Reviewerly/exHarmony huggingface dataset:
Security bound of continuous-variable measurement-device-independent quantum key distribution with imperfect phase reference calibration

Phase reference calibration is a necessary procedure in practical continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) for the need of Bell-State Measurement (BSM). However, the phase reference calibration may become imperfect in practical applications. We explored the practical security of CV-MDI-QKD with imperfect phase reference calibration under realistic conditions of lossy and noisy quantum channel. Specifically, a comprehensive framework is developed to model and characterize the imperfection of practical phase reference calibration operation, which is mainly caused by the non-synchronization of two remote lasers in senders. Security analysis shows that the imperfect phase reference calibration has significant side effects on the performance and security of the CV-MDI-QKD protocol. A tight security bound to excess noise introduced by imperfect phase reference calibration is derived for reverse reconciliation against arbitrary collective attacks in the asymptotic limit, and the tolerance of the CV-MDI-QKD protocol to this excess noise is also obtained. This security analysis framework can eliminate the security hazards caused by imperfect phase reference calibration without changing the existing CV-MDI-QKD system structure.
EFF-ViBE: An Efficient and Improved Background Subtraction Approach based on ViBE

Background subtraction plays an important role in intelligent video surveillance since it is one of the most used tools in motion detection.If scientific progress has enabled to develop sophisticated equipment for this task, algorithms used should be improved as well.For the past decade a background subtraction technique called ViBE is gaining the field.However, the original algorithm has two main drawbacks.The first one is ghost phenomenon which appears if the initial frame contains a moving object or in the case of a sudden change in the background situations.Secondly it fails to perform well in complicated background.This paper presents an efficient background subtraction approach based on ViBE to solve these two problems.It is based on an adaptive radius to deal with complex background, on cumulative mean and pixel counting mechanism to quickly eliminate the ghost phenomenon and to adapt to sudden change in the background model.
The Application of Behavioural Heuristicsto Initial Coin Offerings Valuation and Investment

Blockchain projects have seen a rush of investment in the form of Initial Coin Offerings (ICOs) in 2016 and 2017, yet little is understood about how to valuate these projects.This research explored the application of behavioural heuristics to ICO valuation and investing.Identified were six variables that may impact investment decision making due to key behavioural biases.These variables -coin value, market capitalisation, ease of understanding, market sentiment, maximum ICO bonus level, and pre ICO social media levels -were analysed using Pearson's Correlation against return on investment (ROI).The data was collected from numerous ICO websites and Twitter.Fundamental analysis was taken from Coincheckup due to it being a major source of information for many retail investors and using a well-defined methodology.Sentiment data was collected from Twitter and assessed using Crimson Hexagon's social sentiment analysis tool.Ease of understanding was evaluated using AWS Blockchain business canvas.All information was compiled into a single dataset and the top 47 projects in terms of ROI were utilised for this research.Ease of understanding was found to be significantly correlated with ROI.Ease of understanding was then combined with fundamental analysis to develop a hybrid model of evaluation for cryptocurrency projects.This model substantially outperformed fundamental analysis alone, with a 33.6% improvement on ROI.In conclusion, current methods of fundamental analysis for blockchain projects are an inadequate method for capturing their full potential future value.Investors lacking appropriate tools and with limited knowledge and experience -along with the relatively recent advent of cryptocurrencies -are being influenced by behavioural factors such as ease of understanding.It is therefore important that investors and entrepreneurs alike take such factors into consideration.
Vehicle detection using background subtraction and clustering algorithms

Traffic congestion has raised worldwide as a result of growing motorization, urbanization, and population. In fact, congestion reduces the efficiency of transportation infrastructure usage and increases travel time, air pollutions as well as fuel consumption. Then, Intelligent Transportation System (ITS) comes as a solution of this problem by implementing information technology and communications networks. One classical option of Intelligent Transportation Systems is video camera technology. Particularly, the video system has been applied to collect traffic data including vehicle detection and analysis. However, this application still has limitation when it has to deal with a complex traffic and environmental condition. Thus, the research proposes OTSU, FCM and K-means methods and their comparison in video image processing. OTSU is a classical algorithm used in image segmentation, which is able to cluster pixels into foreground and background. However, only FCM (Fuzzy C-Means) and K-means algorithms have been successfully applied to cluster pixels without supervision. Therefore, these methods seem to be more potential to generate the MSE values for defining a clearer threshold for background subtraction on a moving object with varying environmental conditions. Comparison of these methods is assessed from MSE and PSNR values. The best MSE result is demonstrated from K-means and a good PSNR is obtained from FCM. Thus, the application of the clustering algorithms in detection of moving objects in various condition is more promising.
Secure distributed data geolocation scheme against location forgery attack

