Reviewerly/exHarmony huggingface dataset:
High dynamic range 3D shape measurement based on multispectral imaging

High-speed and high-accuracy three-dimensional (3D) measurement plays an important role in numerous areas. The recent proposed binary defocusing techniques have enabled the speed breakthrough by utilizing 1-bit binary fringe patterns with the advanced digital light processing (DLP) projection platform. To enhance the phase quality and measurement accuracy, extensive research has also been conducted to modulate and optimize the binary patterns spatially or temporally. However, it remains challenging for such techniques to measure objects with high dynamic range (HDR) of surface reflectivity. Therefore, to overcome this problem, this paper proposes a novel HDR 3D measurement method based on spectral modulation and multispectral imaging. By modulating the illumination light and acquiring the fringe patterns with a multispectral camera, high-contrast HDR fringe imaging and 3D measurement can be achieved. Experiments were carried out to demonstrate the effectiveness of the proposed strategy.
Attack and Improvement on a Symmetric Fully Homomorphic Encryption Scheme

Fully homomorphic encryption (FHE) has been regarded as the holy grail of cryptography and supports meaningful computations on encrypted data. Recently, Kumer et al. proposed a symmetric FHE scheme based on the Euler theorem. However, we illustrate in the paper that the secret key can be recovered from the homomorphic computation key and only a pair of known plaintext/ciphertext by computing the greatest common divisor with the Euclidean algorithm. Our observations are well supported by the numerical experiments on the suggested parameters. Furthermore, an improvement in the scheme is presented. We mimic Dijk et al.'s idea to illustrate that the improvement can be reduced to the approximate greatest common divisor (AGCD) problem. Several known attacks, including the proposal in this paper, are also examined.
Some Suggestions on the Reform of Running Schools in Yunnan Higher Vocational Colleges

With the strong support of the provincial government of Yunnan provincial party committee and the university worker committee, Yunnan vocational college and many other vocational colleges explore actively all kinds of educational approaches, and have carried out the reform of talent training mode. But when it comes to the cooperation of production and learning, the integration of production and education, it is still in the prototype stage. This paper analyzes the problems existing in the development of Yunnan vocational colleges, and will provide certain suggestions.
Moderating Effects of Spatial Presence on the Relationship Between Depth Cues and User Performance in Virtual Reality

The user experience of Virtual Reality (VR) is a holistic experience related to internal and external aspects of users. Depth cues, which form depth perception, play an important role in enabling users not only to perceive the three-dimensional Virtual Environment (VE) from the two-dimensional surface but also to make interacts in the VE accurately. Spatial Presence (SP), a cognitive feeling of being there, addresses user’s mental models related to the mediated environment. We assume that SP would have moderating effects on the relationship between depth cues and user performance of VR. We examine it through a user study. Considering the depth cue as an independent variable, participants are exposed to two experimental conditions: non-depth cue condition and depth cue provided condition. As moderator variables, Self-Location and Possible Action, a two-dimensional structure of spatial presence, are measured by a questionnaire. Task completion time is considered as a dependent variable. The results show that in high self-location situations, depth cues are not as useful, but they play an important role in low self-location situations.
The First International Workshop on Knowledge Graph Technology and Applications

Decentralised data solutions bring their own sets of capabilities, requirements and issues not necessarily present in centralised solutions. In order to compare the properties of different approaches or tools for management of decentralised data, it is important to have a common evaluation framework. We present a set of dimensions relevant to data management in decentralised contexts and use them to define principles extending the FAIR framework, initially developed for open research data. By characterising a range of different data solutions or approaches by how TRusted, Autonomous, Distributed and dEcentralised, in addition to how Findable, Accessible, Interoperable and Reusable, they are, we show that our FAIR TRADE framework is useful for describing and evaluating the management of decentralised data solutions, and aim to contribute to the development of best practice in a developing field.
Study on the Calibration Method of Gaze Point in Gaze Tracking

Aiming at the problem of poor robustness of gaze estimation in non-contact gaze tracking system, the gaze tracking system of single-camera multi-light source is improved. Based on the algorithm of cross-invariant gaze estimation, the traditional gaze-point calibration algorithm is improved. Based on the structure of the eyeball model, the influence of corneal curvature of the eyeball on the sight placement error is analyzed. For the error caused by the angle between the optic axis and the optical axis, an improved dynamic adaptive calibration algorithm is proposed, which divides the screen into 3 * 3 sub-regions. For each sub-region, a mapping parameter is calculated respectively to form a parameter matrix. The current parameter is an optimal parameter value dynamically obtained from the matrix in real time and the current region parameter is extracted to obtain the final eye-point placement. Experiments show that this method has significant effect on line of sight calibration.
An evolutionary‐based approach for dealing with numerical and categorical attributes in ILP

