Reviewerly/exHarmony huggingface dataset:
Autonomous Path Planning for Mobile Robots in Multi Obstacle Environments

The mobile robot will suffer from a lack of direction in the search, numerous redundant nodes, and a prolonged algorithm running time when there are more obstacles in the environment. As a result, the attraction force of the goal point to the robot and the repulsive force of the obstacle to the mobile robot are utilized in conjunction to identify the growth direction of the growing tree in this study, which is based on RRT and the operating principle of the artificial potential field technique. The simulation results indicate that in a multi obstacle environment, the enhanced RRT algorithm outperformed the conventional RRT algorithm in terms of autonomous path planning efficiency by 6.15 times, reduced the length of the planned path by 34.24 meters, and decreased the average number of iterations by 46.93%; And the upgraded RRT algorithm has 2.23 times the efficiency in autonomous path planning compared to the RRT* algorithm and 14.50% fewer iterations on average, but its projected ideal path is 0.61 meters longer. Therefore, the improved RRT algorithm has more directional search, more efficient planning efficiency, and relatively fewer average iterations when conducting path planning.
Multimodal Speech Emotion and Text Sentiment Analysis

Today, every industry uses sentiment analysis as machine learning, and artificial intelligence techniques to evaluate client satisfaction with a given product or service. In our study, we investigated how combining voice sentiment analysis with text sentiment analysis influences outcomes compared to employing only one of the analysis methods for small size of data. In our experiments, we had 96% accuracy for the test data of the speech sentiment analysis model and 78% accuracy for the test data of the text sentiment analysis model. By dint of the Multimodal model methods, the combined use of the two models exceeded the sound test results and reached 98% accuracy. This shows the positive effect of the Multimodal model method on sentiment analysis performed with little data.
Nonlinear Observer for Quadrotor Waypoint Navigation Using Only Range Measurements

This paper proposes a novel nonlinear estimator for the position of a quadrotor in waypoint navigation using range measurements. It is assumed that the flying vehicle follows a straight line at a constant altitude between two waypoints. The positions of the waypoints are known and the measurements are assumed to be the squared distance to the point source located at the departure waypoint. The measurement is corrupted by additive noise. Under these conditions it is shown that the dynamics of the estimation error are described by a nonlinear Verhulst logistc equation. This observation unveils a link between navigation of a vehicle using range measurements and population dynamics, which to the best of the authors' knowledge is new. The stability of the error dynamics and the stationary distribution of the error are investigated for both constant and ramp velocity inputs. The performance of the proposed estimator is evaluated and compared with a Kalman filter for an augmented system. A numerical example illustrates the proposed method.
Reactive and Human-in-the-Loop Planning and Control of Multi-Robot Systems under LTL Specifications in Dynamic Environments*

This paper investigates the planning and control problems for multi-robot systems under linear temporal logic (LTL) specifications. In contrast to most of existing literature, which presumes a static and known environment, our study focuses on dynamic environments that can have unknown moving obstacles like humans walking through. Depending on whether local communication is allowed between robots, we consider two different online re-planning approaches. When local communication is allowed, we propose a local trajectory generation algorithm for each robot to resolve conflicts that are detected on-line. In the other case, i.e., no communication is allowed, we develop a model predictive controller to reactively avoid potential collisions. In both cases, task satisfaction is guaranteed whenever it is feasible. In addition, we consider the human-in-the-Ioop scenario where humans may additionally take control of one or multiple robots. We design a mixed initiative controller for each robot to prevent unsafe human behaviors while guarantee the LTL satisfaction. Using our previous developed ROS software package, several experiments are conducted to demonstrate the effectiveness and the applicability of the proposed strategies.
AERed: An Autoencoder–Decoder Dimensionality Reduction Method for Wearable-Based Human Activity Recognition

Human activity recognition (HAR) is the process of identifying a person’s activities accurately. It is possible by placing sensors on the subject’s body and obtaining data from a variety of high-dimensional physiological signals. Recently, sensors such as an accelerometer and gyroscopes have been incorporated directly into wearable devices, making activity identification fairly straightforward. High dimensionality makes it necessary to use an optimization method that might reduce the number of features used in the dataset while taking less time to make activity recognition successful in wearable devices with limited battery life. In this study, we propose the autoencoder reduction method (AERed), a dimensionality reduction (DR) technique based on the symmetric design of a typical autoencoder (AE). With the new structure, there are fewer weights that must be tweaked, which lowers the computational cost. This work makes use of a public-domain dataset from the UCI repository. The dataset’s features were reduced from 561 to 256 using the AERed method. The reduced features are classified using a random Bayesian filter support vector machine classifier and reported 95.95% F1-score. This method also consumed a lot less time than baseline methods at the DR stage. The proposed method is validated by performing parameter sensitivity analysis, complexity analysis, and visualization performance analysis.
3-D Target Tracking for Distributed Heterogeneous 2-D–3-D Passive Radar Network

