Reviewerly/exHarmony huggingface dataset:
A local multiple patterns feature descriptor for face recognition

Human perception of a signal depends on the ratio of the change of stimulus to the stimulus itself while the change of stimulus to the stimulus itself is usually ignored in hand-crafted feature descriptors. However, it is important for extracting discriminant feature. To address this problem, we firstly develop a local multiple patterns (LMP) feature descriptor based on the Weber's law for feature extraction and face recognition. In LMP, (1) the Weber's ratio is modified to contain the change direction, and thus the modified Weber's ratio is quantized into several intervals to generate multiple feature maps for describing different changes; (2) LMP concatenates the histograms of the non-overlapping regions of the feature maps for image representation. Secondly, since LMP could only capture small-scale structures, a multi-scale block LMP (MB-LMP) is presented to generate more discriminative and robust visual feature. Thus, MB-LMP could capture both the small-scale and large-scale structures. In implementation, MB-LMP is computationally efficient using integral image. In experiments, LMP and MB-LMP are evaluated on four public datasets for face recognition. The experimental results demonstrate the promise of the proposed LMP and MB-LMP descriptors with desirable efficiency.
G3raphGround: Graph-Based Language Grounding

In this paper we present an end-to-end framework for grounding of phrases in images. In contrast to previous works, our model, which we call G <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> raphGround, uses graphs to formulate more complex, non-sequential dependencies among proposal image regions and phrases. We capture intra-modal dependencies using a separate graph neural network for each modality (visual and lingual), and then use conditional message-passing in another graph neural network to fuse their outputs and capture cross-modal relationships. This final representation results in grounding decisions. The framework supports many-to-many matching and is able to ground single phrase to multiple image regions and vice versa. We validate our design choices through a series of ablation studies and illustrate state-of-the-art performance on Flickr30k and ReferIt Game benchmark datasets.
Service with a smiley face: Emojional contagion in digitally mediated relationships

The lack of typical nonverbal cues in digitally mediated marketing communication (e.g., text messages, social media etc.) creates special challenges for marketers desiring to manage customer relationships. To overcome these limitations, marketers have turned to emojis to convey subtle meaning regarding the message sender (often a service provider) and to elicit positive emotion in the message recipient. Relying on theories of emotion as social information and emotional contagion, we present three studies that provide initial evidence of a positive conscious and unconscious affective response to smiley-face emojis that mediates relational outcomes when relationships are communal in nature, but not when relationships are based on simple exchange. We also make a methodological contribution by employing biometric measurement of smiling in order to capture unconscious affect and to demonstrate novel emotional contagion effects in a customer service message. We close with a discussion of the average emoji effect observed across reported and unreported studies, which is modest but reliable, as well as discussion of future research opportunities and managerial implications.
Survey: Cybersecurity Vulnerabilities, Attacks and Solutions in the Medical Domain

Recently, an increasing number of cyber-attacks in the medical field has resulted in great losses in the health care industry, since medical information plays an essential role in human health. To introduce a comprehensive survey about possible cyber-attacks and solutions for these attacks, our paper first presents a brief overview of the necessary background of the dataflow in the medical domain and then identifies the vulnerabilities in each stage of the dataflow. Then, according to the weaknesses identified in the medical system, a classification of cyber-attacks is presented. Additionally, the paper presents research on previous work that focuses on solving these cyber-attacks and identifies the strengths and limitations of the solutions for each attack. More importantly, for data storage assurance, our paper discusses several cybersecurity architectures for the medical domain from the existing literature. The countermeasures from previous papers and architectures that are still weak in terms of resource depletion, attack reduction, applicability, etc. are addressed. Finally, the paper discusses and recommends solutions for future work to decrease cyber-attacks in the medical field so that human health can be guaranteed.
PKI4IoT: Towards public key infrastructure for the Internet of Things

