Reviewerly/exHarmony huggingface dataset:
Analysis of Different Interpolation Methods in Watermarking Scheme Based on Prediction Error Expansion, Interpolation and Segmentation

Watermarking is a way to keep secret information hidden in the cover media that is indistinguishable to the human eye but easily noticeable to the decoder. In this paper, a watermarking scheme is analyzed based on prediction error expansion using interpolation and segmentation in order to find an optimal watermarking technique. In this method, watermark is embedded into a cover image using additive prediction error expansion method by interpolating and segmenting the cover image. Interpolation reduces artifacts and segmentation distinguishes sharp areas and smooth areas. Here, interpolation is used instead of other prediction method in order to reduce the computation time and complexity of the method. Segmentation is used so that higher embedding capacity can be used for sharp segments and lower embedding capacity for smooth regions using additive PEE. The interpolation method will be varied to determine the most efficient interpolation method for this scheme. Different noise attacks will be applied to find the robustness of the method. The idea of applying different bits in different segments will be applied. For this purpose, the segments are classified into different classes according to their average slopes. The segments that have higher slopes will get more bits embedded into them. Finally, different performance parameters like PSNR, NCC, MSE & embedding capacity will be calculated for evaluating the proposed scheme.
Multi-purpose Control of a Moving Object Using Computer Vision Algorithms

The article is devoted to the design of control systems for moving objects using computer vision algorithms. The relevance of this research area is associated with the increasing use of autonomous vehicles for solving practically important tasks without human intervention. However, many of these tasks are impossible or inefficient to solve without using of visual information. The problem of visual positioning of a moving object equipped with an onboard video camera is considered. The purpose of the control is to provide the desired position of some observed object in the image plane of the camera. A mathematical model of the joint dynamics of a moving object and a set of observed points in the image plane is derived. Such joint dynamics are taken into account in control design because the camera, mounted on board, has restrictions on freedom of movement in space and on the speed of these movements. A multi-purpose approach to the synthesis of the control law is proposed, allowing to ensure the fulfillment of a set of requirements for the quality of processes in a closed-loop system in various regimes. The choice of adjustable elements of a multi-purpose structure is discussed. The application of the approach is illustrated by an example of control of a wheeled robot. The results of simulation performed in MATLAB/Simulink environment are presented.
Learning and Testing Variable Partitions.

$ $Let $F$ be a multivariate function from a product set $\Sigma^n$ to an Abelian group $G$. A $k$-partition of $F$ with cost $\delta$ is a partition of the set of variables $\mathbf{V}$ into $k$ non-empty subsets $(\mathbf{X}_1, \dots, \mathbf{X}_k)$ such that $F(\mathbf{V})$ is $\delta$-close to $F_1(\mathbf{X}_1)+\dots+F_k(\mathbf{X}_k)$ for some $F_1, \dots, F_k$ with respect to a given error metric. We study algorithms for agnostically learning $k$ partitions and testing $k$-partitionability over various groups and error metrics given query access to $F$. In particular we show that $1.$ Given a function that has a $k$-partition of cost $\delta$, a partition of cost $\mathcal{O}(k n^2)(\delta + \epsilon)$ can be learned in time $\tilde{\mathcal{O}}(n^2 \mathrm{poly} (1/\epsilon))$ for any $\epsilon > 0$. In contrast, for $k = 2$ and $n = 3$ learning a partition of cost $\delta + \epsilon$ is NP-hard. $2.$ When $F$ is real-valued and the error metric is the 2-norm, a 2-partition of cost $\sqrt{\delta^2 + \epsilon}$ can be learned in time $\tilde{\mathcal{O}}(n^5/\epsilon^2)$. $3.$ When $F$ is $\mathbb{Z}_q$-valued and the error metric is Hamming weight, $k$-partitionability is testable with one-sided error and $\mathcal{O}(kn^3/\epsilon)$ non-adaptive queries. We also show that even two-sided testers require $\Omega(n)$ queries when $k = 2$. This work was motivated by reinforcement learning control tasks in which the set of control variables can be partitioned. The partitioning reduces the task into multiple lower-dimensional ones that are relatively easier to learn. Our second algorithm empirically increases the scores attained over previous heuristic partitioning methods applied in this context.
The CitySPIN Platform: A CPSS Environment for City-Wide Infrastructures.