Abstract Inductive logic programming (ILP) induces concepts from a set of positive examples, a set of negative examples, and background knowledge. ILP has been applied on tasks such as natural language processing, finite element mesh design, network mining, robotics, and drug discovery. These data sets usually contain numerical and multivalued categorical attributes; however, only a few relational learning systems are capable of handling them in an efficient way. In this paper, we present an evolutionary approach, called Grouping and Discretization for Enriching the Background Knowledge (GDEBaK), to deal with numerical and multivalued categorical attributes in ILP. This method uses evolutionary operators to create and test numerical splits and subsets of categorical values in accordance with a fitness function. The best subintervals and subsets are added to the background knowledge before constructing candidate hypotheses. We implemented GDEBaK embedded in Aleph and compared it to lazy discretization in Aleph and discretization in Top‐down Induction of Logical Decision Trees (TILDE) systems. The results obtained showed that our method improves accuracy and reduces the number of rules in most cases. Finally, we discuss these results and possible lines for future work.
Detection of optical fringes parameters with global direction metric

A global direction metric method based on Fourier-polar transform is proposed in this paper, which calculates the global fringe direction according to the directional distribution of intensity from the power spectrum of fringe pattern. By introducing polar coordinate transform, the rotation of power spectrum is transformed into translation component, which can make the calculation process simple and fast. Then the original image is projected along the global fringe direction and the mean value of pixel gray is calculated. Also, the fringe pitch can be calculated from the projection curve close to cosine distribution. This detection method of optical fringe parameters uses overall information of the image, and holds good adaptability and robustness to noise and degraded image. Moreover, without any pre-processing operations such as smoothing filter and threshold segmentation is required in this method. It can directly detect two parameters of global fringe direction and fringe spacing, which is of great significance for quantitative analysis of fringe image.
Computer parallel Rijndael algorithm optimization based on data stream decomposition

An order to improve the execution efficiency of Rijndael algorithm, starting from the analysis of the transformation of the core part of the Rijndael algorithm, a method based on data stream decomposition was proposed in the multi-core platform for parallel optimization of Rijndael algorithm. The m ain method adopted is to divide the effect of each component transformation of the wheel transform on the whole state into the effect of each component unit of the state, so that the component transformation can be carried out in parallel. The experimental results show that the average acceleration ratio of the decomposition of Rijndael algorithm based on data stream is 1.0674, and the performance is 6.74% higher than before . Therefore, the scheme of parallel optimization of Rijndael algorithm based on data flow decomposition is feasible, and the performance improvement brought by parallel optimization of Rijndael algorithm basically meets the expected requirements of the experiment.
Favoring the k-Means Algorithm with Initialization Methods

Clustering algorithms are non-supervised algorithms and, among the many available, the k-Means can be considered one of the most popular and successful. The performance of the k-Means, however, is highly dependent on a ‘good’ initialization of the k group centers (centroids) as well as of the value assigned to the number (k) of groups the final clustering should have. This chapter addresses experiments using five initialization algorithms available in the literature namely, the Method1, the k-Means++, the CCIA, the Maedeh&Suresh and the SPSS algorithms, to empirically evaluate their contribution to improving k-Means performance.
Development of a mobile complex for monitoring temperature slices in dispersed environments

One of the tasks in the study of soils is to determine their temperatures. That tasks is associated with a number of difficulties. Therefore, for measuring the temperature distribution in various natural soils in real field conditions a mobile complex was designed. Its modular system allows to make measurements at any depth. In addition, the compactness of the device and the speed of assembly and disassembly makes it quite mobile. The simple design of the assemblies of which the complex is composed provides an inexpensive and fast manufacture and repair of the device.The first paragraph after a heading is not indented (Bodytext style).
Image visualization for infrared cameras using radiometry

Infrared (IR) cameras typically output single channel frames with 13-16 bit dynamic range. On the other hand, typical Commercial Off-The-Shelf (COTS) monitors can display three channel frames with only 8 bit dynamic range. Therefore, visualization of IR images while preserving details is a challenging task for utilizing the full potential of thermal cameras. In this paper, we propose a radiometry based method for converting 13-16 bit IR images to 8 bit single channel images for visualization purposes, which is called temperature quantizer. In the proposed method, we use the histogram of the calibrated temperature values. In this context, we collected data using an uncooled Long Wave IR (LWIR), a cooled Middle Wave IR (MWIR) and a cooled LWIR camera. The proposed method is compared with a basic min-max scaler and a Digital Number (DN) quantizer that uses raw image histogram. For quantitative comparison, we used standard deviation and entropy values. Experiments show that the proposed method performs better than the reference methods quantitatively and qualitatively.
Measuring Term Relevancy Based on Actual and Predicted Co-occurrence

