Reviewerly/exHarmony huggingface dataset:
High-resolution and real-time spectral-depth imaging with a compact system

We develop a compact imaging system to enable simultaneous acquisition of the spectral and depth information in real time. Our system consists of a spectral camera with low spatial resolution and an RGB camera with high spatial resolution, which captures two measurements from two different views of the same scene at the same time. Relying on an elaborate computational reconstruction algorithm with deep learning, our system can eventually obtain a spectral cube with a spatial resolution of 1920&times;1080 and a total of 16 spectral bands in the visible light section, as well as the corresponding depth map with the same spatial resolution. Evaluations on both benchmark datasets and real-world scenes show that our reconstruction results are accurate and reliable. To the best of our knowledge, this is the first attempt to capture 5D information (3D space + 1D spectrum + 1D time) with a miniaturized apparatus and without active illumination.
Non-classical correlations beyond Bell’s inequality violation in qubit-pairs with an intrinsic noise

In this paper, we analytically explore two qubits interacting with a coherent harmonic oscillator field under intrinsic noise. The interaction of the uncorrelated two qubits with a harmonic oscillator initially in the superposition of coherent states leads to the generation of nonclassical correlations (NCCs). The NCCs are quantified by local quantum uncertainty (LQU), uncertainty-induced non-locality (UIN), concurrence and Bell’s inequality. It is found that the generated NCCs are enhanced via the intensity coherence and the parameters of the initial harmonic oscillator state. For the even coherent state, the generated NCCs are more robust against the influence of intrinsic noise rate than that the ones of the coherent state. The appearance of the special type of skew-information correlation (in which, LQU and UIN are equal) depends on the superposition of the coherent states. The phenomena of sudden death and birth of entanglement depend not only on the intrinsic noise rate but also on the initial harmonic oscillator field. The generated NCCs could be controlled by adjusting these parameters.
Cost-Efficient Task Scheduling for Geo-distributed Data Analytics

Geo-distributed data processing is affected by many factors, some countries or regions prohibit the transmission of original user data abroad. Therefore, it is necessary to adopt a non-centralized processing method for these data, but at the same time, many problems will arise. Firstly, it is unavoidable to transfer job’s intermediate data across regions, which will result in data transmission cost. Secondly, the WAN bandwidth is often much smaller than the bandwidth within clusters, which makes it easier to become the bottleneck of geo-distributed job. In addition, because the idle computing resources in the cluster may change with time, it will also cause some difficulties in task scheduling. Therefore, this paper considers the problem of task scheduling for big data jobs on geo-distributed data, considering the budget constraints on intermediate data trans-regional transmission, and without moving the original data. we design a budget-constrained task scheduling strategy CETS. Through the experimental analysis of different scenarios, the effectiveness of the proposed algorithm strategy is verified.
Underwater image color correction algorithm based on scattering statistical characteristics

Different wavelengths of light have different attenuation during underwater transmission, and the attenuation of the same wavelength of light is also inconsistent in various waters. This loss of spectral information causes varying degrees of color distortion in underwater images. In this paper, a novel underwater color correction algorithm based on scattering statistics characteristics is proposed. The algorithm is based on a fact that scattering exists in both atmosphere and underwater. Firstly, we statistically analyze three kinds of images and summarize scattering characteristics. Secondly, a novel spectral compensation strategy is proposed to correct color distortion according to scattering characteristics. Finally, compared with the existing underwater image processing algorithms, there are better subjective and objective indicators. The algorithm is robust for a mass of real underwater images.
Plans, Brokers and Trust

Having discussed the theoretical framework and how to construct Plan-like Architectures, this chapter follows on from the previous principles laid out in Plan Theory.
Content-Based Personalized Dating Recommendation System

Abstract In order to improve the accuracy of recommendation on dating websites, a personalized recommendation algorithm aiming at dating objects is put forward. The algorithm firstly extracts the user’s personal characteristic information and defines quantification criteria to quantify the characteristics. Then, the characteristics will be weighted according to the users’ different degree of emphasis on the eigenvalue and similarity between users will be calculated with Euclidean distance. Finally, possible candidates will be recommended according to similarity. The rationality of the algorithm is verified by some experiments.
The application of algebraic methods to simplify the more complex type of static data structure through visualization approach

