Reviewerly/exHarmony huggingface dataset:
Multi-view kernel consensus for data analysis

Input data is high-dimensional while the intrinsic dimension of this data maybe low. Data analysis methods aim to uncover the underlying low dimensional structure imposed by the low dimensional hidden parameters. In general, uncovering these hidden parameters is achieved by utilizing distance metrics that considers the set of attributes as a single monolithic set. However, the transformation of a low dimensional phenomena into measurement of high dimensional observations can distort the distance metric. This distortion can affect the quality of the desired estimated low dimensional geometric structure. In this paper, we propose to utilize the redundancy in the feature domain by analyzing multiple subsets of features that are called views. The proposed methods utilize the consensus between different views to extract valuable geometric information that unifies multiple views about the intrinsic relationships among several different observations. This unification enhances the information better than what a single view or a simple concatenations of views can provide.
Object Memorability Prediction using Deep Learning: Location and Size Bias

Object memorability prediction is a task of estimating the probability that a human recognises the recurrence of an object after a single view. Initial research on object memorability showed that it is possible to predict the object memorability scores from the intrinsic features of an object. Though the existing works proposed some of the features for object memorability prediction task, the influence of Spatial-location and Spatial-size of an object to its memorability have not been explored yet. In this work, the importance of these two characteristics in determining object memorability prediction is investigated and the same is demonstrated by building a baseline model. Further, a deep learning model is devised for automatic feature learning on these two object characteristics. Experimental results highlight that the Spatial-location and Spatial-size of an object play a significant role in object memorability prediction and the proposed models outperformed the existing methods.
A topic-driven language model for learning to generate diverse sentences

Generating diverse sentences under a topic is a meaningful, yet not well-solved task in the field of natural language processing. We present a neural language model for generating diverse sentences conditioned on a given topic distribution. From the perspective of diversity, the proposed model takes the advantages of variational autoencoders with convolutional neural network and long short-term memory architecture. The proposed model is trained end-to-end to learn topic-level Gaussian distributions in the latent space. Then our model decodes the samples of topics obtained from latent space to generate each sentence. Results on Restaurant Dataset and Yahoo! Answers Dataset show that our model outperforms other methods in terms of language model perplexity. Also, our approach can generate a large set of different coherent sentences related to given topics. The diversity of our sentences provides a novel interpretation of topics.
An Electronic Voting Scheme Based on Revised-SVRM and Confirmation Numbers

This paper improves the performance of the revised-simplified verifiable re-encryption mixnet (R-SVRM) based e-voting scheme by introducing confirmation numbers (CNs) that are used in the CN based e-voting scheme. Although CN based and R-SVRM based schemes had made e-voting schemes more practical by excluding zero knowledge proof (ZKP) that requires large volume of computations, still they are not efficient enough. Namely, the CN based scheme adopts RSA encryption functions that are not probabilistic or commutative, therefore to satisfy essential requirements of elections, extra random factors are necessary for individual votes, election authorities must sign on votes and they must keep encryption keys as their secrets. On the other hand, although the R-SVRM based scheme uses ElGamal encryption functions that are probabilistic and commutative, vote forms in it is complicated, i.e., vote forms consist of at least 3 items and they include information about candidates as exponents. The improved scheme simplifies vote forms by exploiting CNs, and extensively reduces the number of operations required for individual votes. Also, the scheme successfully satisfies all essential requirements of e-voting systems, i.e., it is endowed with features about privacy, robustness, accuracy, incoercibility and fairness as CN based and R-SVRM based schemes are.
Audio-Material Reconstruction for Virtualized Reality Using a Probabilistic Damping Model

Modal sound synthesis has been used to create realistic sounds from rigid-body objects, but requires accurate real-world material parameters. These material parameters can be estimated from recorded sounds of an impacted object, but external factors can interfere with accurate parameter estimation. We present a novel technique for estimating the damping parameters of materials from recorded impact sounds that probabilistically models these external factors. We represent the combined effects of material damping, support damping, and sampling inaccuracies with a probabilistic generative model, then use maximum likelihood estimation to fit a damping model to recorded data. This technique greatly reduces the human effort needed and does not require the precise object geometry or the exact hit location. We validate the effectiveness of this technique with a comprehensive analysis of a synthetic dataset and a perceptual study on object identification. We also present a study establishing human performance on the same parameter estimation task for comparison.
Mobile application based on CCI standards to help children learn English as a foreign language

