Reviewerly/exHarmony huggingface dataset:
UAV Track Planning Algorithm Based on Graph Attention Network and Deep Q Network

To solve the problem that the deviation of the Unmanned Aerial Vehicle (UAV) flight status data collected by the equipment during flight leads to the failure of the mission, this paper proposes a UAV track planning algorithm based on Graph Attention Network and Deep Q Network (DQN). Firstly, we use the camera to collect images and apply pre-trained ResNet to extract image features. Secondly, we adopt the Graph Attention Network to establish the connection between the sensor-measured flight state information and the actual flight state information. Thirdly, we build the optimization model of flight state. Moreover, based on the Deep Reinforcement Learning (DRL) theory, the DQN-based UAV track planning system is trained. Finally, the system combined the optimized flight state to complete the optimal flight action output to realize the track planning. Simulation results show that, compared with the original algorithm which is under the same flight conditions as the proposed algorithm, the velocity deviation rate of the proposed algorithm is improved by 46.79%, which can plan a high-quality track and has good engineering application value.
Formal Definition of Pseudo Code and Mapping Rules to Java Code

Pseudo code is often used to describe the algorithm flow, but developers may spend a lot of time and energy to implement the conversion of pseudo code to Java code. Now automatic code generation technology has become a key issue in the field of software engineering. We designed the formal definition of pseudo code and the mapping rules from the parsing model of the formal pseudo code to the target file in this paper. The method uses formal pseudo code to describe the business requirements and takes it as input. Based on parsing and mapping rules, it automatically generates executable Java code for the algorithm. Experimental results show that this method can quickly obtain the code of the algorithm under the condition of a small amount of manual intervention, and improve the working efficiency of the algorithm developers.
Global Path Planning Based on an Improved A* Algorithm in ROS

We consider the problem of robot global path planning using traditional A* algorithm based on ROS. By means of an improved A* algorithm, we are able to solve the safety problem of robots. The algorithm calculates the threat value of obstacles to the robot and adds it to the evaluation function. During the execution of the algorithm, the security threat value of the node is calculated according to the shortest distance between the node and obstacle, adding the value to the corresponding node evaluation, finding the best path through the evaluation function. The generated path is smoothed with the two-order Bezier curve. This algorithm improves the safety performance and the optimality of the path.
Application of Music Information Analytic Guiding System Based on HCI and 3D Data Mining

Application of the music information analytic guiding system based on HCI and 3D data mining is the primary focus of this manuscript. After processing the task submitted by the user, the result will be sent to the dialog control module, which is composed of multiple non-interactive computing parts for the processing tasks. According to principle of human-computer interaction space gesture recognition, a semantic feedback and interactive channel information integration mechanism is added, and the design supports space gesture recognition. Then, the model is well applied to the applications of the music information guiding with intelligent factors. The combination of the different approaches makes the performance better.
Multi - owner Secure Data Sharing in Cloud Computing Environment

Data sharing in the cloud is a technique that allows users to conveniently access data over the cloud. The data owner outsources their data in the cloud due to cost reduction and the great conveniences provided by cloud services. Data owner is not able to control over their data, because cloud service provider is a third party provider.  The main crisis with data sharing in the cloud is the privacy and security issues. Various techniques are available to support user privacy and secure data sharing. This paper focus on various schemes to deal with secure data sharing such as Data sharing with forward security, secure data sharing for dynamic groups, Attribute based data sharing, encrypted data sharing and Shared Authority Based Privacy-Preserving Authentication Protocol for access control of outsourced data.
X-Based PPDL

We survey the latest publications regarding X-based privacy-preserving deep learning methods based on homomorphic encryption, secure multi-party computation, differential privacy, secure enclaves and their hybrid and summarize the key features of all surveyed publications including learning type and dataset.
PRACTICE-ORIENTED PROBLEMS IN MATHEMATICAL EDUCATION OF STUDENTS

The article presents practice-oriented problems of various complexity levels to increase the motivation of students to Mathematics in the first years of study, which helps master special disciplines, in senior courses.
Dimensionality Reduction of Unbalanced Datasets: Principal Component Analysis

