Reviewerly/exHarmony huggingface dataset:
Fabric wrinkle level classification via online sequential extreme learning machine based on improved sine cosine algorithm

Because it is difficulty to classify level of fabric wrinkle, this paper proposes a fabric winkle level classification model via online sequential extreme learning machine based on improved sine cosine algorithm (SCA). The SCA has excellent global optimization ability, can explore different search spaces, and effectively avoid falling into local optimum. Because the initial population of SCA will have an impact on its optimization speed and quality, the SCA is initialized by differential evolution (DE) to avoid local optimization, and then the output weight and hidden layer bias are optimized; that is, the improved SCA is used to select the optimal parameters of the online sequential extreme learning machine (OSELM) to improve the generalization performance of the algorithm. To verify the performance of the proposed model DE-SCA-OSELM, it will be compared with other algorithms using a fabric wrinkles dataset collected under standard conditions. The experimental results indicate that the proposed model can effectively find the optimal parameter value of OSELM. The average classification accuracy increased by 6.95%, 3.62%, 6.67%, and 3.34%, respectively, compared with the partial algorithms OSELM, SCAELM, RVFL and PSOSVM, which meets expectations.
Framework for Healthcare Security Practice Analysis, Modeling and Incentivization

Healthcare professionals are often the weakest link in the security chain, which is contributing to data breaches in the healthcare sector. A number of reasons account for this. Technological countermeasures for cyber defenses have been heightened and the adversaries tend to exploit easy entry points. Besides, healthcare staffs are usually occupied by their core duty of healthcare provison with little experience in information security.With a Design Science Approach (DSA), observational measures for effective profiling of healthcare staffs were developed. Regulations and security standards such as the Code of Conduct, General Data Protection Regulation (GDPR) of European Union (EU), ISO 7799, and other Norwegian Acts and regulations for personal data protection, were reviewed for the observational measures. A comprehensive Healthcare Security Practice Analysis, Modeling and Incentivization (HSPAMI) framework was proposed for analyzing healthcare staffs' security practices in a comprehensive way.
Authentication and integrity of smartphone videos through multimedia container structure analysis

Nowadays, mobile devices have become the natural substitute for the digital camera, as they capture everyday situations easily and quickly, encouraging users to express themselves through images and videos. These videos can be shared across different platforms exposing them to any kind of intentional manipulation by criminals who are aware of the weaknesses of forensic techniques to accuse an innocent person or exonerate a guilty person in a judicial process. Commonly, manufacturers do not comply 100% with the specifications of the standards for the creation of videos. Also, videos shared on social networks, and instant messaging applications go through filtering and compression processes to reduce their size, facilitate their transfer, and optimize storage on their platforms. The omission of specifications and results of transformations carried out by the platforms embed a features pattern in the multimedia container of the videos. These patterns make it possible to distinguish the brand of the device that generated the video, social network, and instant messaging application that was used for the transfer. Research in recent years has focused on the analysis of AVI containers and tiny video datasets. This work presents a novel technique to detect possible attacks against MP4, MOV, and 3GP format videos that affect their integrity and authenticity. The method is based on the analysis of the structure of video containers generated by mobile devices and their behavior when shared through social networks, instant messaging applications, or manipulated by editing programs. The objectives of the proposal are to verify the integrity of videos, identify the source of acquisition and distinguish between original and manipulated videos.
Cascaded Volumetric Fully Convolutional Networks for Whole-Heart and Great Vessel 3D segmentation

The absence of cardiac chambers, holes in the heart and abnormal connections cause the death of hundreds of people every year. The clinical team involved in the diagnosis and treatment decisions for congenital heart disease (CHD) must be consistent in their choices. Therefore, in cases of CHD there is a need for a system that is capable of segmenting the whole heart and large blood vessels in 3D efficiently, quickly, and accurately. This article proposes to fill this need by using Cascaded Volumetric Fully Convolutional Networks. The approach proposes the use of two Volumetric Fully Convolution Networks (V-Net) in sequence. The first network aims at locating the cardiac region, while the second segment the substructures of the cardiac area and the great vessels. Both networks are trained with the 2016 data set from the MICCAI Workshop on Whole-Heart and Great Vessel Segmentation of 3D Cardiovascular MRI in Congenital Heart Disease (HVSMR). The experimental results show that the proposed method has a promising potential in decision-making in CHD cases (from MR images). The approach obtained on average 98.15% for Accuracy, 94.89% for Precision, 98.81% for Specificity Coefficient, 94.27% for Sensitivity Coefficient, 93.24% for Matthews Coefficient, 80.65% for Jaccard Index, 94.20% for Dice Coefficient, and 1.61 for the Hausdorff Distance. The proposed method enables the visualization and iteration of the segmented volume in 3D so that the doctor can analyze the entire structure of the heart along with the circulatory network.
Improving Scalability of Parallel CNN Training by Adjusting Mini-Batch Size at Run-Time