Cyber-physical Social System (CPSS) are complex systems that spanthe boundaries of the cyber, physical and social spheres. They playan important role in a variety of domains ranging from industryto smart city applications. As such, these systems necessarily needto take into account, combine and make sense of heterogeneousdata sources from legacy systems, from the physical layer and alsothe social groups that are part of/use the system. The collection,cleansing and integration of these data sources represents a majoreffort not only during the operation of the system, but also duringits engineering and design. Indeed, while ongoing efforts areconcerned primarily with the operation of such systems, limitedfocus has been put on supporting the engineering phase of CPSS.To address this shortcoming, within the CitySPIN project we aim tocreate a platform that supports stakeholders involved in the designof these systems especially in terms of support for data management.To that end, we develop methods and techniques based onSemantic Web and Linked Data technologies for the acquisitionand integration of heterogeneous data from disparate structured,semi-structured and unstructured sources, including open data andsocial data. In this paper we present the overall systemarchitecturewith a core focus on data acquisition and integration.Wedemon-strate our approach through a prototypical implementationof an adaptive planning use case for public transportationscheduling.
A new approach of object recognition in encrypted images using convolutional neural network

One of the major challenges in mobile networks and digital technologies is maintaining the security of real time data. In this regard, the research community developed a lot of works to fulfill this goal by proposing secure image encryption algorithms. However, some of these encryption schemes are not secure enough and lack robustness and security. In this paper, we succeed to reveal the weaknesses of a recently published encryption algorithm that is supposed to be secure and robust. We found that although the proposed network is unable to decrypt the ciphered image, it is able to perform classification on this image. We succeeded to build a deep neural network that can recognize encrypted images with an accuracy of 95.8%. Results demonstrate that our proposed approach is efficient for classifying ciphered images. These results could be valuable for further works into the topic of cryptanalysis using deep learning.
A distributed common test platform environment

In order to solve the inconvenience caused by the difference of hardware and software test resources during product testing, realize instrument interchange, meet the rapid change characteristics of test demand and rapid deployment requirements, this paper studies the construction method of a distributed test environment, adopts a fixed general test platform in hardware, and drives Heterogeneous hardware interface standardization technology (middleware technology), isolation module differences, to solve the different manufacturers to the module replacement to bring compatibility problems. Software adopts the design principle of layered software, connecting test elements such as device libraries, ICD libraries, use case libraries, etc. through the use case design layer, resource allocation layer, and hardware environment layer, and through XML between elements Exchange data information, interact with database information via Ethernet, and complete the automatic testing process. Each software runs independently on multiple test devices in the test environment, enabling the automatic execution of corresponding actions or automatically generating the test case software code through XML file.
Analysis of Educational Results of Ukrainian Students in Mathematics and Physics based on External Independent Assessment

The article researches the educational outcomes of mathematics and physics students over the last 10 years based on the External Independent Assessment (EIA). External Independent Assessment is a test, that is make up by all applicants for higher education institutions to determine their educational performance, which is conducted and regulated by a unit of the Ministry of Education of Ukraine. The article deals with the correlations of interdisciplinary student achievement, the number of students and the level of knowledge by years.
Connecting the Humanities through Research Infrastructures : Paper presented at DHN 2019 Conference, Copenhagen

Several Research Infrastructures(RIs)exist in the Humanities and Social Sciences, some –such as CLARIN, DARIAH and CESSDA –which address specific areas of interest, i.e. linguistic studies, digital humanities and social science data archives. RIs are also unique in their scope and application, largely tailored to their specific community needs. However, commonalities do exist and it is recognised that benefits are to be gained from these such as efficient use of resources, enabling multi-disciplinary research and sharing good practices. As such,a bridging project PARTHENOS has worked closely with CLARIN and DARIAH as well as ARIADNE (archaeology), CENDARI (history), EHRI (holocaust studies) and E-RIHS (heritage science) to iden-tify, develop and promote these commonalities. In this paper, we present some specif-ic examples of cross-discipline and trans-border applications arising from joint RI collaboration, allowing for entirely new avenues of research
HABITUATION OF TITLE LITERATION IN EMPOWERING READING INTEREST IN STUDENTS OF STATE 01 PANDEAN MADIUN