In air target tracking with a distributed passive radar network, each local node will track the target separately. For two-dimensional (2-D) passive radar, the target’s altitude is often ignored due to the lack of elevation measurement, leading to an estimation deviation of the target state and thereby reducing the subsequent track fusion accuracy. This article proposes a three-dimensional (3-D) target tracking and fusion method for a distributed heterogeneous network with 2-D and 3-D passive radars. The proposed method solves two problems: associating 2-D tracks with 3-D tracks and fusing them. First, we transform the 2-D and 3-D tracks into a common bistatic coordinate and then perform the track association. Second, we obtain equivalent measurements by recalculating Jacobian matrices and measurement predictions of associated local tracks. Then, the global tracks are updated using the equivalent measurements, which have equal state estimation accuracy as centralized fusion. Monte Carlo simulations also demonstrate the equivalence of the estimation accuracy. Moreover, we verify the effectiveness of the proposed algorithm using the field experiment data of a 2-D–3-D passive radar network.
Calliope-Net: Automatic Generation of Graph Data Facts via Annotated Node-link Diagrams

Graph or network data are widely studied in both data mining and visualization communities to review the relationship among different entities and groups. The data facts derived from graph visual analysis are important to help understand the social structures of complex data, especially for data journalism. However, it is challenging for data journalists to discover graph data facts and manually organize correlated facts around a meaningful topic due to the complexity of graph data and the difficulty to interpret graph narratives. Therefore, we present an automatic graph facts generation system, Calliope-Net, which consists of a fact discovery module, a fact organization module, and a visualization module. It creates annotated node-link diagrams with facts automatically discovered and organized from network data. A novel layout algorithm is designed to present meaningful and visually appealing annotated graphs. We evaluate the proposed system with two case studies and an in-lab user study. The results show that Calliope-Net can benefit users in discovering and understanding graph data facts with visually pleasing annotated visualizations.
High-accuracy low-latency non-maximum suppression processor for traffic object detection

As autonomous driving technology advances, the requirements for object detection are becoming increasingly high. Non-maximum suppression (NMS) algorithm, as a key component in traffic object detection algorithms, is an independent post-processing process in the object detection framework. Due to the complexity of real-world road scenarios and high density of detected entities in urban traffic, the number of candidate bounding boxes generated by the neural network is large. Hence, low-precision processors may generate a significant number of redundant target bounding boxes. The excessive output of redundant target bounding boxes not only imposes a workload on subsequent processing but also has the potential to result in non-optimal decision-making. We propose a high-performance NMS processor that can quickly process a large number of candidate boxes without performing sorting of their scores. Also, it has low precision loss computing units and high parallel computing arrays. Combined with algorithm design, it effectively reduces the computational complexity and reduces the inference time of the end-to-end task of the NMS algorithm. Thus, our NMS processor’s speed is comparable to SOTA architecture, and the average accuracy loss is only 0.4% .
A study on the improvement of student education management in universities based on depth-constrained Boltzmann machine

Abstract In this paper, a student education management platform is constructed using ASP.NET development technology. Secondly, a fast CD method is used to update the main parameters of RBM, and multiple restricted Boltzmann machines are used to jointly form a depth-constrained Boltzmann machine for the personalized recommendation of educational management methods. Finally, a performance test analysis was conducted to verify the platform’s effectiveness presented in this paper. The results show that when the number of concurrent users is 600, the maximum response times of student management, faculty management and education management functions are 2.79s, 2.95s and 2.81s, respectively, and the CPU occupancy rate is less than 20%. The education management platform with good performance can help improve and innovate student education management.
Absorb and Repel: Pseudo-Label Refinement for Intra-Camera Supervised Person Re-identification

Person Re-IDentification (ReID) aims to identify pedestrian images with the same identity across non-overlapping camera views. Intra-Camera Supervised person Re-IDentification (ICS-ReID) is a new paradigm that trains a model using only intra-camera labels, thus reducing the cost of inter-camera identity association. Pseudo-label-based clustering algorithms perform well in the unsupervised ReID task, whereas they inevitably generate noisy pseudo-labels through clustering, especially in the early training stage. Given this, we propose an unsupervised pseudo-labeling method to help in the semi-supervised ICS-ReID task. This method improves the clustering results by reassigning pseudo-labels for the training data and consists of two modules, Absorb and Repel. The Absorb module aims to group all data with the same intra-camera identity into one cluster. The Repel module ensures that images under the same camera view but with different identities do not appear in the same cluster. Both modules are independent yet complementary to reduce the error rate of pseudo-labels generated in each epoch. To our knowledge, this is the first attempt to refine pseudo-labels for ICS-ReID. Our method is a simple, nonparametric, and effective strategy that can be easily integrated into existing clustering-based unsupervised ReID tasks. Extensive experiments demonstrate that our proposed method outperforms the state-of-the-art ICS-ReID approaches on three large-scale benchmark person ReID datasets.
Single-Cell Spatial Analysis of Histopathology Images for Survival Prediction via Graph Attention Network

