Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Cloud Note System in University Classrooms

Abstract In view of the disadvantages of paper notes that are not easy to save, not conducive to record, and easy to miss information, this fast personal note-taking software is designed with simple and efficient operation interface, which can cross different platforms. The system chooses B/S architecture, Spring + Mybatis framework in the background, and HTML + JS + Ajax technology in the foreground. MySql is adopted in the database to realize data storage. It mainly includes six functional modules: Notebook module, Note module, Recycle bin module, Collection module, Activity module and User module. Through the cloud note system, it is convenient for teachers to prepare lessons, record key teaching points, and collect students’ homework. Students are easy to record knowledge points in class. It can improve both work and learning efficiency.
Efficient design methods of low-weight correlation-immune functions and revisiting their basic characterization

Correlation immunity (CI) of Boolean functions is an important concept relevant both in the design of nonlinear combiners and for the protection against side-channel cryptanalysis to name a few applications. In this article we give further simplification of the proofs of some known characterizations of these functions, including some new results related to certain properties of the algebraic normal form of these functions. We also provide two efficient constructions of CI functions, thus extending the known construction methods. In particular, a special case of our first construction method is well suited for construction of low-weight CI functions. It is shown that Carlet and Chen conjecture (Carlet and Chen, 2018) on the minimum weight of 3-CI functions (equivalent to the existence of Hadamard matrices of order n=4k) is valid for a great variety of input sizes n, thus apart from the case n=2k which was already proved in Carlet and Chen (2018). This further confirms that the conjecture might be correct for any input size n.
Real-time human action recognition based on person detection

The task of human action or gesture recogntion is mostly focused on achieving good results on public datasets, but rarely achieving good results in real-time recognition. Real-time action recognition is different from training on datasets in the following aspects:1.From the saptial domain, realistic scenes are gernerally with more complicated background 2.From temporal domain, the individuals often perform actions in different lengths. In order to solve the first problem, we propose a real-time recognition method based on human body detection. We get the position of the human body in the picture through the YOLOv3 network, and then obtain the human-centered ROIs as the input of the recognition network, avoiding the interference of the background on the recognition task. Futhermore, we proposed a temporal random sampling rule that can recognize both short or long range actions. We use the indoor dangerous action detection as the application background, verify the superiority of the method on the NTURGB+D dataset, and achieve a better real-time recognition results.
Cliff-sensor-based Low-level Obstacle Detection for a Wheeled Robot in an Indoor Environment

A ramp and uneven ground formed by a low-level obstacle – whose height is too low from the ground – often stalls a robot’s navigation in an indoor environment. Few-centimeter differences between a low-level obstacle and a low-level non-obstacle are very difficult to be precisely measured in a constant distance at a mobile robot. In this paper, a wheeled mobile robot thus makes physical contact onto a low-level object, in order to measure such subtle differences. We use one or more cliff sensors typically in place for a mobile robot in order to avoid a drop-off. A wheeled robot climbs over a low-level object, classifies an obstacle vs. a non-obstacle using a cliff sensor’s timeseries data, and rapidly backs up before getting stuck onto an obstacle. While adopting a simplified deep-learning architecture, we suggest a rapid and accurate obstacle detection technique in real-time. We implemented our technique on an embedded robot platform of LG Hom-Bot. The supplementary video on the physical robot experiment can be accessed at https://youtu.be/yK57S857_II.
Cooperation and dependencies in multipartite systems

Abstract We propose an information-theoretic quantifier for the advantage gained from cooperation that captures the degree of dependency between subsystems of a global system. The quantifier is distinct from measures of multipartite correlations despite sharing many properties with them. It is directly computable for classical as well as quantum systems and reduces to comparing the respective conditional mutual information between any two subsystems. Exemplarily we show the benefits of using the new quantifier for symmetric quantum secret sharing. We also prove an inequality characterizing the lack of monotonicity of conditional mutual information under local operations and provide intuitive understanding for it. This underlines the distinction between the multipartite dependence measure introduced here and multipartite correlations.
Simulation of Noise Performance in an Imaging System with a Pastel-Color Filter Array

It is common in color imaging to use a single-sensor system with a color filter array (CFA), especially a Bayer CFA. Recently, color filter methods using the white component have been studied to improve both resolution and sensitivity. However, de-mosaicking performance is improved using a correlation of color planes, especially for a Bayer CFA. We propose a new imaging system with a pastel-color CFA. In this system, the color plane reconstruction is realized with an abstraction of the white-like component from RAW images. To improve performance, we discuss the use of the highfrequency component. However, the effect of sensor noise on the imaging performance has not been closely investigated in such imaging systems. In this paper, we evaluate these effects on various imaging systems. In our experiment, the optical color filter and sensor noise are simulated using computer calculations. With these results, it is recognized that the proposed method is less affected by sensor noise.
Newton-Raphson Algorithm for Determination of SIFs and Crack-tip from Photoelastic Images

