Reviewerly/exHarmony huggingface dataset:
Modulation classification based on denoising autoencoder and convolutional neural network with GNU radio

In this article, a machine learning method to classify signal with Gaussian noise based on denoising auto encoder (DAE) and convolutional neural network (CNN) is proposed. We combine denoising autoencoder's denoising ability with CNN's feature extraction capability. First, a six-layer neural network is built, including three CNN layers. Then a dataset containing noiseless signal of 11 modulation is generated. In the simulation, we apply this dataset to train neural network and achieve an accuracy of 94%, which is much higher than performance with noisy signal, meaning that noise can greatly influence the accuracy of neural network. Next, we build a denoising autoencoder and train it with signal of 5 dB signal-to-noise ratio (SNR). Compared with neural network without denoising autoencoder, adding a denoising autoencoder can achieve an accuracy of 84% at signal of 18 dB SNR, improved by 58%.
Implementation of Resilient Methods to Predict Open Unemployment in Indonesia According to Higher Education Completed

Unemployment is a big problem faced by the Indonesian people from year to year besides poverty. Therefore it is necessary to predict the level of open unemployment in Indonesia so that later the government and private parties have the right references and references to work together to overcome this problem. The prediction method used is Resilient Backpropagation which is one method of Artificial Neural Networks which is often used for data prediction. The research data used is open unemployment data according to the highest education completed in 2005-2018 based on the semester obtained from the website of the Indonesian Central Bureau of Statistics. Based on this data a network architecture model will be formed and determined, including 12-6-2, 12-12-2, 12-18-2, 12-24-2, 12-12-12-2, 12-12-18 -2, 12-18-18-2 and 12-18-24-2. From these 8 models after training and testing, the results show that the best architectural model is 12-18-2 (12 is the input layer, 18 is the number of hidden neurons and 2 is the output layer). The accuracy of the architectural model for semester 1 and semester 2 is 75% with an MSE value of 0.0022135087 and 0.0044974696
Multi-Train Path Finding

Multi-agent path finding (MAPF) is the problem of moving a set of agents from their individual start locations to their individual goal locations, without collisions. This problem has practical applications in video games, traffic control, robotics, and more. In MAPF we assume that agents occupy one location each time step. However, in real life some agents have different size or shape. Hence, a standard MAPF solution may be not suited in practice for some applications. In this paper, we describe a novel algorithm, based on the CBS algorithm, that finds a plan for moving a set of train-agents, i.e., agents that occupy a sequence of two or more locations, such as trains, buses, planes, or even snakes. We prove that our solution is optimal and show experimentally that indeed such a solution can be found. Finally, we explain how our solution can also apply to agents with any geometric shape.
Unconstrained self-calibration of stereo camera on visually impaired assistance devices

Stereo cameras are widely used in wearable visually impaired assistance devices (VIADs). However, the inevitable vibration, shock, and mechanical stress may make the camera pair become misaligned and cause a sharp decline in the quality of the acquired depth map, which significantly influences the performance of VIADs. In this paper, we propose an epipolar-constraint-based unconstrained self-calibration method that requires neither user involvement nor specific environment, while achieving a rotation accuracy of 0.83 mrad and a translation accuracy of 0.42 mm. Several approaches are proposed to address the image matching issues, including blurred images removal, mismatched key points removal, etc. Based on correctly matched key point pairs, a planar quadric-distribution approach is proposed to ensure the quality and consistency of the final key point group. These collection approaches ensure the reliability of key point pairs, which is the most important factor to realize high accuracy with minimum constraint. A comprehensive set of experiments demonstrates the high robustness of the proposed methods, which are suitable for VIADs. We also present a field test with blindfolded users to validate the flexibility and applicability of the approach.
Semantic-Aware Visual Abstraction of Large-Scale Social Media Data With Geo-Tags

With the rapid growth of geo-tagged social media data, it has become feasible to explore topics across different areas through text mining and geographical visualization. However, the visual elements of social media data always overlap with each other in the map view, which largely disturbs visual perception of semantic features and their geographical distribution. Thus, it is of great significance to reduce the visual clutter of large-scale social media data, and enhance the visibility of semantic features across local areas. In this paper, we utilize a doc2vec model to transform geo-tagged social media data into high-dimensional vectors, and the semantic correlation can be easily characterized in the dimensionality reduction space. Aiming at the reduction of visual clutter of geographical visualization, a dual-objective blue noise sampling model is proposed to select a subset of social media data, by means of which both the semantic correlation and spatial distribution of large scale social media data are well retained. A rich set of visual designs are implemented enabling users to evaluate the sampled results from multiple perspectives and explore the changes of semantic features across areas, such as heatmap, word cloud and text stream. The effectiveness and validity of the proposed visualization system are further demonstrated through case studies and expert reviews.
PhishLedger