In this digital world, sharing of information is very easy and cost-effective; resulting in a large amount of high-dimensional data, available in a variety of domains such as healthcare, finance, etc. Data available in the healthcare domain is used for disease diagnosis using Machine Learning (ML) models. The data set is the heart of the machine learning model. But the performance of such a model will not be satisfactory due to unbalance of the data set. One of the important points is that we can use sensitivity (true positive rate) and specificity (true negative rate) as performance measures along with accuracy. For ML-based healthcare systems, sensitivity plays a vital role. To balance unbalanced data set, the primary step is data preprocessing like feature selection and feature extraction. The proposed method used here is feature extraction method, Principle Component Analysis technique (PCA). Experimentation was done on Pima Diabetic Data set and calculations were done for accuracy as well as sensitivity. Obtained results proved that PCA is a better option for dimensionality reduction and also, it helps to improve the performance of the systems.
Research on an intelligent path planning algorithm for robot using electrical control technology

In view of the shortcomings of traditional genetic algorithm in path planning, such as infeasible path, falling into local optimization, too many turning times and so on, an improved adaptive genetic algorithm is proposed. A priori knowledge is used to optimize the initial population, and the initial population that does not intersect with obstacles is obtained. The probability formulas of crossover and mutation are designed to avoid falling into local optimization, so as to improve the convergence speed. The smoothness of the path and the shortest path are introduced into the fitness function as the evaluation criteria to make the planned path more efficient. The simulation results show that: compared with the basic algorithm, when the number of obstacles is 20:00, the path of the improved algorithm is reduced by 4.2%; when the number of obstacles is 115, the path is reduced by 25.1%. With the continuous increase of obstacles, the percentage of path reduction shows an upward trend, and the number of iterations of the algorithm and the number of turning points in the path are better than the basic genetic algorithm.
Classification of Beneficial and non-Beneficial Bacteria using Random Forest Algorithm

Contaminated water released from factories and other sources leads to the production of harmful bacteria, which at a later stage could lead to dangerous diseases. Classification and detection of bacteria should be carried out. It is known from the literature review that this can be done by using test tube testing, microscope model, spectrum light using spectroscope, but it is more costly, not affordable everywhere, complex and takes longer to achieve results. So, unlike other models, by using an efficient algorithm suitable for the system, an image processing system can be efficient and successful in producing results very quickly, with not much of a hassle in the process. By extracting its multiple features such as scale, shape, texture, color, size and other characteristics such as arrangement of the hair around her body, classification will be performed over images. The framework extracts the characteristics and categorizes the type of bacteria that can be understood whether or not it is a beneficial bacterium for humans and nature.
Identity-Based Signature from Quadratic Residues and Its Extension to ID-Based Multi-Signature

ID-based signature has drawn the attention of the community since Shamir proposed the concept in 1985. In an ID-based signature scheme, a user can choose any string, which directly links to the user, as her/his public key. Therefore, the management issue of the traditional "certificate authority approach" can be resolved. Due to the usefulness and powerfulness of bilinear maps, most of the existing ID-based signature schemes are constructed from pairing groups. However, pairing is one of the most costly operation in modern cryptography. Thus, lots of cryptographer have paid attention to pairing-free ID-based signature. In this manuscript, we proposed an ID-based signature scheme from quadratic residues. The security of the proposed scheme has been proven based on the hardness of the square root problem. We have also extended our ID-based signature scheme into an multi-signature setting, where multiple signer can simultaneously sign on a same message.
Valentino Braitenberg’s Table: Downhill Innovation of Vehicles via Darwinian Evolution

Evolution has been a topic of interest that has been explored extensively throughout recent history. Ever since the proposal of the evolutionary theory by Darwin, there have been attempts made to validate, extend, explore and exploit the different aspects of this theory. This study sets forth methods to explore elements of what has been proposed by Darwinian evolution using Braitenberg vehicles, simple machines with sensory-motor couplings in an environmental context. In this study, a simulation environment is set up based upon the descriptions of neurophysiologist Valentino Braitenberg. This simulation environment is then utilized to carry out the thought experiments envisioned by Braitenberg for the simplest nontrivial kinds of vehicles. The methodology and results for the carried out experiments are detailed in this paper. Apart from an understanding of whether the environmental setup affects evolution, specifically the number of light sources (stimuli) and location of these light sources, the experiments show an interesting trend regarding dynamic equilibrium of the evolutionary process, the ramifications of which might not have been understood well enough previously. It is concluded that ecological setup, as well as the initial genetic makeup of the vehicles, play a crucial role in the evolution of vehicles in scenarios laid out in this study. Further, the placement of stimuli (location of lights) and the number of the stimuli have a visible effect on the survivability of vehicle types (species).
Look Here: Learning Geometrically Consistent Refinement of Inverse-Depth Images for 3D Reconstruction

