Reviewerly/exHarmony huggingface dataset:
DNN Speaker Embeddings Using Autoencoder Pre-Training

Over the last years, i-vectors have been the state-of-the-art approach in speaker recognition. Recent improvements in deep learning have increased the discriminative quality of i-vectors. However, deep learning architectures require a large amount of labeled background data which is difficult in practice. The aim of this paper is to propose an alternative scheme in order to reduce the need of labeled data. We propose the use of autoencoder pre-training in a speaker verification task. First, we train an autoencoder in an unsupervised way, using a large amount of unlabeled background data. Then, we train a Deep Neural Network (DNN) initialized with the parameters of the pre-trained autoencoder. The DNN training is carried out in a supervised way using relatively small labeled background data. In the testing phase, we extract speaker embeddings as the output of an intermediate layer of the DNN. The training and evaluation were performed on VoxCeleb-2 and VoxCeleb1 databases, respectively. The experimental results have shown that by initializing DNN with the parameters of the pre-trained autoencoder, we have achieved a relative improvement of 21%, in terms of Equal Error Rate (EER), over the baseline i-vector/PLDA system.
Mapping Lexical Knowledge to Distributed Models for Ontology Concept Invention

Ontologies are largely used but the abstraction process required to create them is a complex task that leads to incompleteness. Concept invention offers a valid solution to extending ontologies by creating novel and meaningful concepts starting from previous knowledge. The use of distributed vector representations to encode knowledge has become a popular method in both NLP and Knowledge Representation. In this paper, we show how concept invention can be complemented with distributed representation models to perform ontology completion tasks starting from lexical knowledge. We propose a first approach based on a deep neural network trained over distributed representations of words and ontological concept. With this model, we devise a method to generate distributed representations for novel and unseen concepts and we introduce a methodology to evaluate these representations. Experiments show that, despite some limitations, our model provides a promising method for concept invention.
Medical image DENOISING scheme using discrete wavelet transform and optimization with different noises

Summary In Medical Image Processing (MIP), the images are influenced by various types of noises. Hence, reducing such noises becomes a crucial issue. The main objective is to denoise the noisy images effectively with reduced computational cost. This paper proposes a denoising technique using Discrete Wavelet Transform (DWT) and Social Spider Optimization (SSO) algorithm. Initially, DWT is applied over various noises over the input medical images. Then, the wavelet coefficients are optimized by applying the SSO algorithm. Finally, the inverse of DWT (IDWT) is applied over the optimized coefficients. The denoised image is obtained, and then, the PSNR evaluation is utilized for finding the superior performance. Experimental results using MATLAB show that the proposed algorithm has better performance when compared with the existing approaches.
Cross-Domain Extreme Learning Machine for Classification of Hyperspectral Images

Cross domain extreme learning machine (CDELM) is an unsupervised domain adaptation algorithm. It achieves domain adaptation by minimizing the classification loss on source labeled data and utilizing the maximum mean discrepancy strategy. We apply this algorithm for classification of hyperspectral images, and improve it by introducing a transformation on source features, so that the target data and transformed source data can better share the same output weights in ELM network. The enhanced CDELM is denoted as ECDELM in this paper. The experimental results using Hyperion multi-temporal remote sensing images demonstrated the effectiveness of the improvement.
Real-Time Human Head Imitation for Humanoid Robots

The ability of the robots to imitate human movements has been an active research study since the dawn of the robotics. Obtaining a realistic imitation is essential in terms of perceived quality in human-robot interaction, but it is still a challenge due to the lack of effective mapping between human movements and the degrees of freedom of robotics systems. If high-level programming interfaces, software and simulation tools simplified robot programming, there is still a strong gap between robot control and natural user interfaces. In this paper, a system to reproduce on a robot the head movements of a user in the field of view of a consumer camera is presented. The system recognizes the presence of a user and its head pose in real-time by using a deep neural network, in order to extract head position angles and to command the robot head movements consequently, obtaining a realistic imitation. At the same time, the system represents a natural user interface to control the Aldebaran NAO and Pepper humanoid robots with the head movements, with applications in human-robot interaction.
Semantic segmentation network of uav image based on improved U-net

Abstract The u-net, which is popular in the field of deep learning, is improved on the basis of the Fully Convolutional Network (FCN) [1]. U-net [2] was first published in the field of medical image, because the network has many advantages, so many scholars put their migration to use on the other computer vision problems, finding its on different problems still show the excellent performance. In this paper, U-net is applied to the problem of uav image processing, and the semantic segmentation of uav images at pixel level is carried out to identify the types of ground objects and the pixels contained in them. In order to increase the recognition accuracy of neural network, this paper proposes an improved network model based on u-net, and applies it to this problem. By training, verifying and testing the same small sample data set, it is found that the improved network has better recognition accuracy than U-net.
Suggestive evidence of protective haplotype within TGF-B1 gene region in breast density utilizing fine mapping analysis

