Reviewerly/exHarmony huggingface dataset:
Myocardium Detection by Deep SSAE Feature and Within-Class Neighborhood Preserved Support Vector Classifier and Regressor

Automatic detection of left ventricle myocardium is essential to subsequent cardiac image registration and tissue segmentation. However, it is considered challenging mainly because of the complex and varying shape of the myocardium and surrounding tissues across slices and phases. In this study, a hybrid model is proposed to detect myocardium in cardiac magnetic resonance (MR) images combining region proposal and deep feature classification and regression. The model firstly generates candidate regions using new structural similarity-enhanced supervoxel over-segmentation plus hierarchical clustering. Then it adopts a deep stacked sparse autoencoder (SSAE) network to learn the discriminative deep feature to represent the regions. Finally, the features are fed to train a novel nonlinear within-class neighborhood preserved soft margin support vector (C-SVC) classifier and multiple-output support vector ( ε -SVR) regressor for refining the location of myocardium. To improve the stability and generalization, the model also takes hard negative sample mining strategy to fine-tune the SSAE and the classifier. The proposed model with impacts of different components were extensively evaluated and compared to related methods on public cardiac data set. Experimental results verified the effectiveness of proposed integrated components, and demonstrated that it was robust in myocardium localization and outperformed the state-of-the-art methods in terms of typical metrics. This study would be beneficial in some cardiac image processing such as region-of-interest cropping and left ventricle volume measurement.
Polynomial multiplication over binary finite fields: new upper bounds

When implementing a cryptographic algorithm, efficient operations have high relevance both in hardware and in software. Since a number of operations can be performed via polynomial multiplication, the arithmetic of polynomials over finite fields plays a key role in real-life implementations—e.g., accelerating cryptographic and cryptanalytic software (pre- and post-quantum) (Chou in Accelerating pre-and post-quantum cryptography. Ph.D. thesis, Technische Universiteit Eindhoven, 2016). One of the most interesting papers that addressed the problem has been published in 2009. In Bernstein (in: Halevi (ed) Advances in Cryptology—CRYPTO 2009: 29th Annual International Cryptology Conference, Santa Barbara, CA, USA, August 16–20, 2009. Proceedings, pp 317–336. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009), Bernstein suggests to split polynomials into parts and presents a new recursive multiplication technique which is faster than those commonly used. In order to further reduce the number of bit operations (Bernstein in High-speed cryptography in characteristic 2: minimum number of bit operations for multiplication, 2009. http://binary.cr.yp.to/m.html ) required to multiply n-bit polynomials, researchers adopt different approaches. In CMT: Circuit minimization work. http://www.cs.yale.edu/homes/peralta/CircuitStuff/CMT.html a greedy heuristic has been applied to linear straight-line sequences listed in Bernstein (High-speed cryptography in characteristic 2: minimum number of bit operations for multiplication, 2009. http://binary.cr.yp.to/m.html ). In 2013, D’angella et al. (Applied computing conference, 2013. ACC’13. WEAS. pp. 31–37. WEAS, 2013) skip some redundant operations of the multiplication algorithms described in Bernstein (in: Halevi (ed) Advances in Cryptology—CRYPTO 2009: 29th Annual International Cryptology Conference, Santa Barbara, CA, USA, August 16–20, 2009. Proceedings, pp 317–336. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009). In 2015, Cenk et al. (J Cryptogr Eng 5(4):289–303, 2015) suggest new multiplication algorithms. In this paper, (a) we present a “k-1”-level recursion algorithm that can be used to reduce the effective number of bit operations required to multiply n-bit polynomials, and (b) we use algebraic extensions of $$\mathbb {F}_2$$ combined with Lagrange interpolation to improve the asymptotic complexity.
3D AOA Target Tracking with Two-step Instrumental-variable Kalman Filtering

A two-step pseudolinear Kalman filter (2S-PLKF) was previously proposed for angle-of-arrival (AOA) target tracking in three-dimensional space drawing on the pseudolinear rearrangement of nonlinear azimuth and elevation angle measurement equations. Despite enjoying low computational complexity, this algorithm suffers from a severe bias problem arising from the nonlinear-to-linear transformation of the angle measurement equations. This paper presents a new two-step instrumental-variable Kalman filter (2S-IVKF) exploiting the use of pseudolinear estimation, as well as instrumental-variable estimation to overcome the bias problem. The performance advantage of the proposed 2S-IVKF algorithm over the 2S-PLKF, as well as extended and sigma-point Kalman filters is demonstrated with simulation examples.
Fuzzy Description Hemispherical Resonator Gyro Error

This paper addresses models of errors caused by irregularity of density, radius defect, different Q-factor and irregular resonator thickness of a hemispherical resonator gyro based on fuzzy description. A fuzzy algorithm has been proposed for processing error and its simulation results are given.
Gamification in an Augmented Reality Based Virtual Preparation Laboratory Training

