Reviewerly/exHarmony huggingface dataset:
Underwater Enhancement Model via Reverse Dark Channel Prior

The interference of suspended particles causes the problems of color distortion, haze effect and visibility reduction in complex underwater environment. However, existing methods for enhancement often result in overexposure of the low contrast area or distortion of the seriously turbid area. The main factor is the diversity of the underwater images. In this paper, we propose a novel underwater imaging model which can recover different kinds of underwater images, especially the image from the murky water. Specifically, we develop a reverse dark channel prior algorithm which can separate the image with large degradation by setting a maximum filter dark channel threshold. Using the algorithm, we can calculate the adaptive transmissivity and restore the blurred images. The restored image will be sent into an end-to-end generative model for coloring transformation and enhancement. Furthermore, we build an edge deepening module for edge recovery. Comprehensive experimental evaluations show that our method performs promising on image enhancement for different kinds of turbid water. Compared with other previous methods, our method is more universal and more suitable for practical applications.
Secure Data Transmission of Smart Home Networks Based on Information Hiding

Smart home is an emerging form of the Internet of things (IoT), which can enable people to master the conditions of their smart homes remotely. However, privacy leaking in smart home network is neglected. Therefore, it is strongly necessary to design a secure and effective scheme to ensure the privacy protection of the smart home network. Considering the privacy leaking in the smart home network system, this paper proposes a scheme combining encryption and information hiding to ensure the security of data transmission. In the scheme, the sensitive data are encrypted to ciphertext before it is transmitted, and then the ciphertext is embedded into the Least Significant Bit (LSB) of the cover image. On the receiving terminal, the ciphertext can be extracted from the stego image, then the ciphertext is decrypted into the sensitive data. Comparing to the method of using encryption algorithm merely, the scheme greatly improved the security of data transmission, making it more suitable for the real environment.
A SHORT GUIDE TO DEVELOPING COMMUNICATIVELY ORIENTED TEXBOOK ON SPEECH CULTURE

Abstract The article describes the longitude experiment on creating the communicatively orientated textbook on Russian speech culture. The problem of the majority of modern textbooks on speech culture is the lack of communication potential as they are usually based on texts from classical literature and that is why the language material is far from the professional needs of the university students (and especially the students of technical, mathematical and economical directions of education). The authors of the article conducted the linguistic researches in different language levels and proposed the principles of creating the textbook on speech culture with orientation on the professional needs of the university students. Then the principles were realized in the textbook “Russian Language and Speech Culture”. The efficiency of the principles is affirmed during the experiment; students who studied the speech culture with the help of the textbook showed better results than the control groups. The textbook became one of the most popular manuals for practically orientated university students.
A Salience &amp; Motion State Based Quality Adjustment for 360-Degree Video Transmission

The rapid development of Virtual Reality technology and the video sharing market of VOD (video-on-demand) stream nowadays makes online 360-degree video a new form of internet media. In the past few years, many 360-degree video delivery schemes are proposed. Yet, high fps requires a very high network band, otherwise will suffer from significant video quality drop. In this paper, we are focusing on using salient object detection and frame motion state detection for quality adjustment to reset 360-degree image quality of salient foreground and unimportant background. The system predicts the salient portion of video and then separate one frame in to foreground frame and back ground frame. Then by combining MS SSIM (Modified Structural Similarity of Images) algorithm, it also detects important frames and estimate the motion state of each view. The following procedure includes methods which lower the quality of dynamic background view and meanwhile maintain the high quality of foreground. This stage separates a 360-degree video into two videos with a much smaller file size of the background video. The core contribution of the scheme is that it is able to detect salient foreground and less noticed dynamic background of VR graphics. As a result in the transmission of the VR graphics, the network can execute a parallel transmission of a foreground and a economized background, costing less network resources.The results prove that this proposed scheme can effectively reduce global transmission bandwidth of the network with a wide adaptation of all genres of 360-degree videos.
Disjunctive Propositional Logic and Scott Domains

Based on the investigation of the proof system of a disjunctive propositional logic, this paper establishes a purely, syntactic representation of Scott-domains. More precisely, a category of certain proof systems with consequence relations is shown to be equivalent to that of Scott-domains with Scott-continuous functions.
Boosting Cross-lingual Entity Alignment with Textual Embedding

