Reviewerly/exHarmony huggingface dataset:
A Chinese Ancient Book Digital Humanities Research Platform to Support Digital Humanities Research

With the rapid development of digital humanities, some digital humanities platforms have been successfully developed to support digital humanities research for humanists. However, most of them have still not provided a friendly digital reading environment and practicable digital humanities analysis tools to support humanists on interpreting texts and exploring characters' social network relationship. Moreover, the advancement of digitization technologies for the retrieval and use of Chinese ancient books is arising an unprecedented challenge and opportunity due to the development of international Chinese study. For these reasons, this paper presents a Chinese ancient books digital humanities research platform (CABDHRP) to support historical Chinese studies. In addition to providing digital archives, digital reading, basic search, and advanced search functions for Chinese ancient books, this platform still provides two novel functions that can more effectively support digital humanities research including an automatic text annotation system (ATAS) for interpreting texts and a character social network relationship map tool (CSNRMT) which can show the character relationships as a knowledge graph with triples containing two character names and their corresponding relationship attribute for exploring characters' social network relationships. This study adopted DSpace, an open-source institutional repository system, to serve as a digital archives system for archiving scanned images, metadata, and full texts to develop the CABDHRP for supporting digital humanities research. To understand the research participants' perception when interpreting the historical texts and character social network relationships with the support of ATAS and CSNRMT, the semi-structured interviews with 21 research participants were preceded. An automatic text annotation system (ATAS) embedded in the reading interface of CABDHRP can collect resources from different databases through linked data (LD) for automatically annotating ancient texts to support digital humanities research. Additionally, the CSNRMT provided by the CABDHRP can semi-automatically identify characters' names based on Chinese word segmentation technology and humanists' support to confirm and analyze characters' social network relationships from Chinese ancient books based on visualizing characters' social networks as a knowledge graph. The CABDHRP not only can stimulate humanists to explore new viewpoints in a humanistic research, but also can promote the public to emerge the learning interest and awareness of Chinese ancient books.
Multiclass Single Label Model for Web Page Classification

Web is a huge repository of information and there is a need of categorization of web pages to facilitate better search and retrieval of pages. Web page classification has become a challenging task due to the exponential growth of the World Wide Web and this study augments classification model with a general facility for automatically assigning class label (e.g., sport, news) to web pages based on the output of a Naive Bayes classifier. For the purpose of build classification model, yahoo Open Directory Project (ODP) data set has been used for create training and testing set. In this research work web page classification was done using Uniform Resource Locator (URL) features, Meta data, Meta keywords, Internal Links and text, which gives better result than URLs based method.
Research on AUV Global Path Planning Based on Multi-Objective Ant Colony Strategy

In order to overcome the limitation of traditional path planning with a single path length, a multi-objective ant colony algorithm for underwater robot path planning in marine environment is proposed. The route distance, energy consumption and safe navigation are the comprehensive planning goals. The pheromone is classified, and the pheromone update mechanism is improved based on different target performance rankings. At the same time, the three targets are optimized, and the Pareto solution set idea is introduced to find the optimal path under different navigation targets and improve the comprehensiveness of the solution. The simulation results verify the feasibility and effectiveness of the improved algorithm.
Benchmarking for End-to-End QoS Sustainability in Cloud-Hosted Data Processing Pipelines

The end-to-end QoS of a data processing pipeline (DPP) depends upon the performance of its constituent software services. Their performance, in turn, depends upon several factors including (a) the amount of computing resources allocated to each service, (b) the configuration of each service, (c) the inter-dependencies between adjacent services, (d) the data ingestion rate, and (e) the specific tasks each service is assigned. This makes end-to-end quality-assured cloud resource allocation for DPPs a complex and challenging task. Our overall research objective is to develop mechanisms for dynamic resource allocation for services in a pipeline so as to ensure end-to-end QoS while optimizing the associated costs. To that end, in this paper, we propose a systematic approach for building a sustainable QoS model for constituent services, which can then be used to inform resource allocation decisions for the pipeline in response to fluctuating workloads. We demonstrate the proposed approach using a custom DPP for traffic monitoring deployed on the NeCTAR cloud infrastructure.
Research on Emergency Parking Instruction Recognition Based on Speech Recognition and Speech Emotion Recognition

