Reviewerly/exHarmony huggingface dataset:
Drone based user and heading detection using deep learning and stereo vision

The problem of assisting a low-vision person with environment awareness using a drone is addressed. Specifically, the first stage task of detecting the User and their heading which does not require any user adaptive training is tackled. The modalities of 3D and 2D vision on a drone are compared for this task. 3D data is provided using a stereo sensor mounted on the drone that communicates using RF to a mobile device based android application. For the task of localization, a Single Shot MulitBox Detector is utilized. Different networks in terms of input modalities and related structure are developed including a 2D only network and a 3D+2D fused network. Performance of these networks are compared and results discused. In addition, a comparison of retrained networks versus training from scratch is made. In all cases, approximately 34,000 user heading samples were collected for training. Real data from outdoor drone flights that communicate with the Android based application are shown. Detecting both the user in the scene and their heading is an important first step necessary in a drone-based system that helps low-vision persons with environment awareness. The success and challenges faced are presented along with future avenues of work.
Low Dynamic Range Image Set Generation from Single Image

Due to the higher exposure, the information in the highlight area usually clipped. This is one of the bottlenecks for the generation of high dynamic range (HDR) image. In this paper, we describe a simple technique to retrieve the information from an overexposed area by creating low dynamic range (LDR) image set from a single LDR image. These images are fused to generate HDR image. We evaluate our result by the single image HDR generation technique. Our technique shows better visual results compared to other techniques.
Human Activity Identification in Smart Daily Environments

Research in human activity recognition (HAR) benefits many applications such as intelligent surveillance systems to track humans’ abnormal activities. It could also be applied to robots to understand human activity, which improves smart home efficiency and usability. This chapter aims to accurately recognize different sports types in the Sports Video in the Wild (SVW) dataset employing transfer learning. The dataset consists of noisy and similar classes shot in daily environments, not in controlled laboratory environments. Heretofore, different methods have been used and developed for this purpose. Transfer learning is the process of using pre-trained neural networks. The experimental results on different splits of the dataset, size, and pre-trained models show that accuracy of 80.7% is achievable. In another experiment, we have used the famous UCF101 dataset which is collected from YouTube and trained a convolutional neural network (CNN) with Batch Normalization (BN). The achieved accuracy for the test dataset is around 91.2%. One application of the proposed system is to integrate it with a smart home platform to identify sports activities of individuals and track their progress.
AtoMixer: Atom-based interactive visual exploration of traffic surveillance data

Massive traffic surveillance data extracted from vehicle detectors such as cameras provide essential information for revealing urban traffic pattern. However, most existing tools only allow users to analyze the data in specific time periods and regions with particular requirements. In this paper, we work closely with traffic domain experts and investigate a novel way of reframing visual traffic analysis tasks into the combinations of various atom categorical/numerical features and visual presentation. The categorical features contain primitive attributes such as vehicle type, O/D status and driving direction, and the numerical features contains information such as vehicle frequency and speed. The combination of above features includes four basic operations, namely and, or, xor and not to support diversified user requirements. Basic and advanced visualization methods such as trajectory view and flow distribution view are provided to demonstrate the combination results. Through interactive assembling of various atom operations, analysts could derive different query conditions to meet existed and potential upcoming analysis requirements such as locating suspicious vehicles (e.g., fake plate vehicles). Furthermore, AtoMixer, a visual analytic system is developed to support spatio-temporal investigative tasks for traffic surveillance data. We evaluate the effectiveness and scalability of our approach with real world traffic surveillance data.
Using Aerial Drone Photography to Construct 3D Models of Real World Objects in an Effort to Decrease Response Time and Repair Costs Following Natural Disasters

When a natural disaster occurs, there is often significant damage to vitally important infrastructure. Repair crews must quickly locate the structures with the most damage that are in need of immediate attention. These crews need to determine how to allocate their resources most efficiently to save time and money without having to assess each area individually. To streamline this process, drone technology can be used to take photographs of the affected areas. From these photographs, three dimensional models of the area can be constructed. These models can include point clouds, panoramas, and other three-dimensional representations. This process is called photogrammetry. The first step in constructing a three-dimensional model from two dimensional photographs is to detect key features that match throughout all the photos. This is done using David Lowe’s Scale Invariant Feature Transform (SIFT) algorithm which detects the key features. Pairwise matches are then computed by using a k nearest neighbor algorithm to compare all the images one pair at a time finding pixel coordinates of matching features. These pixel matches are then passed to an algorithm which calculates the relative camera positions of the photos in a 3D space. These positions are then used to orient the photos allowing us to generate a 3D model. The purpose of this research is to determine the best method to generate a 3D model of a damaged area with maximum clarity in a relatively short period of time at the lowest possible cost; therefore, allowing repair crews to allocate resources more efficiently.
Propositional Rules Generated at the Top Layers of a CNN