Abstract: This study aims to describe the implementation of habituation of fairy tale literacy activities in empowering reading interest in sisawa, the location of this study in SDN 01 Pandean, Taman Kota Madiun District. The method used in this research is a qualitative approach. Data sources used in this study are primary data sources and secondary data sources. Data collection techniques by interview, observation, and documentation / archives. While validation is used to test the truth of data using source triangulation, and technique triangulation. The results showed that the habituation of fairy tale literacy activities called DOPARI (Fairytale Morning) can empower students' interest in reading especially IVC class SDN 01 Pandean. Empowerment of interest in reading is done is giving all students the opportunity to follow the activities of DOPARI in listening to a fairy tale , became a storyteller, giving a fairy tale about the question and answer session which was read and member i chance students to answer questions, summarizing the content of reading as well as giving gifts or rewards. There are facilities in the form of reading books in each class and in the library. Keywords: habituation, literacy, interest in reading
Global evaluate-and-rescale network: an efficient model for action recognition

Human action recognition in videos is a challenging task in the field of computer vision. Based on the idea of integrating temporal and spatial feature, many works have proposed a variety of methods for extracting spatiotemporal features, such as two-stream network and 3D convolution neural network (3D-CNN). However, due to the huge computational cost of optical flow for two-stream network and the huge number of parameters of 3D-CNN, the computational time required for action recognition is very long, therefore it is difficult to meet the requirements of real-time recognition. This paper aims to explore an efficient architecture of 3D-CNN for action recognition. On the premise of guaranteeing the recognition accuracy, we aim to greatly reduce the computational cost. In order to ensure good performance while reducing the amount of input data, we present Global Evaluate-and-Rescale (GER) Network, which is able to automatically extract the key frames of input data. We have evaluated the performance of our proposed model on two challenging human action recognition datasets UCF101 and HMDB51. The experimental results show that GER Network can reduce up to 50% of the computation time for recognition while achieving approximate accuracy with state-of-the-art 3D-CNN models.
The structure interpretation of remote sensing image in Weijing area of Inner Mongolia

In order to study the tectonic characteristics and its relationship with uranium mineralization in Weijing area of Inner Mongolia, the remote sensing technique and visual interpretation are applied to interpret the linear structures in Weijing area with GF-2 remote sensing data, based on the atmospheric correction and another preprocessing. The distribution regularities of linear structures can be got from the field geological examination and the available geological data. The results indicates that the intersecting areas of NE-NEE trending structure F12 and NNE trending structure F11 with EW or NW trending structures are the favorable uranium metallogenic regions. The NE-NEE trending structure F12 and NNE trending structure F11 are the most important ore-controlling structures in the area.
Research on advanced image analysis and it’s applications

An image is an artefact that depicts visual perception and helps to capture a moment in life. With the advancements in computer vision and machine learning, it is now possible to analyse the content of the images and use it for various applications. Using advanced machine learning models of convolutional neural networks it is possible to detect objects or faces within images. Further, Optical Character Recognition can be used for the text extraction and analysis. As a whole, these techniques would contribute in classification of various images based on their content. This powerful analysis and classification of images can be used in various domains to make the life of each individual easier and much smarter.
Spatio-temporal Saliency for Microscopic Medical Data

Manual tracking and analysis of medical images have become tedious and impractical because of the advancement in medical modalities and the amount of medical streams being produced daily. The analysis of the motion of the immune system can be used for diagnostic purposes. A spatio-temporal saliency map is proposed to detect and segment the moving immune system from microscopic video data. The temporal saliency is computed based on tracklets computed using extended Lucas Kanade technique and their clustering is performed based on coherence. The spatial saliency is computed by feature extraction. Experimental results demonstrate the significance of the proposed spatial and temporal saliency maps fusion. In addition, a database for moving the immune system (DMI) has also been developed.
The objective assessment of aesthetic using improved convolution neural network

This paper proposes an improved neural network model based on CNN for evaluating the aesthetic quality of images. Firstly, we create three input sources from different angles of the target image and establish a three-channel parallel network model. Secondly, mlpconv layer is used to replace the traditional linear convolution layer to obtain more nonlinear abstract features in the network model based on CNN; Then, the combination of the global average pooling and full connection layers are used to replace the full connection layer in traditional CNN, and the three-channel features are merged. Finally, the EMD function is used as the loss function in the softmax layer. The output is probability density mass function from 1 to 10, and the mean and variance are used as objective qualitative score of picture quality. Experiments show that the proposed algorithm is feasible and effective, which solves the problem that the traditional method only obtains the binary classification of aesthetic. And this method gives the objective quantization score of the image. At the same time, the algorithm can get the evaluation value which is consistent with the actual situation in the real-time aerial experiment.
Saturating the quantum Cramér-Rao bound using LOCC

