Reviewerly/exHarmony huggingface dataset:
Design and Implementation of Visualized Marine Landscape Logo based on ARM

Data explosion is a major challenge in the field of contemporary information science. Data visualization can transform disordered data into graphic images that people can easily understand, and find their rules. Data visualization plays an important role in the study of geographic and marine data. For this reason, this paper studies and analyzes the visual marine identification of marine environment(ME) based on arm. Firstly, the concept of arm and the selection of ARM processor are briefly introduced; The visual design based on ARM ME is discussed and analyzed, including trend chart analysis of real-time data based on database, Element Visualization Based on space and histogram visualization based on map; Secondly, an ocean model algorithm is proposed; Finally, by testing the performance of the Visual Ocean identification processing mechanism based on arm, the usability and user experience of the remote interactive visualization service supported by this computing mode are tested. The test results show that arm parallel computing processing mechanism has good speedup performance for large-scale data processing of long-time series.
Covert Communication and Image Authentication Algorithm Based on Adversarial Examples

The research on the application of adversarial examples mainly focuses on considering adversarial examples as a threat in the past. In order to make better use of the adversarial examples, rather than just taking it as the defects of neural network, a covert communication method based on adversarial examples is proposed in this paper. Using the characteristics that adversarial examples can carry information, we combine it with specific coding rules to develop a covert communication algorithm. Unlike the traditional steganography, the secret information is not contained in the communication content of the sender and the receiver itself in the proposed scheme. The mapping relationship between adversarial examples and secret information is hidden in the neural network model, so as to realize the hidden transmission of information and improve the concealment and security of communication. At the same time, the tamper identification is embedded in the tensor of adversarial output. When the image is modified during transmission, the tamper identification will also change, so that the image can be authenticated. Experiments show the feasibility of the algorithm and verify that it can completely extract secret information from the encrypted image adversarial example and authenticate the integrity of the image.
Segmentation of Ship Satellite Images with Transfer Learning and Attention Mechanism

As an integrated part of national marine supervision system, ship monitoring plays a critical role in ensuing efficient exploitation of marine resources and safety of territorial sea. In this paper semantic segmentation networks are trained using high-resolution satellite ship data to facilitate more detailed ship inspection. Using the UNet network, the network performance is optimized by improving the structure of the convolutional an attention mechanism is incorporated to enhance useful features and suppress useless features so as to improve segmentation accuracy. The encoder module obtains high-level semantic information of Synthetic Aperture Radar (SAR) remote sensing of ship images layer by layer, and the decoder module recovers spatial information step by step. Experimental results show that this method can effectively enhance accuracy of image segmentation and retain more image details.
TMIB: A Traceable Multi-user Image Retrieval Scheme based on Blockchain

With the development of cloud computing technol-ogy, more and more users choose to encrypt their huge image data and store them on the cloud server to save their limited storage resources. In order to conduct a safe and effective query operation, it is necessary to research the technology of searching encrypted images. However, most of existing research schemes on encrypted image retrieval technology are focused on the improvement of efficiency and accuracy, ignoring the threat brought by malicious cloud servers or malicious nodes. To solve this problem, we propose a Traceable Multi-user Image retrieval scheme based on Blockchain (TMIB). TMIB scheme utilizes the blockchain based on characteristics such as de-centralization and data tamper-proofing to prohibit returning wrong query results by a malicious cloud server. Moreover, TMIB scheme adopts a proxy re-encryption approach to achieve availability in multi-user scenarios. Furthermore, a watermark is embedded in the encrypted images to track the malicious users for preventing malicious users from distributing the images again. Finally, we analyze the security and feasibility, proving the high precision and efficiency of TMIB scheme in the actual scene through experiments.
Underwater Image Enhancement Based on AttR2U-Net and Multi-Residual Networks

Underwater optical imaging can intuitively reflect internal state of the ocean. However, due to absorption and scattering of light, the degree of attenuation of different wavelengths of light in seawater is different, and underwater images suffer from low contrast and color distortion. To address the problems, a novel network based on U-Net is proposed for underwater image enhancement in this paper. The network consists of two branches namely Branch1 and Branch2: Branch 1 contains a channel attention module and a recurrent residual convolutional neural network based on spatial attention termed AttR2U-Net that is capable of extracting multiscale features. Branch 2 contains three residual groups, embedded in the residual dense block of the channel attention model, by re-weighting the features of different channels to enhance useful features. Furthermore, through dense cascading and residual learning, convolutional enhancement branches extract high-frequency information such as edges in the corrected image to maintain original structure and edges. Finally, results of the outputs of the two branches are fused. Comparable studies demonstrate that the proposed method is superior to state-of-art methods in color correction on underwater images in different scenes and enhancement of the clarity and contrast of images.
Helmet Wearing Detection Algorithm Based on Lightweight Network Model