In view of the situation that novice drivers are easily out of control in an emergency, this paper proposes an emergency parking instruction recognition system based on speech recognition and speech emotion recognition. The system first extracts the feature vector of the speech signal, and then uses the support vector machine (SVM) to recognize the emotion of the speech. When the emotion is abnormal, the dynamic time warping (DWT) algorithm is used to match the parking instruction template. The test results show that the system can realize the speech recognition of parking instructions very well.
Pedestrian-Aligned Multiscale Features Network for Person Re-identification

Person re-identification intends to seek out and match a target pedestrian from different cameras. In practice, images captured by different cameras typically have variances in complex scenes. In addition, large-scale datasets generally adopt automatic detectors to obtain pedestrian detection boxes, and detection errors can lead to image misalignment problems. Both complex scenes and detection errors reduce the robustness of pedestrian characteristics and exasperate the capability of pedestrian alignment. To tackle the above conundrums, we design a novel end-to-end method called Pedestrian-Aligned Multiscale Features Network (PA-MFN), integrating pedestrian alignment and multiscale features network through spatial transformer networks (STN). We employ pedestrian alignment network to work out image misalignment problems satisfactorily. Additionally, we carefully design multiscale features network to extract both global and local features. Comprehensive experiments indicate that our approach gains superior performance on Market-1501, DukeMTMC-reID as well as CUHK03 datasets.
Numerical Weather Analysis Using Statistical Modelling as Visual Analytics Technique

This research work focuses on analysing weather data, described by plenty of attributes, creating a high-dimensional data. After fetching the real-time observations recorded at the city on interest, the data is pre-processed for the purpose of feature selection; this also ensures data correctness, consistency and prepares data for analysis. Statistical model arrives at the aggregate of the insights observed in the empirical data. The statistical model delivers the parameters which are used to estimate the future weather attributes. Substitution of results in underlying statistical model fitness equation, the predictions can be obtained.
Design of Solar Monitoring Tracking System

The main purpose of the paper is to present a novel idea that is based on design and development of an automatic solar tracker system that tracks the Sun energy for maximum energy output achievement. The proposed system can store the measured data on SD card, and also can visualize the measurements online. The hardware part and programming part have been concurrently developed in order for the solar tracking system to be possible for it to operate accurately.
Steganography using Mode-Based Least Significant Bit (MBLSB) Method

In the Least Significant Bit (LSB) method of steganography, inserting bits into the cover image is generally done using the blue channel while ignoring the red and green channels as the human eye is relatively insensitive to blue color as such making it easier to hide the secret bits. However this approach is also easier to be detected and therefore attacked as one can discover the secret bits by observing the size distribution in each channel of the cover image. Our work introduces a simple method that insert the secret bits into cover image based on mode value of the bits of all channels, as such the insertion is evenly distributed based on the cover image size and is harder to be detected or attacked. Our proposed MBLSB approach divides the cover image into several insertion blocks, of which each pixel value will be taken from all pixels in each channel. Finally a XOR gate is applied to the secret bit and the retrieved pixel value to get the mode value for the purpose of determining the insertion bit value. The resulting stego-image quality are evaluated using PSNR with value above 60 dB (no apparent noise) and MSE with value below 1.0 while the average success rate of secret data extraction in the event of a color noise attack is 66.45%.
Research and Design of Website User Behavior Data Acquisition Based on Customized Event Tracking

Nowadays, it is particularly important to obtain user behavior data accurately, as the big data technology is developing at a high speed and the users of various types of websites is also increasing. The current mainstream practice is that users can design their own user behavior statistical analysis function modules or use other third-party platforms, which can analyze only the general user behavior of network services, such as Baidu Statistics and Zhuge Platform. However, each specific social network has its own user behaviors, to analyze them often requiring a self-designed special software [14], which is very difficult to develop. The embedded script provided by the third party can not be customized according to the needs of its users. In this paper, a new embedded design scheme of self-defined web pages based on general label binding, special attribute specific embedded point, form label binding and back-end reverse scanning monitoring technology is proposed, and it is verified that data acquisition and intelligent analysis of user behavior can be better completed on any website.
ShuDA-RFBNet for Real-time Multi-task Traffic Scene Perception