Many applications can be found on the Internet that are designed to help children learn English as a second language. However, it can be said that only a limited number of applications have been designed and developed by considering the important user interface design principles; these principles play an important role in mobile application design aimed at children. The main aim of this study is to develop an educational application running on a mobile phone based on the user interface design principles, particularly CCI standards together with the use of text-to-speech and voice recognition engines that will help children learn English as a foreign language. A survey was conducted in order to determine the opinions of 37 randomly selected volunteer educational technology experts and 10 randomly selected volunteer children. The opinions of a group of children showed that they were happy to use the developed application. Furthermore, it can be said that the mobile application achieved its aims in teaching English as a foreign language with the CCI standards and that the application is suitable as an educational tool. The application developed by the researchers will be beneficial for those who wish to develop Android-based educational applications for children based on CCI standards.
Fault Attack Countermeasures for Error Samplers in Lattice-Based Cryptography

Lattice-based cryptography is one of the leading candidates for NIST's post-quantum standardisation effort, providing efficient key encapsulation and signature schemes. Most of these schemes base their hardness on variants of LWE, and thus rely heavily on error samplers to provide necessary uncertainty by obfuscating computations on secret information. Because of this it is a clear and obvious target for side-channel analysis, with numerous types of attacks targeting this component to gain secret-key information. In order to bring potential lattice-based cryptographic standards to practical realisation, it is important to protect these modules from past and future fault and side-channel attacks. This paper proposes countermeasures that exploit the distributions expected from these error samples, that is either Gaussian or binomial, by using statistical tests to verify the samplers are operating properly. The novel countermeasures are designed to protect against all previous fault attacks on error samplers. We optimize hardware implementation of the proposed tests to avoid division and square root calculations, however, the countermeasure we propose is sufficiently generic to be suitable also for software. We measure the impact of these countermeasures on performance and area consumption on a Xilinx Artix-7 FPGA. Our countermeasure achieve promising performance while resulting in a minimal overhead.
Path planning and guidance for underactuated vehicles with limited field-of-view

When GPS is not available, autonomous vehicles can obtain navigation information by observing a source with a camera or an acoustic system mounted on the frame of the vehicle. This information can be used in the navigation and to overcome the lack of other sources of positioning. However, these systems often have a limited range and angular FOV. Due to this restriction, motion along some paths will make it impossible to obtain the necessary navigation information as the source is no longer in the vehicle's FOV. This paper proposes both a path planning approach and a guidance control law that allows the vehicle to preserve landmarks that are critical for the navigation inside the FOV while at the same time converging to the proposed path. The suggested approach is experimentally validated by implementation in an autonomous underwater vehicle.
Heavy Metal Contamination Index Using Spectral Variables for White Precipitates Induced by Acid Mine Drainage: A Case Study of Soro Creek, South Korea

We analyzed heavy metal contamination, mineral composition, and spectral characteristics of white aluminum precipitates from an acid mine drainage in Taebaek, South Korea. We introduced a single index for prediction of overall heavy metal contamination level in white precipitates using spectral variables. The white precipitates were severely contaminated with heavy metal elements and consisted of primary and secondary minerals. Due to the contamination in the environment, the precipitation pH values ranged from 4.76 to 7.80. The spectral characteristics of white precipitates are dominated by secondary minerals. The spectral reflectance of white precipitates decreased in all wavelengths, and the absorption depth related with OH, H2O, and Al-OH decreased with increase in heavy metal contamination. We found the distinctive differences at ferric iron and Al-OH absorptions between the white and reddish-brown precipitates. Based on these observations, we developed heavy metal contamination index and built prediction models using the index. The validation tests showed the index, and the regression model can accurately predict the amount of heavy metal from the spectral readings. Given the fact that the stream precipitates resulted by acid mine drainage is typically similar for each type, we expect that the heavy metal contamination index can be used to make reliable estimates of heavy metal contamination in the white precipitates by remote sensing applications.
Path Planning of UGV Based on Bézier Curves

Summary An effective path planner is critical for autonomous traversal of unmanned ground vehicles (UGVs) in harsh environments. This paper describes a novel path planning method considering Bézier curves and a two-layer planning framework. In the two-layer framework, a road centerline (RCL) estimator located on the upper layer works as a global planner to obtain the local target for the bottom local planner. The RCL is estimated from a series of candidate Bézier curves based on a safety criterion. In the bottom layer, an optimal trajectory planner and a speed planner make up the local planner to obtain the desired steering turning angle and linear speed. The criteria for optimal trajectory selection are designed for comfortable driving. Road safety is considered in the speed planner for robust driving. Three sets of simulations are used to evaluate and quantify the relative performance of variations of our path planning algorithm. The proposed path planning method is implemented on a modified Polaris RZR 800 UGV, too. Two experiments based on this UGV are set up in the country road environment to demonstrate the viability of the proposed method.
Can We Prove Time Protection?

