Reviewerly/exHarmony huggingface dataset:
Symbol detection using extreme learning machine network for MU‐SIMO systems

This paper focuses on symbol detection for a multi-user single-input multiple-output (MU-SIMO) uplink using supervised machine learning techniques. An extreme learning machine (ELM) is chosen as the equalizer at the receiver end due to its super-fast learning ability and minimum training error. To tune the parameters, a pilot based online training for an ELM network is proposed, and those parameters are further used to detect the unknown transmitted symbols. Conventionally, the channel matrix is estimated using known pilot symbols explicitly and then the transmitted symbol is recovered using a linear equalizer. Here, instead of directly estimating the channel coefficients, the symbols are detected using an ELM based direct equalizer followed by a hard decision rule. To reduce the pilot requirement of the proposed model, a method is discussed where the channel is estimated using a small number of pilot symbols and the ELM network is trained using a separate training dataset along with the channel estimate. Furthermore, the performance comparison between the proposed ELM based equalizers, linear equalizers, and nonlinear detection techniques is shown.
Analyzing Who, What, and Where in a Mediæval Chinese Corpus

Information extraction from historical text is challenging because of the lack of data to train natural language processing tools. This chapter evaluates the utility of in-domain training data for data-driven profiling of characters, verbs, and toponyms and reports a case study on a corpus of Chinese Buddhist text. As is typical for such a corpus, the Chinese Buddhist Canon has few annotated linguistic resources other than lexica of names, places, and domain-specific terms. We apply a lexicon-based approach for named entity recognition and then report an analysis of the “who,” “what,” and “where” of the Canon: who the characters were, what they did, and where they were. Experimental results also show that even a small amount of word segmentation, part-of-speech, and dependency annotation can improve accuracy in named entity recognition and in extraction of character-verb associations.
Pengenalan penggunaan teknologi kedirgantaraan drone untuk meningkatkan mutu siswa SMK Muhammadiyah Sentolo dalam bidang agroindustri

Sentolo Muhammadiyah Vocational School is located in the Kulon Progo area, more precisely on Sentolo-Magelang, Ngeplang, Sentolo, Kulon Progo roads. His areas of expertise are Motorcycle Business Engineering (TBSM), Automotive Light Vehicle Engineering (TKRO), Computer Network Engineering (TKJ). SMK Muhammadiyah Sentolo continues to carry out the process to improve and develop itself. One of them is by creating a Plus Program with the aim of preparing graduates who have resilience and independence. One of the plus programs is agroindustry Therefore, it is necessary to introduce agricultural processing methods using technology, especially technology that supports the fertilization process using liquid fertilizer. This Community Service Activity will introduce the use of fertilization technology using liquid media with the help of drones . The result of this service activity is that students know that the use of drones to help the fertilization process is meaningful for farmers, so students feel eager and enthusiastic to learn how to use the drone. On the farmer's side, they feel that the presence of drones applied by SMK Muhamaadiyah Sentolo students is a new hope that will be useful for helping farmers
Augmented Reality Based Instructional Approaches to Enhance Understanding Abstract Concepts of Physics among Higher Secondary Students

This research examined the augmented reality based instructional approaches to enhance understanding of abstract concepts in physics among higher secondary students. Augmented reality is the blending of interactive digital elements â like dazzling visual overlays, buzzy haptic feedback, or other sensory projections â into our real-world environments [1]. Augmented Reality, or simply AR, is the incorporation of information in digital format that includes live footage of a certain user's real-time environment [2]. The aim of education is not merely imparting of knowledge or the preparation of a finished product, its aim is to arouse curiosity, develop proper interests, attitudes and values and the build up habits of independent study and the capacity to think and evaluate. Without thinking it is not possible for one to become a responsible member of a democratic society. Augmented Reality turns the environment around into a digital interface by placing virtual objects in the real world, in real-time. Augmented Reality can be seen through a wide variety of new experiences. A teacher will have to integrate the new technology based skills of teaching and also help the students to grow intellectually, emotionally and socially with commitment to a set of values. For this, a teacher must have knowledge of his subject, methods and resent technological based techniques of teaching. Many skills are needed to communicate effectively like the skill of questioning, illustrating, demonstrating, explaining, skill of arranging and logically sequencing the subject matter [3]. These digital experiences, skills and attitudes can be developed through systematic learning.
High-fidelity quantum control via Autler-Townes splitting

