Reviewerly/exHarmony huggingface dataset:
Highlight Removal in Facial Images

In this paper, we present a method for highlight removal in facial images. In contrast to previous works relying on physical models such as dichromatic reflection models, we adopt the structure of conditional generative adversarial network (CGAN) to generate highlight-free images. By taking the facial images with specular highlight as the condition, the network predicts the corresponding highlight-free images. Meanwhile, a novel mask loss is introduced through highlight detection, which aims to make the network focus on more on the highlight regions. With the help of multi-scale discriminators, our method generates highlight-free images with high-quality details and fewer artifacts. We also built a dataset containing both real and synthetic facial images, which is, to our best knowledge, the largest image dataset for facial highlight removal. By comparing with the state-of-the-arts, our method shows high effectiveness and strong robustness in different lighting environments.
Massively Parallel Approximate Simulation of Hard Quantum Circuits

As quantum computers grow more capable, simulating them on conventional hardware becomes more challenging yet more attractive since this helps in design and verification. Some quantum algorithms and circuits are amenable to surprisingly efficient simulation, and this makes hard-to-simulate computations particularly valuable. For such circuits, we develop accurate massively-parallel simulation with dramatic speedups over earlier methods on 42- and 45-qubit circuits. We propose two ways to trade circuit fidelity for computational speedups, so as to match the error rate of any quantum computer. Using Google Cloud, we simulate approximate sampling from the output of a circuit with 7 × 8 qubits and depth 42 with fidelity 0.5% at an estimated cost of $35K.
SoK: A Systematic Study of Attacks in Efficient Encrypted Cloud Data Search

Searchable encryption allows one to search encrypted data hosted by an untrusted cloud. In terms of efficiency, symmetric key-based searchable encryption is promising for practical deployment and has been a rich area of research. In the literature, there are mainly two lines of research accordingly: searchable symmetric encryption (SSE) for encrypted matching queries and order-preserving or order-revealing encryption (OPE/ORE) for encrypted range queries. Despite the efficiency, these searchable encryption techniques all allow leakages in their security frameworks and have been shown to be vulnerable to various attacks in recent years. There has been little work regarding a comprehensive investigation on these attacks. In this paper, we provide a systematic survey on the recent advances on attacks in efficient encrypted cloud data search backed by SSE or OPE/ORE. Specifically, we first provide a categorization of the adversary model from diverse dimensions. Then we systematically examine the existing attacks against SSE and OPE/ORE under different adversary types and the leakages allowed in the security frameworks. The goal of this paper is to provide pointers for understanding the security of efficient searchable encryption techniques and to call for further motivated research activities.
HANMRE - An authenticated encryption secure against side-channel attacks for nonce-misuse and lightweight approaches

Side-channel threat is a form of cryptanalysis that takes advantage of secret information leaked during program implementations, through measurement and evaluation of systematic parameters, such as execution time, power consumption and electromagnetic field (EMF) radiation. Since various side-channel analysis techniques have applied successfully in gathering data and extracting cryptographic keys on variety of devices and platforms, including smartphones, smart cards, tablets, TVs, FPGAs and CPUs, these attacks constitute a significant risk to the security of cryptographic systems. Eliminating serious leakages is a major approach to mitigate side-channel vulnerabilities, in particular Simple Power Analysis (SPA) and Differential Power Analysis (DPA). During the last decade, several research aimed at securing cryptographic primitive algorithms against side-channel attacks, and validating possible countermeasures under assumption which its computational complexity can be estimated precisely. In this paper, we propose a hash-based authenticated nonce-misuse resistant encryption, namely HANMRE which is adaptable for a lightweight leakage resilient authenticated encryption with associated data (AEAD) scheme. The HANMRE construction has been designed for the side-channel security achievement (including SPA and DPA attacks) and highly integrated for restrained environments with limited resource. The advantage of this scheme is ensuring the strong security developed in misuse-resistant schemes against general adversaries for authenticated encryption [1]. It also presents reasonable implementation results (especially long message handling) compared to existing authenticated encryption schemes and is expected to be a novel idea for better approaches of authenticated encryption mechanisms design in the future.
Object Detection based on Fusing Monocular Camera and Lidar Data in Decision Level Using D-S Evidence Theory