Abstract This paper had the result of action research (AR) of Data Structure course. This paper discussed the elaboration of hight level complexity of composit type of data structure applying algebraic method through visualization approach (VA). The purpose of this research was to simplify the students understanding at any levels of complexity of static type of data structures through the application of algebraic methods with visualization approach. The roles of the applications were the elaboration of any level of type complexity of data structures logically and systematically at to identify each of their individual access. The method applied to this AR was an algebraic method through visualization approach of composite type of data structures in two cycles. The first cycle applied manually visualization approach, and the second applied simple MS-Powerpoint animation of visualization approach. Some examples of third complexity level of composite type of data structures were presented to demonstrate the methods. The results showed that, before the method is applied, none of the 43 students participating in the course of data structures that can be logically and systematically identifies each individual access from any type of data structure composites tested. How ever, after the second cycle levels of AR applying VM shown that the average score of learning outcome was 78.00 and classical mastery learning achievement was 76.74%. It meant that the students achievement were very hight. The conclusion was the application of VM significanly improved data structure programmer competency.
Machine Learning Approach to Recommender System for Web Mining

One of the major challenges face by webmasters is the introduction of numerous choices to the customer, which leads to repetitive and difficulty in locating the correct item or data on the webpage. In the traditional approach, if the data was changed, pooling approach was possible, only if data variation was within the cluster information. In case the data exceeds the limit, classification was difficult to perform. Therefore, we need to have a classification approach that can work under these conditions. In the proposed work we have implemented Hybrid of ANN and KNN approach and found improvement in the recommendation system with greater accuracy.
Scenario Referring Expression Comprehension via Attributes of Vision and Language

Referring Expression Comprehension (REC) is a task that requires to indicate particular objects within an image by natural language expressions. Previous studies on this task have assumed that the language expression and the image are one-to-one correspondence, that is, the language refers to the target region must exist in the current image and then the region with the highest score will be located, no matter whether they match or not. However, in practical applications, REC is required to locate the reference target region from a series of matched, semi-matched and mismatched scene image sequences. It is the 3D version of this challenge that refers to as Scenario Referring Expression Comprehension (SREC) in this paper. To accomplish such a task, we made a testset based on the existing real-scenario dataset enhancement, constructed a Dual Attributes Recursive Retrieve Reasoning Model (DA3R) for the first time with the Attributes of both images and expressions, and finally verified the feasibility of the method on the testset by assess with three different types of enhanced expression.
Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce

Information-seeking conversation system aims at satisfying the information needs of users through conversations. Text matching between a user query and a pre-collected question is an important part of the information-seeking conversation in E-commerce. In the practical scenario, a sort of questions always correspond to a same answer. Naturally, these questions can form a bag. Learning the matching between user query and bag directly may improve the conversation performance, denoted as query-bag matching. Inspired by such opinion, we propose a query-bag matching model which mainly utilizes the mutual coverage between query and bag and measures the degree of the content in the query mentioned by the bag, and vice verse. In addition, the learned bag representation in word level helps find the main points of a bag in a fine grade and promotes the query-bag matching performance. Experiments on two datasets show the effectiveness of our model.
Two-Stage Discriminative Feature Selection

Recently, a filter supervised feature selection method, namely discriminative feature selection (DFS), was proposed, which combines linear discriminant analysis (LDA) and sparsity regularization effectively. However, DFS is computationally expensive due to the use of eigenvalue decomposition on large matrix. In this paper, we propose a two-stage DFS method, namely TSDFS, to improve the efficiency and keep the accuracy of classification. A direct LDA based feature selection is firstly performed to achieve dimension reduction preprocessing of the data. Then, a DFS procedure is performed efficiently on the reduced data in the second stage. The high efficiency of the whole TSDFS is credited with the high efficiency of dimension reduction preprocessing. In addition, we employ nested cross-validation technology to achieve automatic parameter selection for TSDFS. Extensive experimental results based on several benchmark data sets validate the effectiveness of TSDFS.
Consistent and Specific Multi-view Relative-Transform Classification

