Reviewerly/exHarmony huggingface dataset:
Research on adaptive interferometric measurement of complex vibration environment

In complex phase-shift interferometry, an adaptive phase algorithm based on complex vibration environment is proposed. The phase shift amount calculation of two or more interferometric images could be achieved by self-adaptive phase-shift algorithm (SPA), a closed-loop feedback system is constructed to adaptively correct the characteristic phase shift amount in which the phase shift step length is a set value, thereby realizing self-correction of the phase shift error. According to the actual examinations, the single-step phase shift error is less than &pi;/50, and PV and RMS values of plane objects were obtained respectively better than &lambda;/20 and &lambda;/100. Additionally, the feasibility of the algorithm and the self-adaptive correction system for complex vibration environment were verified. The results show that the proposed method achieves high-speed and high-precision phase-shift extraction, greatly reducing the phase shift error caused by vibration interference, and the overall operation of measurement process is simple relatively.
An ordered sparse subspace clustering algorithm based on p‐Norm

Abstract Images in video may include both Gaussian noise and geometric rotation. Thus, it is challenging to represent an image sequence in its intrinsically low‐dimensional space in a noise‐robust and rotation‐robust manner. In this paper, we propose a novel‐ordered sparse subspace clustering algorithm based on a p‐norm to achieve an effective clustering of sequential data under heavy noise conditions. We also use the wavelet‐histogram of oriented gradient (HOG) transform in the kernel view to extract both the global features (with the wavelet process) and the local features (with the HOG process) from the image. In addition, we assign different weights to different features to obtain a sparse coefficient matrix that helps to emphasize the global and local correlations in each sample. Similarly, the clustering algorithm based on the p‐norm for sequential images emphasizes the within‐class correlations amongst samples. Therefore, in this paper, we select additional denoising main components under a Laplacian constraint to achieve a better block‐diagonal structure and highlight the independence of different clusters. Extensive experiments performed on various public datasets (including the ordered face dataset, handwritten recognition dataset, video scene segmentation dataset, and object recognition dataset) demonstrate that the proposed method is more resilient to noise and rotation than other representative sparse subspace clustering methods.
Quantization based unified blind watermarking scheme

Quantization parameter based blind watermarking schemes are very popular. These schemes work in spatial domain as well as in various transform domains such as wavelet, discrete cosine, and real oriented wavelet. These schemes utilize one bit (binary) image as a watermark. However, in general, watermarks are any n bit image, where n ≥ 1. Therefore, these schemes are not suitable for the watermarks with n > 1. In this paper, we extend the existing work on blind watermarking schemes for n bits watermark images. The ‘division algorithm’ is used as a key component to develop the extended schemes. The proposed scheme is implemented in spatial domain. Experiments are done on one bit, two bits and eight bits watermark images. The results are investigated qualitatively by visualization, and measured quantitatively by peak-signal-to-noise-ratio (PSNR) and normalized correlation coefficient (NC). The results of the existing and proposed watermarking schemes are same for n = 1. The results of the proposed watermarking scheme are reliable for n > 1. A formula is proven for lower bound on PSNR, which is a key finding in the watermarking community. The formula is verified experimentally.
Programmed Physical Activity for the Elderly as a Motor of Active Ageing

Our population is ageing, and this makes it necessary to implement initiatives that encourage sports activities among people over the age of 65. According to the concept of active ageing, keeping this age group active will allow to improve its health. This research aims to create an ageing model. To this end, we developed a platform for the capture of data and used it to conduct our study. To validate the proposed hypotheses, we examined the correlation between the data captured by the platform with medical data.
Application of Text Classification Method Based on Depth Learning in Email Handwriting Analysis

Natural Language Processing is an important direction in the fields of computer science and artificial intelligence. It has a wide range of applications, such as text classification, machine translation, emotional analysis, etc. where text classification is typically characterized of capturing the language features of e-mails to identify the authors. In this paper, mathematic models are set up and compared based on the traditional text classification method and the Deep Learning text classification method respectively. Finally, the following results are obtained:
Dynamic texture recognition under adverse lighting and weather conditions for outdoor environments

Recognizing dynamic patterns based on visual processing is significant for many applications. In this paper dynamic texture recognition focuses on outdoor scenarios where a crisis event might occur (i.e. fire in a forest, floods/flooding etc.) Real outdoor scenes may include the objects with dynamic behaviour due to illumination, blurring, or weather conditions effects. Under bad weather conditions the imaging systems is degraded and produce low visibility images. In this work precipitation artefacts and lightning effects for dynamic texture analysis were studied. Experimental results show that the proposed method of weather and adverse lighting effects compensation is feasible and effective for videobased dynamic texture analysis under bad weather conditions.
Measuring Ciphertext Randmoness Using Die Hard Randomness Index

