Reviewerly/exHarmony huggingface dataset:
V2RNet: An Unsupervised Semantic Segmentation Algorithm for Remote Sensing Images via Cross-Domain Transfer Learning

The dependence on large-scale pixel-level annotations brings great challenge to semantic segmentation task for remote sensing images (RSIs). To alleviate this issue, we propose V2RNet, an unsupervised semantic segmentation method which introduces adversarial learning into segmentation network. Our method creatively transfers the segmentation model from the synthetic GTA-V data to the real optical remote sensing data via domain adaptation. Additionally, to unify the source domain semantic structures and target domain image style, we design a semantic segmentation discriminator as auxiliary to optimize the domain adaptation efficiency. Thus the proposed method is effective on typical remote sensing targets such densely arranged, intertwined road. Experimental results on Massachusetts Road data set demonstrate our unsupervised semantic segmentation model achieves comparable segmentation accuracy, which also validates the effectiveness of the proposed method.
Deformable scene text detection using harmonic features and modified pixel aggregation network

Although text detection methods have addressed several challenges in the past, there is a dearth of effective methods for text detection in deformable images, such as images containing text embedded on cloth, banners, rubber, sports jerseys, uniforms, etc. This is because deformable regions contain surfaces of arbitrarily shapes, which lead to poor text quality. This paper presents a new method for deformable text detection in natural scene images. It is observed that although the shapes of characters change in a deformable region, the pixel values and spatial relationship between the pixels do not change. This motivated us to explore extraction of Maximally Stable Extremal Regions (MSER) in an image in which pixels that share common features are grouped into components. The unique character shape variations led us to explore harmonic features to represent the component shape variations, using which a classifier classifies text and non-text components from the output of the MSER step. Additionally, the objective of developing a lightweight method with low computational cost motivated us to introduce a modified Pixel Aggression Network (PAN) for text deformable text detection at a component level. Comprehensive experiments which include experiments on our Deformable Text Dataset (DTD) and standard natural scene text datasets, namely, MSRATD-500, ICDAR 2019 MLT, Total-Text, CTW1500, ICDAR 2019 ArT and DSTA1500 datasets show that the proposed model outperforms the existing methods for our dataset as well as the standard datasets.
Are We in-Sync? Students’ Virtual Instructional Experience and Perceived Information Literacy Skills in Time of Pandemic

The COVID-19 outbreak has brought a drastic change in the current educational system. While distance learning and online instruction is not really a new concept in the field of education (Kurzman, 2013 Kurzman, P. A. (2013). The evolution of distance learning and online education. Journal of Teaching in Social Work, 33(4-5), 331–338. doi:https://doi.org/10.1080/08841233.2013.843346[Taylor & Francis Online] , [Google Scholar]), it has been intensified with the aim of minimizing physical interactions without jeopardizing the quality of knowledge transfer. The emergence of technology has also implicated changes in the information-seeking behavior of students over time (Oliveira & Greenidge, 2020). Online library orientations and virtual one-shot information literacy (IL) sessions have been in place to supplement virtual classroom instructions by teachers (Lierman & Santiago, 2019 Lierman, A., & Santiago, A. (2019). Developing online instruction according to best practices. Journal of Information Literacy, 13(2), 206–221. doi:https://doi.org/10.11645/13.2.2649[Crossref] , [Google Scholar]). The Nazarbayev University (NU) Library through its Reference Office took action and managed to implement creative ways to conduct online sessions. This paper documented NU students' virtual library experiences with subject librarians during the pandemic as perceived by students. The 10-item workshop evaluation revealed that students felt very satisfied with the content, coverage, and organization of library sessions. They also highly agreed that the conduct of library sessions was practical, clear, and has an avenue for interaction and development of their IL skills. In a follow-up study with workshop evaluation respondents, a stratified sample size of 101 was determined to identify the current IL skills of students. Students were given the opportunity to conduct a self-assessment of their IL skills. With a 31.68% response rate, the eight-item IL skillset affirmed that students personally believed they were moderate users in searching and evaluating information sources. This paper also determined the frequency of follow-up sessions requested by the students even after attending the online library orientations and IL sessions. Data included in this research was gathered between August 17 to November 27, 2020. To investigate further, an analysis of the reference queries received during the Fall 2020 semester using Reference Analytics was conducted.
Dispelling Misconceptions and Characterizing the Failings of Deepfake Detection

In this article, we highlight problematic trends in our understanding of and approaches to dealing with deepfakes. We discuss five common misconceptions surrounding deepfakes and adopt a security perspective to make three recommendations to avoid backward-facing research efforts.
Massive data clustering by multi-scale psychological observations