The emergence of the Internet of Things (IoT) has been greatly applied for improving people’s lives. Through the combination with cloud computing resources, the IoT is no longer constrained by computing and storage resources. For the storage service of IoT/cloud, it is crucial to keep the data within some specific geolocation borders for improving storage service quality. At the same time, it can protect the security of data against natural or man-made disasters. However, the outsourcing model results in the data owners losing their physical control over their data, which makes it difficult for the data owners to check the geolocation of their outsourced data with the potentially semi-honest cloud service providers (CSPs). To provide a secure cloud data geolocation against semi-honest CSPs, we propose an efficient data geolocation scheme, and it can be applied into distributed cloud data geolocation. It combines both the proof of retrievability technique and the delay-based geolocation technique for cloud users, the purpose is to check both the retrievability and geolocation of their outsourced data stored in CSPs. By using a novel server-centric broadcast measurement, the scheme can provide more effective detection of the location forgery attacks from potentially semi-honest CSPs. Finally, the theoretical analysis and experimental evaluation show that the proposed scheme is secure and effective.
Optimal Feature Selection for Blind Super-resolution Image Quality Evaluation

The visual quality of images resulting from Super Resolution (SR) techniques is predicted with blind image quality assessment (BIQA) models trained on a database(s) of human rated distorted images and associated human subjective opinion scores. Such opinion-aware (OA) methods need a large amount of training samples with associated human subjective scores, which are scarce in the field of SR. By contrast, opinion distortion unaware (ODU) methods do not need human subjective scores for training. This paper presents an opinion-unaware BIQA measure of super resolved images based on optimally extracted perceptual features. This set of features was selected using a floating forward search whose objective function is the correlation with human judgment. The proposed BIQA method does not need any distorted images nor subjective quality scores for training, yet the experiments demonstrate its superior quality-prediction performance relative to state-of-the-art opinion-unaware BIQA methods, and that it is competitive to state-of-the-art opinion-aware BIQA methods.
Software and Hardware Requirements and Trade-Offs in Operating Systems for Wearables: A Tool to Improve Devices’ Performance

Wearable device requirements currently vary from soft to hard real-time constraints. Frequently, hardware improvements are a way to speed-up the global performance of a solution. However, changing some parts or the whole hardware may increase device complexity, raising the costs and leading to development delays of products or research prototypes. This paper focuses on software improvements, presenting a tool designed to create different versions of operating systems (OSs) fitting the specifications of wearable devices projects. Authors have developed a software tool allowing the end-user to craft a new OS in just a few steps. In order to validate the generated OS, an original wearable prototype for mining environments is outlined. Resulting data presented here allows for measuring the actual impact an OS has in different variables of a solution. Finally, the analysis also allows for evaluating the performance impact associated with each hardware part. Results suggest the viability of using the proposed solution when searching for performance improvements on wearables.
Graph Filtering with Multiple Shift Matrices

We propose a novel graph filtering method for semi-supervised classification that adopts multiple graph shift matrices to obtain more flexibility in dealing with misleading features. The resulting optimization problem is solved with a computationally efficient alternating minimization approach. In simulation experiments, we implement both conventional and our proposed graph filters as semi-supervised classifiers on real and synthetic datasets to demonstrate advantages of our algorithms in terms of classification performance.
Multiphoton nonclassical state generated by hyperradiance originating from a quasi-subradiant state

In a driven cavity QED system with two nonidentical qubits of different resonant frequencies, a multiphoton nonclassical state can be generated by single-photon and two-photon cavity-induced transparency (CIT). Due to the existence of the quasi-subradiant state, the parameter of radiance witness $R$ can be greater than unity, which demonstrates that the cavity QED system is in the hyperradiance regime. In this hyperradiance regime, although the second-order correlation function ${g}^{(2)}(0)$ is greater than unity, the photon statistics still exhibits the higher-order sub-Poissonian distribution. Because the single-photon and two-photon processes are suppressed by CIT-like effects, the three-photon process is enhanced, which leads to Klyshko's criterion ${K}_{n}&lt;1$ for $n&gt;2$, indicating the appearance of multiphoton nonclassicality. Based on this principle, we propose a scheme for preparing multiphoton nonclassical light with a three-photon probability of ${10}^{\ensuremath{-}4}$, which is feasible for experiment.
Reading Practices of EFL Students: A Survey of Kuwaiti College Students

