Reviewerly/exHarmony huggingface dataset:
Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm

Due to the complicated characteristics of regional geochemical data from stream sediments as a result of the complexity of geological features, detection of multi-elemental geochemical footprints of mineral deposits of interest is a challenging task. As a way to address this, a hybrid genetic algorithm-based technique, namely genetic K-means clustering (GKMC) algorithm, is proposed here for optimum delineation of multi-elemental patterns (both anomaly and background) in stream sediment geochemical data. To do so, factor analysis and sample catchment basin modeling were coupled with GKMC and traditional K-means clustering (TKMC) methods for identification of anomalous multi-elemental geochemical footprints of deposits of porphyry copper and skarn copper in the 1:100,000 scale Varzaghan map sheet, northwest Iran. Based on higher prediction rate, it can be inferred that the model derived by GKMC is superior to the one derived by TKMC. In addition, the strong anomaly classes of the GKMC and TKMC models predict, respectively, ~83% and ~66% of the porphyry/skarn Cu deposits in ~22% and ~36% of the study district. Thus, the geochemical anomaly targets derived by the GKMC method are more reliable than those generated by the TKMC method. This revealed that the GKMC algorithm is an efficient and robust tool for recognizing multi-element geochemical anomalies for mineral exploration.
Video summarization via block sparse dictionary selection

The explosive growth of video data has raised new challenges for many video processing tasks such as video browsing and retrieval, hence, effective and efficient video summarization (VS) is urgently demanded to automatically summarize a video into a succinct version. Recent years have witnessed the advancements of sparse representation based approaches for VS. However, video frames are analyzed individually for keyframe selection in existing methods, which could lead to redundancy among selected keyframes and poor robustness to outlier frames. Due to that adjacent frames are visually similar, candidate keyframes often occur in temporal blocks, in addition to sparse presence. Therefore, in this paper, the block-sparsity of candidate keyframes is taken into consideration, by which the VS problem is formulated as a block sparse dictionary selection model. Moreover, a simultaneous block version of Orthogonal Matching Pursuit (SBOMP) algorithm is designed for model optimization. Two keyframe selection strategies are also explored for each block. Experimental results on two benchmark datasets, namely VSumm and TVSum datasets, demonstrate that the proposed SBOMP based VS method clearly outperforms several state-of-the-art sparse representation based methods in terms of F-score, redundancy among keyframes and robustness to outlier frames.
AutoMine

Graph mining algorithms that aim at identifying structural patterns of graphs are typically more complex than graph computation algorithms such as breadth first search. Researchers have implemented several systems with high-level and flexible interfaces customized for tackling graph mining problems. However, we find that for triangle counting, one of the simplest graph mining problems, such systems can be several times slower than a single-threaded implementation of a straightforward algorithm.
FashionOn

The image-based virtual try-on system has attracted a lot of research attention. The virtual try-on task is challenging since synthesizing try-on images involves the estimation of 3D transformation from 2D images, which is an ill-posed problem. Therefore, most of the previous virtual try-on systems cannot solve difficult cases, e.g., body occlusions, wrinkles of clothes, and details of the hair. Moreover, the existing systems require the users to upload the image for the target pose, which is not user-friendly. In this paper, we aim to resolve the above challenges by proposing a novel FashionOn network to synthesize user images fitting different clothes in arbitrary poses to provide comprehensive information about how suitable the clothes are. Specifically, given a user image, an in-shop clothing image, and a target pose (can be arbitrarily manipulated by joint points), FashionOn learns to synthesize the try-on images by three important stages: pose-guided parsing translation, segmentation region coloring, and salient region refinement. Extensive experiments demonstrate that FashionOn maintains the details of clothing information (e.g., logo, pleat, lace), as well as resolves the body occlusion problem, and thus achieves the state-of-the-art virtual try-on performance both qualitatively and quantitatively.
Dynamic Context Correspondence Network for Semantic Alignment

Establishing semantic correspondence is a core problem in computer vision and remains challenging due to large intra-class variations and lack of annotated data. In this paper, we aim to incorporate global semantic context in a flexible manner to overcome the limitations of prior work that relies on local semantic representations. To this end, we first propose a context-aware semantic representation that incorporates spatial layout for robust matching against local ambiguities. We then develop a novel dynamic fusion strategy based on attention mechanism to weave the advantages of both local and context features by integrating semantic cues from multiple scales. We instantiate our strategy by designing an end-to-end learnable deep network, named as Dynamic Context Correspondence Network (DCCNet). To train the network, we adopt a multi-auxiliary task loss to improve the efficiency of our weakly-supervised learning procedure. Our approach achieves superior or competitive performance over previous methods on several challenging datasets, including PF-Pascal, PF-Willow, and TSS, demonstrating its effectiveness and generality.
ObliDB

