Reviewerly/exHarmony huggingface dataset:
Feature-Selecting Based Hashing via Deep Convolutional Neural Networks

In the task of image retrieval, the nearest neighbor algorithm is widely used because of its high efficiency, where hashing algorithm is one of typical representatives. In recent years, with the development of deep convolutional neural networks, there have been many deep hashing algorithms for image retrieval. This paper proposes a new deep hashing algorithm that adds a hash layer to the image classification networks to obtain hash codes. A constraint item be added to the classification loss function, which is used to pick out some important nodes from the hash layer, and these selected nodes representing the picture are encoded. Compared with other existing algorithms, the performance of our algorithm has a certain improvement.
Fusion of Body Sensors’ Data and Video Images in Assistive Technology

Modern innovations in the design of sensors and the convergence of computing, cognition and communications have led to many new possibilities in incorporating AI-techniques in Assistive Technology (AT) for elderly people. Combining wearable sensors (body sensors) with sensors and computing capabilities of smartphones, a set of experiments were performed to test various AI-algorithms for the detection of critical events such as accidental falls, prolonged stationary states and going astray from residence of "Elderly Living Independently At Home, (ELIAH)". Selected results from studies related to both critical and trivial events are used to test different AI models (threshold, Artificial Neural Networks (ANN), Support Vector Machines (SVM), k-Nearest Neighbors algorithm (kNN). The AI models are versatile enough to identify clearly fall from non-fall events. After selecting suitable features based on sensor data fusion, AI model using only wrist-based sensors flawless detection of events related to fall. A proposed system architecture for implementing these detection models in an application software for smartwatch and smartphone can serve to alert accidental faults as well as going astray of ELIAH. Data fusion with video images is also discussed.
Research and Improvement of CHI Feature Selection in Sentiment Analysis

Feature selection is a very important step in sentiment classification based on machine learning method. This paper will focus on the better CHI feature selection method and make improvements to overcome the shortcomings of the traditional method. The experimental results show that the improved IM-CHI feature selection method improves the F1 value of emotion classification by 4.9% in different feature dimensions, 4.1% in different classifiers, and 2.0% in data sets of different fields. It proves the effectiveness of this method in emotion classification.
Learning an Object Detector Using Zoomed Object Regions

The single shot multi-box detector (SSD) is one of the first real-time detectors, which uses a convolutional neural network (CNN) and achieves the state-of-the-art detection performance. However, owing to the semantic gap between each feature layer of CNN, the SSD has a room for improvement. In this paper, we propose a novel training scheme to enhance the performance of the SSD. In object detection, ground truth (GT) box is a bounding box enclosing an object boundary. To improve the semantic level of the feature map, we generate additional GT boxes by zooming in to and out from the original GT boxes. Experimental results show that the SSD trained with our scheme outperforms the original one on public dataset.
The relationship between students’ interest in bilingual science learning and students’ English competence

The correlation of students' interest in bilingual science learning and their English competence was investigated. Forty-six students of elementary-school teacher candidate program participated in this study. The students received five topics of science (Heat, Changes in Matters, Plant Life and Environment, Simple Machine, Animal Life and Environment) with English usage embedded. The students' interest in science learning involving English was examined. Meanwhile, the students' mastery of general English was measured. The result shows that the students' interest in bilingual science learning and their English mastery is not significantly correlated. The finding suggests that the students might still enjoy and get benefit from bilingual science learning despite their English abilities. This leads to the opportunity to have more bilingual science classes to enhance not only students' science mastery but also their English competence.
Research on gait recognition algorithm based on multiinformation perception

With the collected multi-information, through the information preprocessing, feature vector extraction, analysis and fusion, the recognition model based on BP neural network, genetic algorithm optimization BP neural network, the extreme learning machine and support vector machine are established. We have accurately identified five gait modes: flat walking, upstairs, downstairs, uphill, and downhill. The highest average recognition rate is 96.5%, and the recognition time is 0.156 s. By analyzing the advantages and disadvantages of the four recognition models, the optimal gait recognition algorithm can be obtained.
Construction of an analytical method for limiting the complexity of neural-fuzzy models with guaranteed accuracy