Timing channels are a significant and growing security threat in computer systems, with no established solution. We have recently argued that the OS must provide time protection, in analogy to the established memory protection, to protect applications from information leakage through timing channels. Based on a recently-proposed implementation of time protection in the seL4 microkernel, we investigate how such an implementation could be formally proved to prevent timing channels. We postulate that this should be possible by reasoning about a highly abstracted representation of the shared hardware resources that cause timing channels.
Optical fiber intrusion signal recognition method based on TSVD-SCN

Stochastic Configuration Networks (SCN) introduce the inequality constraint of a supervised mechanism to ensure the universal approximation property of learner model. However, in the processing of building SCN, due to the properties of used activation function and the way of assigning the random input weights and biases of the hidden nodes, the hidden output matrix is often ill-posed, i.e., the matrix can be of rank deficient or demonstrate multicollinearity. Thus, the least squares method for evaluating the output weights may result in poor generalization performance for data modelling problems. This paper aims to overcome this drawback through modifying the computation of the generalized pseudo inverse of the output matrix by a Truncated Singular Value Decomposition (TSVD) method with an adaptively chosen truncation threshold. The improved SCN model is then applied for recognizing intrusion signals in Optical Fiber Pre-warning System. Experimental results show that the proposed improved algorithm can achieve higher recognition rate compared to the original SCN classifier.
Multitask learning approach for understanding the relationship between two sentences

Understanding the relationship between two sentences through tasks including natural language inference, paraphrase detection, semantic textual similarity, and semantic relatedness is a fundamental step to natural language understanding. We propose an approach to infer the relationship between two sentences using a multitask framework to generate a universal representation of the relationship. Our model consists of a universal layer shared for all tasks with several task-specific layers on top for each task. To generate universal representation, we employ the enhanced sequential inference model based on a deep learning and soft alignment techniques. The task-specific layers are composed of multilayer perceptrons. The main feature of the proposed approach is that a single encoder can model various relationship of sentences at same time on multiple tasks. When we evaluated our approach on four public datasets for four different tasks regarding the relationship between two sentences, it outperformed state-of-the-art methods for two datasets and performed significantly well for the other two datasets. Further investigation of our proposed model showed that it captures comprehensive information together with specific knowledge regarding each task to infer semantic similarity. The detailed analysis supports that the proposed approach is robust over all semantic inference tasks using a single model.
Toward dynamic pain expressions in avatars: Perceived realism and pain level of different action unit orders

The facial expression of pain can be decomposed in three sets of Action Units (AUs), the smallest discriminating facial movements: Brow lowering (B), Nose wrinkling + Upper lip raising (N), and Orbit tightening + Eyelid closure (O). This study compared the perception of realism and pain level from different onset orders of AUs in avatars. Seven videos of facial expressions of pain were created with four different avatars (2 women): six sequential onsets combining the three sets of AUs and one synchronized onset. 45 healthy adults (22 women; aged 23.6 ± 5.2 years) rated the realism of facial movements, and the level of intensity and unpleasantness of perceived pain. A more realistic expression was associated with the onset of O before or at the same time as N, a more intense expression was associated when B occurred last, and a higher level of unpleasantness was associated with the onset of N before B. Therefore, the sequence ONB yielded the highest ratings on both measures of realism and pain levels. These findings describe the perceived content of different orders of facial movements that could contribute to the creation of realistic pain-expressing virtual agents designed to study human-computer interactions.
Towards a Fair Evaluation of Zero-Shot Action Recognition Using External Data

Zero-shot action recognition aims to classify actions not previously seen during training. This is achieved by learning a visual model for the seen source classes and establishing a semantic relationship to the unseen target classes e.g. through the action labels. In order to draw a clear line between zero-shot and conventional supervised classification, the source and target categories must be disjoint. Ensuring this premise is not trivial, especially when the source dataset is external. In this work, we propose an evaluation procedure that enables fair use of external data for zero-shot action recognition. We empirically show that external sources tend to have actions excessively similar to the target classes, strongly influencing the performance and violating the zero-shot premise. To address this, we propose a corrective method to automatically filter out too similar categories by exploiting the pairwise intra-dataset similarity of the labels. Our experiments on the HMDB-51 dataset demonstrate that the zero-shot models consistently benefit from the external sources even under our realistic evaluation, especially when the source categories of internal and external domains are combined.
3D Patchwise U-Net with Transition Layers for MR Brain Segmentation