Reading is important for students by contributing significantly to success in their studies and their career development. A questionnaire-based survey was conducted in the English Department of the College of Basic Education, Public Authority for Applied Education and Training (PAAET), in Kuwait. Based on 410 responses of EFL college students in Kuwait on their reading practices, it was found that students read mainly for entertainment, and reading does not appear to be a popular activity among students. Fiction, fashion, and best sellers were the three main types of reading, indicating that academic reading was not a priority. Only a small proportion of students used e-books. Most students perceived such features of e-books as their portability and ability to store more information as likely to attract more students to e-reading, and indicated that they would be attracted to reading e-books themselves if circumstances change. This indicates a good potential to promote e-reading among students if steps are taken to make e-books and e-readers available to them through libraries and academic institutions. E-reading is also expected to become more popular among students if it could be linked to academic reading, particularly to the availability of text books in e-format. Libraries in Kuwait should start more proactive program to promote e-books and e-reading among college and university students.
Expertise in the Selection of Electronic Educational Resources – Conceptual Vision

The current stage of training of pedagogical specialists is associated with the search for opportunities for the continuous development of their digital competences. It is viewed as a dynamic system that integrates the constantly expanding set of competencies given the dynamic development of information and communication technologies and the requirements for training in a digital environment. The main component (competency) in the digital competency profile of pedagogical specialists is the expertise in the selection and combination of electronic educational resources for the creation of a dynamic technologically enriched digital environment, which guarantees higher quality of education and consequently – higher educational results. This article presents an author's system of criteria and indicators as a conceptual vision for expert evaluation of electronic educational resources, which can be enriched and expanded in a technological and methodological context.
Improving RNN Performance by Modelling Informative Missingness with Combined Indicators

Daily questionnaires from mobile applications allow large amounts of data to be collected with relative ease. However, these data almost always suffer from missing data, be it due to unanswered questions, or simply skipping the survey some days. These missing data need to be addressed before the data can be used for inferential or predictive purposes. Several strategies for dealing with missing data are available, but most are prohibitively computationally intensive for larger models, such as a recurrent neural network (RNN). Perhaps even more important, few methods allow for data that are missing not at random (MNAR). Hence, we propose a simple strategy for dealing with missing data in longitudinal surveys from mobile applications, using a long-term-short-term-memory (LSTM) network with a count of the missing values in each survey entry and a lagged response variable included in the input. We then propose additional simplifications for padding the days a user has skipped the survey entirely. Finally, we compare our strategy with previously suggested methods on a large daily survey with data that are MNAR and conclude that our method worked best, both in terms of prediction accuracy and computational cost.
Building an International Consensus on Multi-Disciplinary Metadata Standards: A CODATA Case History in Nanotechnology

Science today is rapidly becoming both multi-disciplinary and data-driven. These two trends pose new challenges to the capture, management, sharing, and dissemination of research data. Multi-disciplinary science means diverse data generation communities and equally diverse user groups. Data-driven means that sharing data among different communities is more important than ever because of the growth of modeling and knowledge discovery. Nanotechnology is a prime example, involving chemistry, physics, materials science, toxicology, environmental science, and many other disciplines. During the past few years, CODATA has created an international, multi-disciplinary Working Group that has developed a number of critically important metadata standards to facilitate sharing nanomaterials data. In this paper, we discuss the challenges faced in starting and executing this work, as well as the approaches taken to make progress on producing internationally accepted metadata standards. Many of these approaches are directly applicable to other multi-disciplinary subjects.
Faster Linear Transformations in $\textsf{HElib}$ , Revisited

HElib is a C++ library that implements fully homomorphic encryption (FHE). This supports homomorphic linear transformations on plaintext slots which consist of many rotation operations on plaintext slots, and currently, each rotation involves one or two automorphisms. In this paper, we describe a new method for linear transformations that perform only one automorphism for the rotation operation for many kinds of linear transformations. We then further investigate our new method to have algorithmic improvement, which can be understood as a generalization of the baby-step/giant-step strategy on HElib. Finally, we verify that our improvement works well by implementation, and see 2-4 times speedup than the current HElib implementation.
Path Planning of Underwater Terrain-Aided Navigation Based on Improved Artificial Potential Field Method