In this paper we present a novel quantitative measure of randomness based on Diehard randomness tests. The proposed method relies on the results of diehard tests in calculating a randomness index; namely Diehard Randomness Index (DRI). The presented index can be helpful in measuring randomness in bit sequences generated from pseudo-random and random number generators to identify which has better randomness properties. The paper also show sample calculations of the proposed index to measure randomness of ciphertext resulting from encryption using block-ciphers.
A New Training Sample Selection Method Avoiding Over-Fitting Based on Nearest Neighbor Rule

Sample selection is an important task. Now, there are many sample selecting methods using nearest neighbor rule. But most of them never consider the over-fitting problem. For overcoming this disadvantage, this paper gives a new sample selecting method. This method uses pruning tactics and cross-validation to avoid over-fitting. It divided the original sample set to some disjoint subsets. Every time, a subset is used as validation sample set to prune samples selected from other subsets. All the subsets take turns as validation set. And the final result was gotten by combining all the selected sample sets. The experiments show that, compared with the existing methods, the new method can get smaller selected sample set and better classifiers can be trained on its selected samples.
Using Scheduling Apps to Streamline a Social Media Workflow

Creating an account is only the first step in developing a social media presence. In order to have an impact, there must be interaction with the university community. One of the most important steps to increasing engagement is through the creation and regular posting of diverse content. Creating a steady stream of content can be a challenge. Integrating third-party scheduling applications into your library's social media plan can allow for asynchronous posting, which ensures a steady stream of posts. The availability, robustness, and efficiency of third-party scheduling apps varies and there are multiple factors to consider when selecting an app. We will explore the different third-party scheduling apps currently available. In addition, we will discuss what criteria to consider when selecting and implementing scheduling apps. We will share how the librarians at Valdosta State University's Odum Library are using Trello, a project management app, to improve their Instagram account.
Online Collaborative Learning as a Way of Boosting Language and Teaching Proficiency

A pedagogical attempt to increase the effectiveness of foreign language teaching led to pairing Russian students who are obtaining a Master's degree in language teaching methodology with those of a distance department learning English as a foreign language to collaboratively solve educational problems online. As a course instructor plays the role of a facilitator gradually giving students more and more academic freedom, the educational process increases learner autonomy. Current research findings indicate that such a course is effective for both target groups of learners. By directly applying the received competence to online practice, those who study language teaching methodology learn more effectively and acquire new skills associated with successfully working with computer technologies, while those who study by distance learning receive better academic guidance and a more individualized approach from their senior peers.
Reduction of Cognitive Overload in Online Reviews Using Data Visualisation

With the internet taking over many aspects of our lives including the way commercial practices are handled, many business owners are taking what has been posted about them in the forms of online reviews very seriously. While most e-business data visualisation tools focus on website analytics and customer behaviors to determine what customers want and what product or service needs improvement, many dismiss the importance of online reviews, especially the ability to determine if a review should be considered valuable or not. One of the problems of efficiently understanding online reviews is the reader not having enough cognitive strength and working memory to decide if the reviews should be taken seriously or not, and at the same time, understand what the review is about. This paper proposes a novel model to automatically mine online reviews from certain websites, analyses them using decision-tree machine learning and n-grams, and then display a visualisations to highlight how true a review is to be considered. To achieve this, several stages take place in the visualisation system's framework, including retrieving and processing the data, and creating the visualisation. In this study, we focus on reducing cognitive overload by performing some pilot usability studies so that business owners can make better informed decisions.
Elegant and Efficient Communication by Haiku-Like Concise Sentences

Poetic fragmental sentences such as Haiku are often free from grammatical constraint while maintaining full message transmitting power. The author takes Haiku, a classical Japanese concise poetic sentence, as an elegant and efficient communication language for a digital signal system. By using the functional grammar and season-word ontology, the author will throw light on the secret of efficiency in Haiku-like sentences. It is often said that this efficiency comes from artistic mutism—ellipsis or abbreviation. Various events and situations are narrated in a very short and simple sentence, which is composed of a 5-7-5 pattern of letters, words, or phrases. Haiku-like sentences can be composed in non-Japanese, such as English, French, Chinese, etc. The most important Haiku philosophy is “the universality” (Fueki-Ryukou), which was first told by the great poet Basho in 1689. The benefit of universality is even ranging over the digital communication system. That is, the Haiku-like sentence enables highly efficient and concise communication. You can so much as write a cipher by Haiku.
Selected aspects of electricity generation in a private photovoltaic installation