In many practical problems, the same objects can be described in many different ways or from different angles. These multiple descriptions constitute multiple views of objects. Multi-view classification methods try to exploit information from all views to improve the classification performance and reduce the effect of noises. However, how to efficiently exploit the consistency and specificity in multiple views remains a challenge. In addition, it is also worth to explore the processing results of multi-view data more inline with human cognition. For this reason, we propose a new multi-view classification algorithm, Consistent and Specific Multi-View Relative-transform Classification (CSMRtC). CSMRtC firstly explores the underlying subspace structure of different views exhaustively, to evacuate consistency and specificity of multi-view data. Next, these data matrices are processed using the relative transform technique. As for the consistency and specificity, consistent matrix stores the shared information of multiple data matrices, specificity captures the characteristic of each view. Then, we use the relative transformations to transform data from raw space to relative spaces, to achieve the purpose of suppress noise in the data and improve the distinction between the data. Comprehensive evaluations with several state-of-the-art competitors demonstrate the efficiency and the superiority of the proposed method.
Improving NLU Training over Linked Data with Placeholder Concepts

Conversational systems, also known as dialogue systems, have become increasingly popular. They can perform a variety of tasks e.g. in B2C areas such as sales and customer services. A significant amount of research has already been conducted on improving the underlying algorithms of the natural language understanding (NLU) component of dialogue systems. This paper presents an approach to generate training datasets for the NLU component from Linked Data resources. We analyze how differently designed training datasets can impact the performance of the NLU component. Whereby, the training datasets differ mainly by varying values for the injection into fixed sentence patterns. As a core contribution, we introduce and evaluate the performance of different placeholder concepts. Our results show that a trained model with placeholder concepts is capable of handling dynamic Linked Data without retraining the NLU component. Thus, our approach also contributes to the robustness of the NLU component.
A New Approach to Control the Position of Joint Arm Robot Using Image Background Subtraction Technique

Vision-based systems enhance the degree of autonomy of the robot in manufacturing applications and help to increase productivity. Computer vision systems are used to perform multiple tasks by using various algorithms in the view field. In this paper, a new approach based on image subtraction technique using Gaussian mixture model (GMM) to control the position of joint arm robot for pick and place operations to sort the objects is presented. In this work, a simple vision system is used to capture the images of the robot and objects placed within the work volume of the robot, and these images are processed continuously using GMM background subtraction algorithm to find the coordinates of the objects with reference to robot base. These coordinate points are used to pick the object and place in the desired location. In this work, a prototype of 3R servo robot linkage system is fabricated to evaluate the algorithm using web camera, and a program is developed in MATLAB.
Expert System to Diagnosing Heart Attack Disease Using Certainty Factor Method

Abstract Coronary Heart Disease is one of many diseases that cause death and still being a health problem for both developed and developing countries. It is estimated to double in the next two decades, making it the biggest leading cause of death in 2020. The causes of coronary heart disease contain age and sex, with the incidence of males is more than in females but the incidence of females will increase after menopause (the end of the menstrual cycle) around the age of 50 years. This is due to estrogen (steroid compounds) have a protective effect on the onset of atherosclerosis. In this study the author wants to create a system that can detect heart disease by users before ensuring himself to the doctor to diagnose heart disease which is very dangerous. The system that the author will design used the certainty factor method to provide an assessment of the symptoms of user diagnosis. The application was designed with the desktop-based visual studio software assistance that is easy to use by everyone because it is familiar with the windows display design.
Log-Cumulants of the Finite Mixture Model and their Application to Statistical Analysis of Uavsar Data

Since its first flight in 2007, the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR (PolSAR) data in very high spatial resolution. It is possible to observe small spatial features in this type of data, offering the opportunity to explore structures in the images. In general, the structured scenes would present multimodal or spiky histograms. The finite mixture model has great advantages in modeling this kind of data. In this paper, a type of important statistics called log-cumulants are derived for the finite mixture model. They are adopted to UAVSAR data analysis to determine statistical behaviors of different land types.
Siamese Manhattan LSTM Implementation for Predicting Text Similarity and Grading of Student Test Papers

This paper presents a method to grade answer papers written by the students by assessing the semantic similarity between the written answers and the actual answers and grading them accordingly based on the amount of semantic similarity between the two. There is a need for automatic grading of answers for faster checking of papers and to reduce the work of the teachers, also the method of text similarity can be used in search engines to find a particular document on the Internet or by question-answer sites such as Quora to determine similar questions. We have implemented this by using Manhattan LSTM (Long short-term memory) which is a Siamese deep neural network. This method uses word embedding vectors to create embedded matrices which are fed to LSTM and similarity function to get the result of the similarity between answers and then scaled to the appropriate grade.
New concept of universal coding using one step reversible low contrast mapping (1RLCM)