Abstract For underwater terrain-aided navigation systems to collect enough terrain features, a path-planning method for terrain-aided navigation is proposed. First, a distribution model of terrain features is built based on underwater terrain Fisher information content. Then, terrain information and path length are taken as optimization targets, and considering the constraints of autonomous underwater vehicle (AUV) maneuverability, a path applicable to terrain-aided navigation can be provided. Simulation tests show that the terrain feature information in AUVs' sailing area obtained with the method is adequate; therefore, this method can effectively improve the performance of terrain-aided navigation systems.
DRGAN: A GAN-Based Framework for Doctor Recommendation in Chinese On-Line QA Communities

Recently, more and more people choose to seek health-related information in health-related on-line QA communities. Doctor recommendation is very essential for users in these communities since it is difficult for them to find a proper doctor without assistance from medical staffs. In this paper, we develop a Generative Adversarial Nets (GANs)-based doctor recommendation framework utilizing data in Chinese on-line QA communities. We conduct extensive sets of experiments on a real-world dataset. The experimental results show that our framework significantly outperforms the state-of-the-art baselines.
Speaking skill of elementary students reviewed by gender

Speaking skill is one of language skill that used on daily life. Speaking skill include two aspects namely linguistic aspect and non-linguistic aspect. However, not all people have good speaking skill when facing some situation. That condition is available to students on language learning. Every students has difference obstacles on speaking. The obstacles of students’ speaking skill can be identified by speaking aspects. This research aims to identifying gender difference of students’ speaking skill on elementary schools. This research was using quantitative design within expost facto method. Technique of collecting sample used stratified sampling. Samples of this research were 30 students of third grade at one of elementary schools on Kampar Sub-District. Technique of collecting data in this research used speaking test. Technique of validity data used content validity and the validity was done by an expert or obtain expert judgement. Meanwhile, the data analysis used inference statistic. The result of this research useful to identify students’ speaking skill so teacher can give appropriate treatment to improve students’ speaking skill based on their necessities.
Automatic Generation of Chinese Couplets with Attention Based Encoder-Decoder Model

Chinese couplets, as one of the traditional Chinese culture, is the treasure of Chinese civilization and the inheritance of Chinese history. Given a sentence (namely an antecedent clause), people reply with another sentence (namely a subsequent clause) equal in length. Because of the complexity of the semantic and grammatical rules of couplet, it is not easy to create a suitable couplet that meets the requirements of sentence pattern, context, and flatness. In this paper, given the issued antecedent clause, we can automatically generate the subsequent clause by encoder-decoder model. Moreover, to satisfy special characteristics of couplets, we incorporate the attention mechanism into the encoding-decoding process, which greatly improves the accuracy of couplets generated automatically.
ANALISA SISTEM QR CODE UNTUK IDENTIFIKASI BUKU PERPUSTAKAAN

&lt;p&gt;Information books in the library will provide a special identity on any book title. Identity of the books stored in the library will allow the borrower book knows the book title, author, ISBN number, number of shelves where the books are stored and the number of books available in the library. Identity can be summarized in a QR Code.&lt;/p&gt;&lt;p&gt;With the QR Code on the books, the user can obtain information about a book without having to connect to the database, simply scanning the QR Code Reader. To create a QR Code requires an application generator. In this study, the authors will develop case studies QR Code Generator library FTIK University of Semarang. QR Code Generator will convert alphanumeric data from a book into a two-dimensional image. Which will be attached to each book in the library.&lt;em&gt;&lt;/em&gt;&lt;/p&gt;
Optimal Representation for Web and Social Network Graphs Based on ${K}^{2}$ -Tree

With the rapid growth of the Internet, the scale of graphs has increased dramatically, which poses special challenges in representing both web graphs and social network graphs. In the adjacency matrix of web and social network graphs, only a very small proportion of the elements is “1” s. Furthermore, we find that using the aggregation of scattered 1 s to form a high density of adjacency matrices is beneficial to the compression of storage space. Based on these findings, we propose the DGC-K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -tree compression approach based on K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -tree, which can greatly increase the density of 1 s among the existing algorithms and adequately compress the blank area in the adjacency matrix. Then, we design a query algorithm for this mechanism to support the operation on the graph. The experimental results show that compared with the state-of-the-art algorithms, including the K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -tree based on a diagonal clustering mechanism (K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -BDC), the K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -tree, Re-Pair, and LZ78, our approach achieves better compression ratio and shorter time consumption. In terms of storage efficiency, our approach reduces the space by an average of 34.07% compared to the best performing algorithm K <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -BDC. In terms of query efficiency, our approach reduces the time by an average of 80.63% compared to the best performing algorithm LZ78.