Reviewerly/exHarmony huggingface dataset:
Robust Contour Visual Servoing based on B-spline Features with Temporary Occlusion

In visual servoing (VS) tasks, the target object with complex shape brings the challenge to extract effective visual information used for robotic control. Appropriate image features describing the overall contour of the object are critical to implement such tasks. In this work, we propose a B-spline features-based contour VS method. The quasi-uniform B-spline curve is employed to construct image features by control points from contours. With the good shape description capability and concise mathematical expression, B-spline features handle the object with complex shape in a visually intuitive and efficient way. Moreover, to improve the VS system’s robustness to the dynamic environment and temporary occlusion, a real-time estimate framework composed of B-spline features estimator (BFE) and B-spline features predictor (BFP) is proposed. BFE achieves the optimal estimation of current image features and corresponding depth based on adaptive extended Kalman Filter (AEKF), where control points are regarded as observations. BFP mainly tackles the problem of object occlusion, where the perspective projection invariance of B-spline features and Lie algebra model are introduced to predict the occluded features through nonlinear optimization. The effectiveness of the proposed method is validated on VS tasks for objects with complex shapes by simulations and experiments.
Facial Behaviour Realization using Statistical Features

In this article, the authors attempt to characterize the facial emotions utilizing a few statistically measurable elements. The objective is to demarcate the emotions based on their arousal level. Several low and high arousal emotions such as anger, surprise, sadness, happiness, fear, and disgust are investigated to segment them based on the level of arousal. Initially, the facial images are loaded and the sector of interest is extracted to assess the factual component of the face. The versatile Gabor filter is applied to each of the facial images to extract the discriminate feature vectors. Finally, several statistical parameters are computed from the Gabor feature vectors of each facial emotional expression to characterization and identification based on the level of arousal. To exhibit the stated acknowledgment strategy, JAFFE facial information base and the MATLAB 18 (b) platform are incorporated. Simulation results reveal, it is possible to demarcate the high arousal emotional states from the low arousal states graphically for the sake of identification.
Deep Neural Network-based Single Object Tracking

In this paper, we put forward the notion of an approach centered on single object tracking. The single object tracker is going to find one object, and then it is going to track that object over the whole frame of the video. The basic elements of this methodology are images, groundtruths, neural network, and detector which are used to make a single object tracker. The neural network used for this tracking method is RESNET-101. Other trackers are also efficient in tracking the object, but still not getting accurate predicted bounding boxes on the selected object, this field gives other people a chance to make different trackers that can do perfect tracking. The datasets used in this paper are the Online object tracking benchmark(OOTB) and Unmanned Aerial Vehicle(UAV).
A Survey on Fast and Scalable Incremental Frequent Item Set Methods for Big Data

The Frequent Pattern Mining (FPM) algorithm, which is commonly used to find frequent itemsets, does not scale well in today's Big Data, especially for dynamically incremental databases. As a result, the FIM algorithms must be developed to support incremental mining. The aim of the Incremental frequent mining is to discover interesting patterns which is used to support data analysis and for making decisions. Incremental mining aims to extract patterns from the dynamic databases which have applications in domains such as product recommendation, text mining, market basket analysis, and web click stream analysis. Several algorithms have been proposed to mine the frequent patterns from both the original and incremental databases. The field of incremental mining is becoming more vibrant and increasing as new algorithms are developed every day. This paper presents a detailed survey of incremental pattern mining, and its methods.
Evaluation of The Quality of The School Website using WEBUSE and IPA

A website is a collection of interconnected web pages that can be accessed by the public and share a single domain. Individuals, clubs, enterprises, and organizations can construct and maintain websites for a variety of objectives. The website offers an almost limitless number of options that may be used anywhere and at any time. One of them is in the field of education. SMA Harapan 1 Medan is one of the schools that use the internet as a source of information. However, according to the website administrator, the website (sma1.harapan.ac.id) continues to have issues with features, appearance, and insufficient information. The WEBUSE (Website Usability Evaluation Tools) method can be used to tackle the problem on the website. WEBUSE is a questionnaire-based usability evaluation approach for assessing the usability of a website. WEBUSE was chosen because website problems are categorized in WEBUSE, and it can assess usability across all types of websites and domains. The evaluation's findings will be examined using Importance Performance Analysis (IPA). The Importance Performance Analysis approach to determine the level of conformance and how satisfied users are with the website is also used to determine which parts need to be fixed or maintained. The results became insight to formulate improvement strategies. The analysis findings will be considered while creating the improvement plan.
Aspect Extraction on Restaurant Reviews using Domain-Specific Word Embedding

