Reviewerly/exHarmony huggingface dataset:
Hiding data inside images using orthogonal moments

In this contribution we propose a novel steganographic method based on several orthogonal polynomials and their combinations. The steganographic algorithm embeds a secrete message at the first eight coefficients of a high frequency image. Moreover, this embedding method uses the Beta chaotic map to determine the order of the blocks where the secret bits will be inserted. In addition, from a 128-bit private key and the steps of a cryptography algorithm according to the Advanced Encryption Standard (AES) to generate the key expansion, the proposed method generates a key expansion of 2560 bits, with the purpose to permute the first eight coefficients of high frequency before the insertion. The insertion takes eventually place at the first eight high frequency coefficients in the transformed orthogonal moments domain. Before the insertion of the message the image undergoes a series of transformations. After the insertion the inverse transformations are applied to the original transformations in reverse order. The experimental work on the validation of the algorithm consists of the calculation of the Peak Signal-to-Noise Ratio (PSNR), the Universal Image Quality Index (UIQI), the Image Fidelity (IF), and the Relative Entropy (RE), comparing the same characteristics for the cover and stego image. The statistical undetectability of the proposed method is evaluated by using a blind steganalysis. The proposed algorithm improves the level of imperceptibility and security analyzed through the PSNR and RE values, respectively, and thus demonstrated resistance to steganalysis.
A Multi-sensor Based Physical Condition Estimator for Home Healthcare

According to the WHO(World Health Organization) and UNSD(United Nations Statistics Division) definition, when the percentage of elderly people (65 years of age or older) in the population exceeds 7%, it becomes an "aging society", if it exceeds 14%, it becomes an "aged society", and if it exceeds 21%, it becomes a "super-aged society". Some developed countries are becoming super-aged societies. In a super-aged society, there are various problems in medical services for health management. To solve these problems, it is desirable for all generations, including the elderly, to take the initiative to maintain their own health. In this paper, we propose a system aimed at every one of them actively managing their health. The system always monitors and accumulates the biological information of the subject using various contact or non-contact sensors. By analyzing these data in an integrated manner, the subject can easily recognize changes in the physical condition. And also, it promotes the provision of information to remote healthcare professionals when people receive healthcare at home.
A Methodology Approach to Evaluate Cloud-Based Infrastructures in Support for e-Assessment

In the last decade, cloud development has grown exponentially and increasingly companies, administrative and educational institutions decide to make the leap and turn their solutions to a cloud platform. In particular, cloud technologies are applicable to educational contexts, especially in online learning, as online universities historically have their educational services installed on-premises, but the trend is to move them to the cloud. This is mainly because of the evident non-functional benefits of using cloud-based solutions, such as high availability, scalability and real-time responsiveness, which are impossible or too much costly to be achieved on-premises Indeed, these benefits can effectively support the current broad and demanding educational services, such as document sharing, communication, assessment, administrative procedures and reporting tools, which must be available to an increasing number of students and university staff anytime and anywhere. However, from the architectural point of view, cloud-based systems pose some challenges not existing in traditional on-premises systems, such as modularization and separation of services, which require additional work to guarantee performance and data protection against potential risks. In this paper, we focus on the assessment services provided by an innovative cloud-based educational system named TeSLA, which may reduce considerably the university costs and infrastructure maintenance, while offering flexible and effective e-assessment solutions. The ultimate goal of the paper is to propose a methodology to evaluate the TeSLA system from the underlying cloud infrastructure in terms of non-functional requirements.
Research and Application of Question Answering System in the field of Air Traffic Control

In this paper, we will build a question answering system for the field of air traffic control, which can assist controllers in monthly training better. The air traffic control question and answer system consists of a search based question answering system and a generation based question and answering system. The search based question answering system maintains an air traffic control problem set, uses word2vec to segment the questions and cosine theorem to calculate the similarity of the questions in order to match the best answer. To questions that can not be matched, we propose Attention-Based Bidirectional Long Short-Term Memory Networks to generate an answer.
Conception and realization of a parabolic dish

This research paper deals with the development of clean energy production technologies. Due to the current energies, and environmental context (oil prices, pollution, global warming, etc.), the uses of solar energy becomes a new perspective. The main purpose of this topic is to build a parabolic dish mirror to concentrate solar radiation. This study will focus on an experiment to convert solar energy into thermal energy using a solar concentrator. A solar tracking system is deployed to orient the concentrator in two directions of rotation designed according to a geographical parameter (the study area). Finally, a numerical simulation of a theoretical model is exploited in order to make it possible to calculate the degree of radiation at the level of the reflector. This experimental work shows the efficiency of the proposed contribution.
Co-Existence of 87 Mbit/s Quantum and 10 Gbit/s Classical Communications in 37-Core Fiber