A measure of term relevancy is important in various applications such as Web search. Although the co-occurrence probability of terms in a database is a simple way to express term relevancy, it suffers from each term having a different co-occurrence tendency. In this paper, we propose a new measure of term relevancy: a ratio of actual and predicted values of co-occurrence (RAP). We construct a model predicting co-occurrence for each query as piecewise approximation lines.
Advanced Software Engineering

Software rules them all! In every industry now, software plays a dominant role in technical and business innovations, in improving functional safety, and also for increasing convenience. Nevertheless, software is not always designed, (re)developed, and/or secured with the necessary professionalism, and there are unnecessary interruptions in the development, maintenance, and operating chains that adversely affect reliable, secure, powerful, and trustworthy systems. Current surveys such as the annual World Quality Report put it bluntly, directly correlated with the now well-known failures of large-scale, important and/or safetycritical infrastructures caused by software. It is thus high time that software development be left to the experts and that space be created for the use of current methods and technologies. The present article sheds light on current and future software engineering approaches that can also and especially be found in the Fraunhofer portfolio.
A Novel TNN Model for PLI Noise Reduction in ECG Signal

In this paper, the Wavelet transform based Thresholding Neural Network (TNN) is considered for adaptive noise reduction where the conventional wavelet shrinkage methods over denoises the signal due to high threshold value. In this work, two methodologies are proposed. In the first method Gauss Newton algorithm is implemented in TNN and in the second Newton Raphson algorithm is implemented to obtain the optimal threshold value. It is observed that both the methodologies show improved performance in denoising procedure. Their performances are studied using SNR and MSE parameters and the investigated results illustrate that Newton Raphson method is computationally efficient compared to Gauss Newton method.
On Image Based Enhancement for 3D Dense Reconstruction of Low Light Aerial Visual Inspected Environments

Micro Aerial Vehicles (MAV)s have been distinguished, in the last decade, for their potential to inspect infrastructures in an active manner and provide critical information to the asset owners. Inspired by this trend, the mining industry is lately focusing to incorporate MAVs in their production cycles. Towards this direction, this article proposes a novel method to enhance 3D reconstruction of low-light environments, like underground tunnels, by using image processing. More specifically, the main idea is to enhance the low light resolution of the collected images, captured onboard an aerial platform, before inserting them to the reconstruction pipeline. The proposed method is based on the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm that limits the noise, while amplifies the contrast of the image. The overall efficiency and improvement achieved of the novel architecture has been extensively and successfully evaluated by utilizing data sets captured from real scale underground tunnels using a quadrotor.
Dissection on Java Organs in GitHub Repositories

Organ transplantation has brought convenience for software reuse and evolution since it was proposed. However, studies about mature, high-quality organs are still insufficient. It is still unclear about the detailed characteristics of organs in the open-source environment. In this paper, we look deep into organs obtained from software evolution processes of the ten large-scale Java repositories hosted on GitHub, aiming at providing practical information for utilizing organs in the open-source environment. We found that: 1) commits use add as a keyword in their comments possess the most organs, occupying 38% of the total amount, but commits with the keyword fix possess the highest locating accuracy (about 57%); 2) developers prefer to add new classes when they bring new functionalities to the projects in that the proportion of class level organs is 40%, more than statement level organs' and function level organs' (35% and 25%, respectively); 3) nearly 70% of the total amount are cross-file organs with the median of the number of files each organ spans reaching three and the average of this value being around four; 4) a small number (0.55%) of organs are multi-commit; 5) more than 40% of code reuse in the open-source software can be finished by organ transplantation. Based on our findings, we highlight implications for future studies and design the mode of using organs to conduct code reuse.
Artificial Intelligence-Based Latency Estimation for Distributed Systems

Network latency is an important metric specially for distributed systems. Depending on the system size, network latency can be either explicitly measured or predicted. However, prediction methods suffer from several drawbacks which lead to poor performance. The goal of this study is to demonstrate a novel method of network latency estimation which will be considered a valuable addition to the existing works due to its accuracy and efficiency. A number of machine learning techniques such as conventional linear regression, convolutional neural network, and support vector machine are used to predict the value to the end-to-end latency between any given pair of nodes. Two datasets: Ubique and iConnect-Ubisoft are used for training and testing the machine learning algorithms.
CERTAIN INVESTIGATIONS ON REVERSIBLE IMAGE WATERMARKING