Aiming at the problems that the traditional helmet wearing detection algorithm has a large amount of parameters and calculation as well as poor real-time performance, which is not conducive to field deployment, helmet wearing detection algorithm called YOLOv4-MAC based on lightweight network model is proposed. Firstly, the YOLOv4 backbone feature extraction network CSPDarkNet53 is replaced by MobileNet V3, and the Depthwise Separable convolution(DW) is used to replace the traditional convolution in PANet and prediction network, which greatly reduces the amount of parameters and calculation; Then SPPNet is replaced by ASPPNet to increase the network receptive field and reduce the amount of network calculation; Finally, CBAM module is added before YOLOHead module to increase the detection ability of small targets. The experimental results show that the mAP of the improved algorithm is 87.82%, the detection speed is greatly improved, and the helmet wearing detection condition can be judged quickly and accurately.
Operation Parameter Optimization of Cooling Systems in Data Centers Based on NN-HS

The rapid development of Internet services such as cloud computing has accelerated the construction speed and scale of data centers. High energy consumption has become a prominent problem for data centers. Cooling systems account for a considerable proportion in the total energy consumption of data centers. Operation parameter optimization for reducing energy consumption is one of the key technologies to build green data centers. In order to solve this problem, we propose a new method with PUE (Power Usage Effectiveness) as performance index. Setting the operation parameters of cooling systems as the inputs and the corresponding PUE as the output, the neural network is constructed. The PUE function is approximated by this neural network with historical operating data of the data center of a certain bank in Beijing, and it is taken as the optimization objective. Considering other constraints, the relevant mathematical optimization model is established. At the same time, the harmony search algorithm is improved, including the search method of “seeking advantages and avoiding disadvantages” and the adaptive strategy. Therefore, novel improved harmony search algorithm (IHS) is proposed. Finally, the proposed IHS algorithm is applied to solve the above model. Based on the relevant instance data of the bank, the optimal operation parameters of the cooling system under given conditions are obtained. Through the analysis and comparison of optimal results, the effectiveness and feasibility of the proposed method are verified.
Decomposing style, content, and motion for videos

In this paper, we present the first video decomposition framework, named SyCoMo, that factorizes a video into style, content, and motion. Such a fine-grained decomposition enables flexible video editing, and for the first time allows for tripartite video synthesis. SyCoMo is a unified and domain-agnostic learning framework which can process videos of various object categories without domain-specific design or supervision. Different from other motion decomposition work, SyCoMo derives motion from style-free content by isolating style from content in the first place. Content is organized into subchannels, each of which corresponds to an atomic motion. This design naturally forms an information bottleneck which facilitates a clean decomposition. Experiments show that SyCoMo decomposes videos of various categories into interpretable content subchannels and meaningful motion patterns. Ablation studies also show that deriving motion from style-free content makes the keypoints or landmarks of the object more accurate. We demonstrate the photorealistic quality of the novel tripartite video synthesis in addition to three bipartite synthesis tasks named as style, content, and motion transfer. • Decomposing video into style, content, and motion in an unsupervised framework. • Extracting motion from style-free content. • Supporting three bipartite synthesis tasks as style, content, and motion transfer. • Enabling novel tripartite video synthesis. • Performance of video synthesis on various video categories.
Construction of Entity and Entity Relationship Annotation Corpus for Test Identification

Currently, the test identification corpus faces the problems of small scale, poor consistency and poor standardization, which is difficult to meet the needs of intelligent applications such as knowledge mapping, question answering system and knowledge reasoning. Based on the entity labeling system for test identification developed by CCKS evaluation conference, this paper developed and improved the labeling specification. On this basis, the original text of test identification was obtained through news, Wikipedia and other channels. The entities in the original text were manually labeled and collated by the annotator, and the entity annotation corpus for test identification was constructed. The corpus constructed in this paper has a total of 86,000 words, with a total of 5980 entities and 4982 entity relations. The consistency of multi-round annotation is 86% and 85%, respectively.
Chirp-Loc: Multi-factor authentication via acoustically-generated location signatures