Increased mammographic density is a strong and independent attribute factor for breast carcinoma. The density is associated with epithelial cell proliferation and stromal fibrosis development that is regulated by Transforming Growth Factor-Beta 1 (TGF-β1). This study was aimed to fine map the TGF-β1 gene cluster in predicting the role of predisposing haplotypes in breast density development using tagging SNPs. Genotyping was conducted for three tagging TGF-β1 SNPs assigned as rs1800469, rs1800470 and rs4803455 in a total of 50 consented female participants as case group which involved BI-RADS 3 and 4, while the control group (n = 50) consisted of BI-RADS 1 and 2 who underwent a mammography check-up at The Department of Radiology, Hospital Sultanah Nur Zahirah of Kuala Terengganu. A series of bioinformatics analysis including individual SNP-based association analysis, haplotype-based association analysis and fine mapping analysis was conducted spanning the gene region of 20 kb utilizing Chinese Hans Beijing (CHB) and Japanese (JPT) representing the Asian dataset from the HapMap database. The study demonstrated two tagging SNPs of TGF-β1 yielded a significant association (rs1800469, p-value = .034; rs1800470, p-value = .004). However, rs4803455 did not show a significant association with p value of 0.053. We further revealed the susceptible haplotype that was assigned as haplotype GGA with significant P value 0.003 indicated by odd ratio of 3.349 (1.482–7.568). A potential protective haplotype was observed in haplotype AGC with odd ratio 0.585 (0.316–1.080) even though with less significant (p = .085). The fine map revealed four targeted SNPs in which consist of rs12461895, rs10416269, rs7408955 and rs1549934 from Block 2 and five potential SNPs from Block 4 assigned as rs4803457, rs2317130, rs1166933, rs1982072, rs11670143 as another potential genetic variant that might contribute to breast density. This fine map study suggests the potential protective haplotype and genetic variants of TGF-β1 in predisposing to breast density development which is worth to be genotyped and further investigated.
Constructions of MDS convolutional codes using superregular matrices

Maximum distance separable convolutional codes are the codes that present best performance in error correction among all convolutional codes with certain rate and degree. In this paper, we show that taking the constant matrix coefficients of a polynomial matrix as submatrices of a superregular matrix, we obtain a column reduced generator matrix of an MDS convolutional code with a certain rate and a certain degree. We then present two novel constructions that fulfill these conditions by considering two types of superregular matrices.
On the Economic Sustainability of Cloud Sharing Systems Are Dynamic Single Resource Sharing Markets Stable?

The recent emergence of the small cloud (SC), both in concept and in practice, has been driven mainly by issues related to service cost and complexity of commercial cloud providers (e.g., Amazon) employing massive data centers. However, the resource inelasticity problem [29] faced by the SCs due to their relatively scarce resources might lead to a potential degradation of customer QoS and loss of revenue. A proposed solution to this problem recommends the sharing of resources between competing SCs to alleviate the resource inelasticity issues that might arise. Based on this idea, a recent e?ort ([18]) proposed SC-Share, a performance-driven static market model for competitive small cloud environments that results in an e?cient market equilibrium jointly optimizing customer QoS satisfaction and SC revenue generation. However, an important question with a non-obvious answer still remains to be answered, without which SC sharing markets may not be guaranteed to sustain in the long-run - is it still possible to achieve a stable market e?cient state when the supply of SC resources is dynamic in nature?. In this paper, we take a? rst step to addressing the problem of e?cient market design for single SC resource sharing in dynamic environments. We answer our previous question in the a?rmative through the use of Arrow and Hurwicz's disequilibrium process [9, 10] in economics, and the gradient play technique in game theory that allows us to iteratively converge upon e?cient and stable market equilibria.
Research on Damage Tree Bayesian Network Simulation Metamodel Based on Fault Tree Analysis

It is difficult for the fault tree analysis method to fully adapt to the needs of damage tree analysis. Based on the original model of the damage tree, the corresponding Bayesian network simulation metamodel is constructed based on the internal relationship between the damage of equipment components reflected by the damage tree. The analysis of the damage tree from the Bayesian network perspective effectively solves the two key problems of the rapidity of damage location and the common cause of damage bottom event.
Support Method to Generate Catalogs for Orchestrator using Catalog Definition Templates

