Reviewerly/exHarmony huggingface dataset:
Embedding Adversarial Learning for Vehicle Re-Identification

The high similarities of different real-world vehicles and great diversities of the acquisition views pose grand challenges to vehicle re-identification (ReID), which traditionally maps the vehicle images into a high-dimensional embedding space for distance optimization, vehicle discrimination, and identification. To improve the discriminative capability and robustness of the ReID algorithm, we propose a novel end-to-end embedding adversarial learning network (EALN) that is capable of generating samples localized in the embedding space. Instead of selecting abundant hard negatives from the training set, which is extremely difficult if not impossible, with our embedding adversarial learning scheme, the automatically generated hard negative samples in the specified embedding space can greatly improve the capability of the network for discriminating similar vehicles. Moreover, the more challenging cross-view vehicle ReID problem, which requires the ReID algorithm to be robust with different query views, can also benefit from such a scheme based on the artificially generated cross-view samples. We demonstrate the promise of EALN through extensive experiments and show the effectiveness of hard negative and cross-view generation in facilitating vehicle ReID based on the comparisons with the state-of-the-art schemes.
Identifying and Reducing Gender Bias in Word-Level Language Models

Many text corpora exhibit socially problematic biases, which can be propagated or amplified in the models trained on such data. For example, doctor cooccurs more frequently with male pronouns than female pronouns. In this study we (i) propose a metric to measure gender bias; (ii) measure bias in a text corpus and the text generated from a recurrent neural network language model trained on the text corpus; (iii) propose a regularization loss term for the language model that minimizes the projection of encoder-trained embeddings onto an embedding subspace that encodes gender; (iv) finally, evaluate efficacy of our proposed method on reducing gender bias. We find this regularization method to be effective in reducing gender bias up to an optimal weight assigned to the loss term, beyond which the model becomes unstable as the perplexity increases. We replicate this study on three training corpora—Penn Treebank, WikiText-2, and CNN/Daily Mail—resulting in similar conclusions.
AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty

Modern deep neural networks can achieve high accuracy when the training distribution and test distribution are identically distributed, but this assumption is frequently violated in practice. When the train and test distributions are mismatched, accuracy can plummet. Currently there are few techniques that improve robustness to unforeseen data shifts encountered during deployment. In this work, we propose a technique to improve the robustness and uncertainty estimates of image classifiers. We propose AugMix, a data processing technique that is simple to implement, adds limited computational overhead, and helps models withstand unforeseen corruptions. AugMix significantly improves robustness and uncertainty measures on challenging image classification benchmarks, closing the gap between previous methods and the best possible performance in some cases by more than half.
Multi-Scale Vision Longformer: A New Vision Transformer for High-Resolution Image Encoding

This paper presents a new Vision Transformer (ViT) architecture Multi-Scale Vision Longformer, which significantly enhances the ViT of [12] for encoding high-resolution images using two techniques. The first is the multi-scale model structure, which provides image encodings at multiple scales with manageable computational cost. The second is the attention mechanism of Vision Long-former, which is a variant of Longformer [3], originally developed for natural language processing, and achieves a linear complexity w.r.t. the number of input tokens. A comprehensive empirical study shows that the new ViT significantly outperforms several strong baselines, including the existing ViT models and their ResNet counterparts, and the Pyramid Vision Transformer from a concurrent work [47], on a range of vision tasks, including image classification, object detection, and segmentation. The models and source code are released at https://github.com/microsoft/vision-longformer.
Face mask detection using deep learning: An approach to reduce risk of Coronavirus spread

Effective strategies to restrain COVID-19 pandemic need high attention to mitigate negatively impacted communal health and global economy, with the brim-full horizon yet to unfold. In the absence of effective antiviral and limited medical resources, many measures are recommended by WHO to control the infection rate and avoid exhausting the limited medical resources. Wearing a mask is among the non-pharmaceutical intervention measures that can be used to cut the primary source of SARS-CoV2 droplets expelled by an infected individual. Regardless of discourse on medical resources and diversities in masks, all countries are mandating coverings over the nose and mouth in public. To contribute towards communal health, this paper aims to devise a highly accurate and real-time technique that can efficiently detect non-mask faces in public and thus, enforcing to wear mask. The proposed technique is ensemble of one-stage and two-stage detectors to achieve low inference time and high accuracy. We start with ResNet50 as a baseline and applied the concept of transfer learning to fuse high-level semantic information in multiple feature maps. In addition, we also propose a bounding box transformation to improve localization performance during mask detection. The experiment is conducted with three popular baseline models viz. ResNet50, AlexNet and MobileNet. We explored the possibility of these models to plug-in with the proposed model so that highly accurate results can be achieved in less inference time. It is observed that the proposed technique achieves high accuracy (98.2%) when implemented with ResNet50. Besides, the proposed model generates 11.07% and 6.44% higher precision and recall in mask detection when compared to the recent public baseline model published as RetinaFaceMask detector. The outstanding performance of the proposed model is highly suitable for video surveillance devices.
Unified No-Reference Quality Assessment of Singly and Multiply Distorted Stereoscopic Images