Clustering is the discovery of latent group structure in data and is a fundamental problem in artificial intelligence, and a vital procedure in data-driven scientific research over all disciplines. Yet, existing methods have various limitations, especially weak cognitive interpretability and poor computational scalability, when it comes to clustering massive datasets that are increasingly available in all domains. Here, by simulating the multi-scale cognitive observation process of humans, we design a scalable algorithm to detect clusters hierarchically hidden in massive datasets. The observation scale changes, following the Weber-Fechner law to capture the gradually emerging meaningful grouping structure. We validated our approach in real datasets with up to a billion records and 2000 dimensions, including taxi trajectories, single-cell gene expressions, face images, computer logs and audios. Our approach outperformed popular methods in usability, efficiency, effectiveness and robustness across different domains.
Deconfounded and Explainable Interactive Vision-Language Retrieval of Complex Scenes

In vision-language retrieval systems, users provide natural language feedback to find target images. Vision-language explanations in the systems can better guide users to provide feedback and thus improve the retrieval. However, developing explainable vision-language retrieval systems can be challenging, due to limited labeled multimodal data. In the retrieval of complex scenes, the issue of limited labeled data can be more severe. With multiple objects in the complex scenes, each user query may not exhaustively describe all objects in the desired image and thus more labeled queries are needed. The issue of limited labeled data can cause data selection biases, and result in spurious correlations learned by the models. When learning spurious correlations, existing explainable models may not be able to accurately extract regions from images and keywords from user queries.
Schrödinger's tree—On syntax and neural language models

In the last half-decade, the field of natural language processing (NLP) has undergone two major transitions: the switch to neural networks as the primary modeling paradigm and the homogenization of the training regime (pre-train, then fine-tune). Amidst this process, language models have emerged as NLP's workhorse, displaying increasingly fluent generation capabilities and proving to be an indispensable means of knowledge transfer downstream. Due to the otherwise opaque, black-box nature of such models, researchers have employed aspects of linguistic theory in order to characterize their behavior. Questions central to syntax-the study of the hierarchical structure of language-have factored heavily into such work, shedding invaluable insights about models' inherent biases and their ability to make human-like generalizations. In this paper, we attempt to take stock of this growing body of literature. In doing so, we observe a lack of clarity across numerous dimensions, which influences the hypotheses that researchers form, as well as the conclusions they draw from their findings. To remedy this, we urge researchers to make careful considerations when investigating coding properties, selecting representations, and evaluating via downstream tasks. Furthermore, we outline the implications of the different types of research questions exhibited in studies on syntax, as well as the inherent pitfalls of aggregate metrics. Ultimately, we hope that our discussion adds nuance to the prospect of studying language models and paves the way for a less monolithic perspective on syntax in this context.
Privacy-Preserving cloud-Aided broad learning system

Broad Learning System (BLS) is a new deep learning model proposed recently, which shows its effectiveness in many fields, such as image recognition and fault detection. In this paper, we propose a secure, efficient, and verifiable outsourcing algorithm for BLS. This algorithm enables resource constrained devices to outsource BLS algorithm to untrusted cloud server to complete model training, which is of great significance for the promotion and application of BLS algorithm. Compared with the original BLS algorithm, this algorithm not only improves the efficiency of the algorithm on the client, but also ensures that the sensitive information of the client will not be leaked to the cloud server. In addition, in our algorithm, the client can verify the correctness of returned results with a probability of almost 1. Finally, we analyze the security and efficiency of our algorithm in theory and prove our algorithms feasibility through experiments.
Dual-Stream Siamese Network for Vehicle Re-Identification via Dilated Convolutional layers

Vehicle re-identification (V-ReID) is one of the major technologies of intelligent transportation and traffic management systems, which is an important aspect of the development of smart cities. The speedy developments in the field of Deep Learning (DL) methods, re-identification techniques achieved substantial improvement in recent years. But this research area received less attention from computer vision enthusiasts due to the shortage of proper datasets. Earlier studies were mainly focusing on specific views (i.e., front/back), but these techniques are not effective in real-world scenarios, where the angle and visual appearance of the moving vehicle change for the camera. In this article, we propose a dual-stream Siamese network for vehicle re-identification by substitution of few traditional convolutional layers into dilated convolutional layers (DCL). In our proposed framework, both streams are utilizing baseline ResNet-50 model, where one is employed to extract features from registration plates while the next stream capture features from vehicles shape. We trained our model on ten videos and tabulated the details about the proposed two-stream network. Experimental results indicate consistent improvement and accuracy.
Automating Behavior Selection for Affective Telepresence Robot