Visual sensors such as optical camera possess fatal shortcomings like vulnerable to the weather condition and light intensity, which may cause catastrophic disasters. Fortunately, the prevalent 3D sensors like Light Detection and Ranging (Lidar) can overcome these drawbacks. Fusing both monocular camera and 3D Lidar data can often achieve a better performance than solely using one of them. In this paper, we propose an object detection approach by fusing monocular camera data and Lidar data in decision level based on Dempster-Shafer evidence theory. First, the 3D point cloud collected from Lidar is projected onto the image plane and the upsampling method is adopted to receive dense depth maps. Then, the RGB images and dense depth maps are separately fed into YOLOv3 detection framework to extract the object features. Next, the final detection attributes, the class confidence and bounding box, are generated by using Dempster's combination rule and extracting intersection of the associated bounding boxes. Finally, the mean Average Precision (mAP) performance of the proposed method is improved by 0.5% and 3.1% compared with sigle camera method and Lidar method, respectively.
Instability of Successive Deep Image Compression

Successive image compression refers to the process of repeated encoding and decoding of an image. It frequently occurs during sharing, manipulation, and re-distribution of images. While deep learning-based methods have made significant progress for single-step compression, thorough analysis of their performance under successive compression has not been conducted. In this paper, we conduct comprehensive analysis of successive deep image compression. First, we introduce a new observation, instability of successive deep image compression, which is not observed in JPEG, and discuss causes of the instability. Then, we conduct a successive image compression benchmark for the state-of-the-art deep learning-based methods, and analyze the factors that affect the instability in a comparative manner. Finally, we propose a new loss function for training deep compression models, called feature identity loss, to mitigate the instability of successive deep image compression.
Effects of socially assistive robot Hyodol on depressive symptoms and quality of life among community-residing older adults

본 연구는 서울시 구로구에 거주하는 65세 이상 독거노인 169명을 대상으로 한국형 소셜로봇(효돌)이 독거노인의 우울증상과 삶의 질에 미치는 영향을 파악하기 위해 수행되었다. 효돌은 일상생활관리, 정서 지지·인지기능 향상 등을 위한 컨텐츠를 대상자에게 제공하였다. 연구결과 효돌이 독거노인의 우울증상(SGDS-K)(사전: 6.28±4.10, 사후: 3.40±4.03, 95% CI: 2.18 ~ 3.57)을 경감시켰으며, 삶의 질(EQ-5D)(사전: 0.75±0.15, 사후: 0.78±0.01, 95% CI: -0.05 ~ -0.002)을 증진시켰다. 본 연구는 이러한 결과를 바탕으로 독거노인의 우울을 예방하고 삶의 질을 증진시키며, 독거노인 담당자(전담 사회복지사, 생활관리사)의 소진을 예방하기 위한 전략으로써의 소셜로봇의 활용성을 제시하였다.This research aims to examine the effects of the socially assistive robot(Hyodol) on the depressive symptoms and quality of life of the elderly(n=169) living alone in Guro-gu, Seoul via a single group pre-post study, using paired t-test. Hyodol provided daily life management, contents for emotional support and cognitive function improvement, and etc. Socially assistive robots decreased the depressive symptoms(SGDS-K) of the elderly living alone and improved the quality of life(EQ-5D) of them. Based on these results, the potential of the socially assistive robot, which was the appropriate strategy to prevent depression of the elderly living alone to improve the quality of life and to prevent the exhaustion of social workers was discussed.
MORSE: MultimOdal sentiment analysis for Real-life SEttings

Multimodal sentiment analysis aims to detect and classify sentiment expressed in multimodal data. Research to date has focused on datasets with a large number of training samples, manual transcriptions, and nearly-balanced sentiment labels. However, data collection in real settings often leads to small datasets with noisy transcriptions and imbalanced label distributions, which are therefore significantly more challenging than in controlled settings. In this work, we introduce MORSE, a domain-specific dataset for MultimOdal sentiment analysis in Real-life SEttings. The dataset consists of 2,787 video clips extracted from 49 interviews with panelists in a product usage study, with each clip annotated for positive, negative, or neutral sentiment. The characteristics of MORSE include noisy transcriptions from raw videos, naturally imbalanced label distribution, and scarcity of minority labels. To address the challenging real-life settings in MORSE, we propose a novel two-step fine-tuning method for multimodal sentiment classification using transfer learning and the Transformer model architecture; our method starts with a pre-trained language model and one step of fine-tuning on the language modality, followed by the second step of joint fine-tuning that incorporates the visual and audio modalities. Experimental results show that while MORSE is challenging for various baseline models such as SVM and Transformer, our two-step fine-tuning method is able to capture the dataset characteristics and effectively address the challenges. Our method outperforms related work that uses both single and multiple modalities in the same transfer learning settings.
A Study of F<sub>q</sub>R-Cyclic Codes and Their Applications in Constructing Quantum Codes