Hardware enclaves such as Intel SGX are a promising technology for improving the security of databases outsourced to the cloud. These enclaves provide an execution environment isolated from the hyper-visor/OS, and encrypt data in RAM. However, for applications that use large amounts of memory, including most databases, enclaves do not protect against access pattern leaks, which let attackers gain a large amount of information about the data. Moreover, the naïve way to address this issue, using Oblivious RAM (ORAM) primitives from the security literature, adds substantial overhead. A number of recent works explore trusted hardware enclaves as a path toward secure, access-pattern oblivious outsourcing of data storage and analysis. While these works efficiently solve specific subproblems (e.g. building secure indexes or running analytics queries that always scan entire tables), no prior work has supported oblivious query processing for general query workloads on a DBMS engine with multiple access methods. Moreover, applying these techniques individually does not guarantee that an end-to-end workload, such as a complex SQL query over multiple tables, will be oblivious. In this paper, we introduce ObliDB, an oblivious database engine design that is the first system to provide obliviousness for general database read workloads over multiple access methods. ObliDB introduces a diverse array of new oblivious physical operators to accelerate oblivious SQL queries, giving speedups of up to an order of magnitude over naïve ORAM. It supports a broad range of queries, including aggregation, joins, insertions, deletions and point queries. We implement ObliDB and show that, on analytics workloads, ObliDB ranges from 1.1--19x faster than Opaque, a previous oblivious, enclave-based system designed only for analytics, and comes within 2.6 x of Spark SQL, which provides no security guarantees. In addition, ObliDB supports point queries with 3--10ms latency, which is comparable to index-only trusted hardware systems, and runs over 7x faster than HIRB, a previous encryption-based oblivious index system that supports point queries.
HistoSegNet: Semantic Segmentation of Histological Tissue Type in Whole Slide Images

In digital pathology, tissue slides are scanned into Whole Slide Images (WSI) and pathologists first screen for diagnostically-relevant Regions of Interest (ROIs) before reviewing them. Screening for ROIs is a tedious and time-consuming visual recognition task which can be exhausting. The cognitive workload could be reduced by developing a visual aid to narrow down the visual search area by highlighting (or segmenting) regions of diagnostic relevance, enabling pathologists to spend more time diagnosing relevant ROIs. In this paper, we propose HistoSegNet, a method for semantic segmentation of histological tissue type (HTT). Using the HTT-annotated Atlas of Digital Pathology (ADP) database, we train a Convolutional Neural Network on the patch annotations, infer Gradient-Weighted Class Activation Maps, average overlapping predictions, and post-process the segmentation with a fully-connected Conditional Random Field. Our method out-performs more complicated weakly-supervised semantic segmentation methods and can generalize to other datasets without retraining.
Adversarial Fine-Grained Composition Learning for Unseen Attribute-Object Recognition

Recognizing unseen attribute-object pairs never appearing in the training data is a challenging task, since an object often refers to a specific entity while an attribute is an abstract semantic description. Besides, attributes are highly correlated to objects, i.e., an attribute tends to describe different visual features of various objects. Existing methods mainly employ two classifiers to recognize attribute and object separately, or simply simulate the composition of attribute and object, which ignore the inherent discrepancy and correlation between them. In this paper, we propose a novel adversarial fine-grained composition learning model for unseen attribute-object pair recognition. Considering their inherent discrepancy, we leverage multi-scale feature integration to capture discriminative fine-grained features from a given image. Besides, we devise a quintuplet loss to depict more accurate correlations between attributes and objects. Adversarial learning is employed to model the discrepancy and correlations among attributes and objects. Extensive experiments on two challenging benchmarks indicate that our method consistently outperforms state-of-the-art competitors by a large margin.
Wavelet Domain Style Transfer for an Effective Perception-Distortion Tradeoff in Single Image Super-Resolution

In single image super-resolution (SISR), given a low-resolution (LR) image, one wishes to find a high-resolution (HR) version of it which is both accurate and photorealistic. Recently, it has been shown that there exists a fundamental tradeoff between low distortion and high perceptual quality, and the generative adversarial network (GAN) is demonstrated to approach the perception-distortion (PD) bound effectively. In this paper, we propose a novel method based on wavelet domain style transfer (WDST), which achieves a better PD tradeoff than the GAN based methods. Specifically, we propose to use 2D stationary wavelet transform (SWT) to decompose one image into low-frequency and high-frequency sub-bands. For the low-frequency sub-band, we improve its objective quality through an enhancement network. For the high-frequency sub-band, we propose to use WDST to effectively improve its perceptual quality. By feat of the perfect reconstruction property of wavelets, these sub-bands can be re-combined to obtain an image which has simultaneously high objective and perceptual quality. The numerical results on various datasets show that our method achieves the best trade-off between the distortion and perceptual quality among the existing state-of-the-art SISR methods.
Fuzzy Multiple-Source Transfer Learning

