Reviewerly/exHarmony huggingface dataset:
Edge-Guided Panoramic Video Stitching with Limited Overlap

Video stitching remains a challenging problem in computer vision. In this paper, we propose a novel edge-guided method to stitch multiple videos that have small overlapped regions. Our algorithm consists of three steps: (1) spherical projection of the input video frames based on camera calibration, (2) edge detection and edge-guided feature matching for video registration, and (3) seam optimization to eliminate distortions and ghosts in the composited panoramic videos. The experimental results and user studies demonstrate that our method is robust to videos that have small overlapped regions and produces more visually pleasing panoramic videos than state-of-the-art techniques.
Creation of applied intelligent system for diagnostics of cognitive and emotional-volitional sphere of post-stroke patients at different stages of rehabilitation treatment

Abstract In this paper, we describe the creation of the medical diagnostic intelligent system, which is used for the analysis of cognitive and emotional-volitional sphere of post-stroke patients at different stages of rehabilitation treatment (IS DICEV). The applied IS DICEV is constructed with the help of intelligent instrumental software IMSLOG and is based on test methods of the pattern recognition, fault-tolerant mixed diagnostic tests. The use of the applied IS DICEV will allow revealing various kinds of regularities in the cognitive and emotional-volitional sphere of post-stroke patients, as well as finding diagnostic decisions and justifying those using graphical tools. These goals are very important for tracking the dynamics of rehabilitation treatment.
Neural Networks from a Physical Perspective

The investigations on neural networks have attracted a broad interest from lots of research communities in recent years. The evidences of their advantage over traditional methods are found when they are applied to dealing with plenty of practical tasks. However, challenges still remain in the topics, such as understanding the mathematical reason of their effectiveness. Here, we review some of the recent works and present a new look on the neural network from a physical perspective.
Forming Master’s Degree Students’ Projective Skills While Studying the Discipline “Theory and Practice of Educational Process Management”

The paper examines formation of Master’s degree students’ projective skills while studying the discipline “Theory and Practice of Educational Process Management”. The article analyses the concept “educational space” from the viewpoint of the activity-based approach, reveals importance of Master’s degree students’ projective skills, describes the logic of educational space projection within the framework of the Master’s degree course “Theory and Practice of Educational Process Management” taking into account inclusive education. In accordance with the research objective, the author reveals the content and structure of professional competence, describes a sequence of tasks students perform when preparing for intermediate assessment. Scientific originality of the study lies in the fact that the researcher introduces her original approach to formation of students’ projective skills while studying the discipline “Theory and Practice of Educational Process Management”, substantiates criteria to assess the level of professional competence and develops a grading scale, proposes a system of multi-level intermediary tasks on the discipline (threshold level, basic level, advanced level) in accordance with the Federal State Educational Standard of Higher Education (FSES HE 3++) (Master’s degree program, subject area “Pedagogy”). The research findings are as follows: the author justifies relevance of the proposed system of tasks aimed to develop Master’s degree students’ projective skills. In fact, the proposed organizational and methodological model is based on administrative strategies that can be implemented in various forms taking into account results of studies on educational space projection.
Polarity Discrimination of Evaluation Words in The Product's Review

The customer's review plays an important role in deciding the purchase behaviour for online shopping. This paper intends to extract useful information hidden in customer's reviews using the opinion mining technology. In the reviews, the polarity of the evaluation words reflects the consumer's attitude towards the product, so the discrimination of the polarity of the words plays a crucial role in the sentiment analysis. In this paper, we first make a polarity discrimination based on the sentiment lexicons. For the evaluation words that do not exist in the sentiment lexicon, the similarity between evaluation words and emotional seed words is calculated using the linear superposition of Word2Vec and HowNet (LSWH), and then the polarity of the evaluation word is determined. To evaluate the performance of the proposed method, we comparing with the previous two polarity discriminate methods, the result show that the proposed method has the high accuracy.
General Model for Index Recommendation Based on Convolutional Neural Network

With the advent of big data, the cost of index recommendation (IR) increases exponentially, and the portability of IR model becomes an urgent problem to be solved. In this paper, a fine-grained classification model based on multi-core convolution neural network (CNNIR) is proposed to implement the transferable IR model. Using the strong knowledge representation ability of convolution network, CNNIR achieves the effective knowledge representation from data and workload, which greatly improves the classification accuracy. In the test set, the accuracy of model classification reaches over $$95\%$$ . CNNIR has good robustness which can perform well under a series of different learning rate settings. Through experiments on MongoDB, the indexes recommended by CNNIR is effective and transferable.
Graph Traversal Applied to Search for Optimal Minimum Peak Sidelobe Level Binary Sequences