Through technological progress during recent years, Augmented Reality (AR) technology can be used on ordinary smartphones with applications (Apps) in many formal and informal learning environments and educational institutions (e.g. [1, 2]). It is emerging as a suitable technology for teaching psychomotor skills. Simultaneously, gamification has become increasingly popular in the teaching field, providing famous examples, such as Duolingo (for the acquisition of foreign languages) or Codecademy (for learning programming languages) [3]. Many papers have already highlighted the beneficial aspects of gamification and AR for education and teaching (e.g. [1, 2, 4, 5]. While gamification is useful for improving students’ motivation and engagement, AR can be applied to teach them operational skills without any time, costs and place constraints. Hence, this opens up numerous possibilities and forms to combine these two aspects (AR and gamification) for higher education teaching. However, there has been less research focusing on how gamification and AR can be combined in a useful manner to keep up students’ initial motivation aroused through novelty effects of AR learning environments. Accordingly, this paper will present such a gamification concept for an AR based virtual preparation laboratory training to overcome the risk of demotivation, once AR will settle as a mainstream technology such as learning videos. The focus of the AR-App – presently being developed at the University of Paderborn – is to remedy the students’ lack of practical skills when operating electro-technical laboratory equipment during their compulsory laboratory training.
3D modeling and animating of characters in educational game

Bahasa is an identity of the Indonesian nation which grow very fast for the unifying tool of ethnic groups so that it can increase of foreigners' interest in learning Bahasa as a tool to achieve, politics, trade, art and culture, and tourism goals. Many foreigners want to learn Bahasa Indonesia by the following program of BIPA (Bahasa Indonesia untuk Penutur Asing). However, the learning program of BIPA class is still conventional. Games can be used as an alternative educational media for BIPA learning in changing conventional learning methods so that they can visualize real problems. An Android-based game called "Asiknya Berbahasa" is an educational game of BIPA basic level material which has 3 stages; easy, medium and difficult stage and be completed with 3D character models along with the animation. 3D modeling and character animation referring to the Game Development Life Cycle (GDLC) method consists of several stages of initiation, pre-production, production, testing, beta, and release. Testing on 3D models and character animation using a Likert scale shows approval of the average value result test
Performance anomaly detection using isolation‐trees in heterogeneous workloads of web applications in computing clouds

Summary Cloud computing is a model for on‐demand access to shared resources based on the pay‐per‐use policy. In order to efficiently manage the resources, a continuous analysis of the operational state of the system is required to be able to detect the performance degradations and malfunctioned resources as soon as possible. Every change in the workload, hardware condition, or software code can change the state of the system from normal to abnormal, which causes the performance and quality of service degradations. These changes or anomalies vary from a simple gradual increase in the load to flash crowds, hardware faults, software bugs, etc. In this paper, we propose Isolation‐Forest based anomaly detection (IFAD) framework based on the unsupervised Isolation technique for anomaly detection in a multi‐attribute space of performance indicators for web‐based applications. Unsupervised nature of the algorithm and its fast execution make this algorithm most suitable for the environments with dynamic nature where the patterns of data change frequently. The experiment results demonstrate that IFAD can achieve good detection accuracy especially in terms of precision for multiple types of the anomaly. Moreover, we show the importance of validating the accuracy of anomaly detection algorithms with regard to both Area Under the Curve (AUC) and Precision‐Recall AUC (PRAUC) in an extensive set of comparisons including multiple unsupervised algorithms. The demonstration of the effectiveness of each algorithm shown by PRAUC results indicates the importance of PRAUC in selecting suitable anomaly detection algorithm, which is largely ignored in the literature.
Energy efficient VM scheduling and routing in multi-tenant cloud data center

Cloud data center hosting many composite applications of multiple tenants consumes a massive amount of energy. Developing energy efficient mechanisms for data center management has become a vital issue for the cloud providers. Most of the works that deal with mechanisms for energy-efficient resource provisioning focus either on reducing the energy consumption of servers or reducing the energy consumption of network elements, but not both. We have formulated the problem of jointly optimizing the energy consumption of servers and network elements by optimal VM scheduling and routing as an integer programming problem. To solve it a phase-wise optimization approach with two ant colony based meta-heuristic algorithms is proposed. The topology features of the data center network and the communication patterns of the applications are considered in the construction of the solution. The solution is tested for three standard data center networks, 3-tier, B-Cube and Hyper-tree of different sizes and compared against two standard algorithms, first-fit and round-robin algorithms. The results showed that the proposed solution improves energy savings by 15% and 20% on an average when compared with first-fit and round-robin respectively.
User Perception Towards Cultural-Based E-Government Portal Design

