Reviewerly/exHarmony huggingface dataset:
Convolutional Neural Network for Image Feature Extraction Based on Concurrent Nested Inception Modules

Nowadays, convolutional neural networks are getting deeper and more complex to extract and learn features at multiple levels of abstraction. However, large networks take time to be trained, and the training process is not that easy. In this paper, a new architecture based on Inception module is proposed. In each branch of the normal Inception module, we have nested numbers of secondary Inception module to further extract features on the input image and use the width of the net to expand the quantity of the features being extracted. This structure helps the net build a more comprehensive cognition on the image and get great performance. Multiple experiments and comparisons on MNIST, CIFAR-10 and SVHN datasets have proved the feasibility and performance of the structure, and the structure converges well when combined with deeper networks.
Overlap Aware Active Learning Query Strategies for Pool Based Scenario

Active learning (AL) is an emerging machine learning technique, which works with fewer labeled instances, thereby reducing labeling as well as learning cost. Various AL query strategies are available to identify the candidate query instances for achieving a generalized hypothesis. However, the AL query strategies sometimes produce redundant or overlapped instances as candidate instances, due to the presence of instance and/or class overlapping in the dataset. This overlapping results in a complex decision boundary. We addressed this problem and proposed a solution as an augmented step for AL query strategies, which improves the generalization performance of the AL task. The solution is based on the nearest neighbor assumption and fixed vicinity based approach. This augmentation tries to avoid the selection of overlap instances and provides a smoother decision boundary. We have validated the performance of the proposed method on the available benchmark datasets.
Evaluation of Secure Remote Offering Service for Information Bank

An information bank is a reliable data ecosystem for the distribution and utilization of personal data (PD). In order to maintain the trust of individuals, sharing of personal data between businesses and the information bank is required to be secure. Therefore, the information bank must prevent abuse and leaking of personal data. There are several measures that can be taken to limit the damage imposed upon the individual in the case of data abuse or leakage. However, it is difficult to prevent abuse and leakage once the data has been shared with businesses. This work focuses on the security of an offering service on the information bank. The information bank offers useful information or services to individuals from businesses' based on shared personal data. We devise a remote offering service enabling businesses to target individuals without sharing personal data. Moreover, we consider a malicious threat on the remote offering service and propose a mechanism for detecting this threat. The experimental results suggest that the proposed mechanism is useful in some real security use cases.
An End to End Cloud Computing Privacy Framework Using Blind Processing

Database-as-a-service (DBaaS) is a trend allowing organizations to outsource their databases and computations to external parties. However, despite the many advantages provided by this service in terms of cost reduction and efficiency, DBaaS raises many security issues regarding data privacy and access control. The protection of privacy has been addressed by several research contributions proposing efficient solutions such as encrypted databases and blind queries over encrypted data, called blind processing. In this latter context, almost all proposed schemes consider an architecture of a single user (the data owner) that requests the database server for encrypted records while he is the only one capable of decrypting. From a practical perspective, a database system is set up to support not only a single user but multiple users initiating multiple queries. However, managing various accesses to an encrypted database introduces several challenges by itself, like key sharing, key revocation, and data re-encryption. In this article, we propose a simple and efficient blind processing protocol that allows multiple users to query the same encrypted data and decrypt the retrieved results without getting access to the secret key.
Pedestrian Tracker Utilizing Sensor Fusion and a Gaussian-Mixture Probability Hypothesis Density Filter

This paper presents and evaluates a real-time implementation of Gaussian-mixture probability hypothesis density (GM-PHD) filter aimed at tracking pedestrians in vehicular applications. The GM-PHD is a subsequent development of the probability hypothesis density (PHD) filter first introduced by Mahler as a solution to the multi-target tracking dilemma including computational complexity, data association, and inconsistencies in sensors. An adjusted Gaussian model was utilized to track the measurements persistently in real-time. By fusing sensor information from a frequency modulated continuous wave (FMCW) radar and 2D scanning lidar our system focused on implementing real data for physical implementation in autonomous vehicles. Experiment results document the performance of the GM-PHD framework and exemplify its excellent performance in tracking dynamically changing multi-targets with obfuscations in a real-time scenario.
Influence of Language-Specific Features for Author Identification on Indian Literature in Marathi

