ground distance metric parameters together. The training process is conducted with training triplets of sequences, where each triplet is composed of an anchor sequence, a must-link sequence, and a cannot-link sequence. The corresponding hinge loss function of each triplet is minimized, and we develop an iterative algorithm to solve the optimal transport problem and the attention/ground distance metric parameters in an alternate way. The experiments over sequence similarity search benchmark datasets, including text, video, and rice smut protein sequence data, are conducted. The experimental results show the algorithm is effective.
Blockchain Privacy Preservation by Limiting Verifying Nodes' During Transaction Broadcasting

The increasing awareness of the Blockchain technology has gained interest of researchers and industrialists in recent years, hence several business enterprises are keen on using blockchain technology for their day-to-day transactions and record keeping. However, due to public availability of data on the blockchain, it is not advisable for organizations dealing with sensitive and confidential data to risk their data privacy by using blockchain networks. In this study we talk about privacy vulnerabilities and challenges in blockchain based applications. We verify the extent of the problem by both, literary findings, and empirical analyses. Next, we propose a conceptual framework to strengthen privacy preservation of the blockchain networks. The proposed framework is based on the idea of limiting the number of nodes that a transaction is broadcast to, for verification. The selected nodes will differ for each transaction, decreasing the possibilities of network listening and deanonymization of users. Moreover, limiting the number of verifying nodes will result in drastic reduction of computation overhead of the network, along with improved scalability. The proposed framework is analyzed based on various privacy features and risks. The evaluation results show that the model has a privacy rank of 0.76.
On the Hamming Weight Distribution of Subsequences of Pseudorandom Sequences

In this paper, we characterize the average Hamming weight distribution of subsequences of maximum-length sequences (m-sequences). In particular, we consider all possible m-sequences of dimension <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> and find the average number of subsequences of length <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> that have a Hamming weight <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$t$</tex> . To do so, we first characterize the Hamming weight distribution of the average dual code and use the MacWilliams identity to find the average Hamming weight distribution of subsequences of m-sequences. We further find a lower bound on the minimum Hamming weight of the subsequences and show that there always exists a primitive polynomial to generate an m-sequence to meet this bound. We show via simulations that when a proper primitive polynomial is chosen, subsequences of the m-sequence can form a good rateless code that can meet the normal-approximation benchmark.
Bi-Directional Chains of Neural Nets for Multi-Target Regression

Multi-Target Regression refers to the problem where a set of n independent variables are used to predict the values of k target variables where both k and n are greater than 1. Most methods provide the provision for a regression problem with multiple targets including decision tree regressors and artificial neural networks. However, these methods end up making an assumption that their is no inter-dependency among the target variables. In numerous problems, this assumption turns out to be false which can be notably seen with the variance inflation factor and co-relation of these variables. This consideration was addressed in both classification and regression using chain models, which although mostly competent, possess the issue of a uni-directional dependency. In this work, the significance of directionality problem has been discussed and is addressed by proposing an ensemble based methodology. The comparative analysis of the proposed model is studied against the pre-existing models to explore the improvements in the performance of the model.
Research on the Interactive Installation Based on 3D Projection Technology

With the continuous development of interactive media today, traditional graphic elements are no longer only displayed in paper media or mobile screens, but it’ s more often through three-dimensional, interactive display space. The installation design with interactive effect has made it possible for space, works, and audiences to interact with each other. Meanwhile, this presentation form is able to make the displayed graphic elements more dynamic and three-dimensional in the way they are presented. In this paper, through the use of design software and the real-time comparison of stereoscopic installation works, the main research content is to convert the traditional projection into a 3D projection display which is more conducive to the different dimensions of the space, so as to achieve a more beneficial to the interaction between the space, the work, and the audience design expression.
Comparison of Predicted PV System Performance with SURFRAD versus TMY

Solar resource is important for predicting PV system performance. Historical Typical Meteorological Years (TMY) from 1960-1990 (TMY2) and 1990-2010 (TMY3) are commonly used in the United States, but recently there have been concerns by stakeholders whether these datasets may lead to overpredictions. Therefore we simulated performance of a fictional PV system with over 20-years of accurate ground based surface radiation (SURFRAD) measurements at 7 locations and compared the median year performance with simulations using TMY data. We found that TMY overpredicted the median SURFRAD performance at 5 of the 7 sites, the NREL Physical Solar Model V3 (PSM3) performance predictions were greater than 90% of SURFRAD years at 5 of the 7 sites, and TMY3 predictions were closest on average to the median. Both TMY2 and TMY3 were within their uncertainty range.
Learning to Rank for Biomedical Information Retrieval