The combination of multi-parameter stress field equations and photoelastic images, a nonlinear least-squares method was deduced and applied to determine the parameters in the stress field equations of crack tip by fitting the isochromatic phase field obtained by the ten-step phase-shifting in digital photoelasticity. Not only the parameters in the stress field equations but also the coordinates of crack tip were determined simultaneously and automatically by using the proposed method. A set of over-determined nonlinear equations were established to estimate the unknown parameters by an iterative procedure based on Newton-Raphson method. Simulation and experimental results validated the correctness of the proposed method.
The novel index of the similarity between hand-drawn sketches for machine learning

We have dealt with sketches hand-drawn by human hands. They are creations including human emotions and sensibility. To handle sketches on a computer, it is necessary to have an index that quantitatively evaluates the stroke-order. We have already proposed a sketch similarity index for the pen movement by cosine similarity. However, we also found that human cognition strongly depends on the shape of a sketch when they understand what is drawn. For this reason, we propose a new similarity index, which is the old index added to a shape similarity index. As a result, the similarity index composed of cosine similarity and Structural Similarity (SSIM) has a strong positive correlation with the human evaluation of similarity. Then, the correlation coefficient is 0.7566. Therefore, we define an objective evaluation model for the stroke-order similarity that incorporates not only the pen movement of a sketch but also the shape of one, and we verify its effectiveness by our experiments.
The analysis of current trends in the interaction of public and lo-cal self-governmental education management

The article contains the analysis of the peculiarities of current activities of educational districts. The results of author’s analysis enable to conclude that during the 2000s, the interaction of local self-government and public organizations in education management reveals the periods of both increasing and decreasing the role of the public in management processes. The future growth of the role of public organizations in the management of education is predicted due to delegation of the part of the administrative powers to local self-government .
A Visual Calibration Method of Monocular Camera and Pan-Tilt for Mobile Robots

In the competition of “RoboMaster”, the robot, a mobile platform with a two-axis pan-tilt and a barrel, needs to shoot the target by launching a projectile. In order to detect and shoot the target automatically, we develop a visual system with a monocular camera mounted on the chassis. Based on some target detection algorithms, the target's 3D pose in the camera coordinate system is obtained. As the launching mechanism of the robot is on the two-axis pan-tilt, it demands to further obtain target's pose in the pan-tilt coordinate system to achieve precise shooting. Therefore, how to obtain the relative position between the camera coordinate system and the pan-tilt coordinate system is the main problem in our research. We propose a fast and effective method for the relative position calibration between the camera and two-axis pan-tilt based on specular reflection. We utilize a plane mirror to reflect the laser pointer light path mounted on the barrel. Then, the camera calibration method is applied to obtain the rotation matrix between the camera coordinate system and the world coordinate system. We finally verify the effectiveness of our method through experiments and achieve the first place in the competition.
Improving Performance of DeepCC Tracker by Background Comparison and Trajectory Refinement

DukeMTMCT is the largest and most completely labeled dataset in Multi-Target Multi-Camera Tracking (MTMCT). We investigate a state-of-the-art work on DukeMTMCT named DeepCC, and dig out two main problems. The first problem is that the openpose is prone to false detection, which seriously affects performance. The second problem is that two different persons may be assigned with the same ID. According to the corresponding problems, we not only propose a method to measure the similarity between detected bounding box and its original background avoiding false detection caused by OpenPose, but also design a strategy to correct the tracking trajectories which are affected by the unreliability of the correlation matrix clustering method proposed by DeepCC. Our method outperforms the state-of-the-art on DukeMTMCT.
An Enhancing Approach for Typhoon Cloud Image

We develop an efficient approach for enhancing the contrast of single typhoon cloud image. We first perform an invert operation on an input image for capturing more detail information. Then we employ a simple but effective modified histogram model to generate the distribution function which enhances the contrast of the inverted image. The final enhanced typhoon cloud image is obtained through inverting the enhanced image. Compared with several enhancement methods, the proposed method achieves better subjective results and objective assessments. Furthermore, our method can provide a fast segmentation of typhoon eye.
The Link between Collaboration and Citation