We propose quantum control protocols for the high-fidelity preparation of target states in systems with Autler-Townes splitting. We investigate an approximated three-level system obtained from a four-level one by adiabatically eliminating a state that does not participate in the evolution. In our work we use linear, arctan, and Roland-Cerf functions for transferring population between two eigenstates of the system obtaining a high fidelity for long evolution times. Additionally, in order to overcome the restriction given by the lifetimes of the experimental setup, we propose an accelerated adiabatic evolution with a shortcut to adiabaticity protocol, which allows us to reach fidelities close to one but much faster.
3D Multiple Object Tracking with Multi-modal Fusion of Low-cost Sensors for Autonomous Driving

Millimeter-wave (MMW) radar and monocular camera, are most commonly used sensors in perception system of autonomous vehicles. While radar-camera (R-C) fusion has been widely explored for object detection and tracking, few works utilize them to realize 3D multiple object tracking (MOT). This is because neither of the sensor data provide precise and sufficient 3D information. To tackle this problem, this paper proposes an applicable 3D MOT method with R-C fusion. In this work, a suitable 3D object state space model is constructed. Single sensor results are validated before fusion. The challenging spatial-temporal asynchronization is overcome during fusion process. Then we optimize data asso-ciation parameters which are capable to adapt various scenes and sensor properties without manual adjustment. The field test demonstrates the effectiveness of our method. After our optimization, MOTA of 3D MOT with R-C fusion outperforms the baseline by 13.0 % and the tracking error of object distance is reduced by 1.03m.
Parking Violation Detection on The Roadside of Toll Roads with Intelligent Transportation System Using Faster R-CNN Algorithm

On toll roads, there are several car drivers who often use the roadside of toll road as a lane for driving which should not be used unless there is an urgent situation, this can interfere with the interests of other road drivers who have more urgent interests who should use the roadside of the toll road, so far if this occurs violations on the roadside of the toll road, it is rare for toll officers to know the violation directly, it is still not effective enough because officers cannot go directly to secure the driver who commits a violation. By using a system that is used to detect violations on the roadside of toll road using the Deep Learning Faster Region based Convolutional Neural Network (Faster R-CNN) algorithm. The way it works is by processing the video recorded from CCTV on the HK TOLL APPS application, then the system will detect the vehicle that has committed a violation, if it is proven to have committed a violation, the system will provide a notification to the toll road officer via a Telegram Bot. The output obtained in this research is the system can properly detect vehicles that violate the rule on the roadside of the toll road. By using the model results from testing with several data partition training test schemes and configuration variables testing, obtained the model that has the best value with 90%:10% data partition, and configuration variables with Learning Rate 0.0004, Batch Size 1, Epoch 100. With accuracy values 77.9 %, precision 71.1%, recall 73.6%, and Mean Average Precision (mAP) 77%.
Human Slope Perception with Haptic Sense for Networked Virtual Environments

This paper examines human perception of slopes with haptic sense for networked virtual environments by Quality of Experience (QoE) assessment. Each subject in the assessment makes a comparison between each of eleven slopes (called the standard slopes here) with different slope angles and test slopes by just touching the slopes without watching them. The subject answers whether each test slope is different in angle from the standard slope, and then we obtain the ratio of the number of subjects who notice the difference to the total number of subjects for the standard slope. Assessment results demonstrate that the ratio is largely dependent on the angle of the standard slope and the slope angle difference.
Addressing Class Imbalance for Transformer Based Knee MRI Classification

For assessing knee injuries, Magnetic Resonance Image (MRI) examinations are commonly utilized. Developing an automatic interpretable detection mechanism is an essential task for automating the clinical diagnosis of knee MRI. The imbalanced dataset problem is generally an issue for learning models in which the distribution of classes in the dataset is asymmetrical. The MRI datasets are generally imbalanced in favor of categories with injuries because patients who have an MRI are more likely to suffer a knee injury. Hence, it can be a challenging task to train a machine learning algorithm that can automatically handle class imbalance. In this paper, we propose both a network architecture and a comparison of the handling imbalanced dataset techniques to detect the general abnormalities in knee MR images. A network architecture that consists of CNN and transformer-based layers is proposed. Six different configuration methods for imbalanced data training are developed and compared with evaluation metrics (ROCAUC score, specificity, sensitivity, accuracy). Augmentation of additional data to the under-represented class and use of focal loss yield better classification specificity and AUC.
Object Detection Systems Informing Users of Guardian Space Dangers Through Haptic Displays