The tumor microenvironment is a complex ecosystem consisting of various immune and stromal cells in addition to neoplastic cells. The spatial interaction and organization of these cells play a critical role in tumor progression. Single-cell analysis of histopathology images offers an intrinsic advantage over traditional patch-based approach by providing fine-grained cellular information. However, existing studies do not perform explicit cell classification, and therefore still suffer from limited interpretability and lack biological relevance, which may negatively affect the performance for clinical outcome prediction. To address these challenges, we propose a cell-level contextual learning approach to explicitly capture the major cell types and their spatial interaction in the tumor microenvironment. To do this, we first segmented and classified each cell into tumor cells, lymphocytes, fibroblasts, macrophages, neutrophils, and other nonmalignant cells on histopathology images. Given this single-cell map, we constructed a graph and trained a graph attention network to learn the cell-level contextual features for survival prediction. Extensive experiments demonstrate that our model consistently outperform existing patch-based and cell graph-based approaches in two independent datasets. Further, we used the feature attribution method to discover distinct spatial patterns that are associated with prognosis, leading to biologically meaningful and interpretable results.
Research on the Interactiveness of TEM-4 Writing Tasks

This study aims to explore the interactiveness of Test for English Majors-Band 4 (TEM-4) writing tasks and tries to give some advice for further improvement. Interactiveness, as defined by Bachman and Palmer, is an important quality in any test. In order to achieve such aims, this study proposes two research questions:1.How interactive is TEM-4s writing? 2.What do candidates think of TEM-4s writing tasks? This study mainly adopts a mixed method which collects data by distributing about 50 questionnaires to English majors online and conducting semi-structured interviews with three participants. Through statistical analysis of the interactiveness of TEM-4 writing tasks from four aspects, this study found that the level of interactiveness of TEM-4 writing task is moderate while the tasks are challenging enough which can truly test students comprehensive abilities. However, the topics of writing can be more innovative since students feel that they are told in stereotypes and cliches. Therefore, this study broadens the application field of the theory of interactiveness and applies it to TEM-4 writing tasks in China. Furthermore, it also can provide helpful implications for test makers.
Role of Transformational and Transactional Leadership Styles on Employees’ Performance with the Median of Organization Commitment, in Selected Higher Learning Institutions at Mwanza, Tanzania

This study examined the roles of transformational and transactional leadership styles on the employee’s performance in higher learning institutions in Mwanza. The study specifically aimed to identify the relationship between transaction leadership style and employee task performance, to determine if there is a relationship between transactional leadership style and employee tasks performance and to determine if transformational or transactional leadership style influences employee organization performance. The study was guided by situation and contingency theory as well as Transformation and transaction leadership theory. It employed a quantitative research approach and descriptive research design. The study employed a targeted population of 545 from which a sample size of 217 was used. Data in this study was collected through questionnaires and were analyzed using SPSS. V20 uses inferential statistics, descriptive statistics as well as regression and mediation analysis. The result obtained shows that overall model results R. Square of about 65%which is adjusted to 56%. Generally, R square and Adjusted R square were used to explain the power of the regression model in which the results of the model predictors explain 65% of the variation in the employee performance among the sampled respondents in the targeted higher learning institutions. The findings from the regression analysis show a positive and significant relationship between TRNF and EP. The regression formed a coefficient value of .643 and T-test was 2.092whihc was significant at .0002. Transactional Leadership Style (TRNS) was positively and significantly related to Employee Performance (EP). This can be evidenced by the coefficient of .528 and T-statistics of 2.241 which was significant at a level of. 001. The study also tested the relationship between Organization Commitment (OC) and Employee performance (EP) among the selected respondents. The Coefficient value was .556 and a T-Test was 2.85 which was significant at .000. The results from the analysis show a regression coefficient of .447 and a T-test of 2.040 which was significant at a level of .000. The study concluded both Transformational and Transactional leadership style has positive impacts on the task performance of the employee in the higher learning institution because through is a leader uses his/her acts to build my respect in the working place and also leader acts as a good role model to their employee hence employee as motivated to work in a committed manner to attain the desired organization objective whether long term or short term.
Impact of Social Media Influencers’ Credibility and Similarity on Instagram Consumers’ Purchase Intention