The article discusses basic types of photovoltaic modules and their use in various configurations of home power plants. The astronomical conditions and estimation of intensity of solar radiation outside the Earth's atmosphere as a factor determining the scope of electricity production are described. The construction of a stationary photovoltaic installation with a capacity of 4.16kW, consisting of 16 polycrystalline photovoltaic panels cooperating with the Fronius inverter is presented. Subsequently, 7 very similar installations were analysed. It was ensured that: analysed installations were oriented southwards with a maximum 2-minute deviation, an angle of panels was similar and ranged from 35 to 38 º . Installations have been installed in the same geographical area, at a short distance from one another. Energy produced was monitored during a monitoring of an electricity production process. In an empirical part of this article, energy yields obtained in 2017 are shown. Based on collected data, the analysis showing selected aspects of production, including its repeatability in analysed periods of time, was conducted. Obtained data and mathematical relationships allow to develop models for prediction and diagnostics of plant performance.
Native Language Identification from English Noise Bursts by Chinese Listeners

Identifying speakers by their spoken output is a specialist task for forensic investigators. In the present study we focused on cross-linguistic speaker (Chinese, English, Dutch) identification based on (components of) English stops and fricatives, /p, b, t, d, k, g/ and the fricatives /f, v, θ, ð, s, z, ʃ, ʒ/. The contribution of English noise bursts to native language identification will be presented and the special tokens which contribute the most will be analyzed.
SRC

Object detection is an essential remote sensing data application that attempts to detect and recognize semantically meaningful instances from complicated context and background [1]. Recently, a great number of works have reported that the state-of-the-art convolutional neural networks (CNNs), significantly facilitates automatically detecting a predefined class of object from high resolution aerial or satellite imageries [2]. However, different from the object in a photo, the objects in remote sensing imageries are always represented by largely varied scale or spatial resolution. Scale has become a critical attribute for recognizing an object from aerial or satellite imagery [3]. Up to now, the state-of-the-art DCNNs for object detection requires predefined scale, which produces a big challenge to automatically adjust the representative scale for an object on the ground [3-4].
Robustness Analysis of Coronary Arteries Segmentation

Segmentation of medical scans is the first and fundamental stage of numerical modeling of the human cardiovascular system. In this chapter, we analyze the results of coronary arteries segmentation using our approach for ten contrast-enhanced Computer Tomography Angiography datasets with different image quality and contrast phases. The segmentation is also affected by the patient anatomy, the shape and the scope of images. Our results show that the contrast phase timing is crucial for successful automatic segmentation. These factors form restrictions on the input data for automatic segmentation algorithms. Nevertheless, user guidance such as manual seeding and setting of thresholds can be used to significantly improve segmentation results and weaken the input restrictions.
RMVP: A Real-Time Method to Monitor Random Processes of Virtual Machine

Out-of-box methods can provide strict isolation between the security tool and the target virtual machine (TVM), so they have strong anti-jamming abilities. Current monitoring methods are mostly based on memory snapshots at a time point or a fixed period. Due to the randomness of processes and the discontinuity of monitoring methods, methods based on time point or fixed period snapshots that security tools used to monitor processes may result in a high leakage rate. The real-time monitoring method can be used to resolve this problem. However, all current real-time monitoring methods require a ready monitoring set, and their monitoring range is strictly limited. They are only effective for the inherent objects within the ready set and ineffective for the random processes. This paper presents real-time monitoring of virtual machine processes (RMVP), a real-time monitoring method to monitor a random process in the TVM. First, the RMVP monitors process switch by capturing the switch of kernel stack base addresses outside the TVM in real time. Then, it extracts raw memory of the current process through the memory mapping technology that adopts caching mechanism and multi-task concurrent execution strategy. Finally, the RMVP translates raw memory into high-level semantics according to a semantic knowledge base constructed offline. The RMVP can monitor random processes in real time and overcome the challenge of process randomness. The experimental results show that the capture rate of the random process is over 95% and the capture delay is in the range of 2.3~3.3 ms. In addition, the RMVP is especially effective for detecting the processes hidden by rootkits.
A Grammatical Analysis on Machine Translation Errors