Author identification (AI) is a process of investigating author of an anonymous text document. AI has a great help in digital forensic, copyright issues, plagiarism detection, etc. for making the law process quick and efficient. This paper presents AI on Indian regional language Marathi. Insted of it kindly replce it by this scentence: In this research paper, we proposed 21 language-specific lexical features. Validation of these proposed features is done on “Author wise Marathi Language Typewritten Text Corpus” published by us at Indian Language Technology Proliferation and Deployment Center. Experimentation is performed with proposed 21 features. Performance is compared through various machine learning algorithms like Naïve Bayes, k-Nearest Neighbor and Sequential Minimal Optimization. k-Nearest Neighbor performs well over Naïve Bayes and Sequential Minimal Optimization with average accuracy achieved as 82.06 on comedy articles and 85.44 on mixed articles. Proposed language-specific features provide significant improvement in result of accuracy over traditional features.
Privacy Preserving Lightweight Searchable Encryption for Cloud Assisted e-Health System

Large number of health organizations have started making use of cloud assisted technologies to collect, share and process medical records. Wireless Body Area Sensor Network (BASN) is employed to record information such as blood pressure, heart rate, etc from the patient's body. The collected data is stored on the cloud such that it could be shared among different users for providing better services. However, there are many security challenges in using cloud backed large scale commercial e-health applications. The basic challenge is to maintain confidentiality of data while it is transmitted over the communication channel, or stored and searched by cloud provider. Further, unauthenticated or malicious sensor could upload the fake patient reports to the cloud. These problems are amplified due to resource constrained devices like sensor nodes of BASN as resource exhaustive security operations are not affordable by them. Moreover, a malicious cloud could also violate the privacy of a user by tracking its cloud usage pattern. In this paper, we propose a lightweight and secure searching scheme over encrypted data for e-Health environment. We make use of hybrid encryption scheme including symmetric encryption and optimized identity-based encryption (IBE) scheme to ensure data confidentiality over communication channel and for enhancing cloud privacy. Further, due to its lightweight nature, it is affordable for constrained sensors with minimal computation and storage resources. Implementation results confirm the lightweight nature of our proposed scheme.
Hyperparameter-Free Losses for Model-Based Monocular Reconstruction

This work proposes novel hyperparameter-free losses for single view 3D reconstruction with morphable models (3DMM). We dispense with the hyperparameters used in other works by exploiting geometry, so that the shape of the object and the camera pose are jointly optimized in a sole term expression. This simplification reduces the optimization time and its complexity. Moreover, we propose a novel implicit regularization technique based on random virtual projections that does not require additional 2D or 3D annotations. Our experiments suggest that minimizing a shape reprojection error together with the proposed implicit regularization is especially suitable for applications that require precise alignment between geometry and image spaces, such as augmented reality. We evaluate our losses on a large scale dataset with 3D ground truth and publish our implementations to facilitate reproducibility and public benchmarking in this field.
Splicing Detection Technique Based on the Key-Point

The authenticity of digital image find out by using the Image Forgery Detection technique. In the area of digital forensics and multimedia security it is more important. Photo-editing software, powerful computers, and a device the high resolution images are used for the manipulation. So, it is very difficult for finding the transformation in the digital image by the human eyes. The proposed method to identify splicing forgery for images which are blurred, resized and angular transformed. These images need a special kind of feature extraction namely SWT in order to perform the task. MS-LBP is applied to the SWT for finding key-points and creating the database to show the forgery status as well as features of the image.
Saliency Detection using the Combination of Boundary Aware Color-map and Seam-map

Salient region of an image is usually detected by using contrast and boundary priors. Along with those cues the use of seam importance map has shown promising output previously. In this study, better result is found by further exploiting the seam-map using spatial distance and color information in combination with boundary prior. Color and seam maps are also down-weighted using average spatial distance to other regions. Moreover, passing the superpixelized version of the input image into seam and color map generation procedure has improved the output. Experimental results based on MSRA 1k dataset are presented with ten state of the art methods. F-beta measures are presented along with precision recall curves to better understand the outcome. The performance comparison with compared researches proofs superiority of the proposed method.
Hand tracking and fingertip detection based on Kinect*