Building good 3D maps is a challenging and expensive task, which requires high-quality sensors and careful, time-consuming scanning. We seek to reduce the cost of building good reconstructions by correcting views of existing low-quality ones in a post-hoc fashion using learnt priors over surfaces and appearance. We train a convolutional neural network model to predict the difference in inverse-depth from varying viewpoints of two meshes — one of low-quality that we wish to correct, and one of high-quality that we use as a reference. Our full model runs at 11.3[Formula: see text]Hz when aggregating four input views. In contrast to previous work, we pay attention to the problem of excessive smoothing in corrected meshes. We address this with a suitable network architecture, and introduce a loss-weighting mechanism that emphasizes edges in the prediction. Furthermore, smooth predictions result in geometrical inconsistencies. To deal with this issue, we present a loss function which penalizes re-projection differences that are not due to occlusions. Future applications of this work will incorporate semantic scene understanding in a multi-task learning setting. We explore the efficacy of the proposed system in terms of gross error correction and generalization capability by showing its performance in practice on a subset of the Kitti Odometry dataset, complete with a component-wise ablation study. We evaluate correctness and completeness measures of surface reconstruction across viewpoints and show that the proposed system is introspective in regions lacking sufficient high-quality supervision — indeed, models trained with geometric consistency loss create a lot more surface in areas that were not supervised, in one case filling in 67.97% or 8010[Formula: see text]m 2 of an unlabeled input region. Finally, we assess the practical applicability of our method at large-scale by experiments over the full scope of the Kitti Odometry dataset. Broadly, as a measure of effectiveness, our model reduces gross errors by 45.3–77.5%, up to five times more than previous work. We also assess the practical applicability of our method to 3D reconstruction at large scales and find that compared to the baseline our model shows better stability in correctness when improving completeness of surfaces, and is effective in reducing median total error by up to 21.8[Formula: see text]cm.
Method for Compressing Volume Data Using Perturbation Functions

A method for converting voxel data arrays into a function-based description of surfaces is proposed. When implementing the method, the reverse traversal of the octal tree of division of the object space was used. The transformation takes place through a rigorous mathematical calculation, rather than iterations and approximations that lead to partial loss of information. The method consists in solving a system of equations for each level of subdivision of the object space and minimizing the number of functions. The simulation results confirmed the efficiency and effectiveness of the developed method.
The influence of L1 translation familiarity on the acquisition of L2 Chinese antonyms

Abstract As a partial replication of Tagashira, Kida, and Hoshino (2010) , the present study examined first language (L1) translation familiarity effect on second language (L2) antonym acquisition among English-speaking intermediate-level Chinese learners. Fifteen students learned 15 antonymous pairs with familiar or unfamiliar L1 translations and completed two multiple-choice posttests. Their learning strategies were also collected. Results showed (a) a delayed L1 familiarity effect, (b) better retention and lower interference of L2 pairs with familiar L1 translations, (c) low retention of L2 pairs with unfamiliar L1 translations for both words, and (d) more orthographic elaboration strategies employed. The findings suggest that L2 instructors present antonyms in pairs, avoid providing a single, unfamiliar L1 translation, and encourage the use of orthographic elaboration strategies.
Improving Cross-Project Defect Prediction with Weighted Software Modules via Transfer Learning

Abstract Cross-project defect prediction is investigated to resolve the trouble that software program defect prediction besides historic data. However, there are differences in distribution of software metrics of different software projects, which decreases the overall performance of cross-project defect prediction. This research presents a transfer learning-based cross-project fault prediction approach. The weights of the training software modules are determined by analogy with gravity and compared to those in the test set. The costs associated with different prediction errors are considered. Cost-sensitive C4.5 is employed on weighted training data for cross-project defect prediction. 10 projects from NASA are used in our experiments. Our suggested technique delivers promising cross-project defect prediction results, according to the results, 0.81, 0.41 and 0.8 in average PD value, F-measure value and AUC value, which are best compared to Naïve Bayes based cross-project defect prediction.
Graph NLU Enabled Question Answering System

With a huge amount of information being stored as structured data, there is an increasing need for retrieving exact answers to questions from tables. Answering natural language questions on structured data usually involves se-mantic parsing of query to a machine understandable format which is then used to retrieve information from the database. Training semantic parsers for domain specific tasks is a tedious job and does not guarantee accurate results. Knowledge graphs can be easily leveraged for question answering systems, to use them as the database. In this paper, we used conversational analytics tool to create the user interface and to get the required entities and intents from the query thus avoiding the traditional semantic parsing approach. We then make use of Knowledge Graph for querying in structured data domain. We extract appropriate answers for different types of queries which have been illustrated in the Results section.
Designing a Mixed-Reality Sandbox Game on Implementation in Inbound Logistics