We have proposed an analytical method for limiting the complexity of neural-fuzzy models that provide for the guaranteed accuracy of their implementation when approximating functions with two or more derivatives. The method makes it possible to determine the required minimal number of parameters for systems that employ fuzzy logic, as well as neural models.We have estimated the required number of neurons (terms) in a model, which ensure the accuracy required for the area of a model curve to approach the system one along the sections of function approximation. The estimate for an approximation error was obtained based on the residual members of decomposition, in the Lagrangian form, of areas of the approximated system function into a Maclaurin series. The results received make it possible to determine the required number of approximation sections and the number of neurons (terms) in order to ensure the assigned relative and absolute error of approximation.We have estimated the required number of neurons (terms) that provide for the necessary accuracy of model implementation based on the maximum deviation between the system and model curves along the section of approximation. This makes it possible to select, depending on the assigned required accuracy, the number of terms of fuzzy variables, input and output variables, linguistic rules, coordinates of modal values along the axes of input and output variables.To verify validity of the proposed solutions, we modeled the system curves in the Matlab/Simulink environment, which confirmed the guaranteed accuracy of their implementation in accordance with the analytical calculations reported earlier.The results obtained could be applied in modern intelligent technical systems of management, control, diagnosis, and decision-making. Using the proposed methods for selecting and applying the minimal number of terms (neurons) would help reduce the required computing power in nonlinear systems
Panoramic video stitching based on multi-band image blending

With the fast development and application of virtual reality (VR), panoramic images have been a hot research topic and been widely applied in many areas. Usually, the panoramic images are generated by image stitching with images captured by cameras set in 360 degrees. However, the image captured by the traditional fish-eye lens has serious distortion. Many traditional image fusion algorithms are not suitable for this kind of images. In order to handle this problem, we propose a method based on multi-band image blending to realize the real-time stitching of six-way fish-eye video, and finally generates output video. The principle of the stitching system is to decompose the video into frames. After image alignment, multi-band fusion method is used to fuse the high-frequency features, which makes the degree of fusion in the image area smaller and avoids duplication in the mosaic area. Different from the convolution pyramid stitching algorithm which does not depend too much on the overlap area, the proposed algorithm retains more image details and reduces the distortion. Experimental results in terms of both subjective and objective quality show that the system can stitch panoramic video efficiently, and the output image has no stitching artifact in the overlapping area.
Single Sample Face Recognition Using Multicross Pattern and Learning Discriminative Binary Features

Binary feature descriptors, require considerable amount of information to be applicable in wide appearance variations, which contradicts the single sample per person (SSPP) problem. To address this challenge, a novel binary feature learning method called discriminative binary feature mapping is presented. Then, based on a number of precisely selected objectives, a feature mapping is learned by projecting all of the extracted vectors to a lower-dimensional feature space. The resulting feature vectors are then used to obtain a holistic face representation based on dictionary learning. Extensive experimental results show that the proposed method is able to obtain superior performance.
The impact of interactive visualization on trade-off-based geospatial decision-making

In a previous work we developed GISwaps, a novel method for geospatial decision-making based on Even Swaps. In this paper, we present the results of an evaluation of a visualization framework integrated with this method, implemented within a decision support system. This evaluation is based on two different studies. In the quantitative study, 15 student participants used GISwaps with no visual features, and 15 participants used GISwaps with the integrated visual framework, as the tool in a solar farm site location case study. The results of the quantitative evaluation show positive impact of the visualization in terms of increased coherency in trade-offs. The results also show a statistically significant difference in average trade-off values between the groups, with users from the non-visual group setting on average 20% higher trade-off values compared with the users in the visual group. In the qualitative study, we had one expert in GIS, two experts in decision-making and two experts in solar energy as a focus user group. Data in this study were obtained by observations and semi-structured interviews with the participants. The impact of the visualization framework was assessed positively by all participants in the expert group.
Single Actor Pooled Steganalysis

This paper considers a more practical situation for pooled steganalysis that only a single actor is observed, so that the steganalyst needed to analyze the actor independently without comparing with other actors. We propose a pooled steganalysis method for this situation. For a given actor that has emitted a number of images, feature sets are extracted from each image, respectively, and then feed to a binary classifier popularly used in single object steganalysis. Combining all the results output by the classifier, a final decision is made ensemble to label the given actor as "guilty" or "innocent" with the minimal detection error. Experimental results show that the proposed method is effective for single actor pooled steganalysis.
Undertow: An Intra-Kernel Isolation Mechanism for Hardware-Assisted Virtual Machines