Training Convolutional Neural Network (CNN) is a computationally intensive task, requiring efficient parallelization to shorten the execution time. Considering the ever-increasing size of available training data, the parallelization of CNN training becomes more important. Data-parallelism, a popular parallelization strategy that distributes the input data among compute processes, requires the mini-batch size to be sufficiently large to achieve a high degree of parallelism. However, training with large batch size is known to produce a low convergence accuracy. In image restoration problems, for example, the batch size is typically tuned to a small value between 16 ~ 64, making it challenging to scale up the training. In this paper, we propose a parallel CNN training strategy that gradually increases the mini-batch size and learning rate at run-time. While improving the scalability, this strategy also maintains the accuracy close to that of the training with a fixed small batch size. We evaluate the performance of the proposed parallel CNN training algorithm with image regression and classification applications using various models and datasets.
Optimizing Deep Neural Network Architecture with Enhanced Genetic Algorithm

Only weights and biases are learned by gradient descent-based training of Deep Neural Networks (DNN). The other parameters, i.e., hyperparameters have a huge influence on the quality of the model but finding optimal values for them is not a trivial solution. The hyperparameter space grows exponentially and they also display non-linearity and interactions. A network with twelve hyperparameters, each with five potential values can have about 250 million unique combinations of hyperparameter sequences. If training on each set takes 6 minutes, exhaustive training on all potential combinations of the hyperparameters to find the optimal values will take almost 3000 years. Expert knowledge or random selection are some alternate options, but they are not scalable or consistently reliable. Metaheuristics such as evolutionary algorithms are a great choice for solving combinatorial optimization problems like hyperparameter optimization. While other researchers have used evolutionary algorithms such as standard implementation of genetic algorithm (GA), we introduce additional nature-inspired enhancements to GA for better exploration of the hyperparameter solution space to optimize the DNN architecture. The training is complemented with Monte Carlo based variance reduction method called importance sampling. We demonstrate that these fine-tunings result in improvements in the network accuracy on MNIST and CIFAR-10 datasets that outperforms standard use of genetic algorithm.
Asynchronous Blockchain-based Privacy-preserving Training Framework for Disease Diagnosis

With the emerging artificial intelligence technology, especially machine learning and deep learning, an increasing number of healthcare institutions have designed and implemented various data-driven analysis tools and models to assist disease diagnosis. However, it is difficult for a single healthcare institution to collect sufficient medical records to support a sophisticated automatic disease recognition model, especially for some rare diseases, which urges the collaboration among different medical institutions and hospitals. The current mainstream solutions, like centralized distributed machine learning, heavily rely on a central server, which may be vulnerable to attackers or even malicious itself. Meanwhile, data privacy concerns make hospitals reluctant to share their patients' records with others. To solve these issues, in this work, we utilize the blockchain to build a decentralized privacy-preserving cross-institution disease classification framework, called Health-Chain. Specifically, we combined differential privacy and pseudo-identity mechanism to protect data privacy in distributed stochastic gradient descent (SGD) algorithm. Meanwhile, we equip gradient delay compensation to address the asynchronous issues in the decentralized blockchain-based learning system. In the experiments, we implement our Health-Chain in two popular disease recognition tasks, breast cancer diagnosis, and ECG arrhythmia classification, and demonstrate the efficiency and effectiveness of the proposed framework.
An Extrinsic Calibration Method With Closed-Form Solution for Underwater Opti-Acoustic Imaging System

Forward-looking sonar (FLS) and camera are widely used sensors in multimodal systems for underwater environments. The extrinsic calibration is a precondition for this kind of multimodal systems to fuse optical and acoustic information. Due to the very different imaging formation mechanisms and models of 3-D-to-2-D projection for imaging sonar and optical camera, the multimodal correspondence and the sonar-pose recovery from a 2-D sonar image are two main issues in extrinsic calibration work. In this article, we propose an extrinsic calibration method for the opti-acoustic imaging system combining a camera and an FLS in underwater environment. The proposed method includes an estimation process yielding closed-form expressions to determine the sonar pose with respect to the calibration board, which enables the estimation of extrinsic parameters to be directly computed from sensor measurements and the knowledge of calibration-board structure. A calibration board with two sets of feature arrays is used to generate two 2-D-to-3-D correspondences, which circumvents the 2-D-to-2-D multimodal correspondence problem. An iterative optimization process is provided to further improve the performance, while the extra computation time is negligible. Both simulated and actual experiments show that the method outperforms the existing iterative method based on bundle adjustment.
Power-Law Nonlinearity with Maximally Uniform Distribution Criterion for Improved Neural Network Training in Automatic Speech Recognition