We proposed an orchestrator in which service providers construct multiple services by cooperating with each other at one stop without being conscious of individual services in order to respond to the B2B2X (Business to Business to X) business model which provides services service providers by flexibly and easily combining services of each field. The advantage of this orchestrator is that a Catalog-Driven Architecture which operates by defining service specifications, resource specifications, and collaboration methods in its catalog. This feature provides flexibility for adding new services and APIs. However, as the service specifications become more complicated, the contents of catalogs tend to become more complicated, and there is a problem that knowledge is required to generate catalogs. In this paper, we propose a method to automatically generate a GUI and a catalog output function for supporting catalog creation used in orchestrators based on catalog definition templates. And then, we evaluate and confirm the feasibility of the proposed method through experiments.
A Bowl-Shaped Display for Controlling Remote Vehicles

This paper proposes a bowl-shaped hemispherical display to observe omnidirectional images. This display type has many advantages over conventional, flat 2D displays, in particular when it is used for controlling remote vehicles. First, it allows users to observe an azimuthal equidistant view of omnidirectional images by looking from above. Second, it provides a first-person view by looking into the inside of the hemispherical surface from diagonally above. Third, it provides a pseudo–third-person view as if we watched the remote vehicle from its back, by observing both the inside and outside at the same time from obliquely above. These characteristics solve the issues of blind angles around the remote vehicle. We conduct a VR-based user study to compare the bowl-shaped display to an equirectangular projection on a 2D display and a first-person view used in head-mounted displays. Based on the insights gained in the study, we present a real-world implementation and describe the uniqueness, advantages but also shortcomings of our method.
Automatic Driving of End-to-end Convolutional Neural Network Based on MobileNet-V2 Migration Learning

Convolutional neural network is gradually mature, followed by the arrival of 5G era, autonomous driving will become a development hotspot. MobileNet is a Convolutional Neural Network used depthwise separable convolutions to decrease parameters so that the devices with limited resources can use it to complete image recognition. In this paper, we use MobileNet-V2 migration learning improvement to simulate automatic driving steering on embedded devices. In this experiment, in our data set, we compared Nvidia end-to-end automated driving network with our migration learning neural network based on MobileNet-V2 end-to-end convolution. The improved MobileNet-V2 network can works on raspberries pi only has CPU faster and real-time prediction to keep in the lane line, ensure the model to reduce the number of parameter at the same time, the identification error decreases.
Simple Forward and Backward Private Searchable Symmetric Encryption Schemes with Constant Number of Roundtrips

Searchable Symmetric Encryption (SSE) is a mechanism that facilitates search over encrypted data that is outsourced to an untrusted Server. SSE schemes offer practicality at the expense of some information leakage.
Learning Preferences in a Cognitive Decision Model

Understanding human decision processes has been a topic of intense study in different disciplines including psychology, economics, and artificial intelligence. Indeed, modeling human decision making plays a fundamental role in the design of intelligent systems capable of rich interactions. Decision Field Theory (DFT) [3] provides a cognitive model of the deliberation process that precedes the selection of an option. DFT is grounded in psychological principles and has been shown to be effective in modeling several behavioral effects involving uncertainty and interactions among alternatives. In this paper, we address the problem of learning the internal DFT model of a decision maker by observing only his final choices. In our setting choices are among several options which are evaluated according to different attributes. Our approach, based on Recurrent Neural Networks, extracts underlying preferences compatible with the observed choice behavior and, thus, provides a method for learning a rich preference model of an individual which encompasses psychological aspects and which can be used as more realistic predictor of future behavior.
On the Security of RSA-PSS in the Wild

The RSA Probabilistic Signature Scheme (RSA-PSS) due to Bellare and Rogaway (EUROCRYPT 1996) is a widely deployed signature scheme. In particular it is a suggested replacement for the deterministic RSA Full Domain Hash (RSA-FDH) by Bellare and Rogaway (ACM CCS 1993) and PKCS# v1.5 (RFC 2313), as it can provide stronger security guarantees. It has since been shown by Kavki and Kiltz (EUROCRYPT 2012, Journal of Cryptology 2018) that RSA-FDH provides similar security to that of RSA-PSS, also in the case when RSA-PSS is not randomized. Recently, Jager, Kakvi and May (ACM CCS 2018) showed that PKCS#1 v1.5 gives comparable security to both RSA-FDH and RSA-PSS. However, all these proofs consider each signature scheme in isolation, where in practice this is not the case. The most interesting case is that in TLS 1.3, PKCS#1 v1.5 signatures are still included for reasons of backwards compatibility, meaning both RSA-PSS and PKCS#1 v1.5 signatures are implemented. To save space, the key material is shared between the two schemes, which means the aforementioned security proofs no longer apply. We investigate the security of this joint usage of key material in the context of Sibling Signatures, which were introduced by Camenisch, Drijvers, and Dubovitskaya (ACM CCS 2017). It must be noted that we consider the standardised version of RSA-PSS (IEEE Standard P1363-2000), which deviates from the original scheme considered in all previous papers. We are able to show that this joint usage is indeed secure, and achieves a security level that closely matches that of PKCS\#1 v1.5 signatures and that both schemes can be safely used, if the output lengths of the hash functions are chosen appropriately.
A Decision-Making Framework for Blockchain Technology Selection