Abstract With the rapid development of computer technology and sensor performance, hand gesture recognition (HGR) has become one of the most promising research in human computer interaction field. Among all the procedures of HGR, real-time hand tracking is of great importance, which also faces many existing challenges. In this paper, an effective method was proposed for hand tracking in real time using Kinect, including the hand region extraction and fingertips determination. During the former procedure, we presents a novel two-scan method based on connectivity analysis and recrusion algorithm to extract the hand region in real time. After this, in order to determine the fingertips, we first implemented a fast hand convex hull extraction algorithm to obtain the primary candidate points. Then two typical constraints based on hand shape characteristics are applied to determine the final fingertip points. The subsequent experiments verified that our proposed strategy could significantly improve the quality of real-time hand tracking.
Key innovation concepts of STEM education driven by IT Academy project

The information society places requirements on the profile of graduates, what is unreachable in the inertial education system. We aim for key changes in STEM education at primary and secondary schools through a national wide-impact project IT Academy. Active learning focusing on conceptual understanding and development of selected inquiry skills, together with formative assessment, form our didactic basis for innovation. The development of scientific literacy and computational thinking are also stimulated by changes in the content of science and mathematics curricula. We create a strong link to informatics concepts and practical use of informatics in everyday life, study, work and research. We expect the interest of young people to study computer science and STEM will increase. In this paper, we introduce key concepts of STEM education innovation, with examples of educational activities. The results of the pilot verification are formulated in recommendations to the educational professionals.
Deep Sparse Representation Classification with Stacked Autoencoder

Sparse representation classification (SRC) is a new framework for classification and has been successfully applied to face recognition. However, in some cases it is not well to represent the test sample accurately, which tends to undermine the classification accuracy. In order to alleviate this issue, a deep sparse representation based classification (DSRC) method with a deep dictionary which learned by stacked autoencoder is proposed. Specifically, the proposed method trains a stacked autoencoders by pseudoinverse learning and used the hidden outputs to construct a deep dictionary. Given the deep dictionary, a hierarchical sparse representation based classification method is presented to determine the label for each test sample by a weighted residuals strategy. The experimental results show that the proposed method can achieve a comprehensively better performance compared with the state-of-the-art classification methods.
Ballistic Trajectory Estimation Using Polynomial Chaos Based Square Root Ensemble Filter

The ballistic trajectory estimation problem is challenging, mainly because the dynamic model and the angle-only measurement model are highly nonlinear. In this paper, we propose a polynomial chaos expansion based square root ensemble Kalman filter to solve the ballistic trajectory estimation problem. Between two consecutive measurements, polynomial chaos-based approach is used for uncertainty propagation. Upon a new measurement&#x0027;s arrival, a predicted ensemble generated from the predicted state is corrected through the ensemble square root technique and the obtained analysis ensemble is utilized to form the polynomial chaos representation of the target state. Simulation results show the proposed approach&#x0027;s superiority to previous popular nonlinear estimation methods such as the extended Kalman filter, the unscented Kalman filter, and the polynomial chaos-based ensemble filter with the first order linearization, in terms of the root mean square error (RMSE).
A Custom-Made Photogrammetry Scanner to Support Paediatric Surgery

Photogrammetry is a challenging technique used to digitalize objects and to build their surface points’ meshes by connecting digital photographs taken from different positions. The work presented here is the first phase of a collaboration between the OSI EECruces Hospital and the Faculty of Engineering in Bilbao to develop a portable scanner to perform digital scans of paediatric patients’ faces with enough accuracy to: (i) train surgery operations, (ii) analyse patients’ evolution and (iii) perform virtual reality surgery guides. This first phase deals with the construction and calibration of a custom-made scanner. The scanner was built around 12 Raspberry Pi cameras and single-board computers, assembled on different positions in a custom-made structure inside an ad hoc designed and 3D printed container. The Raspberries were connected and synchronized via Ethernet to allow the 12 photographs to be taken simultaneously. Additionally, a projector was used to generate a particular light pattern to aid connecting the different photographs and generating the resulting two 3D points’ meshes: (i) a real colour and texture of the test subject’s face mesh and (ii) a mathematical mesh. Calibration was performed with a rule placed onto the test subjects’ face. This first study was designed to test the accuracy of the photogrammetry scanner by comparing its performance to that of an industrial portable scanner. The promising results obtained indicate that the described scanner has a big potential to perform clinical studies.
Laser field control of subradiant states of a system of several atoms in a configuration close to a regular polygon

