Reviewerly/exHarmony huggingface dataset:
Latent Simplex Position Model: High Dimensional Multi-view Clustering with Uncertainty Quantification

High dimensional data often contain multiple facets, and several clustering patterns can co-exist under different variable subspaces, also known as the views. While multi-view clustering algorithms were proposed, the uncertainty quantification remains difficult --- a particular challenge is in the high complexity of estimating the cluster assignment probability under each view, and sharing information among views. In this article, we propose an approximate Bayes approach --- treating the similarity matrices generated over the views as rough first-stage estimates for the co-assignment probabilities; in its Kullback-Leibler neighborhood, we obtain a refined low-rank matrix, formed by the pairwise product of simplex coordinates. Interestingly, each simplex coordinate directly encodes the cluster assignment uncertainty. For multi-view clustering, we let each view draw a parameterization from a few candidates, leading to dimension reduction. With high model flexibility, the estimation can be efficiently carried out as a continuous optimization problem, hence enjoys gradient-based computation. The theory establishes the connection of this model to a random partition distribution under multiple views. Compared to single-view clustering approaches, substantially more interpretable results are obtained when clustering brains from a human traumatic brain injury study, using high-dimensional gene expression data. KEY WORDS: Co-regularized Clustering, Consensus, PAC-Bayes, Random Cluster Graph, Variable Selection
Person Re-identification on Mobile Devices Based on Deep Learning

Person Re-identification as an important supplement to face recognition, refers to a network of cameras with non-overlapping vision domains, through the use of computer vision technology to solve the cross-camera and cross-scene pedestrian recognition and retrieval. That is, determine whether there is a specific pedestrian to be detected in the different images or different video sequences. At present, many person re-identification research works mainly carried out through experimental verification and evaluation on large ReID datasets such as Market-1501, DukeMTMC-reID, MSMT17, and CUHK03. In this paper, based on the existing deep-learning person re-identification research, combines with the actual application scenarios, under the premise of analyzing the technical feasibility, we propose a complete process for Person Re-identification based on mobile devices, aims to combine pedestrian detection and person re-identification to perform real-time pedestrian detection and query. In this process, not only the features extracted by pedestrians can be reused, but also the research on person re-identification can be better and effectively applied, such as tracking criminals and searching for missing children.
Empirical analysis of Zipf’s law, power law, and lognormal distributions in medical discharge reports

Bayesian modelling and statistical text analysis rely on informed probability priors to encourage good solutions. This paper empirically analyses whether text in medical discharge reports follow Zipf’s law, a commonly assumed statistical property of language where word frequency follows a discrete power-law distribution. We examined 20,000 medical discharge reports from the MIMIC-III dataset. Methods included splitting the discharge reports into tokens, counting token frequency, fitting power-law distributions to the data, and testing whether alternative distributions—lognormal, exponential, stretched exponential, and truncated power-law—provided superior fits to the data. Discharge reports are best fit by the truncated power-law and lognormal distributions. Discharge reports appear to be near-Zipfian by having the truncated power-law provide superior fits over a pure power-law. Our findings suggest that Bayesian modelling and statistical text analysis of discharge report text would benefit from using truncated power-law and lognormal probability priors and non-parametric models that capture power-law behavior.
On Comparing Different Metric Learning Schemes for Deep Feature based Person Re-identification with Camera Adaption

Person re-identification, as a branch of image retrieval, has an extremely important application in public safety. In the past few decades, researchers have improved its accuracy through a variety of methods, including increasing data volumes and developing feature extraction or metric learning schemes. As the data volumes increased, researchers began to turn handcrafted feature algorithms to deep learning. Now, with the emergence of deep learning and large-scale data, we need to discuss which kind of metric learning performances on the re-identification problem is better. In this paper, to address the data shortage problem and learn invariant property from different cameras, we choose a camera adaptation method to increase the data volumes. We conduct comprehensive experiments on two datasets with camera adaptation and combine recent advances on feature extraction and metric learning. To ensure fairness, all methods use a unified code library that includes 4 deep feature extraction networks and 8 metric learning. The experimental results show that, under the premise of continuous improvement in deep learning, the traditional metric method based on Euclidean distance also achieved commendable results.
EFFECTIVE USE OF VIDEO LECTURES: GUIDELINES AND CRITERIA