Multilingual knowledge graph (KG) embeddings have attracted many researchers, and benefit lots of cross-lingual tasks. The cross-lingual entity alignment task is to match equivalent entities in different languages, which can largely enrich the multilingual KGs. Many previous methods consider solely the use of structures to encode entities. However, lots of multilingual KGs provide rich entity descriptions. In this paper, we mainly focus on how to utilize these descriptions to boost the cross-lingual entity alignment. Specifically, we propose two textual embedding models called Cross-TextGCN and Cross-TextMatch to embed description for each entity. Our experiments on DBP15K show that these two textual embedding model can indeed boost the structure based cross-lingual entity alignment model.
Authorship Link Retrieval Between Documents

In this paper we propose a method for automatic author clustering called Document Authoring Link Retriever, DALIR. Documents are represented using Doc2Vec, experimenting with several parameters; afterwards, vectors are clustered (or linked together) using K-means and Hierarchical Agglomerative Clustering. We experimented with different vector representation sizes, different fixed number of clusters, and clustering methods. We evaluated our method on the author clustering task of PAN @ CLEF 2017. We used the BCubed F-score evaluation scheme of this task, being able to overcome some of the reported results from the first places of this challenge, although our method requires to manually establish a number of clusters a priori.
Design and Implementation of Traversing Machine System Based on msOS Platform

In order to solve the problem of multi-task simultaneous operation of sensor reading data, remote control data receiving, OSD (On Screen Display) data sending and PID (Proportion Integration Differentiation) attitude control, a traversing machine system based on msOS platform was designed. The system implements a traversing machine system with attitude control, fixed height control, OSD data return, and fast system response speed. First, the structure and flight principle of the traversing aircraft are introduced. Then introduced the hardware design of the system. The hardware system adopts the idea of holistic design. Centralize the interface between the sensor and the function module on the flight control board. Then introduced the software design of the traversing machine system in this paper. Then read various sensor data through msOS. The software executes the cascade PID algorithm to realize the attitude control of the rider. Then introduced the debugging process. Finally, the shortcomings of the traversing machine system based on msOS platform in this paper are summarized and the research prospect is prospected.
People Counting in a Public Event-Use Case: Free-to-Ride Bus

In the case of traditional bus travel, there are many ways to gather statistics on bus passengers. Paid tickets can directly contain information about the stop at which the passengers came aboard and where they got off. Alternatively, stamping or reading travel cards can be used to obtain similar statistics. In the case of a free-to-ride bus service, these simple methods are generally not available. Yet, for further route and capacity planning, producing exact – or at least informative – statistics is crucial. This paper presents a real-life use case of collecting statistics about bus passengers on a free-to-ride bus route at a large public event in the summer of 2018 in Pori, Finland. The use case utilized cost-effective and off-the-shelf components such as the Raspberry Pi 3 computer, position sensors, cameras, and the Open Source Computer Vision Library version 3. The hardware and software components of the system, which was based on image analysis and shape detection, as well the results of the study, are explained in this paper. Furthermore, this paper presents a discussion on the challenges faced while developing and implementing the system.
Efficient Deeplearning Technique to person reidendification in content based video system

Person re-identification is very interesting and major difficult in Video surveillance systems which have great impact on safety of public. In spite of broad research endeavors' for quite a long time, it stays one of the most testing open issues that extensively impedes the triumphs of genuine Content Based Image Retrieval frameworks Person re-ID is only issue of distinguishing individuals crosswise over pictures that have been caught by various observation cameras without covering fields of view. With the expanding requirement for computerized video examination, this errand is getting expanding consideration. What's more, it has numerous basic applications, for example, cross camera following, multi-camera conduct investigation and measurable pursuit. In any case, this issue is trying because of the huge varieties of lighting, posture, perspective and foundation. To address these various troubles, in this paper, we propose a few profound learning-based ways to deal with acquire a superior individual re-recognizable proof execution in various manners. We applied ResNet50 and Siamese Network. We use local feature methods to extract the required image from database. We observed the ResNet50 producing great accuracy compare to remaining methods.
Exploring the Use of Reverse Thrust in a Dynamic UAS Landing Maneuver using Kinodynamic RRT

The concept of reversing the direction of the main propeller (and thereby the thrust) of an electric, fixed-wing unmanned aerial system (UAS) during landing maneuvers is examined in this paper. The physical performance of a propeller and motor combination is first tested using a custom thrust test bench. A two-dimensional simulation of a fixed-wing UAS is then created using a nonlinear dynamics model and the dynamics are used in a Rapidly-Exploring Random Tree (RRT) algorithm to search for feasible trajectories and their necessary control inputs. The possible usefulness of the reverse thrust maneuver is then demonstrated and shown in simulation with favorable results.
A Novel Distributed Anomaly Detection Algorithm for Low-density Data