Context-based authentication has been proposed as a way to enable secure authentication with minimal or even no user interaction requirements by using sensor data to ensure the device being authenticated is in possession of the person initiating the authentication request. A key limitation of practically all context-based authentication systems is that they are vulnerable to context manipulation attacks where an attacker manipulates the environment to create a desired response in the sensor data. We contribute Chirp-Loc as a system that has been designed to improve the robustness of context-based authentication solutions against context-manipulation attacks. Chirp-Loc integrates an innovative approach based on room impulse response (RIR) to establish a location fingerprint that characterizes the physical environment instead of the ambient environment. We describe the design and development of an Android prototype of Chirp-Loc. We also conduct extensive accuracy and security analysis of Chirp-Loc by considering a multi-factor authentication solution that uses Chirp-Loc to verify the proximity of an authentication token, such as a smartphone. Through extensive experiments, we demonstrate that Chirp-Loc offers high degree of security and usability. Our work paves the way for improving the resilience of context-based authentication against attackers that manipulate the context information and offers a way to implement authentication systems that minimize user interaction demands while offering a high degree of security.
The role of cartography in presenting information on the distribution of the covid-19 outbreak

Abstract Geospatial information is one of the instruments used by the government in used to map the rate of spread of the Covid-19 disease, the level of Covid-19 vulnerability, and facilitate the distribution of social assistance to people affected by Covid-19. The ability to process geospatial information is needed for the parties who make use of the information. On that basis, the Geographical Information Science Study Program encourages increased ability to manage geospatial data among government staff in Indonesia through cartography training: thematic mapping related to Covid-19 outbreak by utilizing the ArcGIS Online application. The method applied in this training activity is presentation, demonstration, and application operation compiling thematic maps related to Covid-19 using ArcGIS Online to 527 participants. The majority of participants who have never used the ArcGIS Online application are enthusiastic about participating in cartography training, especially since the training is carried out with a tutor model and the participants complete all the work until it’s finished. The expectation, the participants will implement the use of the application in the agencies where they work. Therefore, not a few participants asked for this training to be continued intensively.
UAV Formation Transformation Based on Two-Stage Path Planning Method

With the maturity of unmanned aerial vehicle (UAV) technology, the application of UAV in various fields is more in-depth. A method to quickly and accurately solve the flight paths of a large number of UAVs from one formation to another is crucial for application scenarios that require UAVs to perform formation transformation tasks. In this paper, the path planning of UAV swarm formation transformation is divided into two stages: rough planning and fine planning. In the rough planning stage, a 0-1 variable planning model is established to match the start and end points of the UAV flight path, and the model is solved using a Strengthen Elitist GA Algorithm to quickly obtain high-quality feasible solutions. In the fine planning stage, four models including behavioral mode, obstacle avoidance criterion, optimal waypoint selection and target exchange are established. On this basis, a heuristic fixed-step space search algorithm is designed and implemented to plan the path finely, and the path planning for the UAV formation transformation in two scenarios is finally completed.
Comparison of Various Empirical Models to Estimate Monthly Mean Diffuse Solar Radiation for Humid-Subtropical Climate Region of India

This research shows the performance analysis of different models in estimating monthly average diffuse solar radiation on the horizontal surface (Rd) of humid-subtropical climate region (HSCR) of India. The annual average diffuse, beam and global solar radiation (Rg) values have been obtained from Indian Metrological data as 7.82, 14.48, and 22.30 MJ/m2-day, respectively for Aligarh locations. The location has abundant potential to use solar energy under good conditions in the sky. The suitable estimation of Rd with unique input parameter (i.e., Rg) was investigated. A literature correlating diffuse fraction (DF) (Rd to Rg ratio) to the sky-clearness index (SCI) (Rg to extraterrestrial solar radiation ratio) was selected for 25 representative models. The aim is to get the best precise model among 25 models for the evaluation of Rd in the region under consideration. The result of 25 models had been compared with Indian Metrological data. In the context of well-known statistical indicators, distinctive statistical evaluation of the models was carried out. The outcomes of statistical test gave that model 10 provided smaller MBE (−0.0625), RMSE (0.9938), MPE (−0.0781), MAE (0.9125), and t-stat (0.2090) among all 25 models. This research contributes to the understanding of the importance of Rd, where the lack of essential devices, along with the significant operating and maintenance costs, is a notable barrier to assessment. The results of this paper are helpful for developing nations along with remote places having related weather conditions.
Methodology of application of educational online services in teaching mathematics in the context of the implementation of the updated FGOS LLC