A challenging problem in the no-reference quality assessment of multiply distorted stereoscopic images (MDSIs) is to simulate the monocular and binocular visual properties under a mixed type of distortions. Due to the joint effects of multiple distortions in MDSIs, the underlying monocular and binocular visual mechanisms have different manifestations with those of singly distorted stereoscopic images (SDSIs). This paper presents a unified no-reference quality evaluator for SDSIs and MDSIs by learning monocular and binocular local visual primitives (MB-LVPs). The main idea is to learn MB-LVPs to characterize the local receptive field properties of the visual cortex in response to SDSIs and MDSIs. Furthermore, we also consider that the learning of primitives should be performed in a task-driven manner. For this, two penalty terms including reconstruction error and quality inconsistency are jointly minimized within a supervised dictionary learning framework, generating a set of quality-oriented MB-LVPs for each single and multiple distortion modality. Given an input stereoscopic image, feature encoding is performed using the learned MB-LVPs as codebooks, resulting in the corresponding monocular and binocular responses. Finally, responses across all the modalities are fused with probabilistic weights which are determined by the modality-specific sparse reconstruction errors, yielding the final monocular and binocular features for quality regression. The superiority of our method has been verified on several SDSI and MDSI databases.
Privacy Preserving Machine Learning with Homomorphic Encryption and Federated Learning

Privacy protection has been an important concern with the great success of machine learning. In this paper, it proposes a multi-party privacy preserving machine learning framework, named PFMLP, based on partially homomorphic encryption and federated learning. The core idea is all learning parties just transmitting the encrypted gradients by homomorphic encryption. From experiments, the model trained by PFMLP has almost the same accuracy, and the deviation is less than 1%. Considering the computational overhead of homomorphic encryption, we use an improved Paillier algorithm which can speed up the training by 25–28%. Moreover, comparisons on encryption key length, the learning network structure, number of learning clients, etc. are also discussed in detail in the paper.
A Survey on Human Face Expression Recognition Techniques

Human Face expression Recognition is one of the most powerful and challenging tasks in social communication. Generally, face expressions are natural and direct means for human beings to communicate their emotions and intentions. Face expressions are the key characteristics of non-verbal communication. This paper describes the survey of Face Expression Recognition (FER) techniques which include the three major stages such as preprocessing, feature extraction and classification. This survey explains the various types of FER techniques with its major contributions. The performance of various FER techniques is compared based on the number of expressions recognized and complexity of algorithms. Databases like JAFFE, CK, and some other variety of facial expression databases are discussed in this survey. The study on classifiers gather from recent papers reveals a more powerful and reliable understanding of the peculiar characteristics of classifiers for research fellows.
Anonymous Lightweight Chaotic Map-Based Authenticated Key Agreement Protocol for Industrial Internet of Things

With an exponential increase in the popularity of Internet, the real-time data collected by various smart sensing devices can be analyzed remotely by a remote user (e.g., a manager) in the Industrial Internet of Things (IIoT). However, in the IIoT environment, the gathered real-time data is transmitted over the public channel, which raises the issues of security and privacy in this environment. Therefore, to protect illegal access by an adversary, user authentication mechanism is one of the promising security solutions in the IIoT environment. To achieve this goal, we propose a new user authenticated key agreement scheme in which only authorized users can access the services from the designated IoT sensing devices installed in the IIoT environment. In the proposed scheme, fuzzy extractor technique is used for biometric verification. Moreover, three factors, namely smart card, password and personal biometrics of a legal registered user are applied in the proposed scheme to increase the level of security in the system. The proposed scheme supports new devices addition after initial deployment of the devices, password/biometric change phase and also smart card revocation phase in case the smart card is lost or stolen by an adversary. In addition, the proposed scheme is lightweight in nature. We carry out the formal security analysis using the broadly accepted Real-Or-Random (ROR) model and also the non-mathematical (informal) security analysis on the proposed scheme. Furthermore, the formal security verification using the popularly-used AVISPA (Automated Validation of Internet Security Protocols and Applications) tool is carried out on the proposed scheme. The detailed security analysis assures that the proposed scheme can withstand several well-known attacks in the IIoT environment. A practical demonstration using the NS2 simulation study is also performed for the proposed scheme and other related existing schemes. Also, a detailed comparative study shows that the proposed scheme is efficient, and provides superior security in comparison to the other schemes.
Face Mask Detection Using Transfer Learning of InceptionV3