Modern virtual reality applications rely on the guardian space to guarantee safety to the users during inter-actions. The current state of technology adopts virtual walls or meshes projected to the virtual space to alert users when their reach approaches the boundaries of the guardian space, requiring constant attention to the visual display. In this paper, we explore haptic displays as an alternative modality to alert users about dangers in the guardian space. We adopt object detection to analyse the scene within the guardian space, detecting unseen objects entering the space, i.e. a cat approaching the user's surroundings, causing a haptic display to signal the user the direction of the danger. We develop a proof-of-concept system capable of alerting a user of changes to the scene within the guardian space, demonstrating the feasibility of the system and its potential applications for extended reality platforms.
Ukraine Aftershocks

The major shock of the war in Ukraine draws attention to two aftershocks: asymmetric warfare and Ukraine’s software outsourcing capability. Assessment of these provides a basis for additional speculation about the future of software.
Ethnic Costume Grayscale Image Coloring Method with Improved Pix2Pix

Grayscale image coloring is challenging for high-resolution images of ethnic costume class, and current coloring methods are mainly applicable to low-resolution images, which pay less enough attention to local regions. While ethnic costume images are characterized by rich semantic information, diverse colors and high resolution, the original methods are difficult to show good results on ethnic costume dataset and prone to problems such as inaccurate coloring in local areas. In this paper, we propose a method of colorization that combining global features and attention mechanism with Pix2Pix, by injecting grayscale map into different layers of the generator as global features, and accelerating the speed of the convergence by the attention mechanism. The experimental results show that the proposed method has better coloring effect on ethnic costume images and could generate higher quality images.
Exploring and Analyzing Insights for Software Process Improvement

The goal of this research project was to apply the ideas from Gregory Bateson’s book “Steps To An Ecology Of Mind” to the software development process. In our work, we focused on three fundamental aspects: a) the process to follow, b) the product being built, c) the structure of the team and its organization. While reading the book, we identified the general concepts of the human brain work and drew parallels between the situations described in the book and the conditions that people face during software development. We conducted an anonymous survey among software developers and made sure that many selected ideas could be used to improve the software development process.
Drift Reduction for Monocular Visual Odometry of Intelligent Vehicles Using Feedforward Neural Networks

In this paper, an approach for reducing the drift in monocular visual odometry algorithms is proposed based on a feedforward neural network. A visual odometry algorithm computes the incremental motion of the vehicle between the successive camera frames, then integrates these increments to determine the pose of the vehicle. The proposed neural network reduces the errors in the pose estimation of the vehicle which results from the inaccuracies in features detection and matching, camera intrinsic parameters, and so on. These inaccuracies are propagated to the motion estimation of the vehicle causing larger amounts of estimation errors. The drift reducing neural network identifies such errors based on the motion of features in the successive camera frames leading to more accurate incremental motion estimates. The proposed drift reducing neural network is trained and validated using the KITTI dataset and the results show the efficacy of the proposed approach in reducing the errors in the incremental orientation estimation, thus reducing the overall error in the pose estimation.
Method of Multi-lane Vehicles Speed Continuously Perceiving Based on Single Roadside Camera

Roadside camera has been widely applied to detect the traffic status and now it is an important component composing the digital road infrastructure. A novel method of multi-lane vehicles speed continuously perceiving based on single roadside camera is proposed in this paper. Firstly, extended Haar feature is adopted by identifying objects of roadside camera video to achieve the training data set. Then, an AdaBoost cascade classifier is designed and optimized based on iterative learning of the data set for accurately vehicle identifying. Thirdly, an association tracker is proposed based on MOSSE to realize multi-vehicle tracking in consecutive video frames, and average pixel and Euclidean distance are applied to locate the vehicle position and calculate the vehicle trajectory. At last, a transformation relation of image pixel to physical distance is proposed to obtain the vehicle real time speed. The proposed method has been verified with real roadside camera data. The experimental results show that the vehicle recognizing accuracy is above 98.02%, the vehicle speed perceiving error is within <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\pm 2\%$</tex> , and the proposed method can deal with real time roadside camera data with good.
Simulation Model of Data Consistency Protocol for Multicloud Systems

The development of multi-cloud systems with an acceptable level of service delays that significantly affect the quality of the end-users experience is an open research problem, for which simulation is successfully used. This paper proposes a simulation model in Matlab for estimating the time to write data updates initiated by the end-users to the databases of all cloud service providers for a multi-cloud system with a geo-distributed middleware that implements, as an example, the VIP-grab data consistency protocol. The simulation results indicate that our model can be useful in choosing a data consistency protocol that influences the architecture of a multicloud system.
Exploiting matching local information for person re-identification