The article is devoted to the use of electronic educational resources in teaching mathematics and is based on the key ideas of the updated FGOS LLC, one of which is the idea of forming and developing students' competencies in the field of information technology use. The article will introduce math teachers to the possibility of using an online educational service Learningapps.org to increase motivation and more productive formation of computational skills of students in the process of teaching mathematics. The theme of "Fractions" is taken as a basis. The article provides methodological recommendations for the inclusion of educational resources in the process of studying this topic. The article is addressed to teachers and students of pedagogical universities, methodologists, as well as teachers of mathematics.
A multi-output model based on extreme learning machine with application in the multi-objective optimization of a dental implant

In this work, a multi-output model is proposed based on extreme learning machine, which uses the regression information of training samples and the correlation of outputs to predict multiple responses for the new point. Two kernel matrices are designed to evaluate the regression relationship of training samples and the correlation of the outputs, respectively. A five-fold cross-validation method is used to assess the training error, and a heuristic algorithm is used to optimize the hyperparameters of the proposed model. The results of mathematical problems indicate that the performance of the proposed model is better with more training samples and is positively correlated with the correlation of outputs. The proposed model produces more competitive performance in terms of prediction accuracy and computational cost than the benchmark multi-output models. The proposed multi-output model is applied to the multi-objective optimization of a dental implant, showing its application potential in engineering design and optimization.
The effectiveness of second-person reference in enhancing reader memory in English-Chinese advertisement translation

Abstract This research aims to account for readers’ perception of second-person pronouns and to test their effectiveness in enhancing reader memory in English-Chinese advertisement translation. We conducted an experiment in which 62 participants read the Chinese translations of 16 previously unseen English print advertisements. Two parallel Chinese versions were prepared for the experiment, one with second-person reference and the other without. The participants were first asked to read the translations and indicate which version they liked better. Two weeks later, they were divided into two groups and asked to rate their memory of the two Chinese versions. Statistical analysis shows that the ratings of the second-person version are significantly higher, which implies that second-person reference is effective in enhancing the participants’ memory.
A novel decoder based on Bayesian rules for task‐driven object segmentation

As a challenging problem in computer vision, salient object segmentation has attracted increasing attention in recent years. Though a lot of works based on encoder–decoder have been made, these methods can only recognize and segment one class of objects, but cannot segment the other classes of objects in the same image. To address this issue, this paper proposes a novel decoder based on Bayesian rules to perform task-driven object segmentation, in which a control signal is added to the decoder to determine which class of objects need to be segmented. What's more, a Bayesian rule is established in the decoder, in which the control signal is set as the prior, and the latent features learned in encoder is transferred to the corresponding layer of decoder as observation, thus the posterior probability of each object with respect to the specific-class can be calculated, and the objects belonging to this class can be segmented. This proposed method is evaluated for task-driven salient object segmentation on several benchmark datasets, including MS COCO, DUT-OMRON, ECSSD etc. Experimental results show that the approach tends to segment accurate, detailed, and complete objects, and improves the performance compared with the previous state-of-the-art.
The Relationship between Job Skills and the Level of Job Availability in the Information Technology Industry: An Analysis of Information Technology Job Market

According to the national statistics, the Information Technology (IT) industry provides massive rapidly growing strength of the workforce in Sri Lanka. When consider the job availability of the IT industry it mostly depends on the five skills of the applier. Programming language, full-stack development, front-end development, back-end development, and databases are playing a major role in IT skills. This paper presents a recent survey on those five major skills used by Sri Lankan employees in the IT industry and a recent index of their popularity and industry trends. The internet job database was selected as the data gathering source for this study. Then Statistical Package for the Social Science (SPSS) was used for analyzing the sample dataset. Job skills examine with job opportunities in entry-level, mid-level, and senior-level. Based on the analysis, research found that what are the expectations in each level of jobs in the current IT job market. The result of this research should be useful to job seekers, IT stakeholders, and also fresh undergraduates can be obtained a crystal-clear analysis about the new trends based on programming languages and other essential requirements of the current IT sector.
Design of a river chief incentive mechanism based on blockchain: A principal–agent model

Abstract Information asymmetry caused by centralized databases is the main factor hindering the improved performance of river chief governance, and blockchain can solve this dilemma. In view of the mismatch between traceable feature of blockchain and the heavy punishment mechanism, a model of river governance was constructed based on principal–agent theory, and an incentive mechanism of river chiefs in the context of blockchain was designed. The results we conducted will enable the real river management information to be stored permanently in the distributed ledger. These measures are conducive to long‐term river management and improve the overall environmental and social benefits.