Reviewerly/exHarmony huggingface dataset:
Imperative Dynamic Routing Between Capsules Network for Malaria Classification

Malaria is a severe epidemic disease caused by Plasmodium falciparum. The parasite causes critical illness if persisted for longer durations and delay in precise treatment can lead to further complications. The automatic diag... | Find, read and cite all the research you need on Tech Science Press
HeadFi

Headphones continue to become more intelligent as new functions (e.g., touch-based gesture control) appear. These functions usually rely on auxiliary sensors (e.g., accelerometer and gyroscope) that are available in smart headphones. However, for those headphones that do not have such sensors, supporting these functions becomes a daunting task. This paper presents HeadFi, a new design paradigm for bringing intelligence to headphones. Instead of adding auxiliary sensors into headphones, HeadFi turns the pair of drivers that are readily available inside all headphones into a versatile sensor to enable new applications spanning across mobile health, user-interface, and context-awareness. HeadFi works as a plug-in peripheral connecting the headphones and the pairing device (e.g., a smartphone). The simplicity (can be as simple as only two resistors) and small form factor of this design lend itself to be embedded into the pairing device as an integrated circuit. We envision HeadFi can serve as a vital supplementary solution to existing smart headphone design by directly transforming large amounts of existing "dumb" headphones into intelligent ones. We prototype HeadFi on PCB and conduct extensive experiments with 53 volunteers using 54 pairs of non-smart headphones under the institutional review board (IRB) protocols. The results show that HeadFi can achieve 97.2%--99.5% accuracy on user identification, 96.8%--99.2% accuracy on heart rate monitoring, and 97.7%--99.3% accuracy on gesture recognition.
A Comparative Study of Feature Selection Approaches for Human Activity Recognition Using Multimodal Sensory Data

Human activity recognition (HAR) aims to recognize the actions of the human body through a series of observations and environmental conditions. The analysis of human activities has drawn the attention of the research community in the last two decades due to its widespread applications, diverse nature of activities, and recording infrastructure. Lately, one of the most challenging applications in this framework is to recognize the human body actions using unobtrusive wearable motion sensors. Since the human activities of daily life (e.g., cooking, eating) comprises several repetitive and circumstantial short sequences of actions (e.g., moving arm), it is quite difficult to directly use the sensory data for recognition because the multiple sequences of the same activity data may have large diversity. However, a similarity can be observed in the temporal occurrence of the atomic actions. Therefore, this paper presents a two-level hierarchical method to recognize human activities using a set of wearable sensors. In the first step, the atomic activities are detected from the original sensory data, and their recognition scores are obtained. Secondly, the composite activities are recognized using the scores of atomic actions. We propose two different methods of feature extraction from atomic scores to recognize the composite activities, and they include handcrafted features and the features obtained using the subspace pooling technique. The proposed method is evaluated on the large publicly available CogAge dataset, which contains the instances of both atomic and composite activities. The data is recorded using three unobtrusive wearable devices: smartphone, smartwatch, and smart glasses. We also investigated the performance evaluation of different classification algorithms to recognize the composite activities. The proposed method achieved 79% and 62.8% average recognition accuracies using the handcrafted features and the features obtained using subspace pooling technique, respectively. The recognition results of the proposed technique and their comparison with the existing state-of-the-art techniques confirm its effectiveness.
Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration

This paper presents a novel medical image registration algorithm named total variation constrained graph-regularization for non-negative matrix factorization (TV-GNMF). The method utilizes non-negative matrix factorization by total variation constraint and graph regularization. The main contributions of our work are the following. First, total variation is incorporated into NMF to control the diffusion speed. The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information. Second, we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power. Third, the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given. Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.
Multi-Localized Sensitive Autoencoder-Attention-LSTM For Skeleton-based Action Recognition

One of key challenges of skeleton-based action recognition (SAR) tasks is the complex nature of human motion patterns. Variations such as performers and viewpoints may impose negative effects to the action recognition accuracy. In this work, we propose the Multi-Localized Sensitive Autoencoder-Attention-LSTM (Multi-LiSAAL) for SAR. The Localized Stochastic Sensitive Autoencoder (LiSSA) encodes both spatial and temporal information, and extracts meaningful features from different parts (four limbs and a trunk) from the skeleton. The LiSSA is trained by minimizing the localized generalization error to enhance the robustness of autoencoders via reducing its sensitivity with respect to small variations in inputs. We apply an attention mechanism to assign different weights to different skeleton parts and focus more on informative sections. Then, a backbone classifier network takes weighted features as inputs to differentiates actions. Experimental results on five public benchmarking datasets show that the Multi-LiSAAL outperforms state-of-the-art methods.
CDE-GAN: Cooperative Dual Evolution-Based Generative Adversarial Network