Let R = F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> + uF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> + vF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> + uvF <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> , with u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = u, v <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> = v, uv = vu, where q = p <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> for a positive integer m and an odd prime p. We study the algebraic structure of F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> R-cyclic codes of block length (r, s). These codes can be viewed as R[x]-submodules of F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> [x]/(x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sup> - 1) × R[x]/(x <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sup> - 1). For this family of codes we discuss the generator polynomials and minimal generating sets. We study the algebraic structure of separable codes. Further, we discuss the duality of this family of codes and determine their generator polynomials. We obtain several optimal and near-optimal codes from this study. As applications, we discuss a construction of quantum error-correcting codes (QECCs) from F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> R-cyclic codes and construct some good QECCs.
UAV Swarm Path Planning

We present an algorithm for the modeling and management of groups of autonomous Unmanned Aerial Vehicles (UAV). The algorithm extends a previous work in which aircraft were considered individually. In this paper, we decompose the algorithm into two computational levels: one for the centralized control of groups of UAVs, or "swarms", and one modeling the distributed behavior of individual UAVs within these swarms. We examine a number of scenarios and compare the impact of communication latency on the intra-swarm and inter-swarm separations.
Feminisms through design

column Share on Feminisms through design: a practical guide to implement and extend feminism: position Author: Sai Shruthi Chivukula Purdue University Purdue UniversityView Profile Authors Info & Claims InteractionsVolume 27Issue 6November - December 2020 pp 36–39https://doi.org/10.1145/3427338Published:02 November 2020Publication History 4citation4,877DownloadsMetricsTotal Citations4Total Downloads4,877Last 12 Months1,002Last 6 weeks61 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
Emotional Communication between Remote Couples: Exploring the Design of Wearable Ambient Displays

We explore the potential of limited vocabulary wearable ambient displays and their preferred characteristics in communicating intimacy between couples at a distance. The research comprises two studies: a co-design workshop and a field study of the design concepts generated from the co-design workshops using low-fidelity prototypes. Our findings reveal that wearable ambient displays could potentially help to increase awareness and emotional connections between couples. A limited vocabulary display is considered as a complementary channel to a smartphone, supporting the communication of subtle and lightweight messages that the sender does not necessarily expect a response to. From the user studies, we identify the preferred characteristics, as well as the potential and challenges of low vocabulary wearable ambient displays.
Detecting Adversarial Examples for Speech Recognition via Uncertainty Quantification

Machine learning systems and also, specifically, automatic speech recognition (ASR) systems are vulnerable against adversarial attacks, where an attacker maliciously changes the input. In the case of ASR systems, the most interesting cases are targeted attacks, in which an attacker aims to force the system into recognizing given target transcriptions in an arbitrary audio sample. The increasing number of sophisticated, quasi imperceptible attacks raises the question of countermeasures. In this paper, we focus on hybrid ASR systems and compare four acoustic models regarding their ability to indicate uncertainty under attack: a feed-forward neural network and three neural networks specifically designed for uncertainty quantification, namely a Bayesian neural network, Monte Carlo dropout, and a deep ensemble. We employ uncertainty measures of the acoustic model to construct a simple one-class classification model for assessing whether inputs are benign or adversarial. Based on this approach, we are able to detect adversarial examples with an area under the receiving operator curve score of more than 0.99. The neural networks for uncertainty quantification simultaneously diminish the vulnerability to the attack, which is reflected in a lower recognition accuracy of the malicious target text in comparison to a standard hybrid ASR system.
More to diverse: Generating diversified responses in a task oriented multimodal dialog system

Multimodal dialogue system, due to its many-fold applications, has gained much attention to the researchers and developers in recent times. With the release of large-scale multimodal dialog dataset Saha et al. 2018 on the fashion domain, it has been possible to investigate the dialogue systems having both textual and visual modalities. Response generation is an essential aspect of every dialogue system, and making the responses diverse is an important problem. For any goal-oriented conversational agent, the system’s responses must be informative, diverse and polite, that may lead to better user experiences. In this paper, we propose an end-to-end neural framework for generating varied responses in a multimodal dialogue setup capturing information from both the text and image. Multimodal encoder with co-attention between the text and image is used for focusing on the different modalities to obtain better contextual information. For effective information sharing across the modalities, we combine the information of text and images using the BLOCK fusion technique that helps in learning an improved multimodal representation. We employ stochastic beam search with Gumble Top K-tricks to achieve diversified responses while preserving the content and politeness in the responses. Experimental results show that our proposed approach performs significantly better compared to the existing and baseline methods in terms of distinct metrics, and thereby generates more diverse responses that are informative, interesting and polite without any loss of information. Empirical evaluation also reveals that images, while used along with the text, improve the efficiency of the model in generating diversified responses.
MABNet: A Lightweight Stereo Network Based on Multibranch Adjustable Bottleneck Module