The world is facing a huge health crisis due to the rapid transmission of coronavirus (COVID-19). Several guidelines were issued by the World Health Organization (WHO) for protection against the spread of coronavirus. According to WHO, the most effective preventive measure against COVID-19 is wearing a mask in public places and crowded areas. It is very difficult to monitor people manually in these areas. In this paper, a transfer learning model is proposed to automate the process of identifying the people who are not wearing mask. The proposed model is built by fine-tuning the pre-trained state-of-the-art deep learning model, InceptionV3. The proposed model is trained and tested on the Simulated Masked Face Dataset (SMFD). Image augmentation technique is adopted to address the limited availability of data for better training and testing of the model. The model outperformed the other recently proposed approaches by achieving an accuracy of 99.9% during training and 100% during testing.
Not only Look, But Also Listen: Learning Multimodal Violence Detection Under Weak Supervision

Violence detection has been studied in computer vision for years. However, previous work are either superficial, e.g., classification of short-clips, and the single scenario, or undersupplied, e.g., the single modality, and hand-crafted features based multimodality. To address this problem, in this work we first release a large-scale and multi-scene dataset named XD-Violence with a total duration of 217 h, containing 4754 untrimmed videos with audio signals and weak labels. Then we propose a neural network containing three parallel branches to capture different relations among video snippets and integrate features, where holistic branch captures long-range dependencies using similarity prior, localized branch captures local positional relation using proximity prior, and score branch dynamically captures the closeness of predicted score. Besides, our method also includes an approximator to meet the needs of online detection. Our method outperforms other state-of-the-art methods on our released dataset and other existing benchmark. Moreover, extensive experimental results also show the positive effect of multimodal (audio-visual) input and modeling relationships. The code and dataset will be released in https://roc-ng.github.io/XD-Violence/.
How to play any mental game, or a completeness theorem for protocols with honest majority

research-article Share on How to play any mental game, or a completeness theorem for protocols with honest majority Authors: Oded Goldreich Technion TechnionView Profile , Silvio Micali MIT MITView Profile , Avi Wigderson Hebrew University Hebrew UniversityView Profile Authors Info & Claims Providing Sound Foundations for Cryptography: On the Work of Shafi Goldwasser and Silvio MicaliOctober 2019 Pages 307–328https://doi.org/10.1145/3335741.3335755Published:04 October 2019Publication History 57citation435DownloadsMetricsTotal Citations57Total Downloads435Last 12 Months42Last 6 weeks11 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Discriminative Spatiotemporal Local Binary Pattern with Revisited Integral Projection for Spontaneous Facial Micro-Expression Recognition

Recently, there have been increasing interests in inferring mirco-expression from facial image sequences. Due to subtle facial movement of micro-expressions, feature extraction has become an important and critical issue for spontaneous facial micro-expression recognition. Recent works used spatiotemporal local binary pattern (STLBP) for micro-expression recognition and considered dynamic texture information to represent face images. However, they miss the shape attribute of face images. On the other hand, they extract the spatiotemporal features from the global face regions while ignore the discriminative information between two micro-expression classes. The above-mentioned problems seriously limit the application of STLBP to micro-expression recognition. In this paper, we propose a discriminative spatiotemporal local binary pattern based on an integral projection to resolve the problems of STLBP for micro-expression recognition. First, we revisit an integral projection for preserving the shape attribute of micro-expressions by using robust principal component analysis. Furthermore, a revisited integral projection is incorporated with local binary pattern across spatial and temporal domains. Specifically, we extract the novel spatiotemporal features incorporating shape attributes into spatiotemporal texture features. For increasing the discrimination of micro-expressions, we propose a new feature selection based on Laplacian method to extract the discriminative information for facial micro-expression recognition. Intensive experiments are conducted on three availably published micro-expression databases including CASME, CASME2 and SMIC databases. We compare our method with the state-of-the-art algorithms. Experimental results demonstrate that our proposed method achieves promising performance for micro-expression recognition.
3D fault architecture controls the dynamism of earthquake swarms

Seismic swarms show the structure Faults responsible for earthquakes are idealized into two dimensions, despite fault zones being complicated, three-dimensional structures. Ross et al. used machine learning to find 22,000 seismic events near Cahuilla, California, during a seismic swarm. They used the locations and sizes of these events to show how the complex structure of the fault interacted with natural fluid injections from below. The authors' methods highlight the complexities of one fault and suggest a way to characterize other faults around the world. Science , this issue p. 1357
Guests’ perceptions of robot concierge and their adoption intentions