This paper verifies a probable link between collaboration link and citation link through experiments on scientific papers. The result shows that scholars tend to cooperate with scholars with lower citation numbers. More than 70% of the cooperation between high-citation scholars and low-citation scholars comes from the same institution.
Denoising Deep Boltzmann Machines: Compression for Deep Learning

This work is to improve deep learning training performances via compression-based denoising algorithms. The deep learning models we focus on are Deep Boltzmann Machines (DBM) including Restricted Boltzmann Machines (RBM), Bernoulli DBM, Gaussian Bernoulli RBM, and Gaussian Bernoulli DBM. In theory, DBMs are universal appropximators, however, there is a gap between DBMs' promised theoretical properties and their practical performances. In this paper, we propose a post-hoc signal processing technique, i.e., denoise, to trained DBM. The proposed technique denoises DBMs, and thus the resulting models, denoising DBMs, remain more information about the training data. The proposed denoising DBM has its roots in compression-based denoising techniques, and is unlike other denoising techniques widely used in deep learning areas.
An NLP-Based Cryptosystem to Control Spread of Fake News Through Social-Media

The era of digitalization has seen a transition from traditional methods of providing services to digital means. The advancement of technological innovations has not only impacted the human lifestyle but also has aided the growth of various social channels for people to communicate with one another. But the rise of social media platforms has also led to the spread of fake news. Fake news spread fast and can affect the brand image of various product/service-based organizations. To reduce the effect of fake news, we have proposed a method to temporarily encrypt the message sent from a consumer using the company’s tag for a fixed time, say, 1 day, in Twitter, hence giving the company the time to respond to the concerns raised by the customer. Once the response is received, or the stipulated time ends, the message will be made visible to the public automatically.
Image Cosegmentation via Cosaliency Guided and Spline Regression

Most image cosegmentation techniques often suffer from large intraclass variations or complex background. In this paper, we combine cosaliency and spline regression for cosegmenting a group of related images simultaneously. The cosaliency prior provides information of possible foreground and background regions to improve cosegmentation efficiency. A spline regression is used to measure the smoothness of local spatial adjacent regions. This process is done by minimizing an energy function, which is made up of an interimage energy, a smooth energy and a regularization energy. We use an iterative technique to resolve this energy function. This technique decomposes the energy minimization into subproblems, and renovates each image alternatively for calculation efficiency. The experimental results on two benchmarks, MSRC dataset and iCoseg dataset, display better segmentation results of our proposed technique.
The Impact of Multimedia Synchronization and Buffering on Quality of Experience (QoE) in Audiovisual Transmission

In recent years, multimedia service providers have been focusing on improving the users quality of experience (QoE) of audiovisual services. To examine the factors that affect QoE efficiently, we explore models to reflect the quantitative relationship between influential factors and QoE. We carry out a series of subjective quality tests to investigate the effects of audiovisual and subtitle synchronization, and playback buffering under a limited transmission bandwidth, on perceived quality. We analyze statistics and propose an analytical model to determine the quantitative relationship between the aforementioned factors and perceived audiovisual quality. The proposed model is content-dependent and can monitor QoE when delivering multimedia data through a dynamic network. Therefore, it is capable of providing more accurate guidance for improving audiovisual transmission services.
Sequence to Sequence Network for Learning Network Representation

Network representation learning is an important way for learning the low dimensional vector of nodes in the network, with preserving certain structural information between nodes in the original graph. Most existing network embedding models use truncated random walks and shallow architectures which do not fully obtain the nonlinear information and neighborhood information of the network. In this article, we propose a novel method for network representation learning which generates low-dimensional representation vectors for each node in the graph by obtaining the local and global structure information of the network. Unlike previous work, we use the hybrid BFS and DFS methods to sample the neighbor information of each node instead of using the uniform sampling method in DeepWalk to generate the linear sequences. After obtaining the linear sequences, we use a sequence to sequence network that contains a teaching sequence which is proved effective in capturing the nonlinear information of graph, to learn the reconstruction error of the input sequence and the output sequence. We named our method SSNR which is not only preserve both the local and global network structure information, but also capture the nonlinear information from network to achieve more discriminative node representation. To verify the effectiveness of SSNR, we employ the learned node representation as features in downstream experiments with node classification and graph visualization tasks. The experimental results of different datasets demonstrate that SSNR outperforms many state-of-the-art baseline models in these tasks.
Random Walks and Concurrent Zero-Knowledge