Anomaly detection becomes an interesting and significant problem in data mining area. It, in turn, generates many promising applications such as Intrusion Detection, Fraud Detection, Mobile cellular network fraud, and etc. Most of the existing anomaly detection methods are specifically modeled on certain tasks or datasets. And the density-based model has widely applied in anomaly detection. However, for many real-world applications, domain experts usually confront with large scale low-density data. This paper aims to solve the anomaly detection problem of large-scale low-density data. The Relative Local Density-based Outlier Factor (RLDOF) is a highly efficient and promising method for low-density data. While it is not easy to achieve considerable results concerning large-scale data. In this paper, we propose a distributed RLDOF model for low-density data on a large scale. The experimental results indicate that, compared with the existing methods, the model can effectively improve the anomaly detection performance of large-scale low-density data.
An Approach to Support the Design and the Dependability Analysis of High Performance I/O Intensive Distributed Systems

Frequent service down times and poor system performance can affect aspects such as the availability, quality of experience and generate millions of dollars in lost revenue. High Performance Computing (HPC) environments are often required to comply with performance and dependability requirements. The CHESS methodology provides support for the design and the evaluation of dependability and performance system attributes. In this paper we extend the CHESS methodology to support the design and the dependability analysis of HPC environments. The proposed approach was employed in the Grid’5000, a highly distributed and I/O intensive HPC environment. The application of the proposed approach provided key information for demonstrating dependability, deriving project decisions, agreeing on new design choices and resource allocation strategies.
The Cognitive Diagnosis Based on the Framework of Junior High School Mathematics Knowledge and Ability

With challenges of the traditional "only fractional" in educational assessment, researchers have been paying more and more attention to the intelligent diagnosis and assessment of disciplines, so there are many intelligent assessment systems for students now. However, there are less researches focusing on the studies of special discipline characteristics, such as skills and ability in China. In this paper, we have established a multi-dimensional framework of mathematical knowledge ability attributes and applied it to the Mathematical Diagnostic System (MDS-KA) to help students develop comprehensively. First, the framework of mathematical knowledge ability attributes diagnoses students' cognitive structure from the three dimensions of knowledge, skills and abilities, and accurately locates students' weak points. Second, the MDS-KA system is integrated into the above framework, intelligently assesses students' learning situation, and combine with empirical research to explore the effectiveness of the framework and system. By the analysis of feedback questionnaire data, there were more than 87% of students think that the individual diagnosis report is consistent with their real learning situation and helpful for learning.
Adaptive Dimensionality Reduction for Local Principal Component Analysis

The early detection of upcoming failures in technical systems, e.g. in factory automation, is crucial for worker safety and to avoid downtimes. Machine learning (ML) approaches are commonly used in this context to learn the standard behavior of technical systems and to identify anomalies by determining if new data points are outside of the training data distribution. Reliable anomaly detection becomes even more challenging when ML models are continuously learnt on data streams and required to account for "concept drift". Gaussian mixture models (GMM) of the Neural Gas Principal Component Analysis (NGPCA) type are well suited for online learning and anomaly detection. NGPCA describes non-linear data distributions with a set of locally linear PCA models. They offer better interpretability and less computational effort compared to autoencoders which are often used in this application field. The contribution of this work is to enhance NGPCA (and generally GMMs) by extending local PCA with an adaptive approach that adjusts the dimensionality of each local model individually. This improves the representation by the overall model and therefore its capability to detect anomalies. The proposed method is applicable to streaming data by continuously and individually adapting the dimensionality of each local PCA model. To achieve this, natural characteristics of online PCA are exploited. The algorithms has low computational complexity enabling the usage in technical systems with limited resources. First the mathematical foundation is explained and used to extend NGPCA. Afterwards, the new combined algorithm is numerically evaluated and advantages over non-linear competitors are pointed out.
Research on 3×3 coupler photoelectric detecting technology based on light source modulation in fiber optic hydrophone