Generative adversarial networks (GANs) have been a popular deep generative model for real-world applications. Despite many recent efforts on GANs that have been contributed, mode collapse and instability of GANs are still open problems caused by their adversarial optimization difficulties. In this article, motivated by the cooperative co-evolutionary algorithm, we propose a cooperative dual evolution-based GAN (CDE-GAN) to circumvent these drawbacks. In essence, CDE-GAN incorporates dual evolution with respect to the generator(s) and discriminators into a unified evolutionary adversarial framework to conduct effective adversarial multiobjective optimization. Thus, it exploits the complementary properties and injects dual mutation diversity into the training, to steadily diversify the estimated density in capturing multimodes and improve generative performance. Specifically, CDE-GAN decomposes the complex adversarial optimization problem into two subproblems (generation and discrimination), and each subproblem is solved with a separated subpopulation (<i>E-Generators</i> and <i>E-Discriminators</i>), evolved by its own evolutionary algorithm. Additionally, we further propose a <i>Soft Mechanism</i> to balance the tradeoff between E-Generators and E-Discriminators to conduct steady training for CDE-GAN. Extensive experiments on one synthetic dataset and three real-world benchmark image datasets demonstrate that the proposed CDE-GAN achieves a competitive and superior performance in generating good quality and diverse samples over baselines. The code and more generated results are available at our project homepage <i><uri>https://shiming-chen.github.io/CDE-GAN-website/CDE-GAN.html</uri></i>.
Vaw-Gan For Disentanglement And Recomposition Of Emotional Elements In Speech

Emotional voice conversion (EVC) aims to convert the emotion of speech from one state to another while preserving the linguistic content and speaker identity. In this paper, we study the disentanglement and recomposition of emotional elements in speech through variational autoencoding Wasserstein generative adversarial network (VAW-GAN). We propose a speaker-dependent EVC framework based on VAW-GAN, that includes two VAW-GAN pipelines, one for spectrum conversion, and another for prosody conversion. We train a spectral encoder that disentangles emotion and prosody (F0) information from spectral features; we also train a prosodic encoder that disentangles emotion modulation of prosody (affective prosody) from linguistic prosody. At run-time, the decoder of spectral VAW-GAN is conditioned on the output of prosodic VAW-GAN. The vocoder takes the converted spectral and prosodic features to generate the target emotional speech. Experiments validate the effectiveness of our proposed method in both objective and subjective evaluations.
The impact of automation and artificial intelligence on digital forensics

Abstract Artificial intelligence (AI; broadly defined to include Machine Learning and Deep Learning) and automation are two current and reciprocal computing disciplines. As such, AI‐powered software, programs, operating systems, and devices are developed on a massive scale to automate a wide variety of processes and operations. The principal aims of integrating AI and automation include efficiency, accuracy, and cost‐reduction. While there is still an on‐going cost associated with automation, the cost is typically many magnitudes smaller than the on‐going costs incurred to get the job done manually, which increases the likelihood of generating a high return on investment. One emerging application of AI and automation is digital forensics. For example, US Federal and State Law Enforcement Agencies have started exploring the utility of AI‐powered technology to make the job of digital forensics more impactful. This trend can maximize the accuracy of digital forensic investigations, enabling the resolution of more digital investigations. This article is categorized under: Digital and Multimedia Science &gt; Cyber Threat Intelligence Digital and Multimedia Science &gt; Artificial Intelligence Digital and Multimedia Science &gt; Cybercrime Investigation
Realising a Push Button Modality for Video-Based Forensics