With the continuous development of biomedical, the scale of data has also continued to increase, which makes it difficult for researchers to extract information manually. In order to satisfy the researcher's information requirement, information retrieval techniques for the biomedical field have been resolved. Learning to rank is the field of machine learning and information retrieval combination, it has been shown to improve the retrieval efficiency to a large extent. This paper proposes a query optimization technology based on learning to rank. By using learning to rank for technical research on optimizing, a model combining query improvement and query expansion is proposed. Based on the improvement of the query, our method uses LTR to reorder the query expansion words, so that the original query can retrieve the document with higher accuracy. Only by biological field resources, the query expansion can obtain extended words, but it can not accurately describe the degree of relevance between extended words and query words. Introduce LTR methods can fully consider the relevance of extended words and original queries. Improve the inaccurate problem of single-extension. The experimental results show that compared with traditional algorithms, this algorithm improves retrieval efficiency by 3.31% on average.
A grid anchor based cropping approach exploiting image aesthetics, geometric composition, and semantics

Image cropping aims at the selection of the relevant part of an image maximizing its aesthetic quality and composition. The part of the image that needs to be removed is highly dependent on user preferences and can be related to image aesthetics, composition, informativeness, or other criteria. Since the concept of the perfect crop does not exist, but there are several cropping possibilities, recent cropping algorithms are trained to rank a set of crop candidates based on their compositional quality. To this end, several benchmark databases have been released that provide for each image a series of human-annotated crop candidates with corresponding scores. Many of the image cropping methods rely on a single criterion to define the best crop or crops in an image. However, a single criterion misses the complexity of human opinions which can differ in personal preferences and backgrounds. Motivated by this, we formulate the cropping problem as a ranking problem of candidate crop regions using a grid anchor based approach and multiple criteria. To evaluate the goodness of a crop region, we design a cropping method by combining three efficient and lightweight neural networks specifically designed to evaluate the quality of a crop in terms of aesthetics, composition, and semantics. Our results on standard datasets show that using more criteria yields better crops than state-of-the-art approaches. This result is also confirmed by a subjective study on user preferences that involved a panel of users.
Simulation Modeling as a Means of Solving Professionally-Oriented Problems in Maritime Industry

Simulation modeling is one of the most commonly used methods of studying the functioning of complex systems to assess possible risks. The use of simulation methods allows us not only to conduct a detailed analysis of the situation, but also to assess the probability of avoiding or appearance of a threat. The ship’s crew must be staffed by qualified seafarers who have received a favorable decision from the management of crewing companies. Such commissions assess the applicant’s competence at the international level, as he must work on ships of the world fleet. Therefore, among the tasks facing higher maritime education, one of the main is the formation of key competencies, the degree of formation of which is assessed at the international level. Over the last 5–6 years, Kherson State Maritime Academy has radically changed the training programs for maritime professionals, implementing a competency-based approach to training. The program of “Information Technologies” course, which is studied by 1st-year cadets, contains “Information Technologies in Navigation” module, which examines the algorithms and methods of solving professionally-oriented problems. The specificity of such tasks involves the use of simulation methods, algorithmization, the ability to use modern computer programs. This content of the course contributes to improving the quality of training of future ship navigators, and implementing the focus of this course on their professional activities. In other words, we begin to form both subject and universal professionally-oriented competencies. This article proposes methodological techniques for the formation of subject professionally oriented competencies of future ship navigators. These techniques are shown on the example of solving an applied problem using an information-modeling approach to the analysis and risk assessment of a ship operating in conditions of changing or unpredictable external influences. These influences are one of the factors that can lead to emergencies.
Plain Text &amp; Character Encoding: A Primer for Data Curators

Plain text data consists of a sequence of encoded characters or “code points” from a given standard such as the Unicode Standard. Some of the most common file formats for digital data used in eScience (CSV, XML, and JSON, for example) are built atop plain text standards. Plain text representations of digital data are often preferred because plain text formats are relatively stable, and they facilitate reuse and interoperability. Despite its ubiquity, plain text is not as plain as it may seem. The set of standards used in modern text encoding (principally, the Unicode Character Set and the related encoding format, UTF-8) have complex architectures when compared to historical standards like ASCII. Further, while the Unicode standard has gained in prominence, text encoding problems are not uncommon in research data curation. This primer provides conceptual foundations for modern text encoding and guidance for common curation and preservation actions related to textual data.