Objectives: To protect secret information in a digital medium without compromising robustness. There are many approaches introduced regarding the importance of utilizing medical, military and natural images for image watermarking procedure. Reversible watermarking is appreciated that the original media can be recovered without any loss of information after extracting stego-data. With an aim to provide higher security and robustness, certain investigations have been done on reversible image watermarking. Methods/Statistics: This research work mainly addresses on privacy, reversibility, payload and robustness related goals. Considering these challenges, three parts of image watermarking techniques, namely proposed Bi-orthogonal Wavelet Transform and Importance Measure Model (BWT and IMM), introduced Dynamic Prediction Error Integrated Reversible Image Watermarking (DPEIRIW) and proposed Multi Wavelet Transform based and Oppositional Particle Swarm Optimization (MWT - OPSO) are introduced to improvise the data hiding performance. Findings: The methodologies introduced in this research work not only surpass other approaches with considerations to a variety of incursions, but still maintains a satisfactory image quality. From the experimental results, it is clearly understood that this suggested approach MWT and OPSO attains the average PSNR of 59.84 dB and average NC of 1, which is minimal when comparedwith the all the other existing strategies. Applications/Improvements: As the ongoing trend is now towards multi-core computing and the requirements for the real time video communication is very much in demand, the ownership right information also required to be preserved. It could be a great future direction to deploy this design approach under multi-core platforms. Keywords: Correlation Coefficient, Entropy, Importance Measure Model, OPSO Biorthogonal DWT, Prediction Error, Reversible Watermarking, NC, PSNR
Diachronic Distribution of Clause Combining

In Chap. 4 , we studied the genre distribution of different clause combining patterns based on the Crown corpus, the BNC, and the COCA. The result shows that different types of clause combining are different in genre distribution, but there are no differences between British English and American English. However, the above research is synchronic. Language evolves over time (Leech et al. 2009). Then, will the distributions of different types of clause combining show any diachronic changes?
Novel Evaluation Methodology for Machine Learning Algorithms through API Creation

Datascientist have a need for API's that can evaluate the standards of the code, test functionality of each feature involved in the execution of algorithms. It would be helpful if the datascientist can visualize these standards in an understandable form. In this reported work we present the details about one such API termed as score API. The score API takes input from the workbench, such as user name and password for authentication of the user, once authorized the user can check for status of the developed algorithm. API reports the strength of the algorithms and validates them through automation testing.
Analysis of Outlier Data using Parallel Maximum Likelihood Estimator

In this paper, we investigate about implementation of parallel maximum likelihood estimator (Parallel-MLE) in analysis of outlier data for multiple linear regression model. Parallel method is a method that divides data into clusters. In this study, K-Means Clustering is applied for clustering method. The data in this research was obtained from bankruptcy data (bank32nh). Bank32 is queues data at an XYZ bank, where it consist of 4500 observations, 1 dependent variable, and 31 independent variables. The experimental results show Parallel-MLE show better mean square error (MSE) and performance compare to MLE. Our proposed method achieved smaller MSE for more outlier observations (7-30 outliers) i.e. 0.000059.
Exploiting Supervised Learning for 3D Model Semantic Segmentation Using Multispectral Data

3D model texture-based segmentation using multispectral imagery to define its construction materials is addressed within the scope of this work. An end-to-end pipeline is proposed to digitize a real-world object, construct a spatial consistent multispectral texture map and to identify materials on its surface. A multispectral camera capable of capturing ultraviolet to near infrared imagery is used to create image sequences for its Structure-from-Motion based 3D reconstruction. We utilize computational geometry techniques to create a spatial-consistent texture based on ultraviolet to near infrared imagery. Various supervised learning approaches are utilized and evaluated on the identification of materials on a 3D model's surface. Experimental results are promising and reveal its capabilities in the study of 3D digitized models.
Shortest-Path-Preserving Rounding

Various applications of graphs, in particular applications related to finding shortest paths, naturally get inputs with real weights on the edges. However, for algorithmic or visualization reasons, inputs with integer weights would often be preferable or even required. This raises the following question: given an undirected graph with non-negative real weights on the edges and an error threshold $$\varepsilon $$ , how efficiently can we decide whether we can round all weights such that shortest paths are maintained, and the change of weight of each shortest path is less than $$\varepsilon $$ ? So far, only for path-shaped graphs a polynomial-time algorithm was known. In this paper we prove, by reduction from 3-SAT, that, in general, the problem is NP-hard. However, if the graph is a tree with n vertices, the problem can be solved in $$O(n^2)$$ time.