Person re-identification task with the main aim is to associate the instances of the same person captured by different cameras in a surveillance camera network usually employs the detection results. As a consequence, misalignment of detected bounding boxes and background information are the two main factors that lead to reducing the performance of person re-identification.To tackle with these challenges, the state-of-art in person re-identification methods proposed to employ attention mechanism or body parts detection. However, these methods have high complexity and computational cost, which can be reduced by using Earth Movers Distance (EMD) instead. Therefore, this paper formulates local matching as a distance calculation of two probability distributions and applies Earth Movers Distance (EMD) to compute the optimal matching between two sets of stripes in order to address an issue in the AlignedReID++ method. Different experiments have been conducted on both single-shot and multi-shot person re-identification. The obtained results have shown the improved performance of the proposed method compared with the baseline method. The matching rates at rank1 obtained by the proposed method are 49.59%, 83.36%, and 78.47% on VIPeR, Marketl501-Partial, and DukeMTMCReID-Partial, respectively.
Optimization: data-driven management using deep learning in cloud computing

The data-driven framework is one of the most demanded managements of cloud computing (CC). This study was focused on optimization and deep learning (DL) for the CC framework. It requires different types of frameworks' data or resources for executing various services on the cloud framework. We propose a model-based data-driven framework that explores data-driven management in terms of CC. This research is an optimal selection of CC recovery when the uncertainty of cloud networks can be based on time constraints and objective functions. We consider the emergence of CC data centers such as Amazon Web Services (AWS) and Wikipedia. These data centers can bring a considerable risk to the uncertainty of the data quality in real-time demand from organizational CC. In the present pandemic situation, data centers perform various degradation of the quality of data because the allocation of resources has increased the input of data size, especially in cloud workload. So, we use an Artificial Neural Network (ANN) model to perform a time allocation and elasticity for managing the CC workload and their management. Real-time workload resilience, trace troubleshooting, probability, and availability are implemented to analyze the data quality for better performance, which leads to overhead on the cloud platform performance.
Unknown Object Recognition Using the Manifold Structure of Class Distributions

Recently, most object-recognition techniques have been developed using known classes of objects as inputs. However, in the real world, there is a possibility that "unknown" objects may be used inputs, and conventional methods cannot cope with such cases. In this study, we focus on the unknown object recognition technique, which recognizes an unknown object as an unknown class when it is input, and propose a method to construct a latent space considering the manifold structure of known classes. The basic performance of the proposed method is demonstrated using the CIFAR-10 image dataset, and results of its application to radar image data are also reported.
Improving Query Expansion Performances with Pseudo Relevance Feedback and Wu-Palmer Similarity on Cross Language Information Retrieval

With more information available in multiple languages, the need to search for relevant information is no longer fixated only on one language. Cross language information retrieval, a system that search for relevant information in different language, experienced a decrease in performance due to the loss of meanings during translation process or the initial query that was not descriptive of the information to be sought. One method to improve the performance of the information retrieval system relevance feedback query expansion. Another method utilizes external resources such as WordNet as the basis for generating query expansion term. This study utilizes the Wu-Palmer semantic similarity measurement in WordNet to improve the performance of pseudo relevance feedback query expansion. The terms contained in the feedback document considered as candidate expansion terms. Each candidate is given weight based on IDF score, Wu-Palmer similarity score, and document score using Okapi ranking function. Several candidate terms with the highest weight are used in query expansion. Experiment is conducted by comparing mean average precision value of query expansion based Rocchio feedback with query expansion based Wu-Palmer semantic similarity using NPL collection. The result show that the query expansion method of pseudo relevance feedback with WuPalmer semantic similarity can have better performance than the Rocchio pseudo relevance feedback for document feedback less than five. The highest mean average precision is obtained when two document feedback and ten expansion term is used with 0.5605 compared to Rocchio pseudo relevance feedback with only 0.4499.
HyperStyle-Based Data Augmentation to Improve the Performance of Face Recognition Model

Face recognition is the most stable and robust biometric technique for identifying and authenticating human faces. However, training face recognition models using a deep learning architecture requires large training images. In addition, labeling face image collections manually is a time-consuming and costly process. Augmenting face images using the HyperStyle architecture can solve these issues. Experimental results obtained using the ResNet-50 architecture trained on a dataset of 44,000 face images (the original FaceScrub dataset combined with the HyperStyle age and smile augmented images) demonstrate that the model achieved an F1-score of 79%, therefore outperforming the model trained using the original FaceScrub dataset without HyperStyle augmentation, i.e., F1-score of 63%.