Purpose The hotel industry has witnessed an increasing number of service automation through service robots such as robot concierges. However, few studies have documented how to identify how hotel guests perceive a robot concierge for their service encounter. Therefore, the purpose of this study is to examine the effects of robot concierges on hotel guests’ attitudes and adoption intentions of robot concierges. Design/methodology/approach This study investigated the effects of robot concierges’ morphology and their level of interactivity with guests at different levels of hotel service on guests’ attitudes and their intentions to adopt robot concierges. To achieve the study’s objectives, this study conducted a 3 × 2 × 3 between-subjects factorial design experiment. Moreover, the survey asked questions about subjects’ preferences of their service encounters (e.g. human employees, robot concierges and/or no preference) and reasons for their selected preference. Findings The results demonstrated that the robot’s morphology significantly influenced guests’ attitudes toward robot concierges. In particular, the caricatured robot was the most preferred morphology of robot concierges. The findings showed that even if guests had favorable attitudes toward robot concierges, they preferred human employees to robot concierges because of humans’ sincere and genuine interactions. Originality/value This study contributes to the literature by investigating the causal impacts of the morphology of robot concierges, level of interactivity and level of hotel service on guests’ attitudes toward robot concierges. The thematic analysis of service encounter preference provides an overview of the factors that guests expect for their service encounters in a hotel setting.
Graph representation learning: a survey

Research on graph representation learning has received a lot of attention in recent years since many data in real-world applications come in form of graphs. High-dimensional graph data are often in irregular form, which makes them more difficult to analyze than image/video/audio data defined on regular lattices. Various graph embedding techniques have been developed to convert the raw graph data into a low-dimensional vector representation while preserving the intrinsic graph properties. In this review, we first explain the graph embedding task and its challenges. Next, we review a wide range of graph embedding techniques with insights. Then, we evaluate several state-of-the-art methods against small and large datasets and compare their performance. Finally, potential applications and future directions are presented.
Lightweight Fine-Grained Search Over Encrypted Data in Fog Computing

Fog computing, as an extension of cloud computing, outsources the encrypted sensitive data to multiple fog nodes on the edge of Internet of Things (IoT) to decrease latency and network congestion. However, the existing ciphertext retrieval schemes rarely focus on the fog computing environment and most of them still impose high computational and storage overhead on resource-limited end users. In this paper, we first present a Lightweight Fine-Grained ciphertexts Search (LFGS) system in fog computing by extending Ciphertext-Policy Attribute-Based Encryption (CP-ABE) and Searchable Encryption (SE) technologies, which can achieve fine-grained access control and keyword search simultaneously. The LFGS can shift partial computational and storage overhead from end users to chosen fog nodes. Furthermore, the basic LFGS system is improved to support conjunctive keyword search and attribute update to avoid returning irrelevant search results and illegal accesses. The formal security analysis shows that the LFGS system can resist Chosen-Keyword Attack (CKA) and Chosen-Plaintext Attack (CPA), and the simulation using a real-world dataset demonstrates that the LFGS system is efficient and feasible in practice.
Progressive LiDAR adaptation for road detection

Despite rapid developments in visual image-based road detection, robustly identifying road areas in visual images remains challenging due to issues like illumination changes and blurry images. To this end, LiDAR sensor data can be incorporated to improve the visual image-based road detection, because LiDAR data is less susceptible to visual noises. However, the main difficulty in introducing LiDAR information into visual image-based road detection is that LiDAR data and its extracted features do not share the same space with the visual data and visual features. Such gaps in spaces may limit the benefits of LiDAR information for road detection. To overcome this issue, we introduce a novel Progressive LiDAR Adaptation-aided Road Detection (PLARD) approach to adapt LiDAR information into visual image-based road detection and improve detection performance. In PLARD, progressive LiDAR adaptation consists of two subsequent modules: 1) data space adaptation, which transforms the LiDAR data to the visual data space to align with the perspective view by applying altitude difference-based transformation; and 2) feature space adaptation, which adapts LiDAR features to visual features through a cascaded fusion structure. Comprehensive empirical studies on the well-known KITTI road detection benchmark demonstrate that PLARD takes advantage of both the visual and LiDAR information, achieving much more robust road detection even in challenging urban scenes. In particular, PLARD outperforms other state-of-the-art road detection models and is currently top of the publicly accessible benchmark leader-board.