The tabletop robot Haru, used for affective telepresence research, enables a teleoperator to communicate affects from a distance. The robot&#x2019;s expressiveness offers myriad ways of communicating affects through the execution of emotive routines. The teleoperator reacts to input modalities such as the user&#x2019;s facial expression, gestures and speech-based intent as perceived by the robot&#x2019;s perception system. However, due to the sheer number of routines to select from, the task of choosing the appropriate or the most preferred routine is becoming cumbersome. In this paper, we propose a human-in-the-loop reinforcement learning mechanism in which an agent learns the teleoperator&#x2019;s selection preference as a function of the input modalities and aids the routine selection process by narrowing it to n-best optimal choices. Our experimental results show that with only a few number of interactions from the teleoperator, the system can learn to recommend optimal routine behaviors for all perceived modalities, which greatly reduces the workload of the teleoperator.
Robustness to Incorrect Priors and Controlled Filter Stability in Partially Observed Stochastic Control

We study controlled filter stability and its effects on the robustness properties of optimal control policies designed for systems with incorrect priors applied to a true system. Filter stability refers to the correction of an incorrectly initialized filter for a partially observed stochastic dynamical system (controlled or control-free) with increasing measurements. This problem has been studied extensively in the control-free context, and except for the standard machinery for linear Gaussian systems involving the Kalman filter, few studies exist for the controlled setup. One of the main differences between control-free and controlled partially observed Markov chains is that the filter is always Markovian under the former, whereas under a controlled model the filter process may not be Markovian since the control policy may depend on past measurements in an arbitrary (measurable) fashion. This complicates the dependency structure and therefore results from the control-free literature do not directly apply to the controlled setup. In this paper, we study the filter stability problem for controlled stochastic dynamical systems and provide sufficient conditions for when a falsely initialized filter merges with the correctly initialized filter over time. These stability results are applied to robust stochastic control problems: under filter stability, we bound the difference in the expected cost incurred for implementing an incorrectly designed control policy compared to an optimal policy. A conclusion is that filter stability leads to stronger robustness results to incorrect priors (compared with results in [A. D. Kara and S. Yüksel, SIAM J. Control Optim., 57 (2019), pp. 1929--1964] without controlled filter stability). Furthermore, if the optimum cost is the same for each prior, the cost of mismatch between the true prior and the assumed prior is zero.
Distorted mirror images organised by cuspoid and umbilic caustics

Abstract The anatomy of the distorted and topologically disrupted images of an extended object after reflection by curved mirrors, and of their refraction analogues, e.g. in gravitational lensing, is determined by the caustic surfaces enveloping the ray family issuing from each point of the object. Simulations are presented of reflections of the same object when organised by each of the five caustics (elementary catastrophes) that are stable in three-dimensional space: the fold, cusp, swallowtail, elliptic and hyperbolic umbilic catastrophes. The object is a picture of a face, whose familiar features make the image distortions and disruptions easier to understand. An important distinction is between mirrors curved in one direction and those curved in two.
Multifocal Attention-Based Cross-Scale Network for Image De-raining

Albeit existing deep learning-based image de-raining methods have achieved promising results, most of them only extract single scale features, and neglect the fact that similar rain streaks appear repeatedly across different scales. Therefore, this paper aims to explore the cross-scale cues in a multi-scale fashion. Specifically, we first introduce an adaptive-kernel pyramid to provide effective multi-scale information. Then, we design two cross-scale similarity attention blocks (CSSABs) to search spatial and channel relationships between two scales, respectively. The spatial CSSAB explores the spatial similarity between pixels of cross-scale features, while the channel CSSAB emphasizes the interdependencies among cross-scale features. To further improve the diversity of features, we adopt the wavelet transformation and multi-head mechanism in CSSABs to generate multifocal features which focus on different areas. Finally, based on our CSSABs, we construct an effective multifocal attention-based cross-scale network, which exhaustively utilizes the cross-scale correlations of both rain streaks and background, to achieve image de-raining. Experiments show the superiority of our network over state-of-the-art image de-raining approaches both qualitatively and quantitatively. The source code and pre-trained models are available at https://github.com/zhangzheyu0/Multifocal_derain.
Deep Learning for Videoconferencing: A Brief Examination of Speech to Text and Speech Synthesis

Integrating AI-powered applications into video conferencing systems is further expected to blow up in various industrial scenarios. In this modern era of the video conferencing industry, deep learning techniques are revolutionizing to improve the quality of communication in use cases such as resolution improvement, background noise reduction, video compression, face alignment, transcription, and speech synthesis. This paper reviews the latest works on deep learning-based transcription and speech synthesis methods. They are classified into three categories: Speech to text, text to speech, and speech to text to speech. We included experimental studies conducted in two specific methods in a speech to text and speech synthesis. Experimental results on various test scenarios of two state-of-the-art pre-trained models are also analyzed. Finally, the future development trend of AI- powered video conferencing system is predicted.
A Preliminary Analysis of Communication Preferences in a Global Software Development Environment