Practical lab exercises and haptic games are often used to create understanding and awareness of how technologies can support the quality of warehouse operations. However, not all students have access to a suitable physical lab or game environment. A Mixed reality sandbox mode was planned, a game mode of free form that emphasises freedom of movement and offers the player a lot of flexibility and ingenuity in completing tasks and coming up with solutions to the game objectives with a combination of Augmented Reality and physical tools. The purpose of playing this game is to promote the importance of using sensors to increase the efficiency of inbound logistics remotely.
Graph Attention Network Based Object Detection and Classification in Crowded Scenario

Non-maximum Suppression (NMS) is an essential post-processor for most detectors, responsible for merging excessive detections from detector. The standard NMS is a crude greedy algorithm that is not accurate enough sometimes and even fail entirely in crowded scenario, which results from making inadequate use of information of detection. We regard the detection set as the data of the graph structure, and adopt the Graph Attention Network (GAT), which fits the data structure very well, as the backbone to perform NMS. In addition, our algorithm is aware of the density of objects and therefore can handle partly crowded scenario. The result of our algorithm is better than the traditional algorithm, which shows the superiority of performing NMS based on global information.
Learning-based Initialization Strategy for Safety of Multi-Vehicle Systems

Multi-vehicle collision avoidance is a highly crucial problem due to the soaring interests of introducing autonomous vehicles into the real world in recent years. The safety of these vehicles while they complete their objectives is of paramount importance. Hamilton-Jacobi (HJ) reachability is a promising tool for guaranteeing safety for low-dimensional systems. However, due to its exponential complexity in computation time, no reachability-based methods have been able to guarantee safety for more than three vehicles successfully in unstructured scenarios. For systems with four or more vehicles, we can only empirically validate their safety performance. While reachability-based safety methods enjoy a flexible least-restrictive control strategy, it is challenging to reason about long-horizon trajectories online because safety at any given state is determined by looking up the safety value in a pre-computed table that does not exhibit favorable properties that continuous functions have. This motivates the problem of improving the safety performance of unstructured multi-vehicle systems when safety cannot be guaranteed given any least-restrictive safety-aware collision avoidance algorithm while avoiding online trajectory optimization. In this paper, we propose a novel approach using supervised learning to enhance the safety of vehicles by proposing new initial states in very close neighborhood of the original initial states of vehicles. Our experiments demonstrate the effectiveness of our proposed approach and show that vehicles are able to get to their goals with better safety performance with our approach compared to a baseline approach in wide-ranging scenarios.
Research on Low-Resolution Image Matching and Recognition Algorithm Based on Unified Feature Space

In this paper, the super-resolution method is introduced to analyze and study the low-resolution image matching and recognition. After processing the low-resolution image, the super-resolution method uses the unified feature space method to identify the relevant information in the image, so as to improve the recognition rate.
SPAD+: An Improved Probabilistic Anomaly Detector based on One-dimensional Histograms

In today's world, databases are growing rapidly. Fast automatic detection of anomalous records in these massive databases is a challenging task. Traditional distance-based anomaly detectors are limited to small datasets because of their high time complexities. The univariate histogram-based method is arguably the fastest anomaly detection method. The anomaly score of a data instance is computed as the product of the probability mass of histograms in each dimension. Recent studies proved that such a simple method is comparable with many state-of-the-art methods on several datasets. However, as data features are assumed to be independent, it results in poor performance when features are correlated. Such an issue can be taken care of by using Principal Component (PC) features, which is the primary element of this paper. Our results show that integrating PCs with the original input features improves the performance of histogram-based anomaly detector with no real compromise in computational complexity.
Improving Automatic Recommendation by Modeling Intrinsic and Extrinsic Motivation with Q-learning

To understand users’ needs is an essential problem of automatic recommendation. Current researches often describe users' needs through their side attributes. However, motivation is the direct reason of users’ needs and choice behavior. In real social scenario, it is a challenge to measure the complex motivation of users. Inspired by psychological definitions, in recommendation, we propose to distinguish the Intrinsic Motivation driven by each individual's internal interests and the Extrinsic Motivation coming from the influence of social group. With latent factors of users and items, two motivations are separately modeled and integrated for recommendation with Q-learning (Off-policy), which improve the effectiveness and explainability of recommendation. Experiments on real-world datasets demonstrate the validity of our model compared with state-of-the-art methods.