Quantum key distribution (QKD) guarantees unconditional security and has thus been intensely studied both from a theoretical and an experimental point of view [1]. However, limitations due to transmission loss and noise are still detrimental for these systems performance in terms of reachable distance and key rates, having so far confined QKD implementations to the use of dedicated channels and hence reduced a successful integration with the classical network, with few exceptions [2–6]. In this work we demonstrate a step forward in the co-existence of QKD with classical bright signals, exploiting a 7.9-km long 37-core heterogeneous multicore fiber (MCF) [7], and a space division multiplexing (SDM) technique to achieve a total of 87 Mbit/s secret key generation rate (SKR) in the finite key scenario with a simultaneous co-propagating classical signal of 10 Gbit/s.
Automatic Reconstruction of Cross-Cut Chinese Document Shreds Based on the Feature of Typesetting and Strokes

This paper proposed an algorithm for automatic reconstruction of cross-cut Chinese document shreds based on the similarities of stroke and typesetting features. We started with extracting the typesetting feature including row height, line spacing and word width based on horizontal projection vector of strokes among the shreds, and clustered shreds into groups by means of k nearest neighbor mutual voting clustering algorithm with rejection region on the basis of horizontal projection vector similarity, word height and line spacing match measurement. Then, vertical sorting among the cluster groups was carried out according to word height and line spacing match measurement. Finally, intra-group splicing was accomplished based on greedy algorithm by using similarity of strokes and word width match measurement on both sides of vertical cut. The experiment result showed that the proposed algorithm achieved 100% reconstruction accuracy with the run time less than 5 seconds, and worked well in environment with 10 standard deviation Gauss noise.
Making Deep Neural Networks Robust to Label Noise: A Reweighting Loss and Data Filtration

Deep neural networks (DNNs) have achieved astonishing results on a variety of supervised learning tasks owing to a large scale of expert-labeled datasets. However, as recent researches pointed out, the generalization performance of DNNs deteriorates rapidly when training data contains label noise. To alleviate the problem of easily overfitting to label-corrupted data when training DNNs, this paper proposes a robust loss function by reweighting the standard Cross-Entropy loss. For obtaining more robust DNNs under label noise, we further design a framework to jointly optimize model parameters and filtering noisy data during training. Our methods can be thus viewed as online curriculum learning based on both loss function and training datasets. A great deal of experiments have conducted on CIFAR-10, CIFAR-100 and ImageNet under two types of label noise. The results of our proposed method outperforms other state-of-the-art methods.
A Sampling-Based Algorithm for Planning Smooth Nonholonomic Paths

The ability to navigate in an environment is essential to the autonomy of mobile robots and unmanned autonomous vehicles. Informally, path planning computes a collision-free path from a start location to a goal location in a known environment. Computing such paths accounting for the kinematics of the robot is a problem widely addressed in the literature, often focusing on feasibility and optimality of the planned paths. Although the smoothness of the paths is a major concern in most applications, the widely used sampling-based approaches often produce quirky winding paths. In this paper, we propose a novel path planning algorithm that is able to produce smooth paths, particularly when considering nonholonomic robot kinematics, like the differential drive kinematics. Comparative experiments show the effectiveness of the proposed algorithm in producing smooth paths.
Everyday life information‐seeking behaviors of nomadic pastoralists in their oral culture

ABSTRACT Nomadic pastoralists, due to their lifestyle and the dominance of oral culture, have not received much attention from information service providers and little is known about their information behavior. This qualitative ethnographic study entailed twenty semi‐structured interviews with Iranian Bakhtiari nomadic pastoralists using everyday life information seeking (ELIS) framework. Nomadic pastoralists' ELIS is dominated by reliance on human information sources including elders, chiefs (Khans) and family members. Most of their information needs relate to practical issues of daily life (e.g., animal husbandry, handicraft, and parenting) and therefore they prefer information sources that tap into their local and indigenous knowledge (e.g., elders) and value experience and skill over formal education when obtaining information from people. However, they purify their local knowledge through exposure to modern education. Relevance to daily life matters or practicality, previous successful utilization of information, and accessibility are among the factors they consider when choosing information sources.
Structural Harmony Method in the Context of Deep Learning on Example of Music by Valentyn Sylvestrov and Philipp Glass