In this paper, we describe the Maximum Uniformity of Distribution (MUD) algorithm with the power-law nonlinearity. In this approach, we hypothesize that neural network training will become more stable if feature distribution is not too much skewed. We propose two different types of MUD approaches: power function-based MUD and histogram-based MUD. In these approaches, we first obtain the mel filterbank coefficients and apply nonlinearity functions for each filterbank channel. With the power function-based MUD, we apply a power-function based nonlinearity where power function coefficients are chosen to maximize the likelihood assuming that nonlinearity outputs follow the uniform distribution. With the histogram-based MUD, the empirical Cumulative Density Function (CDF) from the training database is employed to transform the original distribution into a uniform distribution. In MUD processing, we do not use any prior knowledge (e.g. logarithmic relation) about the energy of the incoming signal and the perceived intensity by a human. Experimental results using an end-to-end speech recognition system demonstrate that power-function based MUD shows better result than the conventional Mel Filterbank Cepstral Coefficients (MFCCs). On the LibriSpeech database, we could achieve 4.02 % WER on test-clean and 13.34 % WER on test-other without using any Language Models (LMs). The major contribution of this work is that we developed a new algorithm for designing the compressive nonlinearity in a data-driven way, which is much more flexible than the previous approaches and may be extended to other domains as well.
Training bidirectional generative adversarial networks with hints

The generative adversarial network (GAN) is composed of a generator and a discriminator where the generator is trained to transform random latent vectors to valid samples from a distribution and the discriminator is trained to separate such “fake” examples from true examples of the distribution, which in turn forces the generator to generate better fakes. The bidirectional GAN (BiGAN) also has an encoder working in the inverse direction of the generator to produce the latent space vector for a given example. This added encoder allows defining auxiliary reconstruction losses as hints for a better generator. On five widely-used data sets, we showed that BiGANs trained with the Wasserstein loss and augmented with hints learn better generators in terms of image generation quality and diversity, as measured numerically by the 1-nearest neighbor test, Fréchet inception distance, and reconstruction error, and qualitatively by visually analyzing the generated samples.
Training by feel: wearable fitness-trackers, endurance athletes, and the sensing of data

A wide range of wearable fitness-trackers are currently available that allow users to measure, monitor, visualize, and record numerous training metrics including moving pace, distance traveled, average heart rate, and calories burned. Using qualitative data collected through semi-structured interviews with amateur endurance athletes, this paper examines what individuals do with their wearable fitness-trackers and the data they produce. Drawing on the work of Deborah Lupton and Sarah Maslen, we take up the concepts of "data sensing" and the "more-than-human sensorium" to highlight the embodied and sensory dimensions of digital self-tracking. We argue that while much of the appeal of fitness-tracking technologies lies in their ability to generate objective readings of one's performance, these devices do not supplant less quantifiable and more subjective ways of understanding one's self. On the contrary, the participants in our study use the quantitative data generated by a fitness-tracker in conjunction with their own self-assessments to gain a more holistic sense of what they are experiencing during training or on race day. For many of our research participants, the fitness-tracker became a central part of their identity and daily routine. Most participants were reluctant to train without their fitness-trackers, even when not preparing for an event.
Time and sequence awareness in similarity metrics for recommendation

Modeling the temporal context efficiently and effectively is essential to provide useful recommendations to users. In this work, we focus on improving neighborhood-based approaches where we integrate three different mechanisms to exploit temporal information. We first present an improved version of a similarity metric between users using a temporal decay function, then, we propose an adaptation of the Longest Common Subsequence algorithm to be used as a time-aware similarity metric, and we also redefine the neighborhood-based recommenders to be interpreted as ranking fusion techniques where the neighbor interaction sequence can be exploited by considering the last common interaction between the neighbor and the user. We demonstrate the effectiveness of these approaches by comparing them with other state-of-the-art recommender systems such as Matrix Factorization, Neural Networks, and Markov Chains under two realistic time-aware evaluation methodologies (per user and community-based). We use several evaluation metrics to measure both the quality of the recommendations – in terms of ranking relevance – and their temporal novelty or freshness. According to the obtained results, our proposals are highly competitive and obtain better results than the rest of the analyzed algorithms, producing improvements under the two evaluation dimensions tested consistently through three real-world datasets.
Embeddings for DNN Speaker Adaptive Training