In the realm of digital advertising, the recruitment and use of social media influencers is relatively a novel approach. Social media influencers change how buyers feel about a company or product by sharing photographs, videos and other content on their networks. The investigation’s goal is to look into the relationship between the credibility and resemblance of Instagram influencers and their followers’ intent to purchase on the social media platform. With the help of Google Forms, we can collect 384 usable responses, which we have calculated via the sample size Cochran’s formula. A method of sampling referred to as purposive sampling was utilised in the research study. The sample population for this study consists of residents of Delhi and the adjacent National Capital Region (NCR) in Haryana. This is done to ensure that the results are representative of the situation in India as a whole. The nation’s capital is Delhi. In addition to Delhi, this coverage includes the cities of Gurugram, Faridabad, Sonipat and Jhajjar, which are located on the outskirts of Delhi. Jamovi 2.2.5 version is used to analyse the data. To have a better grasp of the variations, the features of the respondents’ demographic makeup, which includes gender, age and number of followers, are examined. The source credibility theory is used in the study. The Pearson Correlation analysis is used to indicate that all predictor variables possess a favourable association with the consumer’s intent to buy and regression analysis is used for calculating the prediction and strength of influence. Both credibility and similarity with influencers are shown to favourably affect consumers’ propensity to purchase Instagram, however, the beta coefficient indicated that credibility with influencers had a greater impact. The findings of the study provide marketing practitioners with a clear insight into the parts of credibility of social media influencers may have an impact on a company’s target market and their propensity to purchase the product that is being marketed to them. The results recommend that influencer marketing may be an effective branding strategy, that should be considered when developing a marketing strategy. But, to further investigate this outcome, we recommend conducting additional research investigating the subject matter of ‘influencer marketing’.
A Novel Goal oriented Sampling Method for Improving the Convergence Rate of Sampling based Path Planning for Autonomous Mobile Robot Navigation

Autonomous Mobile Robots' performance relies on intelligent motion planning algorithms. In autonomous mobile robots, sampling-based path-planning algorithms are widely used. One of the efficient sampling-based path planning algorithms is the Rapidly Exploring Random Tree (RRT). However, the solution provided by RRT is suboptimal. An RRT extension known as RRT* is optimal, but it takes time to converge. To improve the RRT* slow convergence problem, a goal-oriented sampling-based RRT* algorithm known as GS-RRT* is proposed in this paper. The focus of the proposed research work is to reduce unwanted sample exploration and solve the slow convergence problem of RRT* by taking more samples in the vicinity of the goal region. The proposed research work is validated in three different environments with a map size of 384*384 and compared to the existing algorithms: RRT, Goal-directed RRT(G-RRT), RRT*, and Informed-RRT*. The proposed research work is compared with existing algorithms using four metrics: path length, time to find the solution, the number of nodes visited, and the convergence rate. The validation is done in the Gazebo Simulation and on a TurtleBot3 mobile robot using the Robotics Operating System (ROS). The numerical findings show that the proposed research work improves the convergence rate by an average of 33% over RRT* and 27% over Informed RRT*, and the node exploration is 26% better than RRT* and 20% better than Informed RRT*.
A double encryption protection algorithm for stem cell bank privacy data based on improved AES and chaotic encryption technology

The unique infinite self-renewal ability and multidirectional differentiation potential of stem cells provide a strong support for the clinical treatment. In light of the growing demands for stem cell storage, how to ensure personal privacy security and comply with strict ethical supervision requirements is particularly important. In order to solve the problem of low security of traditional encryption algorithm, we proposed a double encryption protection (DEP) algorithm for stem cell bank privacy data based on improved AES and chaotic encryption technology. Firstly, we presented the hash value key decomposition algorithm, through the hash value dynamic coding, cyclic shift, conversion calculation to get the key of each subsystem in the built algorithm. Secondly, DEP algorithm for privacy data is realized with two level of encryption. The first level of encryption protection algorithm used AES as the main framework, adding dynamic coding and byte filling based on DNA coding, and carries out dynamic shift of rows and simplified mixing of columns. The second level of encryption protection algorithm conducted random encoding, operation, diffusion and decoding based on the results of our proposed sequence conversion algorithm. Finally, we raised two evaluation indexes, the number of characters change rate (NCCR) and the unified average change intensity of text (UACIT) to measure the sensitivity of encryption algorithms to changes in plain information. The experimental results of using DEP shown that the average values of histogram variance, information entropy, NCCR and UACIT are116.7883, 7.6688, 32.52% and 99.67%, respectively. DEP algorithm has a large key space, high key sensitivity, and enables dynamic encryption of private data in stem cell bank. The encryption scheme provided in this study ensures the security of the private information of stem cell bank in private cloud environment, and also provides a new method for the encryption of similar high confidentiality data.