The production of video lectures for e-learning courses are becoming irreplaceable component of distance courses and getting more popular. The key feature of using video lectures is flexible opportunities to make learning independent from time and location. Moreover, video lectures promote to the better comprehension of learning material and motivate self-education of students.The purpose of using video lectures for e-learning courses is to increase the effectiveness of learning through visualization. Visualization of learning process minimize cognitive difficulties of students. Synthesis of body language, verbal, and visual elements (multimedia, abstract signs, visual schemes, specific objects, etc.) within the limits of one e-learning course unit is an instrument of optimization of the process of semantic perception and understanding text information.Thus, the aim of the study is to develop the system of criteria to be considered when developing video lectures for e-learning courses and to give guidelines to teaching staff how to design and to implement video lectures within different training courses.After analysis the number of multimedia educational content of leading Russian and foreign universities, we develop the system of criteria for defining the effectiveness of video lectures usage in teaching process: didactic (active learning, structured input, effective integration in the learning process), technical (flexibility, modularity, ergonomic) and economic.
Design of an Automatic Item Pick-up System for Unmanned Aerial Vehicles

The interest of Unmanned Aerial Vehicles (UAVs) for the purpose of delivery has increased significantly in recent years. However, the abilities of those vehicles are quite limited since the arms have not being designed considering the UAV geometry and the center of gravity (CG) changes. Usual approach taken by various researchers were to use a regular gripper or a robotic manipulator, which is not quite satisfactory for access. In this paper, the design of a mechanism for the purpose of catching and holding various shaped objects is proposed. The mechanism, which is based on a double four-bar mechanism, has been designed for minimum CG change. Moreover, the mechanism has the ability to grap objects from locations below as well as next to UAV. Besides, with the help of the onboard controller and camera, the arm acts as an independent entity to identify the position of the part, catch it, and autonomously hold it. It is also possible to carry more than one object with a net structure inside the mechanism storage area. The gripper has been designed and manufactured with flexible parts so that it can hold several geometric shaped objects. The UAV-arm has been designed and manufactured. It is installed on a UAV. Initial tests verify that it can identify and catch spherical, cylindrical, and box shaped pieces up to 650 grams.
Research on Dynamic Graph Layout by Parallel Computing and Markov Process

In online dynamic graph drawing, constraints over nodes and node pairs are added to help preserve a coherent mental map in a sequence of graphs. However, defining constraints is challenging due to the requirements of both preserving mental map and satisfying the visual aesthetics of a graph layout. The requirements underline the importance of properly evaluating the node stability based on the graph changes. Most existing algorithms basically depend on local changes without evaluating their global propagation, as the calculation on the global propagation recursively takes an expensive time cost. To reach the purpose of evaluating globally and saving time cost at the same time, we introduce a heuristic model on which parallel computing can be implemented to reduce time cost. This model uses inverse analysis of Markov process to simulate the node movement hence to give a global analysis of the layout's change, according to which different constraints can be set. Experiments demonstrate that our method preserves both structure and position similarity to help users track graph changes visually, at the same time has similar time cost as the local evaluation algorithm.
Obtaining More Specific Topics and Detecting Weak Signals by Topic Word Selection

With topic modeling methods, such as Latent Dirichlet Allocation (LDA), we can find topics in large text collections. To efficiently employ this information, there is a need for a method that automatically analyzes the topics with respect to their usefulness for applications like the detection of new innovations. This paper presents a novel method to automatically evaluate topics produced by LDA. The new approach puts the focus on finding topics with topic words that are not only coherent, but also specific. By using the documents associated with each word to calculate background topics, a baseline can be set for each topic word that helps assess whether its context fits the topic well. Experiments indicate that the resulting topics are more manageable in terms of their interpretability. Moreover, we show that the approach can be used to detect weak signals.
Development and Forecast Analysis of English Network Teaching Aid Platform under B/S Technology Framework