Complexity and sophistication among multimedia-based tools have made it easy for perpetrators to conduct digital crimes such as counterfeiting, modification, and alteration without being detected. It may not be easy to verify the integrity of video content that, for example, has been manipulated digitally. To address this perennial investigative challenge, this paper proposes the integration of a forensically sound push button forensic modality (PBFM) model for the investigation of the MP4 video file format as a step towards automated video forensic investigation. An open-source multimedia forensic tool was developed based on the proposed PBFM model. A comprehensive evaluation of the efficiency of the tool against file alteration showed that the tool was capable of identifying falsified files, which satisfied the underlying assertion of the PBFM model. Furthermore, the outcome can be used as a complementary process for enhancing the evidence admissibility of MP4 video for forensic investigation.
Improved Incremental RVFL With Compact Structure and Its Application in Quality Prediction of Blast Furnace

This article proposes an improved incremental random vector functional-link network (RVFL) with a compact structure and presents its application to quality prediction of blast furnace (BF) ironmaking processes. Different from the original RVFL, the improved incremental RVFL has no input-output direct links and no output bias (NLNB) and approaches the desired network output by sequentially updating the output weights of fixed hidden neurons. Moreover, it is proved that this improved incremental RVFL with NLNB (I-I-RVFL-NLNB) is able to approximate a given continuous function with arbitrary small errors with fewer hidden neurons. As a result, the developed network has more compact requiring less execution time, while retaining the conventional incremental RVFL's advantage of monotonically decreasing errors and avoiding the overfitting issue of the basic RVFL-NLNB. Since the zero approximation error is not needed in practical applications, the terminal condition of the existing incremental RVFL is improved by using the difference of root mean squared error (RMSE) between two consecutive iterations as one of the indices to characterize the imperceptible descending trend of RMSE. A series of comparisons are made by both benchmark simulations and a real quality modeling problem for a large BF ironmaking plant in South China, which show that the proposed algorithm has better performance in terms of modeling accuracy and efficiency.
Three-dimensional fractional total variation regularized tensor optimized model for image deblurring

Image deblurring is an important pre-processing step in image analysis. The research for efficient image deblurring methods is still a great challenge. Most of the currently used methods are based on integer-order derivatives, but they typically lead to texture elimination and staircase effects. To overcome these drawbacks, some researchers have proposed fractional-order derivative-based models. However, the existing fractional-order derivative-based models only exploit nonlocal smoothness of spatial dimensions and fail to consider the other dimensional information for three-dimensional (3D) images. To address this issue, we propose a three-dimensional fractional total variation (3DFTV) based-model for 3D image deblurring problem. In this paper, we mathematically formulate the proposed model under the tensor algebra. Furthermore, we develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to solve our model. Experimental results demonstrate the superiority of our model against comparing models in terms of quality metrics and visual effects.
Multi-Label Classification with Label-Specific Feature Generation: A Wrapped Approach

Label-specific features serve as an effective strategy to learn from multi-label data, where a set of features encoding specific characteristics of each label are generated to help induce multi-label classification model. Existing approaches work by taking the two-stage strategy, where the procedure of label-specific feature generation is independent of the follow-up procedure of classification model induction. Intuitively, the performance of resulting classification model may be suboptimal due to the decoupling nature of the two-stage strategy. In this paper, a wrapped learning approach is proposed which aims to jointly perform label-specific feature generation and classification model induction. Specifically, one (kernelized) linear model is learned for each label where label-specific features are simultaneously generated within an embedded feature space via empirical loss minimization and pairwise label correlation regularization. Comparative studies over a total of sixteen benchmark data sets clearly validate the effectiveness of the wrapped strategy in exploiting label-specific features for multi-label classification.
Anonymity on blockchain based e-cash protocols—A survey

The transactions in blockchain framework, based cryptocurrencies are publicly available, thereby accessible to all users by design. However, the anonymity of blockchain transactions is necessary for acceptance of such frameworks. There is a need to preserve the privacy of the identities of the blockchain members and the transaction. The main challenge is that anonymity-provisioning mechanism must allow verification by other members for updating the blockchain. The pseudonymous attribute of most of the blockchain-based e-cash protocols is not sufficient for users to attain identity privacy and transaction privacy. This paper provides a comprehensive study of the threats and attacks that aim to deanonymize the e-cash protocols. We present a detailed survey of methods and protocols required for anonymity-provisioning of blockchain-based e-cash transactions. We discussed their features and limitations. We redefined anonymity on the blockchain and categorized the anonymity-provisioning methods and protocols with their outcomes. We provide guidelines for selecting an approach to design an anonymity-provisioning blockchain-based e-cash system. Furthermore, we provide directions for future research in anonymity-provisioning on blockchain-based e-cash protocols.
E-Mail Spam Classification via Machine Learning and Natural Language Processing

