Reviewerly/exHarmony huggingface dataset:
Generative Type Design

It is through typography that order and form, visible and durable, is given to written communication. Typography has been accompanied by new technologies, which designers and typographers have adapted. The use of these technologies in the design process boosted the exploration of new approaches for type design. Designers began to explore generative processes in their design process in different types of projects, such as dynamic visual identities or generative typography. In this work, we present an algorithmic system capable of generating glyphs from typographical skeletons. This system, which is online at cdv.dei.uc.pt/2019 /letterspecies, fills a typographical skeleton using a drawing technique, both selected by the user, and outputs an OpenType font. With the presented system, we explore the relation between legibility and coherence in an innovative type design approach where the generated typefaces can be applied in the most diverse media.
Obstacle Avoidance Planning for Quadrotor UAV Based on Improved Adaptive Artificial Potential Field

Considering the collision-prone problem near the initial point, goal non-reachable with obstacle nearby (GNWON) and the chattering problem of the traditional artificial potential field algorithm(TAPF), the attractive potential field function and the repulsive potential field function are improved respectively, and an improved adaptive artificial potential field algorithm(AAPF) is proposed. The algorithm can calculate the different potential field of each path point on-line in real time. On this basis, a 2D improved adaptive artificial potential field algorithm is extended to 3D space for autonomous obstacle avoidance planning of quadrotor UAV. A 3D obstacle model is established, and the principle of obstacle avoidance at the nearest contact point is designed to calculate the repulsive potential field. Then the obstacle avoidance planning strategy of Four-rotor UAV based on 3D adaptive potential field algorithm is designed. The simulation results show that the improved adaptive algorithm effectively compensates for the shortcomings of the traditional artificial potential field method and can be applied in 3D.
The Study of using Augmented Reality Technique in Children's Natural Ecology Learning by Technology Acceptance Model

As the quick development of information technology, there are more and more teaching methods of using media to assist teaching by applying APPS with the technology of Augmented Reality (AR). With tablet and smartphone, learning can be fun and more diverse. The reason for making this application is to focus on ecological conservation and environmental education. This APP uses AR effects to introduce "The Guide of Host Plants on Campus. This APP is unique because it is localized and geared to the school's surroundings. Through this kind of innovation, students will be better equipped to learn through self-discovery and outdoor experiences. Through this APP we were able to tell whether students were able to adapt to learning with technology combined with traditional teaching methods. This APP was tested on a group of elementary school students, who were asked to complete a survey, be observed, and then be interviewed by quantity and quality research method to understand the users' feedback. After that the data was carefully analyzed with T-Test for difference between means, correlation analysis, and regression analysis methods and discussed by the team conducting the study.
Introduction to Logic Programming

Logic Programming is a style of programming in which programs take the form of sets of sentences in the language of Symbolic Logic. Over the years, there has been growing interest in Logic Programming
MCA-GAN: Text-to-Image Generation Adversarial Network Based on Multi-Channel Attention

It is an extremely challenging task to text-to-image generation based on generative adversarial network(GAN). Although some researchers have made remarkable progress in the field of image generation in recent years, it is still a problem to be solved in the aspect of texture detail in image generation. An attention generation algorithm based on multi-channel is presented in this paper. The network gradually refine by three branches to produce high-resolution images that conform to the text description. By the iterative training of GAN, the multi-channel feature attention mechanism is used to guide the pixel value deviation and loss in the process of image generation. Through a lot of experiments and people's visual perception of the generated images, the algorithm based on multi-channel attention mechanism proposed in this paper has been significantly improved in the processing of details.
A Hybrid of Hard and Soft Attention for Person Re-Identification

Existing pedestrian re-identification methods based on deep learning have achieved good results under constrained conditions. However, there exist some challenges including large human pose variations, viewpoint changes, severe occlusions and imprecise detection of persons. So we present a Hard/Soft hybrid Attention Network (HSAN) that combines pose information and attention mechanism to deal with the challenges. Our model includes two main parts: Pose-guided Hard Attention (PHA) and Regional Soft Attention (RSA). PHA uses the keypoints generated by pose estimation to enhance the foreground information, and RSA is learned to eliminate the background clutter. We extract reliable features and locate discriminative regions by using these two modules to handle occlusions, pose changes and background noises. We conduct a lot of experiments on public datasets including DukeMTMC-ReID, Market-1501, and CUHK03, and the results show that our method achieves stateof-the-art performance.
Eyeball Movement Detection Using Sector Line Distance Approach and Learning Vector Quantization