The photoelectric detecting technology bas-ed on the 3×3 coupler is widely used in interferometric fiber optic hydrophone systems. Parameters such as DC and AC amplitude and phase difference of the couplers have a crucial influence on the accuracy and stability of demodulation. In this paper, an ellipse fitting algorithm based on light source modulation is used to achieve real-time, accurate measurement and correction of the detection parameters, eliminating the impact of the 3×3 coupler asymmetry on the system. The signal detecting accuracy is improved with the effectively suppressed system noise, realizing the stable signal detecting for the fiber optic hydrophone. Compared with the traditional ellipse fitting algorithm based on external large signal modulation, the proposed algorithm presents higher reliability and practicability. Furthermore, in view of the characteristics of large-scale array data processing and high real-time requirements, this paper adopts a heterogeneous design of ARM+FPGA, using the SDSoc platform to carry out software and hardware collaborative development of the 3×3 coupling demodulation algorithm based on light source modulation. ARM implements task allocation and scheduling functions, and FPGA implements specific function calculation modules. this design greatly reduces the pressure of computer data processing, and ensures that the optical fiber hydrophone system can complete automatic parameter calibration while real-time demodulation. The demodulation efficiency of the 3×3 algorithm is improved, coupled with the high integration and easy expansion of the system itself, provides the possibility for future large-scale arrays of 3×3 detection schemes in fiber optic hydrophone systems.
The Executors Scheduling Algorithm for the Web Server Based on the Attack Surface

In the existing scheduling algorithms of mimicry structure, the random algorithm cannot solve the problem of large vulnerability window in the process of random scheduling. Based on known vulnerabilities, the algorithm with diversity and complexity as scheduling indicators can not only fail to meet the characteristic requirements of mimic's endogenous security for defense, but also cannot analyze the unknown vulnerabilities and measure the continuous differences in time of mimic Executive Entity. In this paper, from the Angle of attack surface is put forward based on mimicry attack the mimic Executive Entity scheduling algorithm, its resources to measure analysis method and mimic security has intrinsic consistency, avoids the random algorithm to vulnerability and modeling using known vulnerabilities targeted, on time at the same time can ensure the diversity of the Executive body, to mimic the attack surface web server scheduling system in continuous time is less, and able to form a continuous differences. Experiments show that the minimum symbiotic resource scheduling algorithm based on time continuity is more secure than the random scheduling algorithm.
Meta-Learning of Compositional Task Distributions in Humans and Machines.

Modern machine learning systems struggle with sample efficiency and are usually trained with enormous amounts of data for each task. This is in sharp contrast with humans, who often learn with very little data. In recent years, meta-learning, in which one trains on a family of tasks (i.e. a task distribution), has emerged as an approach to improving the sample complexity of machine learning systems and to closing the gap between human and machine learning. However, in this paper, we argue that current meta-learning approaches still differ significantly from human learning. We argue that humans learn over tasks by constructing compositional generative models and using these to generalize, whereas current meta-learning methods are biased toward the use of simpler statistical patterns. To highlight this difference, we construct a new meta-reinforcement learning task with a compositional task distribution. We also introduce a novel approach to constructing a task with the same statistical complexity as the compositional distribution but without explicit compositionality. We train a standard meta-learning agent, a recurrent network trained with model-free reinforcement learning, and compare it with human performance across the two task distributions. We find that humans do better in the compositional task distribution whereas the agent does better in the non-compositional null task distribution -- despite comparable statistical complexity. This work highlights a particular difference between human learning and current meta-learning models, introduces a task that displays this difference, and paves the way for future work on human-like meta-learning.
An approach to study medicine in China based on artificial intelligence analysis model for international students’

This study utilizes empirical research method to compile the questionnaire for the motivation of Chinese medical students coming to China. The survey was conducted on 653 medical students from 55 origin countries and 8 universities in China. In addition to motivation research, we also analyzes the relationship between the factors such as university, educational background, gender and origin of students and the motivation. The results show that safe and stable social environment, profound history and culture, and a large number of universities are the most important motives for foreign students to study medicine in China. There are significant differences in the motivations of international medical students from different genders, universities, and educational background to study abroad, and but there were no significant differences among students from different countries of origin.
Data-driven Online Group Detection Based on Structured Prediction

Group detection of crowds is an important and challenging problem in applications of the crowds analysis. Especially for service robots, accurate group detection is the premise to ensure the safe interaction between humans and robots. In this paper, we propose an online group detection method based on Structured Prediction for middle density crowds. First of all, we extend the features of pairwise trajectories with velocity and orientation to obtain more valid information. Then, a fully-connected social network is maintained to improve time efficiency significantly. Finally, we adopt the adaptive-sampling BCFW algorithm to learn the mapping from trajectories to groups. Comparing with current state-of-the-art methods, our experiments demonstrate the group detection capacity on precision and time efficiency.