The quantum Cramer–Rao bound (QCRB) provides an ultimate precision limit allowed by quantum mechanics in parameter estimation. Given any quantum state dependent on a single parameter, there is always a positive-operator valued measurement (POVM) saturating the QCRB. However, the QCRB-saturating POVM cannot always be implemented efficiently, especially in multipartite systems. In this paper, we show that the POVM based on local operations and classical communication is QCRB-saturating for arbitrary pure states or rank-two mixed states with varying probability distributions over fixed eigenbasis. Local measurements without classical communication, however, is not QCRB-saturating in general.
Text Mining for Evaluation of Candidates Based on Their CVs

The problem of CV (or resume) text mining becomes increasingly relevant nowadays as long as it could simplify the evaluation of future employees and their suitability for the post for which they apply. The paper proposes a procedure for automatic information extraction from text documents, namely from candidate’s CVs. The described algorithm is based on Natural Language Processing methods and allows to transform text information into categorical features or classes. These features may further be used as inputs for a machine learning model to predict the suitability of the candidate for the position. Besides the general method, the description of the experiments is given in which the algorithm was used for clusterization of future employees according to their previous position and job spheres they worked in. The obtained classes were used to predict the probability of the candidate’s turnover in the first six months. Their addition allowed to raise the model score.
Playback Speech Detection Application Based on Cepstrum Feature

With the popularity of various portable recording devices, playback speech has become one of the most important means of attack in the speaker authentication system. By comparing with the original speech data, the difference in the high-frequency layer, and the playback speech is also different in the low-frequency layer due to the different recording equipment. According to this finding, a detection algorithm was presented to extract representative data. In the high frequency layer, the inverse-Mel filters (I-Mel) is used to extract speaker eigenvector sequences. In the low frequency layer, linear filters (Linear) is combined with Mel filters (Mel) to avoid superposition of characteristic parameters. Multi-layer fusion to obtain L-M-I filter banks to form new cepstral features. The experimental results show that the method can detect playback speech effectively and the equal error rate is 2.63%. Compared with the traditional feature extraction methods (MFCC, CQCC, LFCC, IMFCC), the equal error rate decreases by 12.79%, 9.61%, 4.45% and 3.28% respectively.
Data Exchange Engine for Parallel Computing and Its Application to 3D Chromosome Modelling

Data Exchange Engine for Parallel Computing (abbreviated as DEEPC) is a universal parallel programming interface for scientific computing environments such as MATLAB, Octave, R and Python. It is a software developed by us to support Bulk Synchronous Parallel (BSP) computing for these mainstream script-driven scientific computing environments. BSP is one of the most dominant parallel program models, and it affects the design of parallel algorithms profoundly. However, most of these scientific computing environments have been lack of the software support of BSP for a long time until the birth of DEEPC. The main features of our DEEPC is its ease of use and high performance, especially that without much modification to the sequential-computing programs, one can combine these programs to a high performance parallel program with a short script. To demonstrate these features, we put DEEPC in use to a MATLAB program for the 3D modelling of chromosomes. It has been observed that DEEPC performs very well even without much modification to the corresponding program for sequential computing. CCS Concepts. Computing methodologies → Parallel programming
A Deep Learning Method Study of User Interest Classification

In this paper, a deep learning method study is conducted to solve a new multiclass text classification problem, identifying user interests by text messages. We used an original dataset of almost 90 thousand forum text messages, labelled for ten interests. We experimented with different modern neural network architectures: recurrent and convolutional, as well as simpler feedforward networks. Classification accuracy was evaluated for different architectures, text representations and sets of miscellaneous parameters.
Il soggetto nullo nel processo di acquisizione del portoghese brasiliano

This paper is part of a research project - still at an early stage - on the process of acquisition of Brazilian Portuguese as a foreign language in the Italian university context. We present here the first data on the acquisition, by Italian-speaking learners (all students of the University of Salento), of morphosyntactic properties associated with the positive/negative value of the pro-drop parameter, also showing the pragmatic-discursive properties that can determine a distribution of null pronominal subjects in different contexts.