So far, many rule extraction techniques have been proposed to explain the classifications of shallow Multi Layer Perceptrons (MLPs), but very few methods have been introduced for Convolutional Neural Networks (CNNs). To fill this gap, this work presents a new technique applied to a CNN architecture including two convolutional layers. This neural network is trained with the MNIST dataset, representing images of digits. Rule extraction is performed at the first fully connected layer, by means of the Discretized Interpretable Multi Layer Perceptron (DIMLP). This transparent MLP architecture allows us to generate symbolic rules, by precisely locating axis-parallel hyperplanes. The antecedents of the extracted rules represent responses of convolutional filters that makes it possible to determine for each rule the covered samples. Hence, we can visualize the centroid of each rule, which gives us some insight into how the network works. This represents a first step towards the explanation of CNN responses, since the final explanation would be obtained in a further processing by generating propositional rules with respect to the input layer. In the experiments we illustrate a generated ruleset with its characteristics in terms of accuracy, complexity and fidelity, which is the degree of matching between CNN classifications and rules classifications. Overall, rules reach very high fidelity. Finally, several examples of rules are visualized and discussed.
Hearthstone AI

Online digital collectible card games have seen a massive rise in popularity recently, none more so than Hearthstone: Heroes of Warcraft. While the game is mainly player vs. player focused, a need for competent game playing AI has arisen as well. This project attempts to tackle this problem by presenting a solution for a game playing AI using a tree-based game simulation and machine learning state evaluator. Additionally, the paper provides a way to breakdown Hearthstone into a feature set usable by machine learning algorithms. The AI based on this solution is shown to perform better than multiple testing solutions when played against them.
A Power Efficient Authentication Model for E-Healthcare System Security Concerns in Context to Internet of Things (IoT)

The growing intellectual advancement in the Internet of Things (IoT) has changed the perception of every individual towards the smart way of using internet and cloud for smart objects interaction. The domain of E-health care application is not away from these advancements in IoT. However, the deployment of these application faces some security concerns as severe obstacle. The authentication of the E-healthcare information is much-concerned issues as the confidential data of patients were exchanged among different entities. Thus, a power efficient authentication model which addresses the security issues of E-health application. Using this model, authentication at both the ends of sensors and a base station (S&BS) is verified and achieved secure exchange of health data. This paper considers arbitrary numbers (i.e., nonces) and Hash Key based authentication during cryptographic communication. The outcomes suggest that with this power consumption is reduced and also lay off the session key arrangement among each S&BS. Also, the performance analysis of HKA approach gives that different kinds of security issues are handled properly with low power consumption.
Video-based face recognition based on deep convolutional neural network

With the rise of artificial intelligence in recent years, the field of object recognition is making rapid progress. Face recognition is a major subarea of object recognition which has already played a significant role in our life. However, despite the extensive study on the field of face recognition, video-based face recognition is still a tough area which needs further research. In this paper, we propose a model based on deep convolutional network for video-based face recognition. Our model split video images into two sets, a set of key frames and the other set is made up with non-keys, for different tasks to lower the computational complexity of the model. Besides, we introduce spatial pyramid pooling and center loss to our method for classification task. Our method presented in this paper reached an accuracy of 96.06% on YouTube Faces dataset. The results indicate our approach possesses high precision as well as a strong real-time performance.
Region-based parallax-tolerant image stitching

Image Stitching with large parallax has always been a challenging task, and accurate image alignment is critical for stitching results. In this paper, an image stitching method based on superpixel segmentation regions is proposed. To solve the problem of insufficient matching feature points under large parallax, an improved multi-plane RANSAC method is used to improve the robustness of matching feature selection algorithm. In terms of image alignment, a mesh optimization method with the global similarity prior is adopted, and a superpixel-based segmentation method is used to obtain reasonable matching points and global similarity transformation parameters. A standard seam-cutting algorithm is finally used to compose images together. Experiments show that the proposed method can effectively improve the performance of image stitching in complex scenes with large parallax.
Summary of recommendation system development