Binary sequences with low aperiodic autocorrelation levels are widely used in improving the ability of anti-intercept and detection of small targets of radar systems. Searching for optimal binary sequences is an intractable problem due to its extremely high complexity. In this paper, we present two exhaustive methods from the perspective of graph traversal. We prove a proposition that an equivalent sequence set is a closed set, and give the exact set. Moreover, an improved method to exclude equivalent sequences is proposed based on this proposition. The experiment results demonstrate that the time complexity of the two proposed methods is slightly reduced compared to the existing methods.
Methods of the Vehicle Re-identification

Most of researchers use the vehicle re-identification based on classification. This always requires an update with the new vehicle models in the market. In this paper, two types of vehicle re-identification will be presented. First, the standard method, which needs an image from the search vehicle. VRIC and VehicleID data set are suitable for training this module. It will be explained in detail how to improve the performance of this method using a trained network, which is designed for the classification. The second method takes as input a representative image of the search vehicle with similar make/model, released year and colour. It is very useful when an image from the search vehicle is not available. It produces as output a shape and a colour features. This could be used by the matching across a database to re-identify vehicles, which look similar to the search vehicle. To get a robust module for the re-identification, a fine-grained classification has been trained, which its class consists of four elements: the make of a vehicle refers to the vehicle's manufacturer, e.g. Mercedes-Benz, the model of a vehicle refers to type of model within that manufacturer's portfolio, e.g. C Class, the year refers to the iteration of the model, which may receive progressive alterations and upgrades by its manufacturer and the perspective of the vehicle. Thus, all four elements describe the vehicle at increasing degree of specificity. The aim of the vehicle shape classification is to classify the combination of these four elements. The colour classification has been separately trained. The results of vehicle re-identification will be shown. Using a developed tool, the re-identification of vehicles on video images and on controlled data set will be demonstrated. This work was partially funded under the grant.
PrivLeAD: Privacy Leakage Detection on the Web

Each person generates a plethora of information just by browsing the Web, some of which is publicly available and some other that should remain private. In recent years, the line between public and private/sensitive information is becoming harder to distinguish and the information generated on the Web is being sold and used for advertising purposes, turning the personal lives of users into products and assets. It is extremely challenging for users and authorities to verify the behavior of trackers, advertisers and advertising websites in order to take action in case of misconduct. To address these issues, we present PrivLeAD, a domain independent system for the detection of sensitive data leakage in online advertisement. Specifically, PrivLeAD leverages Residual Networks to analyze the advertisements a user receives during normal internet browsing and thus detect in real time potential leakage of sensitive information. The system has been tested on real and synthetic data proving its feasibility and practical effectiveness.
Real-time Object Detection with Attention Mask

With the development of deep convolutional neural network, the performance of object detection is obviously improved. However, there are still some challenges such as small size and occlusion. In this paper, we present a novel detector named Attention Mask Detector (AMDet). Our motivation is using mask to enhance foreground features and suppress background ones. The mask is produced by an attention branch which is supervised by weak segmentation ground-truth. This weak segmentation ground-truth is generated by bounding box without extra annotations. Our method is based on one-stage detector. We do experiments on both PASCAL VOC and MS COCO datasets and have a result comparison with other one-stage detectors.
Site optimization for multifunction radar based on big data

Traditional radar does not have the condition to perform site optimization using big data, due to its inborn defects in data acquisition, data storage and data processing. Aiming at multifunction radar, on the basis of analyzing its data characteristics and difficulties in site optimization, the basic framework of site performance optimization based on big data is put forward, the requirements of radar for big data application are analyzed, and the key technologies to be solved for site optimization based on big data are given.
Pose-guided Neural Network with Hybrid Representation for Person Re-Identification

Person re-identification (Re-ID) is a challenging task due to different camera perspectives, distinct lightings and backgrounds. In this paper, we propose a pose-guided neural network, which is aimed at introducing a body posture concerned mechanism into feature extraction process. First, we generate dependable distribution maps of human posture joints. Then an end-to-end pose-guided neural network is proposed to focus on learning the features of human body. As we find that in recent literature, researchers treat person Re-Id mainly as an identification task, a hybrid representation mechanism is proposed to combine effective features of identification and verification to enhance the robustness of image representations. Comprehensive experiments based on two challenging datasets (Market-1501 and MSMT17) are conducted, the results show that our method gains competitive performance over other state-of-the-art algorithms.
The Effect Of Domino Game Learning Media On Student Learning Achievements

