Reviewerly/exHarmony huggingface dataset:
A Novel Keypoint Detector Combining Corners and Blobs for Remote Sensing Image Registration

Keypoint detection is a crucial step for feature-based image registration. The traditional detectors only extract one type of keypoint such as a corner or a blob, which is not quite beneficial to image registration. Accordingly, this letter presents a novel keypoint detector that aims to simultaneously extract corners and blobs. The proposed detector is named as Harris-Difference of Gaussian (DoG), which combines the advantages of the Harris-Laplace corner detector and the DoG blob detector. In the definition of Harris-DoG, we first build an image scale space and extract the corners by using the multiscale Harris detector. Then, these corners are screened by an automatic scale selection technique based on their DoG responses. This can make the corners robust to scale changes. Meanwhile, DoG also is used to detect the blobs in the image scale space by a nonmaxima suppression scheme. Finally, the scale invariant feature transform (SIFT) descriptors are computed for the detected corners and blobs, and they are applied together for image registration. The proposed Harris-DoG has been tested by using three pairs of multisensor remote sensing images. The experimental results show that Harris-DoG can effectively increase the number of correct matches and improve the registration accuracy compared with the state-of-the-art keypoint detectors.
HERA

Partial label learning (PLL) aims to learn from the data where each training instance is associated with a set of candidate labels, among which only one is correct. Most existing methods deal with this type of problem by either treating each candidate label equally or identifying the ground-truth label iteratively. In this article, we propose a novel PLL approach named HERA, which simultaneously incorporates the HeterogEneous Loss and the SpaRse and Low-rAnk procedure to estimate the labeling confidence for each instance while training the desired model. Specifically, the heterogeneous loss integrates the strengths of both the pairwise ranking loss and the pointwise reconstruction loss to provide informative label ranking and reconstruction information for label identification, whereas the embedded sparse and low-rank scheme constrains the sparsity of ground-truth label matrix and the low rank of noise label matrix to explore the global label relevance among the whole training data, for improving the learning model. Comprehensive ablation study demonstrates the effectiveness of our employed heterogeneous loss, and extensive experiments on both artificial and real-world datasets demonstrate that our method achieves superior or comparable performance against state-of-the-art methods.
Prediction of chronological and biological age from laboratory data

Aging has pronounced effects on blood laboratory biomarkers used in the clinic.Prior studies have largely investigated one biomarker or population at a time, limiting a comprehensive view of biomarker variation and aging across different populations.Here we develop a supervised machine learning approach to study aging using 356 blood biomarkers measured in 67,563 individuals across diverse populations.Our model predicts age with a mean absolute error (MAE), or average magnitude of prediction errors, in held-out data of 4.76 years and an R 2 value of 0.92.Age prediction was highly accurate for the pediatric cohort (MAE = 0.87, R 2 = 0.94) but inaccurate for ages 65+ (MAE = 4.30, R 2 = 0.25).Variability was observed in which biomarkers carry predictive power across age groups, genders, and race/ethnicity groups, and novel candidate biomarkers of aging were identified for specific age ranges (e.g.Vitamin E, ages 18-44).We show that predictors for one age group may fail to generalize to other groups and investigate non-linearity in biomarkers near adulthood.As populations worldwide undergo major demographic changes, it is increasingly important to catalogue biomarker variation across age groups and discover new biomarkers to distinguish chronological and biological aging.
Desynchronization in distributed Ant Colony Optimization in HPC environment

Metaheuristics have significant computing requirements, in particular Ant Colony Optimization (ACO) processes a population of individuals (agents/ants) roaming in a graph, leaving the pheromone trails and getting inspired by its amount perceived on the edges. If the considered problem instance is large or the time is crucial, one can try to leverage parallel, hybrid or distributed infrastructure, but the algorithm itself must be properly prepared to deal with new possibilities. We have already presented a method for efficient implementation of distributed ACO, in this paper we follow up with introducing planned desynchronization in the pheromone matrix updates in order to further increase the scalability of the proposed system. The proposed modifications allowed the algorithm to scale up to 400 computations nodes without a significant impact on results quality. Efficacy of the algorithm outperforms the standard Max–Min Ant System by 10%.
Quadrant dynamic clipped histogram equalization with gamma correction for color image enhancement