Reviews on the internet can be an important part of a business and can influence owners or consumers for their decision making. Easy access to information in the form of opinions, experiences, and feedback from others can be used as a reference for taking an action. For businesses in the food and beverage sector, consumers usually provide reviews with negative or positive sentiments based on several aspects of the related business. The taste of the food, atmosphere, price, service are examples of aspects that are commonly written in a review. In this work, aspect extraction on consumer reviews of restaurants in Indonesia is going to carried out. Reviews on the internet usually contains words that are informal and very domain specific. This is where Domain Specific Word embedding can be used to reduce the amount of out-of-vocabulary word (OOV) and give the model more context of the review text given. The model used is Deep Learning with Recurrent Neural Network architecture, using Domain Specific Embedding as Word Embedding, and several attempts to reduce out of vocabulary in the model. The model used is able to reduce OOV from 17.16% (based on previous research) to 3.62%, with an evaluation of the F1-Score model of 79.54% using the Bi-LSTM model.
Friendship Inference in Mobile Social Networks: Exploiting Multi-Source Information With Two-Stage Deep Learning Framework

With the tremendous growth of mobile social networks (MSNs), people are highly relying on it to connect with friends and further expand their social circles. However, the conventional friendship inference techniques have issues handling such a large yet sparse multi-source data. The related friend recommendation systems are therefore suffering from reduced accuracy and limited scalability. To address this issue, we propose a Two-stage Deep learning framework for Friendship Inference, namely TDFI. This approach enables MSNs to exploit multi-source information simultaneously, rather than hierarchically. Therefore, there is no need to manually set which information is more important and the order in which the various information is applied. In details, we apply an Extended Adjacency Matrix (EAM) to represent the multi-source information. We then adopt an improved Deep Auto-Encoder Network (iDAEN) to extract the fused feature vector for each user. Our framework also provides an improved Deep Siamese Network (iDSN) to measure user similarity. To provide a substantial description and evaluation of the proposed methodology, we evaluate the effectiveness and robustness on three large-scale real-world datasets. Trace-driven evaluation results demonstrate that TDFI can effectively handle the sparse multi-source data while providing better accuracy for friendship inference. Through the comparison with numerous state-of-the-art methods, we find that TDFI can achieve superior performance via real-world multi-source information. Meanwhile, it demonstrates that the proposed pipeline can not only integrate structural information and attribute information, but also be compatible with different attribute information, which further enhances the overall applicability of friend-recommendation systems under information-rich MSNs.
An Expectation-Maximization-Based Estimation Algorithm for AOA Target Tracking With Non-Gaussian Measurement Noises

This paper considers angle-of-arrival (AOA) target tracking in the two-dimensional plane with non-Gaussian noise. Practical situations arise where sensor measurement noise is non-Gaussian and the standard extended Kalman filter (EKF) based tracker may not be robust, resulting in large tracking error or even lack of convergence. This motivated development of a new tracker, designed for white non-Gaussian noise cases. The noise is decomposed into a sum of Gaussian components using expectation maximization (EM), and the target state is estimated by adapting an extended Kalman filter (EKF). Adaptations are necessary to combine the EM with the iterative EKF. From the analysis of the estimation error, a closed-form expression was derived revealing the presence of a bias in the estimate. On investigation, a sensor path optimization scheme is found that can eliminate bias. Furthermore, a compensation algorithm is presented, which reduces bias for the case of static sensors. The new tracker is suitable for real-time implementation and simulation results demonstrate significant improvements for the target tracking with non-Gaussian noise.
Robust secret key generation for frequency-selective channels

One effective way of generating and distributing secret keys for secure communication is to exploit the randomness of wireless channels. Since a potential eavesdropper has a different propagation channel than two legitimate users, it can be used to generate unique and secure keys. After reciprocal channel sounding, a frequency-selective channel can be quantised to produce the secret keys. Due to different sampling times at the two legitimate devices, the phase of the frequency response will not be equal and hence compromise the secret key generation. In this paper, we show how to overcome this problem and verify the results through both simulations and hardware measurements. We also compare scalar and vector quantisation, and show how the latter can be designed to be robust to synchronisation offsets.
Dalziel Revisited Application; Analyzing mixed AC/DC waveforms

This paper continues the evaluation of touch current generated by switching circuits within the Dalziel space. This work further confirms the inclusion of impulses below half cycle when compared in the normalized Weiss-LaPique space. Al this confirms the usefulness of the Frequency Factor compensated peak touch current measurements for evaluating touch current waveforms as generated by equipment.
SwarmHive: Heterogeneous Swarm of Drones for Robust Autonomous Landing on Moving Robot