The established bounds on the round-complexity of (black-box) concurrent zero-knowledge ( $$\mathrm {c}\mathcal {ZK}$$ ) consider adversarial verifiers with complete control over the scheduling of messages of different sessions. Consequently, such bounds only represent a worst case study of concurrent schedules, forcing $$\widetilde{\varOmega }(\log n)$$ rounds for all protocol sessions. What happens in “average” cases against random schedules? Must all sessions still suffer large number of rounds? Rosen and Shelat first considered such possibility, and constructed a $$\mathrm {c}\mathcal {ZK}$$ protocol that adjusts its round-complexity based on existing network conditions. While they provide experimental evidence for its average-case performance, no provable guarantees are known. In general, a proper framework for studying and understanding the average-case schedules for $$\mathrm {c}\mathcal {ZK}$$ is missing. We present the first theoretical framework for performing such average-case studies. Our framework models the network as a stochastic process where a new session is opened with probability p or an existing session receives the next message with probability $$1-p$$ ; the existing session can be chosen either in a first-in-first-out ( $$\mathsf {FIFO}$$ ) or last-in-first-out ( $$\mathsf {LIFO}$$ ) order. These two orders are fundamental and serve as good upper and lower bounds for other simple variations. We also develop methods for establishing provable average-case bounds for $$\mathrm {c}\mathcal {ZK}$$ in these models. The bounds in these models turn out to be intimately connected to various properties of one-dimensional random walks that reflect at the origin. Consequently, we establish new and tight asymptotic bounds for such random walks, including: expected rate of return-to-origin, changes of direction, and concentration of “positive” movements. These results may be of independent interest. Our analysis shows that the Rosen-Shelat protocol is highly sensitive to even moderate network conditions, resulting in a large fraction of non-optimal sessions. We construct a more robust protocol by generalizing the “footer-free” condition of Rosen-Shelat which leads to significant improvements for both $$\mathsf {FIFO}$$ and $$\mathsf {LIFO}$$ models.
i2Graph: An Incremental Iterative Computation Model for Large Scale Dynamic Graphs

Due to the rapid changes in graph data sets, the mined information will quickly become obsolete, thus the entire data set needs to be re-computed from the beginning, which will result in the waste of computing time and resources. To reduce the cost of such computations, this paper proposes a model called i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Graph to support incremental iterative computation for dynamic graphs. Different from the way of traditional iteration, i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Graph executes the graph algorithm by reusing the results of the previous graph and performs computation on parts of the graph that has changed. i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Graph contains two components: (1) an incremental iterative computation model to improve the execution efficiency of the iterative graph algorithm; and (2) an incremental update method to accelerate the iterative process within the iterative graph algorithm. It is implemented based on Spark GraphX, a popular parallel and distributed computing framework for large-scale graph processing. Experiment results verify the performance advantages of i <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Graph model when performing some iterative graph algorithms on the dynamic graph, compared with the traditional iteration.
Foreign free software for computerized library information systems

Free software enables its free use by anyone under observance of user proprietary rights. The authors review the market of free computerized library information systems. The origins for applying free ALIS are specified; free Evergreen и Koha ALIS are characterized and compared; the list of foreign free ALIS, date and state of development are specified. The methodology for ALIS market estimation is suggested; the role of ALIS developing and related communities is emphasized. The library and designer aspects of ALIS development are described. The new phenomenon of «donation culture» emerged due to implementing free software in libraries. The typical copyright conflict around Koha ALIS between proprietors and independent designers (the latter won with establishing community trust) is described. Using free ALIS follows the general trend of applying free software in public offices, with all advantages and disadvantages of this information infrastructure. As a result, free software is often opposed by the proprietary one, however both components make the competitive market offering users to make choice of ALIS of appropriate price/quality balance.
Information Hiding Based on Typing Errors

Steganography hides secret in carrier. The carrier can be an image, a soundtrack, a video, a text or some other. Text steganography is sometimes more difficulty than others because of the high density of information carried by text. However, we found that most of the online texts is of a kind that tends to contain many typing errors, known as typos, but humans are very good at disambiguation, so these typos often do not frustrate understanding and sometimes it is even difficult to be recognized. This phenomenon can be found both in English and Chinese. In this paper, we propose a text steganography method using carefully injected typos and guarantee the security of the secret and the readability of the texts. With the help of a natural language processing (NLP) model named BERT, we can extract the secret message without the original text. Different from those format-based steganography algorithms, our method can resist format adjustments, OCR re-inputs, etc. Inspired by the text steganography in English text, text steganography in Chinese text with a similar principle is practical. Furthermore, social media platforms always contain many kinds of media, so Cross-Media or even Cross-Social-Network information hiding is practical when combining multi steganography algorithms.