Abstract The detailed examination of low contrast image is a challenging issue. Thus, it makes difficult for the viewer to bring out the detailed features of the image. Histogram equalization (HE) is an efficient way to intensify the contrast of images. However, classical HE techniques result in immoderate intensification. Hence, an efficient contrast enhancement algorithm called quadrant dynamic clipped HE with gamma correction is proposed. This transformation addresses both over‐enhancement and fine detail preservation, which ensures no false contouring. In the proposed method, histogram of the input image is partitioned into four sections using its mean value. Histogram clipping and gamma correction is used to control the color enhancement rate. Then, clipped subhistogram is equalized independently and then they combined together to form an enhanced image. The performance assessment of the proposed and other existing methods is evaluated in terms of entropy, contrast, colorfulness, and saturation. Test results demonstrates that the proposed method outperforms the other existing HE methods in terms of preserving entropy, colorfulness, saturation, and obtaining uniform degree enhancement.
Flexible one-shot geometric calibration for off-axis deflectometry

Off-axis deflectometry is widely applied in the measurement of specular surfaces. However, the measuring accuracy depends on the reliability of geometrical calibration. Existing methods are inconvenient to be utilized due to their disadvantages of low efficiency and operational complexity. A simple geometrical calibration method is proposed by applying a flat mirror with markers, and only one image needs to be captured. A compensation process is introduced to correct the form error of the mirror. Experimental results show that the re-projection errors decrease from 0.319 pixels down to 0.12 pixels; thus the measuring efficiency and accuracy of optical surfaces can be greatly improved.
Benchmarking microservice performance

Benchmarking microservices serves to understand and check their non-functional properties for relevant workloads and over time. Performing benchmarks, however, can be costly: each microservice requires the design and implementation of a benchmark, possibly repeatedly as the service evolves. As microservice APIs differ, benchmarking tools that assume common interfaces - like ones for databases - do not exist.
Assertive Ads for Want or Should? It Depends on Consumers’ Power

Advertisements using assertive language are commonly seen in marketing communications, yet assertive language has often been shown to be ineffective in communication and even decrease compliance. Recent research began to examine factors that influence the effectiveness of assertive advertising messages, but little research has studied the potential moderating role of consumer‐related factors. The current research fills this gap by investigating the moderating role of consumers’ power. Across five studies, the authors find that for high‐power consumers, assertive ads are effective in promoting want products but ineffective in promoting should products. For low‐power consumers, however, assertive ads are effective in promoting should products but ineffective in promoting want products. Moreover, the authors show that this pattern of results is driven (mediated) by consumers’ perception of the fit between the advertising message and the advertised product. Theoretical and practical implications of the findings are discussed.
Bit Allocation for Multi-Task Collaborative Intelligence

Recent studies have shown that collaborative intelligence (CI) is a promising framework for deployment of Artificial Intelligence (AI)-based services on mobile devices. In CI, a deep neural network is split between the mobile device and the cloud. Deep features obtained at the mobile are compressed and transferred to the cloud to complete the inference. So far, the methods in the literature focused on transferring a single deep feature tensor from the mobile to the cloud. Such methods are not applicable to some recent, high-performance networks with multiple branches and skip connections. In this paper, we propose the first bit allocation method for multi-stream, multi-task CI. We first establish a model for the joint distortion of the multiple tasks as a function of the bit rates assigned to different deep feature tensors. Then, using the proposed model, we solve the rate-distortion optimization problem under a total rate constraint to obtain the best rate allocation among the tensors to be transferred. Experimental results illustrate the efficacy of the proposed scheme compared to several alternative bit allocation methods.
Trilingual Semantic Embeddings of Visually Grounded Speech with Self-Attention Mechanisms

We propose a trilingual semantic embedding model that associates visual objects in images with segments of speech signals corresponding to spoken words in an unsupervised manner. Unlike the existing models, our model incorporates three different languages, namely, English, Hindi, and Japanese. To build the model, we used the existing English and Hindi datasets and collected a new corpus of Japanese speech captions. These spoken captions are spontaneous descriptions by individual speakers, rather than readings based on prepared transcripts. Therefore, we introduce a self-attention mechanism into the model to better map the spoken captions associated with the same image into the embedding space. We hope that the self-attention mechanism efficiently captures relationships between widely separated word-like segments. Experimental results show that the introduction of a third language improves the average performance in terms of cross-modal and cross-lingual retrieval accuracy, and that the self-attention mechanism added to the model works effectively.
Constructing self-dual codes from group rings and reverse circulant matrices