Abstract As the concept of the visible product lifecycle and product tracking becomes important to consumers, emerging technologies such as Internet of Things (IoT)-based networks and Blockchain platforms are considered promising solutions by Original Equipment Manufacturers (OEMs) to increase their brand reputation, improve their consumers’ experiences, and reduce their operational costs. However, costly implementation, data verification issues, and the risk of uploading inaccurate information on those platforms have limited the adoption rate and capabilities offered by the aforementioned technologies. Companies often face the question of what type of distributed ledger technology and verification protocol is more efficient in terms of operational costs as well as security. The purpose of this study is to develop an optimization framework to make a trade-off between the cost of implementation of the technology and the security level. The framework is developed based on utility theory principles. The main focus is to discuss the cost associated with various aspects of technology implementation. Finally, this general cost framework is shown as a demo for an example of the food supply chain.
Makeup Interpolation Based on Color and Shape Parametrization

In this paper, we address the problem of synthesizing continuous variations with the appearance of makeup by taking a linear combination of the examples. Makeup usually shows a vague boundary and does not form a clear shape, which makes this problem unique from the existing image interpolation problems. We approach this problem as an interpolation between semi-transparent image layers and tackle this by presenting new parametrization schemes for the color and for the shape separately in order to achieve an effective interpolation. For the color parametrization, our main idea is based on the observation of the symmetric relation between the color and transparency of the makeup; we provide an optimization framework for extracting a representative palette of colors associated with the transparent values, which enables us to easily set up the color correspondence among the multiple makeup samples. For the shape parametrization, we exploit a polar coordinate system, that creates the in-between shapes effectively, without ghosting artifacts.
An Efficient Method for Data Integrity in Cloud Storage Using Metadata

Cloud computing is the fast, reliable and effective fix for the rising storage expenditure of IT companies. The data storage devices available is not cost effective because the data being generated at a faster pace makes it costlier for the IT companies and individual users to repeatedly update their hardware. Cloud computing not only reduces the storage costs but also reduces the protection procedures. Cloud storage shift the user's data to remotely located large data centers where the user does not have any control. However, as the case of many advanced technologies, this unique feature of the Cloud may create many new security challenges which are to clearly understood and resolved. The user should be guaranteed of the correctness of data in the Cloud. Since the data cannot be accessed physically by the user, it has to provide a tangible solution for the user to ensure that the data integrity is retained or not. In this, a system is developed which provides a substantiation of data integrity in the Cloud so that the user can utilize it in order to ensure the correctness of the user data in the Cloud. This proof of data integrity is mutually agreed in cooperation with the Cloud and the client and can also be included in the Service Level Agreement (SLA). This system gives an assurance that the data storage can be minimized at the client side which will be of immense benefit to smaller clients.
Chinese Sign Language Identification via Wavelet Entropy and Support Vector Machine

Sign language recognition is significant for smoothing barrier of communication between hearing-impaired people and health people. This paper proposed a novel Chinese sign language identification approach, in which wavelet entropy was adopted for feature reduction and support vector machine was employed for classification. The experiment was implemented on 10-fold cross validation. Our method (WE+SVM) yielded overall accuracy of 85.69 ± 0.59%. The results indicated this method was effective and superior to three state-of-the-art approaches.
Binary semantic Similarity Comparison based on software gene

Abstract The similarity of binary code is widely used in code copyright protection, vulnerability mining, malicious code analysis and etc. In this paper, we proposed a method for measuring/evaluating the similarity of two binary files based on software genes. Some of Natural language processing methods were introduced into program semantics analysis, including word2vec and doc2vec models to generate assembly instruction embeddings and gene semantic embeddings. Then the longest common subsequence method was applied to evaluate the software similarity. Experiments show that our method can effectively evaluate the similarity of binary files.
ISAR: An Authoring System for Interactive Tabletops

Despite more than three decades of research in augmented reality and shown positive effect of AR in educational settings, we still don't witness spread of this technology in the schools. Complex technology and limited educational content are among the reasons for this absence. Authoring systems can play a positive role in introduction of AR into the school settings. In this paper we introduce ISAR, a domain-independent authoring system for a camera-projector based interactive tabletop. ISAR allows teachers to create learning content and define interactions for the tabletop themselves, without the need for programming, and hence reduces the entrance barrier of educational practitioners for experimenting with augmented reality and tangible interactions.