Traffic scene perception is key of autonomous driving. Computer vision and deep learning are popular basis of algorithms in this field. Current researches are mainly on single task, leading to high computing power demands and low operational efficiency. And algorithms may be trained on a small dataset, which means easier overfitting and more susceptible to noises. To overcome above shortcomings, we propose an end-to-end multi-task deep convolutional network model named ShuDA-RFBNet for multiple object detection and drivable area segmentation simultaneously. ShuDA-RFBNet adopts RFBNet for multiple object detection and DenseASPP for drivable area segmentation task. Both networks share a light-weight ShuffleNet V2 as the base net to extract features. ShuDA-RFBNet uses BDD100K to train, which is the current largest and diverse driving video dataset. Test results show that the mAP of multiple object detection task is 32.71% and the MOU of drivable area segmentation task is 82.67%. ShuDA-RFBNet can inference in real time.
In Which Department(s) the Robots Might Be Employed

The development of service robotics comes along with the development of industrial robotics. Service robots are designed to perform professional job tasks as well as for service users in areas of everyday life. One of the fields for service robots' application is medicine. This chapter determines the opinions of the university students who have tourism education towards tasks that may be performed by the robots. The research question was “in which hotel department the robots might be employed”. In total, 396 valid surveys were collected from the students, Mann Whitney U and Kruskal Wallis-H Tests were conducted to test the hypotheses. It was found that gender, department, and grade are significantly effective on the students' opinions towards tasks that might be performed by service robots.
Probabilistic Image Quality Assessment: An Economics Point of View

We analyze the problem of image quality assessment from economics point of view with two main arguments: 1) Visual contents is a kind of product (good/service) to be utilized by consumers and therefore the image quality assessment process can be modeled using the tools from economics, e.g the law of diminishing marginal utility/returns, the expected utility theory and the principle of risk aversion. 2) The subjective quality rating of a given visual signal is better depicted with a probability distribution than a deterministic value.
Significance of Conventional and Nonconventional Features in Classification of Face through Human Intelligence

Feature extraction is the most vital stage in edifice of any system under image processing. The prominent features play important role in the process of image recognition. Features are broadly classified as Conventional and Non-Conventional. The type of features is being selected affects the image classification phase. The state of the art work has achieved commendable success under face recognition using conventional features like skin tone, face shape, eyes, eyebrows, nose and mouth etc.,. Moving slight away from traditional approach of working with only conventional facial features the underlying work aimed to establish an empirical research to understand the significant role of non-conventional features like dressing style, physic, moustache, style of applying vermillion, regional amulet thread, traditional jewellery along with conventional features in face recognition process. These two features can effectively be used to pursue emotions, behaviour and geographic regional information of a person who is under surveillance. Human intellect is significantly effective in recognising face even in unfavourable conditions like occlusion, blurred image and variation in pose. In this research work ample empirical evidences are gathered and are analysed quantitatively and qualitatively to comprehend that not only the conventional features but many non-conventional features prompted human intelligence to make out native information of a person in given face image. Human Intelligence ensured 96% accuracy in this research work.
Recommendation Model for Trust Circle Mining Based on Users' Interest Fields

A trust-based recommendation system recommends the resources needed for users by system rating data and users' trust relationship. In current relevant work, an over-generalized trust relationship is likely to be considered without exploiting the relationship between trust information and interest fields, affecting the precision and reliability of the recommendation. This research, therefore, proposes a users' interest-field-based trust circle model. Based on different interest fields, it exploits potential implicit trust relationships in separated layers. Besides, it conducts user rating by combining explicit trust relationships. This model not only considers the matching between trust information and fields, but also explores the implicit trust relationships between users do not revealed in specific fields, thus it is able to improve the precision and coverage of rating prediction. The experiments made with the Epinions data set proved that the recommendation model based on trust circle exploiting in users' interest fields proposed in this research, is able to effectively improve the precision and coverage of the recommendation rating prediction, compared with the traditional recommendation algorithm based on generalized trust relationship.
Architecture Design and Research Based on Distributed Configuration Center

With the development of business, there are more and more configurations in the application system. Different configurations are needed when the same program corresponds to different business. At the same time, more and more distributed applications are needed. By studying the specific requirements of configuration management in distributed environment, this paper presents a design scheme of a distributed configuration center, which is based on unified configuration management and provides gray configuration and rollback of historical configuration.
Correlations in Two-Qubit Systems under Non-Dissipative Decoherence