In this work, we describe a construction for self-dual codes in which we employ group rings and reverse circulant matrices.By applying the construction directly over different alphabets, and by employing the well known extension and neighbor methods we were able to obtain extremal binary self-dual codes of different lengths of which some have parameters that were not known in the literature before.In particular, we constructed three new codes of length 64, twenty-two new codes of length 68, twelve new codes of length 80 and four new codes of length 92.
Entanglement teleportation with photons from quantum dots: towards a solid-state based quantum network

Semiconductor quantum dots are currently emerging as one of the most promising sources of non-classical light on which to base future quantum networks.They can generate single photons as well as pairs of entangled photons with unprecedented brightness, indistinguishability, and degree of entanglement.These features have very recently opened up the possibility to perform advanced quantum optics protocols that were previously inaccessible to single quantum emitters.In this work, we report on two experiments that use the non-local properties of entanglement to teleport quantum states: three-photon state teleportation and four-photon entanglement teleportation.We discuss all the experimental results in light of a theoretical model that we develop to account for the nonidealities of the quantum source.The excellent agreement between theory and experiment enables a deep understanding of how each parameter of the source affects the teleportation fidelities and it pinpoints the requirements needed to overcome the classical limits.Finally, our model suggests how to further improve quantum-dot entangled-photon sources for practical quantum networks.
Jhu-HLTCOE System for the Voxsrc Speaker Recognition Challenge

The VoxSRC speaker recognition challenge comprises data obtained from YouTube videos of celebrity interviews in a wide range of recording environments. The challenge provides FIXED and OPEN training conditions to allow cross-system comparisons and to characterize the effects of additional amounts of training data on system performance. This paper describes our submission to this challenge where we have explored x-vector extractor topologies, classification head alternatives, data augmentation, and angular margin penalty. Our final entry to the FIXED condition (which achieved 2nd place) is the score average of 4 diverse systems. We find that this system outperforms a large single DNN with similar number of parameters.
Mango: A Python Library for Parallel Hyperparameter Tuning

Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually. To enable automated hyperparameter tuning, recent works have started to use techniques based on Bayesian optimization. However, to practically enable automated tuning for large scale machine learning training pipelines, significant gaps remain in existing libraries, including lack of abstractions, fault tolerance, and flexibility to support scheduling on any distributed computing framework. To address these challenges, we present Mango, a Python library for parallel hyperparameter tuning. Mango enables the use of any distributed scheduling framework, implements intelligent parallel search strategies, and provides rich abstractions for defining complex hyperparameter search spaces that are compatible with scikit-learn. Mango is comparable in performance to Hyperopt [1], another widely used library. Mango is available open-source [2] and is currently used in production at Arm Research to provide state-of-art hyperparameter tuning capabilities.
CSANet: Channel and Spatial Mixed Attention CNN for Pedestrian Detection

Current mainstream pedestrian detectors tend to profit directly from convolutional neural networks (CNNs) that are designed for image classification.While requiring a large downsampling factor to produce high-level semantic features, CNNs cannot adaptively focus on the useful channels and regions of the feature maps, which limits the accuracy of pedestrian detection.To obtain a higher accuracy, we propose a single-stage pedestrian detector with channel and spatial attentions (CSANet), which can locate useful channels and regions automatically while extracting features.The backbone of CSANet is different from that of mainstream pedestrian detectors, which can effectively highlight the pedestrian-likely regions and suppress the background.Specifically, we model contextual dependencies from channel and spatial dimensions of the feature maps, respectively.The channel attention module can selectively promote CNNs to focus on key channels by integrating associated features.Meantime, the spatial attention module can illuminate semantic pixels by aggregating similar features of all channels.Eventually, the two modules are connected in series to further enhance the representation of feature maps.Experiment results show that CSANet achieves the state-of-the-art performance with MR -2 of 3.55% on Caltech dataset and obtains competitive performance on CityPersons dataset while maintaining a high computational efficiency.
Evaluating facial recognition web services with adversarial and synthetic samples

Face recognition as an identity authentication has been widely used in commodity mobile systems. However due to increasing attacks against face recognition algorithms, mobile systems users begin to concern about the security of face recognition modules in their mobile devices. In this paper, we focus on synthetic and adversarial face image attack on both Baidu and Face++ face comparison APIs. To make a fair comparison, we also tested on Google facenet as the baseline. The results show Both APIs are vulnerable to synthetic and adversarial attacks. Although liveness detection mechanisms could mitigate the synthetic attack in our experiment, not all APIs have this feature. The API without liveness detection mechanism(Face++) is vulnerable to presentation attack. The accept rate in Baidu API could be affected by security level of liveness detection control. Both dodging and impersonation adversarial image attacks are correlated to the perturbation level, the higher perturbation level the more likely to be successful in dodging attack, in the contrast,the lower perturbation level is more likely to lead a successful impersonation attack. In our experiment, Baidu Liveness detection mechanism can detect adversarial images as synthetic images and reject a majority of adversarial image attacks.
Supporting Teamwork in Industrial Virtual Reality Applications