Public Key Infrastructure is the state-of-the-art credential management solution on the Internet. However, the millions of constrained devices that make of the Internet of Things currently lack a centralized, scalable system for managing keys and identities. Modern PKI is built on a set of protocols which were not designed for constrained environments, and as a result many small, battery-powered IoT devices lack the required computing resources. In this paper, we develop an automated certificate enrollment protocol light enough for highly constrained devices, which provides end-to-end security between certificate authorities (CA) and the recipient IoT devices. We also design a lightweight profile for X.509 digital certificates with CBOR encoding, called XIOT. Existing CAs can now issue traditional X.509 to IoT devices. These are converted to and from the XIOT format by edge devices on constrained networks. This procedure preserves the integrity of the original CA signature, so the edge device performing certificate conversion need not be trusted. We implement these protocols within the Contiki embedded operating system and evaluate their performance on an ARM Cortex-M3 platform. Our evaluation demonstrates reductions in energy expenditure and communication latency. The RAM and ROM required to implement these protocols are on par with the other lightweight protocols in Contiki’s network stack.
Message Passing for Complex Question Answering over Knowledge Graphs

Question answering over knowledge graphs (KGQA) has evolved from simple single-fact questions to complex questions that require graph traversal and aggregation. We propose a novel approach for complex KGQA that uses unsupervised message passing, which propagates confidence scores obtained by parsing an input question and matching terms in the knowledge graph to a set of possible answers. First, we identify entity, relationship, and class names mentioned in a natural language question, and map these to their counterparts in the graph. Then, the confidence scores of these mappings propagate through the graph structure to locate the answer entities. Finally, these are aggregated depending on the identified question type. This approach can be efficiently implemented as a series of sparse matrix multiplications mimicking joins over small local subgraphs. Our evaluation results show that the proposed approach outperforms the state of the art on the LC-QuAD benchmark. Moreover, we show that the performance of the approach depends only on the quality of the question interpretation results, i.e., given a correct relevance score distribution, our approach always produces a correct answer ranking. Our error analysis reveals correct answers missing from the benchmark dataset and inconsistencies in the DBpedia knowledge graph. Finally, we provide a comprehensive evaluation of the proposed approach accompanied with an ablation study and an error analysis, which showcase the pitfalls for each of the question answering components in more detail.
Computational Hyperspectral Imaging Based on Dimension-Discriminative Low-Rank Tensor Recovery

Exploiting the prior information is fundamental for the image reconstruction in computational hyperspectral imaging. Existing methods usually unfold the 3D signal as a 1D vector and treat the prior information within different dimensions in an indiscriminative manner, which ignores the high-dimensionality nature of hyperspectral image (HSI) and thus results in poor quality reconstruction. In this paper, we propose to make full use of the high-dimensionality structure of the desired HSI to boost the reconstruction quality. We first build a high-order tensor by exploiting the nonlocal similarity in HSI. Then, we propose a dimension-discriminative low-rank tensor recovery (DLTR) model to characterize the structure prior adaptively in each dimension. By integrating the structure prior in DLTR with the system imaging process, we develop an optimization framework for HSI reconstruction, which is finally solved via the alternating minimization algorithm. Extensive experiments implemented with both synthetic and real data demonstrate that our method outperforms state-of-the-art methods.
A Hybrid R-BILSTM-C Neural Network Based Text Steganalysis

With the emergence of the generation-based steganography, the traditional text steganalysis methods show the unsatisfactory detection performance as the manually extracted features are simple and non-universal. The recently proposed deep learning-based text steganalysis methods can obtain the great detection accuracy by extracting the high-level features. In this letter, a hybrid text steganalysis method (R-BILSTM-C) is proposed through combining the advantages of Bidirectional Long Short Term Memory Recurrent Neural Network (Bi-LSTM) and Convolutional Neural Network (CNN). The proposed method can efficiently capture both local features and long-term semantic information from text to improve the detection accuracy. In the proposed method, the Bi-LSTM architecture is used to capture the long-term semantic information of texts. And the asymmetric convolution kernels with different sizes are applied to extract the local relationship between words. In addition, the high dimensional semantic feature space is visualized. Experimental results show that the proposed method adapts to the different steganographic algorithms efficiently, and achieves the comparable or superior detection performance for the various sentence lengths compared with other state-of-the-art text steganalysis methods.
Efficient Homomorphic Comparison Methods with Optimal Complexity