The article shows the potential of structural harmony method application in deep learning models training.Harmonic sequences, being represented as the paths in the system of graphs and then schematised, serve as the source of the deep learning models input.The visual character of the dataset generated using schemes allows the application of supervised machine learning technics and is suitable for time series analysis.Therefore, two neural network architectures -Convolutional and Convolutional Long-Short Term Memory were tested on example of music by postmodern composers Valentyn Sylvestrov (represented by vocal cycle "Silent songs") and Philipp Glass (represented by piano cycle "Metamorphosis"). Harmonic sequences clustering.
Cross-View Adaptation Network for Cross-Domain Relation Extraction

In relation extraction, directly adopting a model trained in the source domain to the target domain will suffer greatly performance decrease. Existing studies extract the shared features between domains in a coarse-grained way, which inevitably introduce some domain-specific features or suffer from information loss. Inspired by human beings often using different views to find connection between domains, we argue that, there exist some fine-grained features which can be shared across different views of origin data. In this paper, we proposed a cross-view adaptation network, which use adversarial method to extract shared features and introduce cross-view training to fine-turn it. Besides, we construct some novel views of input data for cross-domain relation extraction. Through experiments we demonstrated that the different views of data we construct can effectively avoid introducing some domain-specific features into unified feature space and help the model learn a fine-grained shared features of different domain. On the three different domains of ACE 2005 dataset, Our method achieved the state-of-the-art results in F1-score.
Analytical Comparison of Virtual Machine and Data Placement Algorithms for Big Data Applications Based on Cloud Computing

Data generation proliferation and big data utilization increase in different areas, cause to highlight data and virtual machine placement problem in MapReduce framework. Since problem NP-hardness is proven, different algorithms using various techniques have been proposed in recent years and every solution targets some objectives in this regard. But, it has not proposed any troubleshooting solution to placement problem till now and it is still an issue for service providers. In this paper, we present a comprehensive evaluation of current researches and highlight the new paths for researchers by identifying the weaknesses of existing studies. To reach to this goal, we evaluate many of current researches about vm placement in cloud computing and big data applications on cloud computing and select some of them to be presented in this paper. Also, we propose our method to solve the problem and show how it could be more effective than available methods.
An Optimal Route Recommendation Method for a Multi-purpose Travel Route Recommendation System

With the rapid development of tourism, the demand for travel is becoming increasingly personalized. Travelers are increasingly traveling to places that they have not visited previously. When travelers decide to visit unfamiliar scenic spots, they need to spend a great deal of time making relevant travel plans. Therefore, we consider a system that is specifically designed to make travel plans for travelers when they visit a country or city for the first time. Simultaneously, the optimal path result is obtained using a genetic algorithm. This system can provide travelers with highly satisfying travel paths that only require the traveler to enter the degree of destination and time constraints.
Efficient Resource Utilization Using Blockchain Network for IoT Devices in Smart City

With rapid increase in the use of technology, the world is now moving towards smart cities which require the communication and collaboration of Internet-of-Things (IoT) devices. The smart city enhances the use of technology to share information and data among devices. These devices are producing a huge volume of data that needs to be tackled carefully. Different works have already been proposed to provide a communication in a network for the IoT devices; however, nothing has been founded more effective in terms of resource utilization. Hybrid network architecture is the combination of a centralized and distributed network architectures. The centralized network is used for the communication of IoT devices with edge nodes and distributed network for communicating miner nodes with edge nodes. In this way, the network utilize a lot of resources. In this paper, we are proposing a single network which is the combination of both edge nodes and miner nodes. Blockchain is also implemented in this network to provide secure communication between the devices. The evaluation of the proposed model is done using different performance parameters such as time and cost against the number of devices. Limited number of devices are used to perform this evaluation. Furthermore, the results are obtained by utilizing Proof-of-Work consensus mechanism.
A Pen-Based Device for Signature Verification

The digital pen is the main tool for obtaining online signature writing information. Since the measuring coordinate system of the digital pen is established at the pen body, it must be held at the same position each time to ensure the consistency of the data. In this paper, by combining the tilt sensor with the force, a pen-based solution using high-quality sensor is designed, and the accuracy of the data is verified. Finally, the limitation of the digital pen during writing is eliminated.
Video DeCaptioning Using U-Net with Stacked Dilated Convolutional Layers