The paper focuses on a heterogeneous swarm of drones to achieve a dynamic landing of formation on a moving robot. This challenging task was not yet achieved by scientists. The key technology is that instead of facilitating each agent of the swarm of drones with computer vision that considerably increases the payload and shortens the flight time, we propose to install only one camera on the leader drone. The follower drones receive the commands from the leader UAV and maintain a collision-free trajectory with the artificial potential field. The experimental results revealed a high accuracy of the swarm landing on a static mobile platform (RMSE of 4.48 cm). RMSE of swarm landing on the mobile platform moving with the maximum velocities of 1.0 m/s and 1.5 m/s equals 8.76 cm and 8.98 cm, respectively. The proposed SwarmHive technology will allow the time-saving landing of the swarm for further drone recharging. This will make it possible to achieve self-sustainable operation of a multi-agent robotic system for such scenarios as rescue operations, inspection and maintenance, autonomous warehouse inventory, cargo delivery, and etc.
Adaptive and Stabilized Streaming for Edge-Assisted Connected Vehicles under Heterogeneous Computing Constraints

Recently, emerging media services over edge-assisted vehicular-to-infrastructure (V2I) networks are widely studied. A joint design of scheduling and streaming algorithms is essential for seamless and high-quality media services. In scheduling, the algorithm is designed under the consideration of heterogeneous computing at edges. In addition, the streaming algorithm is also proposed for maximizing the time-average streaming quality maximization subject to stability. It is verified that our proposed joint scheduling and streaming algorithm achieves desired performance improvements.
Optimizing Graph-based Approximate Nearest Neighbor Search: Stronger and Smarter

Approximate Nearest Neighbor Search (ANNS) is widely used in many fields (e.g., recommender systems). In recent years, the graph-based ANNS methods have attracted the attention of many researchers due to their superiority compared to non-graph-based methods. Compared with traditional recommender systems, mobile recommender systems have higher latency requirements. The graph-based ANNS method faces the following challenges that make it difficult to meet the requirements. (1) Poor connectivity. Due to the limitation of the construction algorithm, the connectivity of the graph is poor, which in turn affects the search performance. (2) Redundant search. The existing search algorithm uses sufficiently long search steps for all queries to achieve high search accuracy. However, the query search steps follow the long-tailed distribution that brings the redundant search, e.g., for more than 40 % of the queries, 87.4 % of the search overhead is redundant. We propose two optimization strategies to tackle the above challenges. (1) Reverse connection enhancement strategy. In the graph construction process, we increase the in-degree of the point to be inserted to enhance the graph connectivity, while keeping the out-degree low to maintain the high search efficiency. (2) Query aware early termination strategy. We identify regional features to predict the number of remaining search steps to achieve dynamic search termination and reduce the redundant search overhead. Finally, we verify the proposed solutions on multiple representative datasets. Compared with the state-of-the-art graph-based algorithm, our solutions can improve the search speed up to 1.21x when the recall rate equals 0.95.
Online Compressive Channel Learning Using Untrained Deep Generative Model

In this paper, we propose an untrained deep image prior-based compressive channel estimation (CE) algorithm in massive MIMO systems. We adopt deep convolutional generative network (DCGN) to learn the sparse prior of massive MIMO channels, whose weights can be optimized to match the compressed measurements. Unlike various supervised deep neural networks (DNN)-based CE algorithms, which require offline training over large simulated channel datasets, our DCGN does not require pre-training and can be learned online based on the real-time measurements to make it adaptable to the time-varying channels. Besides, DCGN can exploit the channel structure automatically without any prior knowledge. We further devise a learned regularization technique to improve the CE performance when the measurements are noisy and highly compressive. Simulations show that the proposed method can achieve more accurate and robust online CE performance than traditional compressive sensing (CS) and DNN-based methods.
The Synthetic Off-road Trail Dataset for Unmanned Motorcycle

The Unmanned Motorcycle (UM) is a robotic system aiming to autonomously drive in off-road trail environments, keep the tracked routes under surveillance, and provide help within its ability. One core component of the UM is its vision module that utilizes deep learning techniques to get pixel-accurate trail understanding. By learning valuable features from the off-road trail datasets, the UM is expected to understand traversable areas in a large variety of off-road trail scenes. However, currently, there is no publicly available dataset on the off-road trail scene dataset for UMs, which motivates us to conduct a comprehensive dataset generation and collection. In this paper, we build four virtual worlds to generate Synthetic Off-road Trail (SORT) dataset. The dataset seeks to positively influence the development of data-driven trail segmentation for UMs, which we hope other UM researchers will use the dataset and contribute to it. By this method, the dataset can be easily regenerated and tailored for other robot platforms. The whole workflow of tackling the scene parsing task of the UM is provided, and we hope our work can inspire the robot perception research community and help push the usage of realistic visual simulation to develop learning-based algorithms in their research fields. Multimedia material of our dataset is available at https://www.youtube.com/watch?v=9biV7fKxKRo&list=PLxSoUS6AFfb-KlqzxpqlMfKoF9qur4h0p.
QoE-driven Link Quality Prediction for Video Streaming in Mobile Networks