Transfer learning is gaining increasing attention due to its ability to leverage previously acquired knowledge to assist in completing a prediction task in a related domain.Fuzzy transfer learning, which is based on fuzzy systems and particularly fuzzy rule-based models, was developed due to its capacity to deal with uncertainty.However, one issue with fuzzy transfer learning, even in the area of general transfer learning, has not been resolved: how to combine and then use knowledge when multiple source domains are available.This study presents new methods for merging fuzzy rules from multiple domains for regression tasks.Two different settings are separately explored: homogeneous and heterogeneous space.In homogeneous situations, knowledge from the source domains is merged in the form of fuzzy rules.In heterogeneous situations, knowledge is merged in the form of both data and fuzzy rules.Experiments on both synthetic and real-world datasets provide insights into the scope of applications suitable for the proposed methods and validate their effectiveness through comparisons with other state-of-the-art transfer learning methods.An analysis of parameter sensitivity is also included.
Human activity recognition using machine learning methods in a smart healthcare environment

The rapid developments in information and communication technologies and wireless communication networks have led to the utilization of the smart sensors. In modern healthcare applications, the utilization of smart sensors brings health professionals and patients together for intelligent and automated regular monitoring the activity of elderly people. Smart body sensors and smartphones are increasingly used for personal healthcare monitoring and wellbeing. One of the key improvements of smart sensor technologies in the healthcare monitoring system is the wearable sensor technology. Integrating of smart wearable sensors in healthcare has led to the development of smart applications such as smart healthcare and intelligent healthcare monitoring systems. Today, health informatics represent an important area to improve healthcare efficiency by optimizing the acquisition, storage, and the retrieval of crucial patient’s health information. In this context, there is a lot of excitement about the progress of machine learning techniques which play a crucial role in recognizing human activity. In this chapter, an intelligent smart healthcare system is presented to deliver pervasive human activity recognition (HAR) in an automated manner by using machine learning techniques in order to model and recognize activities of daily living in an accurate and efficient manner. Moreover, we focus on the dataset containing body motion and vital sign recordings from volunteers of diverse profiles while carrying out different physical activities for HAR purpose. In addition, this chapter shows how these approaches are used in two different datasets, a mobile phone and wearable body sensors, for robust and precise HAR. This study has shown that the identification of human activity based on sensor data is very challenging, especially with the existence of a variety of machine learning methods. When it comes to machine learning methods, there is no one solution to all.
Adaptive Reconstruction Network for Weakly Supervised Referring Expression Grounding

Weakly supervised referring expression grounding aims at localizing the referential object in an image according to the linguistic query, where the mapping between the referential object and query is unknown in the training stage. To address this problem, we propose a novel end-to-end adaptive reconstruction network (ARN). It builds the correspondence between image region proposal and query in an adaptive manner: adaptive grounding and collaborative reconstruction. Specifically, we first extract the subject, location and context features to represent the proposals and the query respectively. Then, we design the adaptive grounding module to compute the matching score between each proposal and query by a hierarchical attention model. Finally, based on attention score and proposal features, we reconstruct the input query with a collaborative loss of language reconstruction loss, adaptive reconstruction loss, and attribute classification loss. This adaptive mechanism helps our model to alleviate the variance of different referring expressions. Experiments on four large-scale datasets show ARN outperforms existing state-of-the-art methods by a large margin. Qualitative results demonstrate that the proposed ARN can better handle the situation where multiple objects of a particular category situated together.
The SPHINCS <sup>+</sup> Signature Framework

We introduce SPHINCS+, a stateless hash-based signature framework. SPHINCS+ has significant advantages over the state of the art in terms of speed, signature size, and security, and is among the nine remaining signature schemes in the second round of the NIST PQC standardization project. One of our main contributions in this context is a new few-time signature scheme that we call FORS. Our second main contribution is the introduction of tweakable hash functions and a demonstration how they allow for a unified security analysis of hash-based signature schemes. We give a security reduction for SPHINCS+ using this abstraction and derive secure parameters in accordance with the resulting bound. Finally, we present speed results for our optimized implementation of SPHINCS+ and compare to SPHINCS-256, Gravity-SPHINCS, and Picnic.
CKD: Cross-Task Knowledge Distillation for Text-to-Image Synthesis