Recently, end-to-end CNNs have presented remarkable performance for disparity estimation. But most of them are too heavy to resource-constrained devices, because of enormous parameters necessary for satisfactory results. To address the issue, we propose two compact stereo networks, MABNet and its light version MABNet_tiny. MABNet is based on a novel Multibranch Adjustable Bottleneck (MAB) module, which is less demanding on parameters and computation. In a MAB module, feature map is split into various parallel branches, where the depthwise separable convolutions with different dilation rates extract features with multiple receptive fields however at an affordable computational budget. Besides, the number of channels in each branch is adjustable independently to tradeoff computation and accuracy. On SceneFlow and KITTI datasets, our MABNet achieves competitive accuracy with fewer parameters of 1.65M. Especially, MABNet_tiny reduces the parameters 47K by cutting down the channels and layers in MABNet.
Salient Object Detection with Edge Recalibration

Salient Object Detection (SOD) based on Convolutional Neural Networks (CNNs) has been widely studied recently. How to maintain a complete and clear object boundary structure is still a key issue. Existing works with the utilization of edge information have already improved this issue to some extent. However, these methods extract boundary features indiscriminately, which may weaken useful edge information and mislead edge construction. To address this problem, we present an Edge Recalibration Network (ERN) model for image-based SOD to perform edge-guided features effectively. In a specific, a progressive Fully Convolutional neural Networks (FCNs) for SOD is adopted to incorporate multi-scale and multi-level features. Besides, to locate the edge position and preserve the boundary features accurately, we propose an edge enhancement module with pixel-wise semantic-edge integration and channel-wise feature recalibration. Based on pixelwise semantic-edge integration, the semantic features and boundary features are integrated into the holistic feature maps. Based on channel-wise feature recalibration, the boundary features selectively recalibrate salient semantic features on channel dimension, aiming to enhance useful features and suppress useless features, for the similarity of boundary features and salient semantic features. Experimental results on five popular benchmark datasets show that the proposed model ERN outperforms other state-of-the-art methods under different evaluation metrics.
An Analysis of Spam from Predatory Publications in Library and Information Science

Predatory journals pose a significant threat to the quality of scholarly publication across disciplines, including in library and information science (LIS). These publications are known to target researchers in developing countries and researchers for whom English is not their primary language. Such publications threaten the legitimacy of researchers who may already face an uphill battle to establish a name for themselves in their field. How predatory publishers target researchers in LIS through spam invitations is the subject of this study, which partly replicates a 2017 study by Clemons et al., who examined spam invitations in the field of oncology. The data set of the present study consists of ninety-eight spam invitations from predatory publishers received by the two co-authors over a six-week period. Data analysis shows the frequency of problematic elements of content in these invitations.
IPOD: A Large-scale Industrial and Professional Occupation Dataset

In today's job market, occupational data mining and analysis is growing in importance as it enables companies to predict employee turnover, model career trajectories, screen through resumes and perform other human resource tasks. As such, there has been growing interest in utilizing occupational data mining and analysis, and a key requirement to facilitate these tasks is the need for an occupation-related dataset. However, most research use proprietary datasets or do not make their dataset publicly available, thus impeding development in this area. To solve this issue, we present the Industrial and Professional Occupation Dataset (IPOD), which comprises 475,073 job titles belonging to 192,295 Linkedin users. In addition to making IPOD publicly available, we also: (i) manually annotate each job title with its associated level of seniority, domain of work and location; and (ii) provide embedding for job titles and discuss various use cases. This dataset is publicly available at https://github.com/junhua/ipod.
From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders

Imbalanced datasets pose severe challenges in training well performing classifiers. This problem is also prevalent in the domain of outlier detection since outliers occur infrequently and are generally treated as minorities. One simple yet powerful approach is to use autoencoders which are trained on majority samples and then to classify samples based on the reconstruction loss. However, this approach fails to classify samples whenever reconstruction errors of minorities overlap with that of majorities. To overcome this limitation, we propose an adversarial loss function that maximizes the loss of minorities while minimizing the loss for majorities. This way, we obtain a well-separated reconstruction error distribution that facilitates classification. We show that this approach is robust in a wide variety of settings, such as imbalanced data classification or outlier- and novelty detection.