E-earning cannot only improve the lack of teaching resources; it also brings in new teaching modes such as remote teaching. The content is vivid and can discuss by many people, so that the quality of teaching can improve to the greatest extent. Although there have been certain developments in the application of online teaching in China, and there are still many problems in the implementation of technology and systems. These have affected the quality and efficiency of teaching from different perspectives. According to the analysis, this paper describes the conception and construction process of an online assistant teaching system based on the B/S technology framework. Combined with the practical experience of developing the university English network education platform, the paper explains the concept of college English education platform based on the foundation of the university. The model and content of the construction of the English network education platform elaborated the system architecture of the platform based on the B/S system and the design method of the platform database.
Multi-level Threat Analysis in Anomalous Crowd Videos

Crowd anomaly detection is a challenging problem in the field of computer vision. An abnormal event in a crowd scene can be labeled as threat in a video. Several existing solutions in this area have marked video frames either normal or abnormal event. Such categorization of frames can be referred as two-class threat labeling problem. However, this notion of two-class threat labeling is not well defined in literature. An event can have multiple aspects as it can be treated as anomalous or non-anomalous based on the situation of occurrence. Based on this argument, we propose a new paradigm of extending this two class threat labeling problem to multi-class labeling. As a solution to this multi-class labeling problem, we cluster frames with low, medium and high threat. We also propose a new feature known as pseudo-entropy for better clustering of threats. Our framework consists of two main components, namely, Earth mover distance (EMD) based anomaly detection system and multi-level threat analysis. As an outcome frame-wise and segment-wise threat representation are also presented to facilitate real time video search for relevant events. Exhaustive internal comparison and statistical analysis over benchmark UCSD and UMN dataset clearly indicates the merit of the proposed framework.
Animal image retrieval algorithms based on deep neural network.

Nowadays, with the development of science and technology, image data has become an important part of network data, especially animal images. It is of great significance to effectively retrieve animal images from massive image data of network data for discovering animals or protecting animals. The traditional image retrieval algorithm has the insufficient ability in feature extraction, which is not suitable for effective network image retrieval. The application of depth neural network in image retrieval brings a breakthrough for solving this problem. Aiming at the problem of network animal image data retrieval, a hash network algorithm based on deep neural network is proposed. This method firstly extracts the high-level semantic features of animal images by using depth neural network, and optimizes the depth neural network. Then, the obtained image semantic features are input into the sparse self-coding hash network to obtain short hash codes. Finally, an image retrieval algorithm based on the weight of hash codes is proposed. The simulation results show that the improved depth neural network is used to extract the high-level semantic information of the image, and then the sparse self-coding hash network is used for further training. The obtained short hash codes are retrieved by using the image retrieval algorithm based on the weight of hash codes, which can not only realize animal image retrieval, but also improve the accuracy of image retrieval.
A Study of College English Autonomous Learning Model based on Computer and Network

Abstract Taking the non-English majors as the research object, the traditional college English teaching mode is combined with computer, network, multimedia and other advanced technologies to form a new teaching mode, that is, relying on the computer network platform, building English network course resources, developing an objective and feasible multiple evaluation system, so as to realize the purpose of students’ autonomous learning. The experiment shows that the new teaching mode, which is composed of classroom teaching and online autonomous learning, is effective.
AN EFFICIENT GUSTURE RECOGNITION FOR DUMB AND DEAF PEOPLE USING IOT

Human beings convey all their thoughts and experiences through interactions. But it is not possible for every single person mainly deaf and mute persons. They convey their thoughts through sign language but this is not known to every others. This work aims to reduce the communication gap between people with impairment and normal people. Compared to other gestures hand gesture is much easier to perform and take only less time. In this work we use sensor based recognition module of flex which are used to catch users gestures and convert it to text.
Research on distance measurement method of moving objects in video

Abstract We propose a smart car loading camera to simulate the vehicle driving process, the purpose of which is using monocular vision to solve the collision problem that may occur when passengers open the door after the vehicle stops. First of all, Youzheng Zhang’s calibration method is used to obtain the internal and external parameters of the camera, and then the inter-frame difference method is used to lock the moving target in the video. After preprocessing the image, an iterative method is proposed to automatically obtain the threshold to complete the target object segmentation. Combined with the calibration parameters, the distance of the target object is calculated based on the proportion of similar triangles. The test results of videos taken in multiple different scenes show that the average measurement error of the algorithm is 44mm.
Heterogeneous Kernel Based Convolutional Neural Network for Face Liveness Detection