The purpose of this paper is to analyze user perception towards cultural-based e-government portal design. User perception is measured based on feedback on emotional state received from participants towards an e-government portal that has been developed based on cultural and emotional through look and feel. Cultural studies in the context of e-government development and implementation is still lacking. User testing was conducted with 33 participants. The data obtained have been analyzed. User testing specifically focus on the High and Low Power Distance Dimension. The participants gave their perceptions based on the prototype of e-government portal representing High and Low PD design. The results are based on the mean analysis. The PD design with a higher mean score indicates that it poses a stronger feeling on a particular emotion state. It can be conclude that the emotion states are perceived differently between High and Low PD design.
Phoneme Dependent Speaker Embedding and Model Factorization for Multi-speaker Speech Synthesis and Adaptation

This paper presents an architecture to perform speaker adaption in long short-term memory (LSTM) based Mandarin statistical parametric speech synthesis system. Compared with the conventional methods that focused on using fixed global speaker representations in utterance level for speaker recognition task, the proposed method extracts speaker representations in utterance and phoneme level, which can describe more pronunciation characteristics in phoneme level. And an attention mechanism is deployed to combine each level representations dynamically to train a task-specific phoneme dependent speaker embedding. To handle the unbalanced database and avoid over-fitting, the model is factored into an average model and an adaptation model and combined by an attention mechanism. We investigate the performance of speaker representations extracted by different methods. Experimental results confirm the adaptability of our proposed speaker embedding and model factorization structure. And listening tests demonstrate that our proposed method can achieve better adaptation performance than baselines in terms of naturalness and speaker similarity.
Capacity Investigation of Markov Chain-Based Statistical Text Steganography

Nowadays, there is a high motivation to develop techniques of sending secret information as a result of the need to transfer some data in a secret way. Steganography is one of the important techniques in this field. In particular, statistical text steganography is classified as an immune technique against the majority of steganalysis techniques compared to other steganography techniques. On the other hand, although Arabic is the official language of many countries, many steganography techniques remain unexplored for it. In this work, we implemented a Markov Chain (MC) encoder/decoder combined with Huffman Coding (HC) for Arabic text steganography. Additionally, we computed an upper bound and a lower bound for the stego-text length that depends on the designed encoder/decoder parameters. The proposed Arabic steganography technique capacity performance was investigated for different encoder parameters and secret message length. We found that applying some constraints on the MC increases the embedding capacity till it reaches an upper limit floor.
A new 3D eyeball tracking system to enhance the usability of page scrolling

Nowadays with rapid advancement in computer vision technology, 3D eyeball tracking, and its movement is the most focus on interest in researchers due to its simplification and vastly used in authentication by iris, handling screen by gaze and control in gaming world applications. It is quite challenging to capture the activity of eyeball with the highest functionality and additionally hardware becomes the source of increasing the overhead of cost. We have proposed the 3D eyed model to extract the internal features of eye i.e. pupil, eyelids and eye corners for notifying the position of the 3D eyeball. Based on the proposed 3D eye model, a 3D pupil is estimated the main feature embedded as a reference to identify points in locating eyeball position. The proposed system will track four movements of 3D eyeball i.e. up, right, left and straight. It is working in real time by acquiring snapshot from a webcam from the real world and is implemented to scroll page by eye movements. Setting up the system is easier than compared to the IR illumination method that provided an accurate result of low cost and more user satisfaction.
Inpainting in Omnidirectional Images for Privacy Protection

Privacy protection is drawing more attention with the advances in image processing, visual and social media.Photo sharing is a popular activity, which also brings the concern of regulating permissions associated with shared content.This paper presents a method for protecting user privacy in omnidirectional media, by removing parts of the content selected by the user, in a reversible manner.Object removal is carried out using three different state-of-the-art inpainting methods, employed over the mask drawn in the viewport domain so that the geometric distortions are minimized.The perceived quality of the scene is assessed via subjective tests, comparing the proposed method against inpainting employed directly on the equirectangular image.Results on distinct contents indicate our object removal methodology on the viewport enhances perceived quality, thereby improves privacy protection as the user is able to hide objects with less distortion in the overall image.
Robust Tuned Controller Based on Interval Type 2 Fuzzy Logic for Robotic Manipulators Exposed to Perturbations and Parametric Uncertainties

Industrial robotic arms require a reliable ability to operate in the presence of unexpected perturbations. In this paper, control of a robotic arm is contemplated in the presence of parametric uncertainties which are coupled by significant external perturbations. The proposed method is based on tuning of the higher-order sliding mode controller parameters by the interval type 2 fuzzy logic. Initially, the common chattering effect of the classic sliding mode is eliminated by using higher-order SMC and saturation function, which makes it more robust than the classic algorithm. However, the high-order sliding mode still cannot deal with strong external perturbations properly. This issue is addressed by combining the algorithm with fuzzy type 2 membership functions which add the self-tuning feature to the controller. Simulations show the superiority of the proposed controller over the classic and the higher-order sliding mode controllers in dealing with various significant perturbations.
Quasi-fully Convolutional Neural Network with Variational Inference for Speech Synthesis