In this work, we investigate the use of embeddings for speaker-adaptive training of DNNs (DNN-SAT) focusing on a small amount of adaptation data per speaker. DNN-SAT can be viewed as learning a mapping from each embedding to transformation parameters that are applied to the shared parameters of the DNN. We investigate different approaches to applying these transformations, and find that with a good training strategy, a multi-layer adaptation network applied to all hidden layers is no more effective than a single linear layer acting on the embeddings to transform the input features. In the second part of our work, we evaluate different embed-dings (i-vectors, x-vectors and deep CNN embeddings) in an additional speaker recognition task in order to gain insight into what should characterize an embedding for DNN-SAT. We find the performance for speaker recognition of a given representation is not correlated with its ASR performance; in fact, ability to capture more speech attributes than just speaker identity was the most important characteristic of the embed-dings for efficient DNN-SAT ASR. Our best models achieved relative WER gains of 4% and 9% over DNN baselines using speaker-level cepstral mean normalisation (CMN), and a fully speaker-independent model, respectively.
Weighted ReliefF with threshold constraints of feature selection for imbalanced data classification

Summary Feature selection is a useful method for fulfilling the data classification since the inherent heterogeneity of data and the redundancy of features are often encountered in the current data exploding era. Some commonly used feature selection algorithms, which include but are not limited to Pearson, maximal information coefficient, and ReliefF, are well‐posed under the assumption that instances are distributed homogenously in datasets. However, such an assumption might be not true in the practice. As such, in the presence of data imbalance, these traditional feature selection algorithms might be invalid due to their prejudices to the minority class, which includes few samples. The purpose of the addressed problem in this article is to develop an effective feature selection algorithm for imbalanced judicial datasets, which is capable of extracting essential features while deleting negligible ones according to the practical feature requirements. To achieve this goal, the number and the distribution of samples in each class are fully taken into consideration for the correlation analysis. Compared with the traditional feature selection algorithms, the proposed improved ReliefF algorithm is equipped with: (i) different weights of features according to the characteristics of heterogeneous samples in different classes; (ii) justice for imbalanced datasets; and (iii) threshold constraints resulting from the practical feature requirements. Finally, experiments on a judicial dataset and six public datasets well illustrate the effectiveness and the superiority of the proposed feature selection algorithm in improving the classification accuracy for imbalanced datasets.
Multi-View Subspace Clustering based on Tensor Schatten-p Norm

In this paper, we focus on the multi-view clustering problem. A novel multi-view clustering framework, called multi-view subspace clustering based on tensor Schatten-p norm (MVSC-TSP), is proposed for clustering task. In our method, the tensor Schatten-p norm, which is based on tensor singular value decomposition, is utilized to explore the global low-rank structure of multi-view self-representations. Since 0<; p<; 1, using tensor Schatten-p norm to relax the tensor multi-rank is more effective than the commonly used tensor nuclear norm. Furthermore, we present a new generalized tensor soft thresholding algorithm to solve the tensor Schatten-p norm minimization problem. Based on this, the proposed non-convex optimal problem can be efficiently solved by the alternating direction method of multipliers. Experimental results on image clustering demonstrate that the proposed method is superior to the state-of-the-art methods in term of various evaluation metrics.
SENATE: A Permissionless Byzantine Consensus Protocol in Wireless Networks for Real-Time Internet-of-Things Applications

The blockchain technology has achieved tremendous success in open (permissionless) decentralized consensus by employing Proof of Work (PoW) or its variants, whereby unauthorized nodes cannot gain a disproportionate impact on consensus beyond their computational power. However, PoW-based systems incur a high delay and low throughput, making them ineffective in dealing with the real-time Internet-of-Things (IoT) applications. On the other hand, the Byzantine fault-tolerant (BFT) consensus algorithms with better delay and throughput performance cannot be employed in permissionless settings due to vulnerability to Sybil attacks. In this article, we present a Sybil-proof wireless network coordinate-based Byzantine consensus (SENATE), which has the merits of both real-time consensus reaching and Sybil-proof, i.e., it is based on the conventional BFT consensus framework yet works in open systems of wireless devices where faulty nodes may launch Sybil attacks. As in a Senate, in the legislature, where the quota of senators per state (district) is a constant irrespective with the population of the state, “senators” in SENATE are selected from participating distributed nodes based on their wireless network coordinates (WNCs) with a fixed number of nodes per district in the WNC space. Elected senators then participate in the subsequent consensus reaching process and broadcast the result. Thereby, the SENATE is a proof against Sybil attacks since pseudonyms of a faulty node are likely to be adjacent in the WNC space and hence fail to be elected. The simulation results reveal that the SENATE can achieve real-time consensus (consensus delay under one second) in a network of hundreds of nodes.