We built a new set of suitable measures of correlations for bipartite quantum states based upon a recently introduced theoretical framework [Bussandri et al. in Quantum Inf. Proc. 18:57, 2019]. We applied these measures to examine the behavior of correlations in two-qubit states with maximally mixed marginals independently interacting with non-dissipative decohering environments in different dynamical scenarios of physical relevance. In order to get further insight about the physical meaning of the behavior of these correlation measures we compared our results with those obtained by means of well-known correlation measures such as quantum mutual information and quantum discord. On one hand, we found that the behaviors of total and classical correlations, as assessed by means of the measures introduced in this work, are qualitatively in agreement with the behavior displayed by quantum mutual information and the measure of classical correlations typically used to calculate quantum discord. We also found that the optimization of all the measures of classical correlations depends upon a single parameter and the optimal value of this parameter turns out to be the same in all cases. On the other hand, regarding the measures of quantum correlations used in our studies, we found that in general their behavior does not follow the standard quantum discord D . As the quantification by means of standard quantum discord and the measures of quantum correlations introduced in this work depends upon the assumption that total correlations are additive, our results indicate that this property needs a deeper and systematic study in order to gain a further understanding regarding the possibility to obtain reliable quantifiers of quantum correlations within this additive scheme.
A Machine learning technique dedicated for biological data

In a data-driven world, semi-supervised learning methods are motivated by the availability of large unlabeled datasets than a small amount of labeled data. However, incorporating unlabeled data into learning does not guarantee an improvement in classification performance. In this paper, we present an approach based on a deep learning system to predict missing classes by integrating a model of semi-supervised learning which is the self-training. In order to evaluate its performance, we used a set of diabetes data and four performance measures: Precision, Recall, F-Measure and Area Under the ROC Curve (AUC).
Extraction of causality and related events using text analysis

Identification and extraction of causality from the text have always been an active domain of research since long. Along with the identified causalities, it is also important to identify the associated causal events. Modelling of a system that can automatically extract the causalities becomes more tedious with the need of a large annotated corpus to train the model. Often the performance of the system is influenced by the application domain and the terminologies that are vital and specific to the domain itself. Through this paper, we propose a process framework that learns to extract causality and other relevant information from the text input through applied Machine learning. The test data that we used is from the domain of social science where the text contains the narratives from women about the experiences of their life. The results are represented as a causal graph model which can be further used for visualization and interpretation of causality.
Comparison of Naive Bayes and K-nearest neighbours for online transportation using sentiment analysis in social media

Abstract Nowadays, online transportation is one of the transportation that is increasingly preferred by people. It becomes important because people need transportation to be more effective and efficient. However, sentiment analysis is necessary to improve the quality of services on online transportation. Sentiment analysis includes the process of extracting opinions, sentiments, evaluations, and emotions of people about online transportation services on Twitter social media. To get more accuracy in classification, the opinion is taken in large amounts and classify into positive and negative class. There are several steps that use sentiment analysis. Data collection, pre-processing data, POS Tagging, and opinion classification use the Naive Bayes Classifier method, compared to the accuracy of the K-Nearest Neighbours method. The results of the comparison of Naive Bayes Classifier and K-Nearest Neighbours algorithms use 565 data tweets from Twitter, divided 500 trained data, and 65 test data. The result showed that the Naive Bayes Classifier algorithm had achieved the accuracy rate of 66.15%, and K-Nearest Neighbours algorithm produces the accuracy rate of 67.69%. From the results, the K-Nearest Neighbours algorithm perform better accuracy in sentiment classification than Naive Bayes Classifier.
Study on Denoising and Enhancement Method in SAR Image based on Wavelet Packet and Fuzzy Set

Aiming at problem of denoising and enhancement in Synthetic Aperture Radar (SAR) image, this paper proposes a fusion of wavelet packet transform and fuzzy set algorithm for freqnency domain and fuzzy domain collaborative and joint processing to improve denoising and enhancement effect in SAR image. Firstly, wavelet packet transform was used to decompose SAR image in frequency domain, and noise was removed by threshold quantization. Then, the low frequency part of SAR image was linearly transformed to enhance frequency domain of SAR image. Finally, fuzzy set algorithm was nsed to enhance fuzzy domain of SAR image. In summary, this paper comprehensively proposes a SAR image denoising and enhancement algorithm combining wavelet packet and fuzzy set. The effectiveness and snperiority of proposed method are verified by experiments.