The shifting of education to learning supported by technology facing challenges. One of the challenges is to able to detect student learning processes, especially student affective state. Some studies implemented affective detection. However, the detection in obtrusive manner. Thus, there is a need to propose a method to detect student learning state in unobtrusive manner. This research propose a method to take advantage of eyeball movements as the measuring media as a feedback in the e-learning process. This study focuses in detecting eyeball movement. The eye movement to be detected in this study is in 5 directions center, upward, downward, leftward, and rightward. The eyeball movements is detected using the Sector Line Distance and classification method namely LVQ (Learning Vector Quantization). The result of the experiment shows that the average accuracy in detecting 5 eyeball direction shows a value of 81,72% accuracy. In addition, the proposed method also able to detect 5 directions of the eyeball movements including center. This result outperforming the previous method which only can detect 4 gaze of eyeball movements. Using center and 4 other directions of the eyeballs, the interesting value of the content can be measured and discovered.
Disturbance Magnitude Estimation using Artificial Neural Network Method

The approach of this paper is to estimate the generated power of a generation that encounters an outage in a power system from the frequency response under a few seconds after transient state of the disturbance. By knowing the magnitude of the supply, the removed load may be adjusted with adaptive under frequency load shedding (AUFLS) relay that lead stable in frequency system. The method of estimation is using the Artificial Neural Network (ANN), the data training is obtained from the swing equation, then tested using The New England IEEE 39 Bus System from the frequency response after disturbance for every generator. The objective is to compare the error of three minimum sampling time that used shortly after the disturbance.
Ontology Modeling of Intangible Cultural Heritage Terengganu Brassware Craft

Cultural heritage needs to be preserved for the younger generation. Terengganu brassware craftsmanship is one of the local intangible heritage which is now becoming extinct with the death of craftsmen and lack of interest and continuity by the younger generation. Therefore, the development of Terengganu brassware craft ontology is one approach to preserve this heritage. This paper discusses the process of modeling domain knowledge of this traditional craftsmanship. Ontology Development 101 (OD 101) is as ontology modeling method that consists of five phases; specification, integration, conceptualization, implementation and evaluation. This ontology has successfully modelled the domain knowledge of traditional craftsmanship. Verification of accuracy and correctness of information was done using SPARQL query. The iterative process emphasized by OD 101 enables new ontology developers to model ontologies effortlessly and with more flexibility.
Social Emotion Prediction with Attention-based Hierarchical Neural Network

Text emotion mining has attracted many researchers' attention in recent years. Most existing studies focus on identifying emotion of the text's author. In this paper, we are concerned with the problem of predicting reader's emotion response from the text content of news article, aka social emotion prediction. Social emotion prediction can not only be used for analyzing people's reaction to news events, but also help news producers to forecast the underlying influence of their unpublished news articles. We propose a hierarchical LSTM network with attention mechanism to identify the relation between news article and evoked emotion of reader. Compared with existing word-level and topic-level methods which don't consider the order of words, our method with hierarchical LSTM structure is more efficient to construct the news article representation. With attention mechanism, our method can also find out readers' emotion reaction to specific word or phrase and generate social emotion lexicon. Through experiments on real-world datasets, we demonstrate that our approach outperforms word-level and topic-level baselines, and a state-of-the-art word2vec-based method.
Ticket Text Detection and Recognition Based on Deep Learning

In traditional OCR (Optical Character Recognition) technology, a series of complex preprocessing of the image is needed in the process of detecting and recognizing of the ticket texts. This paper proposes a method based on deep learning in order to detect and recognize the ticket texts automatically. This method consists of two parts: ticket texts detection and recognition. First, CTPN text detection model is used for detect the horizontal texts of the ticket. Then, cropping the detected text image by its coordinate position, resizing them and changing to gray. After that, improved CRNN model which adding self-attention in feature extraction layer is used for text recognition. Experiments show that the proposed method can automatically detect and recognize the texts in the ticket. It not only solves the problem that the traditional method takes a lot of time and waste too much manpower to extract features, but also has good generalization ability and scope of application.
HUSBoost: A Hubness-Aware Boosting for High-Dimensional Imbalanced Data Classification