Comparison of two numbers is one of the most frequently used operations, but it has been a challenging task to efficiently compute the comparison function in homomorphic encryption (HE) which basically supports addition and multiplication. Recently, Cheon et al. (Asiacrypt 2019) introduced a new approximate representation of the comparison function with a rational function, and showed that this rational function can be evaluated by an iterative algorithm. Due to this iterative feature, their method achieves a logarithmic computational complexity compared to previous polynomial approximation methods; however, the computational complexity is still not optimal, and the algorithm is quite slow for large-bit inputs in HE implementation. In this work, we propose new comparison methods with optimal asymptotic complexity based on composite polynomial approximation. The main idea is to systematically design a constant-degree polynomial f by identifying the core properties to make a composite polynomial $$f\circ f \circ \cdots \circ f$$ get close to the sign function (equivalent to the comparison function) as the number of compositions increases. We additionally introduce an acceleration method applying a mixed polynomial composition $$f\circ \cdots \circ f\circ g \circ \cdots \circ g$$ for some other polynomial g with different properties instead of $$f\circ f \circ \cdots \circ f$$ . Utilizing the devised polynomials f and g, our new comparison algorithms only require $$\varTheta (\log (1/\epsilon )) + \varTheta (\log \alpha )$$ computational complexity to obtain an approximate comparison result of $$a,b\in [0,1]$$ satisfying $$|a-b|\ge \epsilon $$ within $$2^{-\alpha }$$ error. The asymptotic optimality results in substantial performance enhancement: our comparison algorithm on 16-bit encrypted integers for $$\alpha = 16$$ takes 1.22 ms in amortized running time based on an approximate HE scheme HEAAN, which is 18 times faster than the previous work.
Foreground-Aware Pyramid Reconstruction for Alignment-Free Occluded Person Re-Identification

Re-identifying a person across multiple disjoint camera views is important for intelligent video surveillance, smart retailing and many other applications. However, existing person re-identification methods are challenged by the ubiquitous occlusion over persons and suffer performance degradation. This paper proposes a novel occlusion-robust and alignment-free model for occluded person ReID and extends its application to realistic and crowded scenarios. The proposed model first leverages the fully convolution network (FCN) and pyramid pooling to extract spatial pyramid features. Then an alignment-free matching approach namely Foreground-aware Pyramid Reconstruction (FPR) is developed to accurately compute matching scores between occluded persons, regardless of their different scales and sizes. FPR uses the error from robust reconstruction over spatial pyramid features to measure similarities between two persons. More importantly, we design a occlusion-sensitive foreground probability generator that focuses more on clean human body parts to robustify the similarity computation with less contamination from occlusion. The FPR is easily embedded into any end-to-end person ReID models. The effectiveness of the proposed method is clearly demonstrated by the experimental results (Rank-1 accuracy) on three occluded person datasets: Partial REID (78.30%), Partial iLIDS (68.08%), Occluded REID (81.00%), and three benchmark person datasets: Market1501 (95.42%), DukeMTMC (88.64%), CUHK03 (76.08%).
Resource Management in a Containerized Cloud: Status and Challenges

Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research.
Guided Super-Resolution As Pixel-to-Pixel Transformation

Guided super-resolution is a unifying framework for several computer vision tasks where the inputs are a low-resolution source image of some target quantity (e.g., perspective depth acquired with a time-of-flight camera) and a high-resolution guide image from a different domain (e.g., a grey-scale image from a conventional camera); and the target output is a high-resolution version of the source (in our example, a high-res depth map). The standard way of looking at this problem is to formulate it as a super-resolution task, i.e., the source image is upsampled to the target resolution, while transferring the missing high-frequency details from the guide. Here, we propose to turn that interpretation on its head and instead see it as a pixel-to-pixel mapping of the guide image to the domain of the source image. The pixel-wise mapping is parametrised as a multi-layer perceptron, whose weights are learned by minimising the discrepancies between the source image and the downsampled target image. Importantly, our formulation makes it possible to regularise only the mapping function, while avoiding regularisation of the outputs; thus producing crisp, natural-looking images. The proposed method is unsupervised, using only the specific source and guide images to fit the mapping. We evaluate our method on two different tasks, super-resolution of depth maps and of tree height maps. In both cases, we clearly outperform recent baselines in quantitative comparisons, while delivering visually much sharper outputs.
Clustering With Outlier Removal