A recommendation system is an information service system that connects users and projects: for one thing, it helps users discover potential projects of interest; for another thing, it helps project providers to deliver projects to users who are interested in it. The recommendation system is a powerful system that can add value to the company or business. In the future, it will continue to be researched and developed to bring a better experience to users.
Defect Prediction Model for Object Oriented Software Based on Particle Swarm Optimized SVM

In terms of the security problem of power information system, this paper analysed the importance of the software defect prediction method in object-oriented software development, and proposed a software prediction model based on particle swarm optimized Support Vector Machine (SVM) corresponding to the features of object-oriented software. The model mainly consists of three parts: the first is the pre-processing module which normalizes the original data and selects feature, then the second is adaptive inertia weight particle swarm module which optimizes the parameters of SVM with the prediction accuracy as the fitness. Finally, the last SVM classification module predicts categories of reduced-dimension data using the optimal parameters from the second module. Experimental results show that the accuracy of the proposed model is 8.2%—12.2% higher than the comparative model, and 9.9%, 5.6% and 7.7% higher on the precision, recall and F value, which proves the validity of the proposed model.
Cooperative Warp of Two Discriminative Features for Skeleton Based Action Recognition

The study of human motion recognition has attracted many attentions in recent years. In this paper, a simple but effective feature combination is proposed by us for human action recognition based on skeletal points. The two features that make up this combination are the preprocessed 3D joint positions and the velocities of these 3D joints respectively. Then we use a combination of DTW [1], Fourier transform and SVM to model these actions and classify them. Because in the process of DTW, we use these two features to cooperate with each other to warp action samples, we call it the cooperative warp of two discriminative features (CWTDF). We also considered another scenario that we use these two features to warp their action samples respectively and then combine them before the classification process. We call it the separate warp of two discriminative features (SWTDF). But in this case, the classification performance is not as good as the cooperative warp situation. Although our feature representation is relatively simple, it is so sufficiently discriminative that our classification performance outperforms many state-of-the-art strategies based on skeletal points.
Analysis of the Software Project Estimation Process: A Case Study

In this paper is presented an analysis of the software project estimation process for the case of conditions in Czech companies. Key findings of the survey were that (a) the most widely used estimation method is Expert judgments with 57% incidence, (b) the most of surveyed companies (39%) operate in Web applications, and (c) the 54% companies fail to complete a quarter of projects in the estimated timeframe.
Network security of prefix span algorithm for data mining

In order to strengthen the overall security of the network, this paper analyzes the network security issues. The study of the network security is carried out based on the Prefix Span algorithm for data mining. The classical data mining algorithm, Prefix Span algorithm and its improvement are propo sed. Then, combined with the characteristics of network security, the proposed algorithm is applied to network security intrusion detection. From the algorithm flow and evaluation model, an optimization and update scheme is proposed, and an effective data transmission evaluation model is established by effectively evaluating the status of data analysis. The experimental results show that the efficiency of the algorithm is higher than the original one. In the long sequence mode mining, the algorithm has more advantages and can better meet the high requirements of intrusion detection. However, due to the diversity and complexity of intrusion methods, data mining still needs deeper research and performance improvement in intrusion detection.
ALPACA

Software engineering corpora often contain domain-specific topics and linguistic patterns. Popular text analysis tools are not specially designed to accommodate such topics and patterns. In this paper, we introduce ALPACA, a novel, customizable text analysis framework. The main function of ALPACA is to analyze topics and their trends in a text corpus. It allows users to define a topic with a few initial domain-specific keywords and expand it into a much larger set of similar topic words. This new set of words can be further expanded into a set of self-contained phrases to describe the topic more precisely. ALPACA extracts those phrases by matching input sentences with linguistic patterns, which are long sequences mixing both specific words and part-of-speech tags frequently appeared in the corpus. In this paper, we demonstrate using ALPACA to continue analyzing CVE security reports and detect a new topic of mobile device's vulnerability. Youtube link: https://wwwyoutube.com/watch?v=UTcMYb2o1pU
Using Multimedia in Blended Learning

The paper is focused on the use of modern methods, especially computers and the Internet in the educational process. The so-called Blended learning combines printed and electronic materials, online and offline teaching methods, etc. in the class and e-learning is phenomenon nowadays mainly in distance learning. The contribution also mentions Khan Academy as maybe the famous non-profit educational portal in the world. The goal of this contribution is to give an initial overview into this field of education and presents examples of educational videos from the field of Information Technologies that were created, verified and published on the Khan Academy portal.
Recognizing Cognitive Activities Through Eye Tracking