We propose a new patch based 3D convolutional neural network to automatically segment multiple brain structures on Magnetic Resonance (MR) images. The proposed network consists of encoding layers to extract informative features and decoding layers to reconstruct the segmentation labels. Unlike the conventional U-net model, we use transition layers between the encoding layers and the decoding layers to emphasize the impact of feature maps in the decoding layers. Moreover, we use batch normalization on every convolution layer to make a well generalized model. Finally, we utilize a new loss function which can normalize the categorical cross entropy to accurately segment the relatively small interest regions which are opt to be misclassified. The proposed method ranked 1 $$^{st}$$ over 22 participants at the MRBrainS18 segmentation challenge at MICCAI 2018.
A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles

Nowadays, automatic multi-objective detection remains a challenging problem for autonomous vehicle technologies. In the past decades, deep learning has been demonstrated successful for multi-objective detection, such as the Single Shot Multibox Detector (SSD) model. The current trend is to train the deep Convolutional Neural Networks (CNNs) with online autonomous vehicle datasets. However, network performance usually degrades when small objects are detected. Moreover, the existing autonomous vehicle datasets could not meet the need for domestic traffic environment. To improve the detection performance of small objects and ensure the validity of the dataset, we propose a new method. Specifically, the original images are divided into blocks as input to a VGG-16 network which add the feature map fusion after CNNs. Moreover, the image pyramid is built to project all the blocks detection results at the original objects size as much as possible. In addition to improving the detection method, a new autonomous driving vehicle dataset is created, in which the object categories and labelling criteria are defined, and a data augmentation method is proposed. The experimental results on the new datasets show that the performance of the proposed method is greatly improved, especially for small objects detection in large image. Moreover, the proposed method is adaptive to complex climatic conditions and contributes a lot for autonomous vehicle perception and planning.
Multi-objective evolutionary algorithm for optimizing the partial area under the ROC curve

The measure called partial area under the curve (pAUC) has attracted increasing interest in recent years due to the wide range of real-world applications it can be used for, such as biomarker selection and pedestrian detection. Compared with AUC, pAUC only acts upon a partial (instead of a full) area under the ROC curve. In this paper, a multi-objective evolutionary algorithm termed MOPA is proposed to optimize the pAUC in an arbitrary false positive range. Within this range, two important components are proposed to focus on a particular region within the AUC. First, a new metric (K-FPR), is proposed that is created by considering the partial range of the false positive rate (FPR). This is combined with the true positive rate (TPR) to provide the two optimized objectives of MOPA. Second, a preference-based multi-objective evolutionary algorithm is developed within the framework of a recently proposed algorithm (AR-MOEA), by which the search is concentrated on a partial area under the ROC curve. Numerical experiments on different data sets demonstrate the competitiveness of the proposed method in comparison with state-of-the-art algorithms.
Pose Guided Human Image Synthesis by View Disentanglement and Enhanced Weighting Loss

View synthesis aims at generating a novel, unseen view of an object. This is a challenging task in the presence of occlusions and asymmetries. In this paper, we present View-Disentangled Generator (VDG), a two-stage deep network for pose-guided human-image generation that performs coarse view prediction followed by a refinement stage. In the first stage, the network predicts the output from a target human pose, the source-image and the corresponding human pose, which are processed in different branches separately. This enables the network to learn a disentangled representation from the source and target view. In the second stage, the coarse output from the first stage is refined by adversarial training. Specifically, we introduce a masked version of the structural similarity loss that facilitates the network to focus on generating a higher quality view. Experiments on Market-1501 and DeepFashion demonstrate the effectiveness of the proposed generator.
Blockchain Technology for the Improvement of SCM and Logistics Services: A Survey

Since the advent, blockchain has found its application in numerous industries, thus disrupting the current way of design and development of the new applications for supply chain management and logistics. Nowadays for business providers, the efficiency of the service they provide is crucial for the long-term improvement of their operations. This efficiency depends on consumer satisfaction with service delivery and reliable information related to goods, correct delivery and timeliness. These providers are strongly dependent on the application and models they use for planning and managing their daily activities. In this context, information sharing is crucial to ensure a reliable and efficient way of collaborating. This papers aims at surveying the current range of academic literature and applications from the business perspective related to the application of blockchain technology in supply chain management and logistics.