This study aims to find out how the effect of domino game learning media on students learning achievement of class VIII at SMP N 1 Padang, West Sumatera. The methodology of the research which is used in this research is experimental research. The research population was 256 students. The sampling technique used in the research is cluster random sampling, so that the sample acquired was class VIII-E which consisted of 34 students. The techniques of data collection used are documentation and tests. The techniques of data analysis are normality test, homogeneity test, and hypothesis test. Based on the research finding, there is significant effect of using domino game learning media on students learning achievements of class VIII at SMP N 1 Padang, West Sumatera.
Impact of Artificial and Natural Intelligence Technologies With Avatar-Based Teaching, Learning, and Research in Russian Modern Universities

This chapter has presented the idea and use of artificial and natural intelligence technologies. A system of professional education, on the one hand, is one of the main institutions of human socialization, and the formation of a harmoniously developed, active, creative personality, on the other hand, ensures the reproduction and development of the personnel potential of the society. Realization of the goals of education is carried out in interaction with the socioeconomic environment, the dynamic change of which predetermines the conditions for the activity of educational institutions. An acceleration of scientific and technological progress in all areas of knowledge raises the task for ensuring the continuous growth of professional skills throughout the whole life. Structural and institutional replacing in professional education is reflected in various models of higher professional education. These alternative technology-mediated environments reflect the increasing need to serve a generation of learners who prefer to learn through experience or by interacting with learning tools.
Accelerating block propagation in PoW blockchain networks with pipelining and chunking

Blockchain is an open, verifiable, and distributed consensus of transactions among different parties, relying on P2P technology for connectivity between nodes. However, the long time of block propagation limits inceptions of another consensus. We propose a novel method that accelerates block propagation in PoW blockchain networks by pipelining message transaction and verifications in parallel over a network with chunks of a block (PiChu). We have conducted extensive evaluations to present the significance of the network pipelining with many parallel chunk connections. Various simulation results exhibit that the proposed method achieves significantly less latency of block propagation than traditional method as the size of a P2P network increases.
Theoretical and experimental research on the use of the blinking reflex for command and control of human body movement

Abstract The blink reflex is a behavioural motor response that is normally found in the process of seeing at newborns and human adults. As with the corneal reflex, the supraorbital branch electrical stimulation of the trigeminal nerve causes a facial nerve bilateral response, namely eyes blinking. Therefore, this sensitive process can also be a neurological control element for performing movements, related to the natural blinking process amplitude, speed and frequency. In the first part of the paper, a series of blinking process physiological aspects are analyzed, in order to identify the movements command and control components. In the second part of the paper, it is analyzed the devices constructive variants for evaluating the blinking process and the connecting possibility with separate sensory elements that can collect information from the visual system neuro-motor level. In the third part of the paper, there are mentioned some aspects related to the experimental installation design and the command and control mode for setting in motion a system using the coordinated ocular and neurosensory blinking mechanism. The final part presents the conclusions of this approach to the development of a system with the use of oculomotor and neurosensory stimuli.
A Novel Audio Extraction and Restoration System for Optical Soundtrack

In this article, we presented an audio extraction and restoration system for film optical soundtrack. Different from the traditional audio reproduction methods that extract audio waveform by laser scanning and then do some repair processing in audio domain, we performed noise analysis and restoration algorithms to improve the quality of soundtrack pictures before the audio waveform extraction. By inputting the soundtrack pictures, we first analyze the noise components for each frame, then the restoration algorithms will be carried out, finally we stitch the pictures for a consistent soundtrack to gain the corresponding audio sequence. A database consisted of 19125 soundtrack pictures was built in this paper to evaluate the performance of our framework. The experiment results demonstrated that our framework can achieve similar audio quality as traditional audio extraction method in subject evaluation, meantime show the potential to form the new film restoration technology which can avoid the laser damage.
HOSA

Distributed triangle enumerating has attracted a lot of interest for the capability to process large graphs quickly. However, existing solutions usually suffer from low speed and/or poor graph-size scalability due to the massive short messages passed via the network or the huge amount of intermediate data. To address the issues, we propose HOSA, a distributed out-of-core triangle enumerating algorithm. HOSA first introduces an I/O-efficient graph placement strategy to divide and place a graph across a cluster in a load-balanced manner to reduce intermediate data and exploit the aggregated I/O bandwidth of the cluster. To enumerate triangles, HOSA proposes an efficient algorithm which not only eliminates short messages, the main cause of the low speed of most previous works, but also effectively uses the aggregated network and I/O bandwidths to further improve performance, and achieves high speed and graph-size scalability. Extensive evaluations show that HOSA can process much larger graphs with better or at least comparable speed than state-of-the-art algorithms, including the best distributed-memory algorithm LiteTE.
Improving Semantic Segmentation via Label Propagation and Temporal Consistency