Machine translation errors are classified into groups of three grammatical levels: clause errors, clause-complex errors and textual errors, with a purpose to unravel causes leading to these errors. As illustrated with examples, clause complex presents different grammatical features from clause and the structural differences between Chinese and English at clause-complex level are the fundamental source of machine translation errors. This research, from perspectives of translation from Chinese to English and translation from English to Chinese, categorized clause-complex level structural differences between English and Chinese. The effects of these differences on machine translation are also analyzed, while future improvement suggestions on machine translation technology are provided accordingly.
Report on the Quantum Information Access and Retrieval Theory Winter School

QUARTZ (Quantum Information Access and Retrieval Theory) is an Innovative Training Network that aims to educate its Early Stage Researchers to adopt a novel theoretically and empirically motivated approach to Information Access and Retrieval based on the quantum mechanical framework that gives up the notions of unimodal features and classical ranking models disconnected from context. The present document reports on the QUARTZ Winter School, one of the activities of the QUARTZ training programme. The school was held at the Department of Information Engineering of the University of Padova, Padova, Italy, from February 7 to February 14, 2018. This report describes the goal of the school framed within the training activities of the QUARTZ project, how the school was structured in terms of lectures and activities devoted to the acquisition of transversal skills.
A digital filtering algorithm based on four-channel wavelet and its application in active optical system

Active optical technology used in large-caliber astronomical telescope depends on micro displacement sensors capable of real-time data processing. Proposed for a kind of differential capacitance micro-displacement sensor, a multi-channel digital ﬁlter based on a wavelet transform is designed in this paper to remove noise and gain a clean output signal. This algorithm includes: 1. From the multi-channel wavelets and refinement equation, a multi-channel filter bank is established and thus a down-sampling of M can be made to improve the real-time performance of the filter. 2. Based on the above, filter coefficients are truncated to integers by the way of wavelet lifting scheme. 3. An efficient iterative method is adopted to improve the wavelet decomposition process and simplify the algorithm itself. Through combining the above three methods, the computation amount of the multiplication and rms in the entire filtering process is reduced greatly, which leads to saving of the hardware resource and reducing of the computation time and benefits the designing of the real-time filter on FPGA platform in future. Verified by Matlab, the algorithm described in this paper achieves good noise processing capability.
Reply to “Geochemical Characteristics of Anatolian Basalts: Comment on ‘Neogene Uplift and Magmatism of Anatolia: Insights from Drainage Analysis and Basaltic Geochemistry’ by McNab et al.”

Abstract Uslular and Gençalioğlu‐Kuşcu (2018) have written a lengthy, and highly critical, comment about McNab et al. (2018, https://doi.org/10.1002/2017GC007251 ) which states that our data compilation for Neogene (and Quaternary) volcanic rocks from Anatolia is selective, inconsistent, and not fit for purpose. We state for the record that our compilation is not based on analyses from the published GEOROC database. Uslular and Gençalioğlu‐Kuşcu (2018) also state that our subdivision of this database into three broad longitudinal categories is unrealistic since it does not consider the full range of different tectonic units. They conclude that our interpretation of the link between Neogene‐Quaternary volcanism and uplift of Anatolia is erroneous. We refute this rather strongly worded comment by carefully addressing the five substantive issues raised.
Decision Model for the Security and Utility Risk Evaluation (SURE) Framework

The Security and Utility Risk Evaluation (SURE) framework is a framework for specifying and calculating risk to enable dynamic and autonomous decisions about cyber security and utility risk in generic computer-based systems. The SURE framework's decision model provides the ability to select between multiple alternative mitigation strategies in order to optimise security and utility risk during the operation of a system. This paper presents the decision model of the SURE framework and an example illustrating how the decision model operates in a mobile networking scenario. The example shows that the SURE framework's decision model enables a better fit than existing security decision models between the context of the requested action, security and utility requirements and the selected mitigation strategy, giving greater flexibility to both policy makers and users.
Movie Recommended System by Using Collaborative Filtering

Recommendation system helps people in decision making an item/person. Recommender systems are now pervasive and seek to make profit out of customers or successfully meet their needs. Companies like Amazon use their huge amounts of data to give recommendations for users. Based on similarities among items, systems can give predictions for a new item’s rating. Recommender systems use the user, item, and ratings information to predict how other users will like a particular item. In this project, we attempt to under- stand the different kinds of recommendation systems and compare their performance on the Movie Lens dataset. Due to large size of data, recommendation system suffers from scalability problem. Hadoop is one of the solutions for this problem.