Abstract Universal coding was developed for compressing data that the probability distribution of a symbol is unknown. Universal coding methods encode a symbol using some bits as code based on the coding algorithm. This research introduces a new concept of universal coding by encoding two symbols using Reversible Low Contrast Mapping (RLCM) transform. The proposed method transforms a pair non negative integer to a non-negative integer and a binary number. This research also introduces a compression scheme namely Average Encoding (AE) to compress a sequence of data using the proposed coding method. This method generally yields a higher compression ratio than the Elias Delta, Elias Gamma, and Fibonacci coding when it is tested using the results of differential encoding of some testing images.
Exploring an In-house Online Reputation Monitoring Implementation

Abstract Today, our online reputation can be developed through subjective opinions communication by netizen on social media. Online reputation especially for business entities can affect in many aspects such as sales and customer loyalty. Due to high amount of social data (e.g. comments), the manual approach in monitoring subjective opinions towards our brands, products or name is no longer relevant. Therefore, entities either organizations or individuals should monitor their online reputation using social media analytics tools such as sentiment analysis to mitigate reputation attack. However, Online Reputation Monitoring (ORMo) is yet a common practice where most practitioners are large corporations. Outsourcing is a good option but entities must allocate some costs which a burden for most small and medium entities. Thus, implementing social media analytics in-house ORMo by entities is a reasonable option. However, the guideline to implement in-house ORMo is still not well explored including what are the needed features in an ORMo tool. Therefore, this research attempts to explore on how to implement an in-house ORMo at affordable cost but reliable. In achieving the objective, this research involved four stages of investigations which are needs assessment of ORMo tool features, prototype development, simulation and expert survey for validation. This research found that in-house ORMo can be implemented at minimal cost using existing resources and the accuracy can be improved by updating the collection of words with its sentiment polarity. The results of this research can be the basis for an in-house ORMo tool implementation and for reviewing the existing ORMo tool.
Structural Role Enhanced Attributed Network Embedding

In recent years, network embedding methods based on deep learning to process network structure data have attracted widespread attention. It aims to represent nodes in the network as low-dimensional dense real-value vectors and effectively preserve network structure and other valuable information. Most network embedding methods now only preserve the network topology and do not take advantage of the rich attribute information in networks. In this paper, we propose a novel deep attributed network embedding framework (RolEANE), which can preserve network topological structure and attribute information well at the same time. The framework consists of two parts, one of which is the network structural role proximity enhanced deep autoencoder, which is used to capture highly nonlinear network topological structure and attribute information. The other part is that we proposed a neighbor optimization strategy to modify the Skip-Gram model so that it can integrate the network topological structure and attribute information to improve the final embedded performance. The experiments on four real datasets show that our method outperforms other state-of-the-art network embedding methods.
A Conversation Analysis Of Repair In Incredibles 2 Movie

The research paper investigates the types of repair in the conversation of Incredible 2 movie manuscript. The research is aimed at finding out 1) the types of repair, and 2) the intentions of repair in the conversation of Incredible 2 movie script. The research adopts descriptive-qualitative research design. The object of the research is repair analysis found in the conversation of Incredible 2 movie script. The data source of the research is the script of Incredible 2 movie. The methods used by the researcher in collecting data are search the movie and the script in google, determine Incredible 2 movie and its script as the data source, download Incredible 2 movie and its script from https://transcripts.fandom.com/wiki/Incredible on Friday, February 8 2019 at 8.07 AM, watch the movie and read Incredibles 2 movie script, mark the repairs used in Incredibles 2 movie on its script, and retype the data in Microsoft Word.In analyzing the data, the researcher employs the repair theory of Schegloff, Jefferson and Sack (1977) to determine the types of repair in the conversation and the theory of Leech (1983) to determine the intentions of repair in the conversation. The findings reveal that 1) from 4 types of repair theory by Schegloff, Jefferson and Sack (1977), there are only three types found in the conversation of Incredible 2 movie namely SISR (Self Initiated Self Repair), OISR (Other Initiated Other Repair), and OIOR (Other Initiated Other Repair), and 2) there are 11 intentions of repair of Incredible 2 movie script such as to ask, to assert, to assure, to clarify, to correct, to explain, to joke, to prove, to request, to specify, and to suggest.