Semantic segmentation is a fundamental task in indoor scene understanding. Most previous supervised approaches rely on densely annotated image data sets. Due to the limited amount of images with segmentation labels, the performance of existing networks is greatly limited. In this paper, we exploit temporal correlation in video frames to improve the performance and robustness of segmentation networks. Two effective learning strategies are proposed to propagate the information from a few labeled frames to their immediate neighbor frames. First, we scale up training dataset for supervised semantic segmentation networks by generating pseudo ground-truth for neighboring frames from a labeled frame using filtered homography transformation. Furthermore, we introduce a self-supervised loss function to ensure temporal consistency between the segmentation results of adjacent frames. The experimental results demonstrate that our proposed method outperforms state-of-the-art techniques for semantic segmentation on NYU-Depth V2 dataset.
Generalization or Instantiation?

Learning from multi-modal data is very often in current data mining and knowledge management applications. However, the information imbalance between modalities brings challenges for many multi-modal learning tasks, such as cross-modal retrieval, image captioning, and image synthesis. Understanding the cross-modal information gap is an important foundation for designing models and choosing the evaluating criteria of those applications. Especially for text and image data, existing researches have proposed the abstractness to measure the information imbalance. They evaluate the abstractness disparity by training a classifier using the manually annotated multi-modal sample pairs. However, these methods ignore the impact of the intra-modal relationship on the inter-modal abstractness; besides, the annotating process is very labor-intensive, and the quality cannot be guaranteed. In order to evaluate the text-image relationship more comprehensively and reduce the cost of evaluating, we propose the relative abstractness index (RAI) to measure the abstractness between multi-modal items, which measures the abstractness of a sample according to its certainty of differentiating the items of another modality. Besides, we proposed a cycled generating model to compute the RAI values between images and text. In contrast to existing works, the proposed index can better describe the image-text information disparity, and its computing process needs no annotated training samples.
Deductron—A Recurrent Neural Network

The current paper is a study in Recurrent Neural Networks (RNN), motivated by the lack of examples simple enough so that they can be thoroughly understood theoretically, but complex enough to be realistic. We constructed an example of structured data, motivated by problems from image-to-text conversion (OCR), which requires long-term memory to decode. Our data is a simple writing system, encoding characters 'X' and 'O' as their upper halves, which is possible due to symmetry of the two characters. The characters can be connected, as in some languages using cursive, such as Arabic (abjad). The string 'XOOXXO' may be encoded as '${\vee}{\wedge}\kern-1.5pt{\wedge}{\vee}\kern-1.5pt{\vee}{\wedge}$'. It is clear that seeing a sequence fragment '$|\kern-1.8pt{\wedge}\kern-1.5pt{\wedge}\kern-1.5pt{\wedge}\kern-1.5pt{\wedge}\kern-1.5pt{\wedge}\kern-1.8pt|$' of any length does not allow us to decode the sequence as '\ldots XXX\ldots' or '\ldots OOO \ldots' due to inherent ambiguity, thus requiring long-term memory. Subsequently we constructed an RNN capable of decoding sequences like this example. Rather than by training, we constructed our RNN ``by inspection'', i.e. we guessed its weights. This involved a sequence of steps. We wrote a conventional program which decodes the sequences as the example above. Subsequently, we interpreted the program as a neural network (the only example of this kind known to us). Finally, we generalized this neural network to discover a new RNN architecture whose instance is our handcrafted RNN. It turns out to be a three-layer network, where the middle layer is capable of performing simple logical inferences; thus the name ``deductron''. It is demonstrated that it is possible to train our network by simulated annealing. Also, known variants of stochastic gradient descent (SGD) methods are shown to work.
Perfect Reconstruction Condition for Rational Wavelet Transform with Reducible Rational Dilation Factor

Wavelet analysis is a modern and very powerful technique for effective analysis of data of different nature (time series, various kinds of multimedia data such as images, video and audio, and so on). Dyadic wavelet transforms are commonly used due to their simple and effective software implementation. Applying of non-dyadic wavelet transforms gives more precise separation of data features. The most general approach of non-dyadic wavelet transforms is the rational multiresolution analysis. Usually irreducible fraction is used as a value of dilation factor for rational wavelet transforms. But using of reducible dilation factor can improve detection of singularities. Property of perfect reconstruction of source data is required for any wavelet transform. It was proved that this condition is satisfied in the case of irreducible dilation factor. In this paper we will show that perfect reconstruction condition is also satisfied for the reducible dilation factor by the example of its value of 6/4.