Recurrent neural networks, such as gated recurrent units (GRUs) and long short-term memory (LSTM), are widely used on acoustic modeling for speech synthesis. However, such sequential generating processes are not friendly to today’s massively parallel computing devices. We introduce a fully convolutional neural network (CNN) model, which can effiently run on parallel processers, for speech synthesis. To improve the quality of the generated acoustic features, we strengthen our model with variational inference. We also use quasi-recurrent neural networks (QRNNs) to smoothen the generated acoustic features. Finally, a high-quality parallel WaveNet model is used to generate audio samples. Our contributions are twofold. First, we show that CNNs with variational inference can generate highly natural speech on a par with end-to-end models; the use of QRNNs further improves the synthetic quality by reducing trembling of generated acoustic features and introduces very little run-time overheads. Second, we show some techniques to further speed up the sampling process of the parallel WaveNet model.
Efficient parameter selection for support vector machines

The support vector machines (SVM) is a popular classification method. Many users may not well tune hyperparameters because this step is time-consuming. However, the performance of SVM relies on the values of hyperparameters. To get around the problem, users may resort to anecdotal methods or default values set by software developers, but these methods may compromise the performance of classification accuracy. We investigate the theory that justifies P-SVM for tuning (γ,C) P-SVM significantly improved accuracy for classifying the business intelligence data. Experiments of simulation and real datasets show that P-SVM reducescomputational time substantially without much loss in accuracy.
ILP-based Compressive Speech Summarization with Content Word Coverage Maximization and Its Oracle Performance Analysis

We propose an integer linear programming (ILP)-based compressive speech summarization method that maximizes the coverage of content words in a resultant summary. It is an unsupervised method and, under the designed constraints, it performs a single-step globally optimal summarization of a given long speech recording, which is decoded as a confusion network form of an automatic speech recognition (ASR) hypothesis sequence. It selects as many different content words as possible from the speech input that inevitably includes a high level of redundancy (e.g. the repetition of the same word) under a given length constraint. In experiments using a lecture speech corpus, we obtained higher summarization performance in terms of ROUGE scores than with a baseline extractive summarization method. We further conduct experimental analyses to obtain the oracle (upper bound) performance of the summarization methods. The analysis results show that the oracle performance is very high even though the ASR hypotheses include recognition errors. It is significantly higher than the system performance and, in addition, the oracle performance of the compressive method is significantly higher than that of the extractive method. These results confirm that our method is a promising approach.
Security-enhanced nonlinear cryptosystem based on joint transform correlator

A security-enhanced optical cryptosystem is designed by introducing a time-varying nonlinear term in the encryption system based on joint transform correlator (JTC). The nonlinear term, which has no influence on the decryption of original image, is a real-valued distribution that is added to the encryption result of JTC-based encryption system. This term is not transmitted to receivers of ciphertexts, therefore, there is no need to manage and distribute it. The cryptosystem is demonstrated to be resistant to various attacks including ciphertext-only attacks, known-plaintext attacks and chosen-plaintext attacks. In addition, the cryptosystem is simple and has negligible computation cost, and the ciphertext is still non-negative real-valued, which is easy to transmit and store.
Information Constrained Control for Visual Detection of Important Areas

In this work, we propose a method for detection of locations with subjective significance in the visual environment using Information Constrained Control (ICC). ICC is a model that takes into consideration not only the goal but also the complexity of the control needed to achieve it, characterized by its deviation from default behavior. This concept resonates with the human visual attention system, which includes an interaction between top-down goal oriented pressure and bottom-up processes of common gaze behavior. We start by providing rationale and intuition for ICC based analysis, then formalize it. Specifically, we formalize a mechanism for estimation of subjective significance. Later, we theoretically compare the ICC to the commonly used STD based mechanism, present the latter as a special case of ICC, suggesting an ICC reward visualization mechanism for STD. Finally, we describe our experiment in a real-world driving environment and present empirical finding to support our claim.
Categorization of Crowd Varieties using Deep Concurrent Convolution Neural Network

Visual understanding of crowd scenes is a challenging and important issue in computer vision domain. Identification of crowd type is a basic requirement for analyzing crowd scenarios. With the advancement of deep convolution neural networks image recognition problems have become easy. In this paper, we propose a novel architecture (DeepCrowd) inspired by Resnet to incorporate spatial features comprehensively. To train and evaluate proposed system, a robust and unique dataset of nearly six thousand images is generated. Evaluating the system extensively highlighted accuracy of 83.11% that is comparable with others state-of-the-art methods.