We present a supervised video decaptioning algorithm driven by an encoder-decoder pixel prediction. By analogy with auto-encoders, we use U-Net with stacked dilated Convolution layer which is a convolutional neural network trained to generate the decaptioned version of an arbitrary video with subtitles of any size, colour or background. Moreover, our method doesn’t require the mask of the region with text to be removed. In order to succeed at this task, our model needs to both understand the content of the entire frames of video, as well as produce a visually appealing hypothesis for the missing part behind text overlay. When training with our model, we have experimented with both a standard pixel-wise reconstruction loss, as well as total variation loss. The latter produces much sharper results because it enforces inherent local nature in the generated image. We found that our model learns a representation that captures not just appearance but also the semantics of visual structures. We quantitatively demonstrate the effectiveness of including dilated convolution layers and residual connections in the bottleneck layer in the reconstruction of videos without captions. Furthermore, our model can be used for semantic inpainting tasks, either stand-alone or as initialization for non-parametric methods.
Polynomial semantics for modal logics

It is shown here that the modal logic K and any extension of it with a finite number of axioms can be characterised by a polynomial semantics. Moreover, some comments are made about the possibility of using algebraic computation to determine deducibility on these logics.
Analysis of KNN Algorithm with Mapreduce Technique on Big Data

Abstract Due the fast growth of new technology application like social media analysis, web data analysis and medical information network analysis, here the various types of data are processed frequently. The large amount of effective data management and analysis is very vital goal. To reduce the data processing complexity, time complexity, and space complexity in Big Data, the paper going to propose the k-nearest neighbor join (KNN) operation. KNN is used to find the K nearest points in S. It is a computational task that will handle the large range of applications such as knowledge discovery or data mining. When the volume and the dimension of data increases, then only distributed approaches can perform the big operations in a given time. Recent works have done on implementing the efficient solutions using the map reduce programming model because it is used for distributing the large scale data processing. Although these works provide different solutions for the same problem, each one has particular constraints and properties. This paper compares the existing of different computation of KNN on MapReduce. First the paper compares the solutions in to three steps for KNN computation on MapReduce: 1) Data processing, 2) Data partitioning and 3) Computation. The Experiment in this paper explains the variety of different data sets, and analyzes the data volume, data dimension and the value of k from many perspectives like time and space complexity, and accuracy.
Optimizing Horn- $$\mathcal {SHIQ}$$ Reasoning for OBDA

The ontology-based data access (OBDA) paradigm can ease access to heterogeneous and incomplete data sources in many application domains. However, state-of-the-art tools are still based on the DL-Lite family of description logics (DLs) that underlies OWL 2 QL, which despite its usefulness is not sufficiently expressive for many domains. Accommodating more expressive ontology languages remains an open challenge, and the consensus is that Horn DLs like Horn- $$\mathcal {SHIQ}$$ are particularly promising. Query answering in Horn- $$\mathcal {SHIQ}$$ , a prerequisite for OBDA, is supported in existing reasoners, but many ontologies cannot be handled. This is largely because algorithms build on an ABox-independent approach to ontological reasoning that easily incurs in an exponential behaviour. As an alternative to full ABox-independence, in this paper we advocate taking into account general information about the structure of the ABoxes of interest. This is especially natural in the setting of OBDA, where ABoxes are generated via mappings, and thus have a predictable structure. We present a simple yet effective approach that guides ontological reasoning using the possible combinations of concepts that may occur in the ABox, which can be obtained from the mappings of an OBDA specification. We implemented and tested our optimization in the Clipper reasoner with encouraging results.
The Territory of my Body: Testosterone Prevents Limb Cooling in the Rubber Hand Illusion

The Rubber Hand Illusion (RHI) is an experimental paradigm for assessing changes in body ownership. Recent findings in the field suggest that social emotions can influence such changes and that empathic motivation in particular appears to positively predict the malleability of body representations. Since the steroid hormone, testosterone, is well known to interrupt certain forms of empathic processing, in the current study we investigated whether 0.5 mg of testosterone affected ownership indices of the RHI. Forty-nine females participated in a double-blind, placebo-controlled experiment in which the RHI was induced. Compared to placebo, testosterone had no effects on the alteration of subjective ownership over the rubber limb or on subjective sense of proprioceptive drift. However, unlike the placebo group, testosterone-treated participants did not display an objective decline in the temperature of their own (hidden) hand following induction of the illusion. These findings suggest that testosterone strengthens implicit but not explicit bodily self-representations. We propose that effective maintenance of implicit body boundaries can be regarded, conceptually, as a primary defensive state facilitating integrity of the self.
Linear Passive Open Quantum Systems

Linear passive open quantum systems are a simple yet interesting class of open quantum systems. In this paper, we first define the class of linear passive open quantum systems. Then some properties of these systems developed in recent years are reviewed.