In recent years, phishing has become one of the biggest security threats on the Internet. To combat phishing, it requires multiple steps and multi-agency participation and thus desperately need uniform data sharing format and unobstructed sharing channels, which unfortunately is just what is lacking currently. This paper proposes a novel phishing data sharing mechanism based on the consortium blockchain. It designs four types of nodes, including reporting node, accounting node, servicing node and supervising node and illustrates the roles of each type. Then it demonstrates the process of reporting, accounting and servicing and designs the process of post-supervision, which ensures the operation of the mechanism stable and fastest; and then discusses its implementation on Hyperledger Fabric. The proposed mechanism includes multi-source reporting, anti-tamper accounting, multi-channel disposal of phishing data and post-supervision. It provides a platform for multi-party participation, transparent and efficient coordination and unified standard and overcomes the current prominent problems of phishing data sharing; and the participants on the consortium blockchain all have a strong desire to combat phishing, which ensures the proposed mechanism is also very practical and highly feasible.
Occlusion Management in VR: A Comparative Study

VR applications rely on the user's ability to explore the virtual scene efficiently. In complex scenes, occlusions limit what the user can see from a given location, and the user has to navigate the viewpoint around occluders to gain line of sight to the hidden parts of the scene. When the disoccluded regions prove to be of no interest, the user has to retrace their path, making scene exploration inefficient. Furthermore, the user might not be able to assume a viewpoint that would reveal the occluded regions due to physical limitations, such as obstacles in the real world hosting the VR application, viewpoints beyond the tracked area, or viewpoints above the user's head that cannot be reached by walking. Several occlusion management methods have been proposed in visualization research, such as top view, X-ray, and multiperspective visualization, which help the user see more from the current position, having the potential to improve the exploration efficiency of complex scenes. This paper reports on a study that investigates the potential of these three occlusion management methods in the context of VR applications, compared to conventional navigation. Participants were required to explore two virtual scenes to purchase five items in a virtual Supermarket, and to find three people in a virtual parking garage. The task performance metrics were task completion time, total distance traveled, and total head rotation. The study also measured user spatial awareness, depth perception, and simulator sickness. The results indicate that users benefit from top view visualization which helps them learn the scene layout and helps them understand their position within the scene, but the top view does not let the user find targets easily due to occlusions in the vertical direction, and due to the small image footprint of the targets. The X-ray visualization method worked better in the garage scene, a scene with a few big occluders and a low occlusion depth complexity' and less well in the Supermarket scene, a scene with many small occluders that create high occlusion depth complexity. The multi-perspective visualization method achieves better performance than the top view method and the X-ray method, in both scenes. There are no significant differences between the three methods and the conventional method in terms of spatial awareness, depth perception, and simulator sickness.
Deterministic Quantum Secure Direct Communication Protocol Based on Hyper-Entangled State

A quantum secure direct communication (QSDC) protocol based on the hyper-entangled state is proposed to improve the efficiency of eavesdropping detection in an improved ''ping-pong'' protocol, in which we use the hyper-entangled state to detect eavesdropping during the quantum communication.In the security analysis, we use the entropy theory method to calculate the amount of information and compare the detection strategy of two QSDC protocols quantitatively by comparing the constraint between the maximal amount of information that the eavesdropper can get, and the probability that the eavesdropper is detected.The probability of being detected will be 50% in the original QSDC protocol which based on the Bell states if the eavesdropper attempts to get all the information, however, in the proposed protocol this probability can reach 75%, which indicates that the proposed protocol is more secure.
Spectral Embedded Deep Clustering