Text-to-image synthesis (T2IS) has drawn increasing interest recently, which can automatically generate images conditioned on text descriptions. It is a highly challenging task that learns a mapping from a semantic space of text description to a complex RGB pixel space of image. The main issues of T2IS lie in two aspects: semantic consistency and visual quality. The distributions between text descriptions and image contents are inconsistent since they belong to different modalities. So it is ambitious to generate images containing consistent semantic contents with the text descriptions, which is the semantic consistency issue. Moreover, due to the discrepancy of data distributions between real and synthetic images in huge pixel space, it is hard to approximate the real data distribution for synthesizing photo-realistic images, which is the visual quality issue. For addressing the above issues, we propose a cross-task knowledge distillation (CKD) approach to transfer knowledge from multiple image semantic understanding tasks into T2IS task. There is amount of knowledge in image semantic understanding tasks to translate image contents into semantic representation, which is advantageous to address the issues of semantic consistency and visual quality for T2IS. Moreover, we design a multi-stage knowledge distillation paradigm to decompose the distillation process into multiple stages. By this paradigm, it is effective to approximate the distributions of real image and understand textual information for T2IS, which can improve the visual quality and semantic consistency of synthetic images. Comprehensive experiments on widely-used datasets show the effectiveness of our proposed CKD approach.
Towards secure big data analytic for cloud-enabled applications with fully homomorphic encryption

Cloud computing empowers enterprises to efficiently manage big data and discovery of useful information which are the most fundamental challenges for big data enabled applications. The cloud offered unlimited resources that can store, manage and analyze massive and heterogeneous data to improve the quality assurance of application services. Nevertheless, cloud computing is exposed to enormous external and internal privacy breaches and leakage threats. In this paper, we introduce a privacy-preserving distributed analytics framework for big data in cloud. Fully Homomorphic Encryption (FHE) is used as an emerging and powerful cryptosystem that can carry out analysis tasks on encrypted data. The developed distributed approach has the scalability to partition both data and analysis computations into subset cloud computing nodes that can be run independently. This rapidly accelerates the performance of encrypted data processing while preserving a high level of analysis accuracy. Our experimental evaluation demonstrates the efficiency of the proposed framework, in terms of both analysis performance and accuracy, for building a secure analytics cloud-enabled application.
The Eighty Five Percent Rule for optimal learning

Abstract Researchers and educators have long wrestled with the question of how best to teach their clients be they humans, non-human animals or machines. Here, we examine the role of a single variable, the difficulty of training, on the rate of learning. In many situations we find that there is a sweet spot in which training is neither too easy nor too hard, and where learning progresses most quickly. We derive conditions for this sweet spot for a broad class of learning algorithms in the context of binary classification tasks. For all of these stochastic gradient-descent based learning algorithms, we find that the optimal error rate for training is around 15.87% or, conversely, that the optimal training accuracy is about 85%. We demonstrate the efficacy of this ‘Eighty Five Percent Rule’ for artificial neural networks used in AI and biologically plausible neural networks thought to describe animal learning.
Statistical Modeling of Craniofacial Shape and Texture

Abstract We present a fully-automatic statistical 3D shape modeling approach and apply it to a large dataset of 3D images, the Headspace dataset , thus generating the first public shape-and-texture 3D morphable model (3DMM) of the full human head. Our approach is the first to employ a template that adapts to the dataset subject before dense morphing. This is fully automatic and achieved using 2D facial landmarking, projection to 3D shape, and mesh editing. In dense template morphing, we improve on the well-known Coherent Point Drift algorithm, by incorporating iterative data-sampling and alignment. Our evaluations demonstrate that our method has better performance in correspondence accuracy and modeling ability when compared with other competing algorithms. We propose a texture map refinement scheme to build high quality texture maps and texture model. We present several applications that include the first clinical use of craniofacial 3DMMs in the assessment of different types of surgical intervention applied to a craniosynostosis patient group.
GODS: Generalized One-Class Discriminative Subspaces for Anomaly Detection

One-class learning is the classic problem of fitting a model to data for which annotations are available only for a single class. In this paper, we propose a novel objective for one-class learning. Our key idea is to use a pair of orthonormal frames - as subspaces - to "sandwich'' the labeled data via optimizing for two objectives jointly: i) minimize the distance between the origins of the two subspaces, and ii) to maximize the margin between the hyperplanes and the data, either subspace demanding the data to be in its positive and negative orthant respectively. Our proposed objective however leads to a non-convex optimization problem, to which we resort to Riemannian optimization schemes and derive an efficient conjugate gradient scheme on the Stiefel manifold. To study the effectiveness of our scheme, we propose a new dataset Dash-Cam-Pose, consisting of clips with skeleton poses of humans seated in a car, the task being to classify the clips as normal or abnormal; the latter is when any human pose is out-of-position with regard to say an airbag deployment. Our experiments on the proposed Dash-Cam-Pose dataset, as well as several other standard anomaly/novelty detection benchmarks demonstrate the benefits of our scheme, achieving state-of-the-art one-class accuracy.