Learning from high-dimensional imbalanced data is a challenging research problem in machine learning, due to the curse of dimensionality caused by high dimension and the learning bias resulted from class imbalance. The existing works generally apply dimension reduction methods to reduce the dimensionality of features first, and then deal with the class imbalance problem by traditional imbalanced learning technologies. However, dimensionality reduction may cause the loss of useful information and cannot effectively address the problem of hubness which is an important aspect of the curse of dimensionality. In this paper, we present a hubness-aware cluster-based ensemble algorithm, HUSBoost, for learning high- dimensional imbalanced data. For hubs induced by high dimensionality, HUSBoost introduces discount factors to slow down the excessive growth of their weights, so as to alleviate the negative impacts of "bad" hubs on the classification decisions of component classifiers. To address the class imbalance problem, HUSBoost utilizes a cluster-based majority undersampling method to correct imbalanced class distribution. Specifically, k- hubs clustering technology is used to divide the majority samples into multiple clusters, and then the representative majority samples are selected from each cluster so as to form the balanced class distribution. Experimental results based on sixteen high-dimensional imbalanced data sets show the effectiveness of HUSBoost.
Combination of Steganography with K Means Clustering and 256 AES Cryptography for Secret Message

Steganography is a technique that evokes secret messages on the media. The cover requested for the message does not arouse suspicion, while cryptography is a senior art and science that maintains the confidentiality of data. Least Significant Bit (LSB) is one of the steganography methods for hiding secret messages carried out at the last bit in several bytes of media dataset values. The location of LSB embedding in the image cannot be randomized. This makes the location of the message that will be inserted (embedding) easier to detect which results in a decrease in the quality of the results of steganography and the similarity of the data inserted. To overcome the weaknesses of the LSB steganography method, improve the K Means Clustering method on steganography and AES 256 for message security. Grouping is a method that is carried out to select the object area, then the embedding of the message is done on the object obtained. As additional security, cryptography used is AES 256.
Combining Virtual Reality Advertising and Eye Tracking to Understand Visual Attention: A Pilot Study

This paper aims to design a virtual reality eye tracker for the analysis of dynamic behavior of viewers when watching a virtual reality (VR) commercial advertising. Seven experimental participants were recruited for the pilot study. Experiment contents included a one-minute VR commercial advertising designed with Unity 3D development tool and through integration into eye tracking device of a head-mounted display (HMD), eye movements of participants were recorded. The finding results of the paper showed that: 1) when watching the VR commercial advertising, participants did not put the first sequence of gaze in each dynamic regions of interest (ROIs) in the commercial object; 2) in terms for concentration span sequence of gaze in each dynamic ROIs, if participant gazed the commercial object, more concerns were put and there was longer time for gazing; on the contrary, there was no eye movement information about gazing at the commercial object; 3) when watching the VR commercial advertising, the commercial object did not receive longer total gazing time and numbers of relative concentration gazing of participants spending on all dynamic ROIs. According to experimental results, we concluded that the VR 360-degree VR film affects the expected outcome in the operation of eye tracking technology.
Description of Processes of Blockchain and Cryptocurrency with Quantum Mechanics Theory

One of the crucial cornerstone in cryptocurrency is the ability to generate random sequences and aleatory numbers with a null chance that any eavesdropper could extract information about the one-to-one transactions. Under the assumption that the Quantum Mechanics transition probability is perceived as a fully blockchain operation, random sequences are generated as a tool to protect transactions entirely based on Bitcoins. We test the algorithm with integer-order Bessel functions inside of a full blockchain-based algorithm. Simulations have demonstrated the reliability of a simple model of blockchain in the order of 75.
Pattern Recognition of Wireless Modulation Signals Based on Deep Learning

Wireless modulation signal pattern recognition is widely used in military and civilian applications. This paper proposes a method based on deep learning for wireless modulation signal pattern recognition for many types in practical applications. In this paper, a 34-layer convolutional neural network is designed to identify 16 typical wireless modulated signals. The robustness of the recognition algorithm is improved by using various methods. The data is enhanced by means of interpolation and extraction, power normalization, Gaussian noise, etc. The feature information matrix is constructed by extracting the time domain, frequency domain and phase domain information of the signal by Hilbert transform. The Additive Margin Softmax [1] method is used to make the signal have larger inter-class spacing and smaller intra-class spacing. Experiments show that under the condition of 0dB signal-to-noise ratio, the recognition rate can reach more than 95%, which is better than the traditional machine learning algorithm.
Feature Space Regularization for Person Re-identification with One Sample