We propose a new clustering method based on a deep neural network. Given an unlabeled dataset and the number of clusters, our method directly groups the dataset into the given number of clusters in the original space. We use a conditional discrete probability distribution defined by a deep neural network as a statistical model. Our strategy is first to estimate the cluster labels of unlabeled data points selected from a high-density region, and then to conduct semi-supervised learning to train the model by using the estimated cluster labels and the remaining unlabeled data points. Lastly, by using the trained model, we obtain the estimated cluster labels of all given unlabeled data points. The advantage of our method is that it does not require key conditions. Existing clustering methods with deep neural networks assume that the cluster balance of a given dataset is uniform. Moreover, it also can be applied to various data domains as long as the data is expressed by a feature vector. In addition, it is observed that our method is robust against outliers. Therefore, the proposed method is expected to perform, on average, better than previous methods. We conducted numerical experiments on five commonly used datasets to confirm the effectiveness of the proposed method.
Some generalizations of good integers and their applications in the study of self-dual negacyclic codes

Good integers introduced in 1997 form an interesting family of integers that has been continuously studied &nbsp;due to their rich number theoretical &nbsp; properties and wide applications. In this paper, we have focused on classes &nbsp;of &nbsp;$ 2^\beta $-good integers, $ 2^\beta $-oddly-good integers, and $ 2^\beta $-evenly-good integers which are &nbsp;generalizations &nbsp;of good integers. &nbsp;Properties of such integers have been given as well as their applications in &nbsp; characterizing and enumerating self-dual negacyclic codes over finite fields. &nbsp;An alternative proof for &nbsp;the characterization of &nbsp;the existence of a &nbsp;self-dual negacyclic code over finite fields has been given in terms of such generalized good integers. &nbsp;A general enumeration formula for the number of &nbsp; self-dual negacyclic codes of length $ n $ &nbsp;over &nbsp;finite fields &nbsp;has been established. &nbsp;For some specific lengths, explicit formulas have been provided as well. &nbsp;Some known results on self-dual negacyclic codes over finite fields can be formalized and viewed as special cases of this work.
Extreme Self-Paced Learning Machine for On-Orbit SAR Images Change Detection

With the rapid development of earth observation satellites, on-orbit data processing is becoming more and more desirable. In this paper, a new on-orbit change detection method for Synthetic Aperture Radar (SAR) images, is proposed via an Extreme Self-paced Learning Machine (ESLM). First, a reflectivity-spatial affinity is defined to measure the similarity between two segmented super-pixels, to identify the initial three groups of pixels: strictly changed, strictly unchanged and fuzzy pixels. Then a new extreme self-paced learning machine is developed, by gradually selecting the most confident changed pixels and predicting the changed pixels in an incremental pattern. Moreover, both the labeled and unlabeled samples are explored to realize semi-supervised classification. Different with the available methods, ESLM works in a self-paced learning pattern and achieves accurate detection, for it can automatically choose the training samples and explore unlabeled samples to enhance the online prediction of changes. Therefore, ESLM has the characteristics of accurate and robust detection, parameter free, low-complexity and rapid implementation, which is very suitable for on-orbit processing. Some experiments are taken on five real benchmark datasets, and the results verify the effectiveness of ESLM.
Improving Keypoint Matching Using a Landmark-Based Image Representation

Motivated by the need to improve the performance of visual loop closure verification via multi-view geometry (MVG) under significant illumination and viewpoint changes, we propose a keypoint matching method that uses landmarks as an intermediate image representation in order to leverage the power of deep learning. In environments with various changes, the traditional verification method via MVG may encounter difficulty because of their inability to generate a sufficient number of correctly matched keypoints. Our method exploits the excellent invariance properties of convolutional neural network (ConvNet) features, which have shown outstanding performance for matching landmarks between images. By generating and matching landmarks first in the images and then matching the keypoints within the matched landmark pairs, we can significantly improve the quality of matched keypoints in terms of precision and recall measures. The proposed method is validated on challenging datasets that involve significant illumination and viewpoint changes, to establish its superior performance to the standard keypoint matching method.
An Improved Pi-Sigma Neural Network with Error Feedback for Physical Time Series Prediction

Forecasting natural occurring phenomena has been addressed and analyzed in many domains of science and gets more attention because of its vast range of applications.Traditional time series forecasting tools have some limitations like slow training process, less efficient training methods that effect on performance.Higher Order Neural Network (HONN) using recurrent feedback appear as a powerful technique and it has the ability to expand the input space, make them more efficient for solving complex problems and perform high learning abilities in the time series forecasting.Recurrent networks commonly used the network output as the feedback terms.This study proposed a model called improved Pi-Sigma Neural Network with Error Feedback (PSNN-EF) which combines the properties of Pi-Sigma Neural Network (PSNN), recurrence and error feedback.PSNN-EF uses back propagation gradient descent algorithm for training purpose and tested with time series signals namely; humidity, evaporation and wind direction datasets.The benefit of using recurrence and error feedback is that it generates more accurate results of prediction and provide more promising results.Based on the obtained results, our proposed method shows better performance and can be an alternative solution to Jordan Pi-Sigma Neural Network (JPSN) and PSNN for one step ahead prediction of those three datasets.
Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use

Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing or maintaining craving, a core factor of relapse of addiction, and is extensively employed in addiction treatment. In a previous study, we proposed a machine-learning-based method to detect craving for gaming using multimodal physiological signals including photoplethysmogram, galvanic skin response, and electrooculogram. Our previous study demonstrated that a craving for gaming could be detected with a fairly high accuracy; however, as the feature vectors for the machine-learning-based detection of the craving of a user were selected based on the physiological data of the user that were recorded on the same day, the effectiveness of the reuse of the machine learning model constructed during the previous experiments, without any further calibration sessions, was still questionable. This "high test-retest reliability" characteristic is of importance for the practical use of the craving detection system because the system needs to be repeatedly applied to the treatment processes as a tool to monitor the efficacy of the treatment. We presented short video clips of three addictive games to nine participants, during which various physiological signals were recorded. This experiment was repeated with different video clips on three different days. Initially, we investigated the test-retest reliability of 14 features used in a craving detection system by computing the intraclass correlation coefficient. Then, we classified whether each participant experienced a craving for gaming in the third experiment using various classifiers-the support vector machine, k-nearest neighbors (kNN), centroid displacement-based kNN, linear discriminant analysis, and random forest-trained with the physiological signals recorded during the first or second experiment. Consequently, the craving/non-craving states in the third experiment were classified with an accuracy that was comparable to that achieved using the data of the same day; thus, demonstrating a high test-retest reliability and the practicality of our craving detection method. In addition, the classification performance was further enhanced by using both datasets of the first and second experiments to train the classifiers, suggesting that an individually customized game craving detection system with high accuracy can be implemented by accumulating datasets recorded on different days under different experimental conditions.
Learning to Refine 3D Human Pose Sequences

We present a basis approach to refine noisy 3D human pose sequences by jointly projecting them onto a non-linear pose manifold, which is represented by a number of basis dictionaries with each covering a small manifold region. We learn the dictionaries by jointly minimizing the distance between the original poses and their projections on the dictionaries, along with the temporal jittering of the projected poses. During testing, given a sequence of noisy poses which are probably off the manifold, we project them to the manifold using the same strategy as in training for refinement. We apply our approach to the monocular 3D pose estimation and the long term motion prediction tasks. The experimental results on the benchmark dataset shows the estimated 3D poses are notably improved in both tasks. In particular, the smoothness constraint helps generate more robust refinement results even when some poses in the original sequence have large errors.
Hybrid Recommendation Algorithm Based on Weighted Bipartite Graph and Logistic Regression

Bipartite graph-based recommendation is a key topic in the field of recommender systems. Using logistic regression, a new recommendation algorithm based on a bipartite graph is proposed. First, the weights of the bipartite graph and the similarity of users are defined. Then, a recommendation list based on the bipartite graph is generated. Next, items in the recommendation list are re-sorted using the classification results of logistic regression. Furthermore, a balance factor is proposed to measure the accuracy and diversity of a recommender system comprehensively. Experimental results show that the recommendation results of the proposed algorithm are good.
High Dynamic Range Image Watermarking Based on Tucker Decomposition

High dynamic range (HDR) imaging technique has received much attentions in the recent years for its abundant details and wide dynamic range of luminance.In order to display HDR images on low dynamic range display devices, tone mapping (TM) process is required.However, the TM can be deemed as an inevitable attack in the field of HDR image copyright protection.In this paper, a highly robust HDR image watermarking algorithm based on Tucker decomposition is proposed.Firstly, Tucker decomposition is carried out on HDR color image to obtain the first feature map of the core tensor, which contains most of the energies in the HDR host image.Then, a Auto-Regressive prediction method is used to establish a local correlation model of the first feature map, so that watermark can be embedded in the first feature map according to the prediction result and its true value.Furthermore, a low-complexity luminance masking approach is designed based on modified specular free map to exclude the areas not suitable for watermark embedding, and an embedding intensity selection strategy is used to balance the imperceptibility and robustness of the watermark.The experimental results show that the proposed algorithm is robust against existing TM attacks while keeping better image imperceptibility and embedding capacity.