The link quality prediction facilitates high quality video streaming over mobile networks. However, the existing link quality prediction algorithms focus on minimizing the gap between the ground truth and the prediction result, while it remains a challenge to exploit such information to achieve high quality video streaming with minimum Quality of Experience (QoE) degradation. The accurate link quality prediction is one of keys to enable beyond 5G/6G world. In this paper, we produce artificial intelligence (AI) based link quality prediction which consists two steps: 1. We explore and exploit the temporal correlation in time series to adaptively learn and predict its short-term behavior based on Gaussian Process (GP). 2. The GP-based prediction is tailored to maximize QoE by finding a proper piece-wise convex envelope of the predicted link quality in an online manner. By using the measured uplink throughputs, the video streaming QoE of the proposed framework were evaluated.
Efficient Split Learning with Non-iid Data

Distributed machine learning such as split learning can train a model using data on mobile devices while protecting privacy. However, its training time is impractical when the number of devices is large, and the model performance will decrease greatly if data are non-iid. To solve these problems, an efficient parallel split learning algorithm is proposed. Specifically, the parallel algorithm with a distillation loss function instead of parameter synchronization reduces the training time without losing the accuracy. And an incentive mechanism based on Stackelberg Game is designed to adapt to the training environment with non-iid mobile data. The experiments on the CIFAR-10 dataset demonstrate the superior performance of the proposed algorithm in terms of training time and model accuracy.
Analyzing Federated Learning with Enhanced Privacy Preservation

For machine learning in online social networks or mobile environments, data privacy-preserving is a very important issue. In the past, artificial intelligence and machine learning algorithms that operating on a stand-alone machine must analyze each data in order to build an accurate model when training a model. However, this method is insufficient in terms of privacy protection, because it must collect data from different sources; on the other hand, if these algorithms analyze only a part of the data, the accuracy of the models constructed by artificial intelligence and machine learning algorithms will be very low. To solve the above problems, this study starts from the privacy protection aspect of industrial data management, combined with the concept of federated learning, and hopes to improve the accuracy of algorithm modules while ensuring data privacy. Therefore, in this paper, we analyze the impact of federated learning under different protocol mechanisms on the accuracy of algorithm modules, and based on this, we can apply this module to integrate blockchain smart contract technology in the future.
Support Vector-Based Unsupervised Learning Approaches for Radio Frequency Interference Detection

The presence of unwanted signals in the radio frequency (RF) spectrum, called RF interference (RFI), is a major drawback in wireless communication systems. The detection of REI has been dealt mainly with signal processing and supervising machine learning approaches. In this paper, we investigate two unsupervised machine learning alternatives for REI detection, the one-class support vector machine (SVM) and the support vector data description (SVDD) algorithm, which delimit the class boundaries of normal signals in high-dimensional space, and view REI contaminated signal as novelty, i.e., outsiders from unknown classes. Similar to the popular binary SVM classifier, these two algorithms can learn from a relatively small training set, and they use unsupervised training to learn from typically unbalanced RFI data sets without need for data augmentation techniques as in supervised training. The experimental results for detecting three types of RFI, using scaling features to a range as a standardization method, show that SVDD has a low computational complexity and an accuracy of 90.74 % versus 91.67 % for the One-class SVM.
Swift Estimation Method of Available Bandwidth to Realize Robust Wireless Video Transmission Systems

Because of the arrival of local 5G, real-time services using videos through wireless networks, such as remote surveillance and teleoperation, have attracted considerable attention. However, drastic changes in available bandwidth (AB) that are caused by moving obstacles or rapid changes in radio traffic pose challenges in providing a guaranteed quality of video streams. Robust video streams without disconnection over wireless networks necessitate an estimation of the AB reflected by the changing wireless environment and controlling of the video quality by the AB. For conventional studies, the AB is rapidly calculated using a table that maps an indicator of wireless-link quality to the AB. However, because the fixed table does not reflect changes in radio traffic, there are discrepancies between the estimated and real AB. Therefore, to precisely estimate the AB that reflects the changing wireless environment, we propose a hybrid calibration method, comprising long-term and short-term calibrations in this study. The long-term calibration determines invariant parameters of the table, which map the received signal strength indicator (RSSI) to the AB (RSSI–AB table). The short-term calibration enables a variant parameter to follow changes in traffic from other terminals. For experimentally evaluating the proposed method, a real Wi-Fi network scenario with a video transmission connection was implemented and traffic from another terminal connected via the same access point was added. Based on the mean absolute logarithmic deviation, the results showed that the proposed method was 1.23 times more accurate than the conventional method.