Few Shot Learning is a solution to relieve the huge annotation cost in Person Re-Identification. We concentrate on one sample setting in this work, where each identity has only one labeled sample along with many unlabeled samples. Training with one sample setting, the model is easily biased towards certain identities. Moreover, a reliable pseudo-label estimation scheme can greatly improve the final performance of the model. Targeting to solve the issues above, we propose two simple and effective solutions. (a) We design the Feature Space Regularization (FSR) Loss to adjust the distribution of samples in feature space. The FSR loss make the difference in distance of all labeled samples to unlabeled samples as small as possible. (b) We propose combining the Nearest Neighbor distance with inter-class distance to estimate pseudo-label for unlabeled data, which we called Joint-Distance. Notably, the Rank-1 accuracy of our method outperforms the state of the art method by a large margin of 12.1 points (absolute, i.e., 67.9% vs. 55.8%) on Market-1501, and 10.1 points (absolute, i.e., 58.9% vs. 48.8%) on DukeMTMC-reID, respectively. We will release all the code in https://github.com/Freedomxt/Feature_Space_Regularization_for_person_Re-Identification_with_One_Sample.
Research on Human Motion Recognition System Based on MEMS Sensor Network

The traditional human motion perception and behavior recognition methods are based on machine vision and image processing. This method has the disadvantages of strong intrusion of individual data privacy, large environmental impact and tedious data processing, and cannot meet the practical requirements in real time. This paper introduced a solution of accurately capture human motion by using multi-sensor of MEMS to construct Zigbee wireless network, and utilizing SVM classification algorithm to realize human action recognition efficiently. The system is used to detect the abnormal human fall behavior, which is perfect reliability and real-time performance and the recognition accuracy of the action is above to 90%.
Light-Field Raw Data Synthesis From RGB-D Images: Pushing to the Extreme

Light-field raw data captured by a state-of-the-art light-field camera is limited in its spatial and angular resolutions due to the camera's optical hardware. In this paper, we propose an all-software algorithm to synthesize light-field raw data from a single RGB-D input image, which is driven largely by the need in the research area of light-field data compression. Our synthesis algorithm consists of three key steps: (1) each pixel of the input image is regarded as a spot lighting source that emits directional light rays with an equal strength; (2) the optical path of each directional light ray through the camera's main lens as well as the corresponding micro lens is considered as accurately as possible; and (3) the occlusion of light rays among objects at different distances within the input image is handled with the depth information. The spatial and angular resolutions of our synthesized light-field data can be scaled up when the input RGB-D image has a higher and higher spatial resolution. Meanwhile, for a given input image with a fixed size, we pay a special attention to what would be the extreme we can push the parameters involved in our synthesis algorithm, such as the number of rays emitted from each pixel, the number of micro lenses, and the number of sensors associated with each micro lens. The usefulness of our synthesized data is validated by refocusing, all-in-focus, and sub-aperture reconstructions. In particular, all-in-focus images are evaluated objectively by computing the structural similarity (SSIM) index, which allows us to reach the goal of pushing to the extreme through selecting various parameters mentioned above.
An Improved Adaptive Extended Kalman Filter Used for Target Tracking

Traditional EKF filter depends on the condition that the noise parameters are known accurately in advance or maintain unchanged during the filtering process. When the covariance of noises cannot be obtained accurately or if the noises are time-varying signals, the performance of standard EKF may be poor and even diverge. In order to solve this problem, an improved adaptive EKF (AEKF) based on fading weight factors and prior guess of limited window length is proposed to update the covariance of measurement noise in real time. Moreover, the introduction of chi-square test based on the innovation sequences makes the time of adaptive introduction more reasonable, and avoids the deterioration or even divergence of the filter. The simulation results have showed that the performance of the improved AEKF is better than that of the traditional EKF and AEKF in target tracking.
Individual Identification of Electronic Equipment Based on Electromagnetic Fingerprint Characteristics

The individual identification of electronic equipment is mainly based on the characteristics of different electronic equipment hardware transmitting signals to identify which communication equipment the signal comes from and realize equipment tracking. The contents of this paper include the following aspects:(1) A fingerprint extraction method based on histogram and signal level co-occurrence matrix (SLCM) is proposed. A relatively complete signal feature library is established combined with other signal characteristics, which can be used as signal fingerprint template for individual identification. (2) Starting point detection based on mean sliding window is proposed to detect the effective signal segment from the continuous signal stream and improve the recognition rate. The experiment of individual recognition is carried out by integrating several base classifiers and the final accuracy of the model is up to 98% and solved the problem of individual identification of the same type of communication equipment under the condition of small sample, low signal-to-noise ratio and varying signal-to-noise ratio.