Cluster analysis and outlier detection are two continuously rising topics in data mining area, which in fact connect to each other deeply. Cluster structure is vulnerable to outliers; inversely, outliers are the points belonging to none of any clusters. Unfortunately, most existing studies do not notice the coupled relationship between these two tasks and handle them separately. In this article, we consider the joint cluster analysis and outlier detection problem, and propose the Clustering with Outlier Removal (COR) algorithm. Specifically, the original space is transformed into a binary space via generating basic partitions. We employ Holoentropy to measure the compactness of each cluster without involving several outlier candidates. To provide a neat and efficient solution, an auxiliary binary matrix is introduced so that COR completely and efficiently solves the challenging problem via a unified K-means— with theoretical supports. Extensive experimental results on numerous data sets in various domains demonstrate the effectiveness and efficiency of COR significantly over state-of-the-art methods in terms of cluster validity and outlier detection. Some key factors including the basic partition number and generation strategy in COR with an application on abnormal flight trajectory detection are further analyzed for practical use.
Secure dynamic multiparty quantum private comparison

Abstract We propose a feasible and efficient dynamic multiparty quantum private comparison protocol that is fully secure against participant attacks. In the proposed scheme, two almost-dishonest third parties generate two random keys and send them to all participants. Every participant independently encrypts their private information with the encryption keys and sends it to the third parties. The third parties can analyze the equality of all or some participants’ secrets without gaining access to the secret information. New participants can dynamically join the protocol without the need for any additional conditions in the protocol. We provide detailed correctness and security analysis of the proposed protocol. Our security analysis of the proposed protocol against both inside and outside attacks proves that attackers cannot extract any secret information.
WeatherNet: Recognising Weather and Visual Conditions from Street-Level Images Using Deep Residual Learning

Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it has still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used in practice. What has been achieved to-date are rather sectorial models that address a limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep convolutional neural network (CNN) models, so-called WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as dawn/dusk, day and night for time detection, glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used but are not limited to autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers.
Position-aware hierarchical transfer model for aspect-level sentiment classification

Recently, attention-based neural networks (NNs) have been widely used for aspect-level sentiment classification (ASC). Most neural models focus on incorporating the aspect representation into attention, however, the position information of each aspect is not studied well. Furthermore, the existing ASC datasets are relatively small owing to the labor-intensive labeling that largely limits the performance of NNs. In this paper, we propose a position-aware hierarchical transfer (PAHT) model that models the position information from multiple levels and enhances the ASC performance by transferring hierarchical knowledge from the resource-rich sentence-level sentiment classification (SSC) dataset. We first present aspect-based positional attention in the word and the segment levels to capture more salient information toward a given aspect. To make up for the limited data for ASC, we devise three sampling strategies to select related instances from the large-scale SSC dataset for pre-training and transfer the learned knowledge into ASC from four levels: embedding, word, segment and classifier. Extensive experiments on four benchmark datasets demonstrate that the proposed model is effective in improving the performance of ASC. Particularly, our model outperforms the state-of-the-art approaches in terms of accuracy over all the datasets considered.
Rank-Based Local Self-Similarity Descriptor for Optical-to-SAR Image Matching

Automatic optical-to-synthetic aperture radar (SAR) image matching is still a challenging task due to the existence of severe nonlinear radiometric differences between the images and the presence of strong speckles in the SAR images. To address this problem, we propose a novel feature descriptor called rank-based local self-similarity (RLSS) for optical-to-SAR image template matching. The RLSS descriptor is an improved version of the local self-similarity (LSS) descriptor, inspired by Spearman's rank correlation coefficient in statistics. It can describe the local shape properties of an image in a discriminable manner. To further improve the discriminability, a dense RLSS (DRLSS) descriptor is formed with a dense scheme by integrating the RLSS descriptors for multiple local regions into a dense sampling grid. Experimental results conducted based on the optical and SAR image pairs demonstrated that the proposed descriptor was robust to nonlinear radiometric differences and it outperformed two state-of-the-art descriptors [dense LSS (DLSS) and histogram of orientated phase congruency (HOPC)].