In our current modern scenario, majority of the correspondence and exchange in all business sectors take place through Emails. As the rate of exchange of information via emails is increasing exponentially, the amount of unsolicited bulk mail or Spam. These Emails are sent for a number of reasons: Extracting confidential information from individuals, promotion of adult content and marketing/advertising of products and services. Thus, keeping this mind, it is of paramount importance to build a comprehensive system for Spam Classification based on semantics based text classification using NLP and URL based filtering. Various Machine Learning algorithms have been surveyed and the objective is to create a model with high performance and efficiency.
Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning

Opioid use disorder (OUD) is an epidemic that costs the U.S. healthcare systems $504 billion annually and poses grave mortality risks. Existing studies investigated OUD treatment barriers via surveys as a means to mitigate this opioid crisis. However, the response rate of these surveys is low due to social stigma around opioids. We explore user-generated content in social media as a new data source to study OUD. We design a novel IT system, SImilarity Network-based DEep Learning (SINDEL), to discover OUD treatment barriers from patient narratives and address the challenge of morphs. SINDEL significantly outperforms state-of-the-art NLP models, reaching an F1 score of 76.79 percent. Thirteen types of treatment barriers were identified and verified by domain experts. This work contributes to information systems with a novel deep-learning-based approach for text analytics and generalized design principles for social media analytics methods. We also unveil the hurdles patients endure during the opioid epidemic.
Multi-Source Contribution Learning for Domain Adaptation

Transfer learning becomes an attractive technology to tackle a task from a target domain by leveraging previously acquired knowledge from a similar domain (source domain). Many existing transfer learning methods focus on learning one discriminator with single-source domain. Sometimes, knowledge from single-source domain might not be enough for predicting the target task. Thus, multiple source domains carrying richer transferable information are considered to complete the target task. Although there are some previous studies dealing with multi-source domain adaptation, these methods commonly combine source predictions by averaging source performances. Different source domains contain different transferable information; they may contribute differently to a target domain compared with each other. Hence, the source contribution should be taken into account when predicting a target task. In this article, we propose a novel multi-source contribution learning method for domain adaptation (MSCLDA). As proposed, the similarities and diversities of domains are learned simultaneously by extracting multi-view features. One view represents common features (similarities) among all domains. Other views represent different characteristics (diversities) in a target domain; each characteristic is expressed by features extracted in a source domain. Then multi-level distribution matching is employed to improve the transferability of latent features, aiming to reduce misclassification of boundary samples by maximizing discrepancy between different classes and minimizing discrepancy between the same classes. Concurrently, when completing a target task by combining source predictions, instead of averaging source predictions or weighting sources using normalized similarities, the original weights learned by normalizing similarities between source and target domains are adjusted using pseudo target labels to increase the disparities of weight values, which is desired to improve the performance of the final target predictor if the predictions of sources exist significant difference. Experiments on real-world visual data sets demonstrate the superiorities of our proposed method.
The surprising impact of mask-head architecture on novel class segmentation

Instance segmentation models today are very accurate when trained on large annotated datasets, but collecting mask annotations at scale is prohibitively expensive. We address the partially supervised instance segmentation problem in which one can train on (significantly cheaper) bounding boxes for all categories but use masks only for a subset of categories. In this work, we focus on a popular family of models which apply differentiable cropping to a feature map and predict a mask based on the resulting crop. Under this family, we study Mask R-CNN and discover that instead of its default strategy of training the mask-head with a combination of proposals and groundtruth boxes, training the mask-head with only groundtruth boxes dramatically improves its performance on novel classes. This training strategy also allows us to take advantage of alternative mask-head architectures, which we exploit by replacing the typical mask-head of 2-4 layers with significantly deeper off-the-shelf architectures (e.g. ResNet, Hourglass models). While many of these architectures perform similarly when trained in fully supervised mode, our main finding is that they can generalize to novel classes in dramatically different ways. We call this ability of mask-heads to generalize to unseen classes the strong mask generalization effect and show that without any specialty modules or losses, we can achieve state-of-the-art results in the partially supervised COCO instance segmentation benchmark. Finally, we demonstrate that our effect is general, holding across underlying detection methodologies (including anchor-based, anchor-free or no detector at all) and across different backbone networks. Code and pre-trained models are available at https://git.io/deepmac.