The prevalence of Cloud Computing has appealed many users to put their business into low-cost and flexible cloud servers instead of bare-metal machines. Most virtual machines in the cloud run commodity operating system(e.g., linux), and the complexity of such operating systems makes them more bug-prone and easier to be compromised. To mitigate the security threats, previous works attempt to mediate and filter system calls, transform all unpopular paths into popular paths, or implement a nested kernel along with the untrusted outter kernel to enforce certain security policies. However, such solutions only enforce read-only protection or assume that popular paths in the kernel to contain almost no bug, which is not always the case in the real world. To overcome their shortcomings and combine their advantages as much as possible, we propose a hardware-assisted isolation mechanism that isolates untrusted part of the kernel. To achieve isolation, we prepare multiple restricted Extended Page Table (EPT) during boot time, each of which has certain critical data unmapped from it so that the code executing in the isolated environment could not access sensitive data. We leverage the VMFUNC instruction already available in recent Intel processors to directly switch to another pre-defined EPT inside guest virtual machine without trapping into the underlying hypervisor, which is faster than the traditional trap-and-emulate procedure. The semantic gap is minimized and real-time check is achieved by allowing EPT violations to be converted to Virtualization Exception (VE), which could be handled inside guest kernel in non-root mode. Our preliminary evaluation shows that with hardware virtualization feature, we are able to run the untrusted code in an isolated environment with negligible overhead.
Support Vector Machine Failure in Imbalanced Datasets

Imbalanced datasets often pose challenges in classification problems. In this work we study and quantify the problem of imbalanced classification using support vector machines (SVM). We identify the conditions under which a SVM failure occur, both theoretically and experimentally, and show that it can be relevant even in cases of very weakly imbalanced data. The guidelines for exploratory data analysis are presented to avoid the SVM failure.
DynFace: A Multi-label, Dynamic-Margin-Softmax Face Recognition Model

Convolutional neural networks (CNN), more recently, have greatly increased the performance of face recognition due to its high capability in learning discriminative features. Many of the initial face recognition algorithms reported high performance in the small size Labeled Faces in the Wild (LFW) dataset but fail to deliver same results on larger or different datasets. Ongoing research tries to boost the performance of Face Recognition methods by modifying either the neural network structure or the loss function. This paper proposes two novel additions to the typical softmax CNN used for face recognition: a fusion of facial attributes at feature level and a dynamic margin softmax loss. The new network DynFace was extensively evaluated on extended LFW and much larger MegaFace, comparing its performance against known algorithms. The DynFace achieved state-of-art accuracy at high speed. Results obtained during the carefully designed test experiments, are presented in the end of this paper.
THE PEDAGOGUE'S MOBILITY AS THE WAY OF REALIZATION CONDITIONS OF THE NEW PEDAGOGICAL CONCEPT OF EDUCATION

The article deals with professional mobility (the ability for successfully switching another activity or change the type of work) as a necessary quality of a modern pedagogical worker. It is highlighted the main qualities, which are important in the process of forming a professional mobile specialist. The result of the formed qualities is the creation of a professional style in accordance with the views of specialists, personal principles and orientations; constant search for optimal educational forms of learning, the implementation of a pedagogical experiment, production and generalization of experience in educational activities. The proposed criteria for evaluating pedagogical activity of educational workers according to the standards put forward by the society (professional self-development, professional skills, introduction of innovative technologies into the educational process, creativity in the period of reformation and modernization, the latest pedagogical technologies of activity). It is outlined the importance of the employees' professional mobility formation in accordance with the tasks of the new Concept of Pedagogical Education Development: the formation of universal skills of activity ensuring mobility as a personality; orientation to innovation activity; provision of fundamental social and humanitarian training as a mastering of professions of another profile; provision of wide professional training as a basis for possible mastering of other professions; strengthening of managerial training of future pedagogical staff; The result of the educational staff professional mobility formation is a sharp increase in the quality of training of all future teachers. The presence in society of a sufficiently large number of active, professionally mobile teachers becomes a guarantee of increasing its sustainability.
Human action recognition based on two-stream Ind recurrent neural network

In order to avoid the influence of external factors on the subsequent recognition of RGB video and improve the accuracy of human motion recognition, an algorithm of human action recognition based on Two-Stream Ind Recurrent Neural Network is proposed. In terms of extracting features, the temporal network extracts the information on the 3D coordinate of different joints at each time and classifies it by a softmax layer. The spatial network converts the spatial positional relationship of the joints at each moment into a skeleton sequence and inputs it into the softmax layer to classify. Finally, the results of the classification of the temporal network and the spatial network are weighted and summed to obtain the final classification result. Experiments verify the validity of the model on the largest 3D skeleton action recognition dataset NTU RGB + D and SBU interactive dataset.
The Convolution Neural Network with Transformed Exponential Linear Unit Activation Function for Image Classification