Liveness detection is a part of living biometric identification. While the face recognition system is promoted, it is also vulnerable to deceived and attacked from fake faces. Face liveness detection in traditional method needs network take long time to training and easy to appear over-fitting. Therefore, this paper proposed a Heterogeneous Kernel-Convolutional Neural Network (HK-CNN), the method replaces the standard convolutional kernel with heterogeneous convolutional kernel to detect the real face. In the two classic databases of NUAA and CASIA-FASD, the algorithm can improve accuracy and reduce the training cost of the model. Compared with traditional convolutional neural network methods, this algorithm has higher efficiency.
Grazing incidence interferometry for testing rough aspherics: experimental results and data analysis

In the past, grazing incidence interferometry has been applied to plane, cylindrical, acylindrical and general rod-like surfaces using diffractive beam splitters. Here, we demonstrate that also rough convex steep rotationally symmetric aspherics can be measured along one meridian in a single step using diffractive beam splitters and phase shifting techniques. The measurement of rough surfaces is possible i.e. without the need to polish the surfaces, due to the large effective wavelength (here about 10&mu;m) of the test. The whole surface can be measured by rotating it stepwise around its symmetry axis and measuring successive small meridian regions. These meridian regions have to be stitched together to get the whole surface. Besides the presentation of the measurement principle, simulation results of the procedure for aspherical specimens are given. The corresponding experimental setup with discussion of the measured phase distributions is presented and the misalignment analysis is performed for the special case of spherical objects under test.
A working human abnormal operation recognition approach based on the deep multi-instance sorting model

Modeling abnormal behavior detection in the intelligent video monitoring system for recognition or detection of special event has attracted significant research interest in recent years. In order to achieve more effective recognition of abnormal behavior detection of the intelligent video surveillance system, this paper proposes a working human abnormal operation recognition approach based on deep multi-instance sorting model. Firstly, the uncut long video is sparse to obtain normal and abnormal behavior video segments, and the RGB and optical flow features in the segment are extracted by the deep convolution network. Video feature vectors are obtained by the consensus function and feature extractor. Then, multi-instance sorting learning is used to assign abnormality scores between 0 and 1 to feature vectors. When the abnormal values of abnormal packets are higher than those of normal packets, the abnormality score is returned and the high abnormality score is determined as an abnormal behavior. The experiments on the open THUMOS14 data set and our own XAGCWD data set using CUDA GPU accelerated computing to demonstrate that our approach improves the recognition accuracy about 10.8% and high accuracy of abnormal detection. The main purpose of this research is to apply the model proposed in this paper to the intelligent workshop behavior monitoring system to effectively realize the safety management of workshop personnel.
Research and Implementation of Text Watermarking Technology Based on PDF Document Structure

Aiming at the problems of illegal copying, malicious tampering, information leakage, copyright disputes and other issues caused by the convenience of the Internet, a watermarking algorithm based on the PDF document structure was developed, a watermarking algorithm model based on the PDF document structure was established, and a DES algorithm was used to encrypt the watermark. Based on the research of the physical structure and logical structure of the PDF document, the algorithm of modifying the color attribute value of the text to embed the watermark information is proposed. Experimental results show that the algorithm achieves information hiding and meets the requirements of digital copyright protection, has good security, and can resist multiple attacks.
Design Thinking's Resources for in-situ Co-Design of Mobile Games

Location-based Mobile Games have been growing in popularity in recent years. But these are widely explored only for outdoor spaces, since it has the advantage of using GPS data to obtain the user's current location. There is no unified solution to sense the user's location in indoor spaces; so, games for these spaces are little explored. The design of Location-based Mobile Games, in many cases, requires the participation of multidisciplinary teams; so, they are co-designed by a group of people. However, there is no clear way of knowing how this co-design should be conducted. The aim of this paper is to propose a conceptual framework for in-situ co-design for indoor spaces on Location-based Mobile Games through the use of Design Thinking's resources. This conceptual framework could help facilitators with this kind of experience to consider what resources can be useful to this task. An experience of in-situ co-design of a Location-based Mobile Game for indoor spaces is presented using the proposed framework. In addition, a discussion is generated in relation to this kind of co-design.