Eye detection and tracking is usually performed by using specific devices that allow to determine the pupil position in many different situations. We propose to use these techniques for recognizing cognitive activities that a potential user is carrying out in front of a computer. We use the images captured by a conventional web camera located over the computer display. Those image are processed and, after the face and facial landmarks are found, the user gaze is analyzed and the ethogram and several statistics associated to the eyes and gaze destination are computed. They are used for determining what is doing the user from a set of predefined activities.
Emotion Detection in Cross-Lingual Text Based on Bidirectional LSTM

Emotions in cross-lingual text can be expressed in either monolingual or bilingual forms. Current researches have focused on analyzing emotions in monolingual text, whereas such approaches may achieve low performances in the case of identifying emotions in cross-lingual texts, which appear frequently in social media. In this paper, a bidirectional LSTM neural network with emotional knowledge is introduced to detect emotions in cross-lingual texts. This approach also employs the cross-lingual feature and the lexical level feature to analyze texts with multilingual forms and take advantage of emotional knowledge. The evaluation results show that our approach is effective for detecting emotion in cross-lingual texts.
Three-element optical system with a given fixed position of optical center

Our paper presents a detailed theoretical analysis of the three-element optical system with zero separation of principal planes and approximately corrected Petzval sum, which has the required value of the axial position of its optical center for a given value of focal length and a given value of axial distance of the image focal point from the last element of the system. Formulas that make it possible to calculate paraxial parameters of such types of optical systems are derived, and the application of the derived formulas is presented in several examples. Such optical systems represent a subset of the new family of optical systems with the constant position of the optical center. The optical center of such an optical system is at a given required axial position, and this position does not change with the object distance, which could be advantageous for certain applications.
Text Classification Based on Keywords with Different Thresholds

Text classification is a supervised learning task for assigning text document to one or more predefined classes/topics. These topics are determined by a set of training documents. In order to construct a classification model, a machine learning algorithm was used. The training model is used to predict a class for new coming document. In this paper, we propose a text classification approach based on automatic keywords extraction with different thresholes. We use 3000 Vietnamese text documents, which belong to ten topics, downloaded from two electronic magazines vnexpress.net and vietnamnet.vn to create ten sets of the keywords. These keywords are used to predict the topic of new text document. The experimental results confirm the feasibility of proposed model.
Domain transform for spherical geometry images

This work presents the Domain Transform for Spherical Geometry Images (DT-SGIM): a filter designed for smoothing triangle meshes represented as spherical geometry images (SGIM) and, at the same time, preserving the meshes’ important curvatures. It is based on the domain transform filter (DT), a high-performance, real-time algorithm for edge-aware smoothing of color images. Our main contribution is the extension of the DT filter to the SGIM domain, resulting in the first method specifically designed for filtering 3-D objects represented by SGIMs, as well as the first demonstration of a domain transform-based filter applied to the problem of polygonal mesh filtering.
Intelligent piezoelectric peristaltic linear driving model based on neural network

Active vibration control is a new subject developed in the last twenty years. It mainly studies the theory, method and measure of active vibration control of structures. After introducing the linear quadratic optimal control algorithm, the numerical simulation of the linear quadratic optimal contro l for a piezoelectric flexible cantilever beam is carried out. The model of the flexible cantilever beam is established firstly, including the differential equation of motion, the moment equation of the actuator and the output equation of the sensor. Then the optimal feedback gain matrix of the system is obtained by using the linear quadratic optimal control. In this paper, genetic algorithm is used to optimize the above parameters of BP neural network, and the improved BP neural network is applied to the study of nonlinear model of dynamic coal blending. A method based on piezoelectric self-sensing method is proposed as a test method. A piezoelectric wafer of piezoelectric bimorph is used as the sensing element, analyzing the relationship between the acceleration parameters of the piezoelectric bimorph and the induced charge generated by the sensing element, and a test circuit device for driving force is designed. The method uses genetic algorithm to calculate the control force online and uses the neural network to simulate the dynamic characteristics of the plate, thus replacing the cantilever plate for dynamic analysis. The system fully utilizes the characteristics of genetic algorithms and neural networks and is a new type of vibration control system with promising future.