Activation functions play an important role in deep learning and its choice has a significant effect on the training and performance of a model. In this study, a new variant of Exponential Linear Unit (ELU) activation called Transformed Exponential Linear Unit (TELU) is proposed. An empirical evaluation is done to determine the effectiveness of the new activation function using state-of-the-art deep learning architectures. From the experiments, TELU activation function tends to work better than the conventional activations functions on deep models across a number of benchmarking datasets. TELU achieves superior classification accuracy on Cifar-10, SVHN and Caltech-101 dataset on state-of-the-art deep learning models. Additionally, it shows superior AUROC, MCC, and F1-score on the STL-10 dataset. This proves that TELU can be successfully applied in deep learning for image classification.
Long‐term adaptive tracking via complementary trackers

In recent years, kernelised correlation filter-based trackers have been employed to manage short-term tracking problems and help long-term trackers achieve excellent accuracy and robustness under challenging conditions, such as geometry/photometry changes, heavy occlusion, fast motion, motion blur, and out-of-camera view. Nonetheless, the inherent boundary effects and risky update strategy of correlation filters constrain the performance of short-term tracking, which limits the performance of long-term trackers. Moreover, the complicated redetection module leads to high-computational cost, which results in the long-term trackers to run at a low speed, thereby significantly restricting their applications. In the present work, the authors propose to employ complementary trackers in designing an efficient long-term tracker. Furthermore, a sigmoid penalty coefficient is proposed to update the tracking model with an adaptive learning rate that adjusts the learning rate while the target encounters appearance variation. Finally, they propose a novel redetection method that combines a redetection classifier with a short-term component to redetect the target while satisfying the explicit condition. The long-term tracker proposed in this study is proven to perform real-time speed of more than 65 frames per second and state-of-the-art accuracy by the experimental result on several challenging benchmarks.
Mimic Defense Structured Information System Threat Identification and Centralized Control

With the speeding up of the informatization process of our national economy and social development, the degree of informatization in our country is getting higher and higher. Information system has become the key infrastructure of our country. The security of these basic information networks and important information systems has been seriously related to national security and social stability, to the collective interests of enterprises, and to the vital interests of the broad masses of the people. Increasing application in improving work efficiency and economic benefits at the same time, the increasingly com-plex information system has also brought greater challenges to operation and maintenance managers. The use of known or unknown vulnerabilities, causing damage to information systems, stealing sensitive information, endangering the normal operation of business systems and other behaviors, has become one of the main causes of various security incidents. How to effectively deal with the vulnerability threat and various vulnerability problems of enterprise information systems, concentrate on the implementation of information security vulnerability warning related risks, and provide solutions to related vulnerability threats, have become an urgent problem to be solved. Therefore, we construct the mimic structured enterprise information system by establishing the threat identification system through unified management to form a comprehensive and secure cyber threat management system.
BRAIN TUMOR DETECTION AND SEGMENTATION USING MULTISCALE INTUITIONISTIC FUZZY ROUGHNESS IN MR IMAGES

The magnetic resonance imaging technique is mostly used for visualizing and detecting brain tumor, which requires accurate segmentation of brain MR images into white matter, gray matter, cerebrospinal fluid, necrotic tissue, tumor, and edema. But brain image segmentation is a challenging task because of unknown noise and intensity inhomogeneity in brain MR images. This paper proposed a technique for the segmentation and the detection of a tumor, cystic component and edema in brain MR images using multiscale intuitionistic fuzzy roughness (MSIFR). Application of linear scale-space theory and intuitionistic fuzzy image representation deals with noise and intensity inhomogeneity in brain MR images. Intuitionistic fuzzy roughness calculated at proper scale is used to find optimum valley points for segmentation of brain MR images. The algorithm is applied to the real brain MR images from various hospitals and also to the benchmark set of the synthetic MR images from brainweb. The algorithm segments synthetic brain MR image into three regions, gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF) and also separates tumor, cystic component and edema accurately in real brain MR images. The results of segmentation of proposed algorithm for synthetic images are compared with nonlocal fuzzy c-means (NLFCM), rough set based algorithms, intervalued possibilistic fuzzy c-means (IPFCM), robust modified Gaussian mixture model with rough set (RMGMMRS) and three algorithms, recursive bias corrected possibilistic fuzzy c-means (RBCPFCM), recursive bias corrected possibilistic neighborhood fuzzy c-means (RBCPNFCM) and recursive bias corrected separately weighted possibilistic neighborhood fuzzy c-means (RBCSPNFCM). The quantitative and qualitative evaluation demonstrates the superiority of the proposed algorithm.