Virtual Reality (VR) systems allow for novel modes of visualization and interaction to support engineering design reviews. However, there are still research challenges to be addressed until companies can fully benefit from the technology's potential. Our previous research showed that social exclusion of VR users sharing the same physical space with colleagues during a design review session has a negative influence on the communication and cooperation among team members. The work in this paper presents approaches to counteract this issue in a shared VR space for industry purpose. We describe the implementation of our concepts based on touch input and visual cues in an interactive VR environment for design review. Our evaluation in laboratory setup reveals that simple visual cues provide effective means to reduce the time to find certain details in complex VR scenes. We conclude our work with thoughts on future development steps to foster the communication between team members in a diversified VR environment.
Blockchain and Decentralised Autonomous Organisations (DAOs): The Evolution of Companies?

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An analysis of boosted ensembles of binary fuzzy decision trees

Abstract Classification is a functionality that plays a central role in the development of modern expert systems, across a wide variety of application fields: using accurate, efficient, and compact classification models is often a prime requirement. Boosting (and AdaBoost in particular) is a well-known technique to obtain robust classifiers from properly-learned weak classifiers, thus it is particularly attracting in many practical settings. Although the use of traditional classifiers as base learners in AdaBoost has already been widely studied, the adoption of fuzzy weak learners still requires further investigations. In this paper we describe FDT-Boost, a boosting approach shaped according to the SAMME-AdaBoost scheme, which leverages fuzzy binary decision trees as multi-class base classifiers. Such trees are kept compact by constraining their depth, without lowering the classification accuracy. The experimental evaluation of FDT-Boost has been carried out using a benchmark containing eighteen classification datasets. Comparing our approach with FURIA, one of the most popular fuzzy classifiers, with a fuzzy binary decision tree, and with a fuzzy multi-way decision tree, we show that FDT-Boost is accurate, getting to results that are statistically better than those achieved by the other approaches. Moreover, compared to a crisp SAMME-AdaBoost implementation, FDT-Boost shows similar performances, but the relative produced models are significantly less complex, thus opening up further exploitation chances also in memory-constrained systems.
Structured authoring for AR-based communication to enhance efficiency in remote diagnosis for complex equipment

Remote diagnosis procedures are prone to communication errors due to varying levels of experience and knowledge between expert maintainers and technicians. These result in inefficiencies that delay the diagnosis process. The aim of the paper is to develop a Structured-Message Authoring framework for Augmented Reality (AR) Remote Communication (SMAARRC) and to evaluate its ability to enhance the efficiency of remote diagnosis services. The framework proposes a message structure and automatic AR content creation rules for it that enable data capture and sharing within a remote context. Laboratory experiments present an average time reduction of 56% for remote calls while maintaining same quality compared to traditional remote communication methods (phone calls and emails). Remote experts feedback evidence the usability and feasibility of this framework to work in real-life conditions.
Frame-Level Phoneme-Invariant Speaker Embedding for Text-Independent Speaker Recognition on Extremely Short Utterances

This paper investigates a phoneme-invariant speaker embedding approach for speaker recognition on extremely short utterances. Intuitively, phonemes are nuisance information for text-independent speaker recognition task since the contents of the speech are usually mismatched between enrolling and testing time. However, many studies have shown that incorporating phoneme information is quite effective to improve the performance of the speaker recognition system. One reasonable explanation for this counter-intuitive result is that the pooling mechanism of segment-based speaker embedding can focus on the specific phonemes which contain rich speaker information, and phoneme information may help this. From this insight, we hypothesize that the pooling mechanism and phoneme-aware training are harmful to extract the speaker embeddings from extremely short utterances. To verify this hypothesis, an adversarial framework is introduced to remove phoneme-variability from the frame-wise speaker embeddings. The experimental results on the Librispeech corpus confirm that our frame-wise, phoneme-adversarial approach outperforms the conventional segment-wise, phoneme-aware approach for short utterances of less than about 1.4 seconds.