Abstract Some collective excitations of an ensemble of closely spaced (two-level) atoms can be characterised by extremely low rates of radiation decay, which is of considerable interest for storing quantum information. Such (dark) states are realised, for example, in planar geometry when atoms are located at the nodes of a regular polygon. However, the decay rate of such excitations increases markedly when the geometry deviates from ideal. Using the example of small deformation of a square in a rhombus, we show that an increase in the decay rate of the dark state due to symmetry breaking can be compensated for by adjusting the transition frequency of some atoms exposed to external laser radiation.
Sources of Evidence for Interactive Table Completion

An important question in interactive information retrieval (IIR) is: How can we support searchers with specific types of search tasks? We describe an auxiliary support tool referred to as the "Matrix''. The Matrix tool was designed to support searchers with comparative search tasks, which require comparing items along different dimensions. The Matrix was designed as a grid of rows and columns representing the items and dimensions related to a comparative task. The Matrix was integrated with a custom-built search interface, which allowed users to search for information and drag-and-drop relevant passages directly into cells in the Matrix. We investigate the following general question: Given a partially completed Matrix, can a system automatically populate empty cells in the Matrix with relevant passages? To this end, we conducted two crowdsourced studies in which participants were assigned comparative tasks and asked to use our system (integrated search interface + Matrix) to populate every cell in the Matrix. After gathering this data, we evaluated machine-learned models for ranking passages in response to an empty Matrix cell and partially completed Matrix. We address two research questions: (RQ1) What are useful types of features for this predictive task? and (RQ2) How does performance vary based on the level of Matrix completion? We view our research as a step towards designing support tools that: (1) help users organize information while searching and (2) can autocomplete search tasks by exploiting the task structure and a searcher's partial solution.
A Poisson multi-Bernoulli filter with target spawning

Previous Poisson multi-Bernoulli (PMB) filters use a model, including unknown targets and detected targets, to describe target birth and surviving. Although such a model can handle target birth, surviving, and death well, its performance may degrade when target spawning arises. This is due to the previous PMB filters treat spawned targets as birth targets. In this paper, a new PMB filter with spawning (PMBS) is proposed, in which spawned targets are described as unknown targets, modelled by a Poisson point processes. Simulation results demonstrate the validity of the proposed filter, by using the spawning model in the measurement-oriented marginal MeMBer/Poisson (MOMB/P) filter and the track-oriented marginal MeMBer/Poisson (TOMB/P) filter.
Education improving thorough information technology and their impact on the students career pathways within the “IT Academy” Project

The concept of building an information and knowledge society is the center of interest of all developed countries of the world, the European Union and Slovakia including. It is a trend that is conditioned by technological developments in the processing and use of information and communication technologies (ICT), which affect various aspects of everyday life. The urgent need for new ICT knowledge and skills, as well as the demographic changes that Europe is facing, have highlighted the importance of young people’s IT education. Improving digital literacy and inclusion, interoperability, the application of ICT in problem-solving is also one of the objectives of the national project “IT Academy - Education for the 21st Century which strives significantly at primary, secondary and higher education levels to support this trend. The aim of the paper is to analyze the impact of popularization and motivational activities within formal and non-formal education on various target groups of the project and to analyse the efficiency, effectivity and usefulness of ICT utilization in education. Although the interpretations of digital literacy may differ, they share a common view on the need to include a critical dimension in teaching and focusing on the social aspects of literacy. We monitored the digital literacy index of secondary school teachers, we focused on comparing the ring activity of 3 groups of selected exact indicators at secondary schools, which had scientific laboratories in comparison with schools that did not have laboratories in their equipment. We used general methods of scientific work, such as analysis, comparison, deduction and others. The primary data were collected by a questionnaire survey. The data were processed in MS Excel and evaluated by statistical methods.
Survey on Automatic Script Identification Techniques

Script identification is an essential task especially in India because of existence of 13 different scripts for writing 22 languages. Major applications of script identification are document sorting, automatic translation, selecting of OCR (Optical Character Recognition) and text area identification. Traditionally, researchers have used feature based methods for script identification but it can be automated through deep learning techniques to reduce comprehensive time. Moreover, CNN (Convolutional Neural Network) based deep learning approaches are less explored for script identification problem. Intention behind this survey on script identification is to make researchers more perceived about the usefulness of deep learning techniques and more specifically, the significance of CNN to deal with script identification problem. This paper contains a useful survey on both feature-based and deep learning based domains on popular foreign and Indian scripts. We have shown comparative analysis between both domains which clearly concludes that deep learning methods could be more useful as compared to feature based methods. In addition, limitations and usefulness of CNN based best practices are also presented.