Effective communication is a crucial asset for workers and institutions, specially in knowledge intensive businesses. In this paper, we present a preliminary analysis of communication preference in a global software development environment. We conducted a survey with developers and designers to assess different aspects of the communication and their perceptions during the communication with multicultural teams and partners.
Multi-Source Remote Sensing Image Registration Based on Local Deep Learning Feature

Due to huge differences in radiation characteristics and geometric characteristics of multi-source remote sensing images, presenting a big challenge for high-precision registration. In this paper, a new registration method based on deep learning is proposed. First, we use the convolutional neural network to extract deep learning features of the reference and sensed image after adaptive down-sampling, and extract 512-dimensional descriptor on the feature map to calculate the matching result, homography matrix and overlap area. Then, the circumscribed rectangle of the overlapping area is divided into blocks, and the same name point information extracted from all sub-blocks is combined to obtain matching result of source image pair, and then homography matrix of the source image pair is estimated. Finally, the registration result is obtained. Results show that the proposed algorithm has strong adaptability and robustness in the registration of multiple heterogeneous images in mountains, hills and plains.
Deep Imitation Learning for Autonomous Navigation in Dynamic Pedestrian Environments

Navigation through dynamic pedestrian environments in a socially compliant manner is still a challenging task for autonomous vehicles. Classical methods usually lead to unnatural vehicle behaviours for pedestrian navigation due to the difficulty in modeling social conventions mathematically. This paper presents an end-to-end path planning system that achieves autonomous navigation in dynamic environments through imitation learning. The proposed system is based on a fully convolutional neural network that maps the raw sensory data into a confidence map for path extraction. Additionally, a classification network is introduced to reduce the unnecessary re-plannings and ensures that the vehicle goes back to the global path when re-planning is not needed. The imitation learning based path planner is implemented on an autonomous wheelchair and tested in a new real-world dynamic pedestrian environment. Experimental results show that the proposed system is able to generate paths for different driving tasks, such as pedestrian following, static and dynamic obstacles avoidance, etc. In comparison to the state-of-the-art method, our system is superior in terms of generating human-like trajectories.
Result diversification by multi-objective evolutionary algorithms with theoretical guarantees

Given a ground set of items, the result diversification problem aims to select a subset with high “quality” and “diversity” while satisfying some constraints. It arises in various real-world artificial intelligence applications, such as web-based search, document summarization and feature selection, and also has applications in other areas, e.g., computational geometry, databases, finance and operations research. Previous algorithms are mainly based on greedy or local search. In this paper, we propose to reformulate the result diversification problem as a bi-objective maximization problem, and solve it by a multi-objective evolutionary algorithm (EA), i.e., the GSEMO. We theoretically prove that the GSEMO can achieve the (asymptotically) optimal theoretical guarantees under both static and dynamic environments. For cardinality constraints, the GSEMO can achieve the optimal polynomial-time approximation ratio, 1/2. For more general matroid constraints, the GSEMO can achieve an asymptotically optimal polynomial-time approximation ratio, 1/2−ϵ/(4n), where ϵ>0 and n is the size of the ground set of items. Furthermore, when the objective function (i.e., a linear combination of quality and diversity) changes dynamically, the GSEMO can maintain this approximation ratio in polynomial running time, addressing the open question proposed by Borodin et al. [7]. This also theoretically shows the superiority of EAs over local search for solving dynamic optimization problems for the first time, and discloses the robustness of the mutation operator of EAs against dynamic changes. Experiments on the applications of web-based search, multi-label feature selection and document summarization show the superior performance of the GSEMO over the state-of-the-art algorithms (i.e., the greedy algorithm and local search) under both static and dynamic environments.
High-speed chromatic confocal microscopy using multispectral sensors for sub-micrometer-precision microscopic surface profilometry

A full-field chromatic confocal microscopy (CCM) using a multispectral sensor was developed for quasi-one-shot microscopic 3D surface measurement. An innovative optical configuration employs a digital micromirror device (DMD) and a multispectral sensor is used to realize CCM with full-field area scanning. In the optical design, an area-scan type chromatic dispersive objective is specially designed to achieve measuring specification. Based on a self-developed chromatic dispersive objective, the FOV for one shot measurement can be reached to 1.8×1.3 mm2 which is immersive to microscopic profilometry. The spectral image captured by the multispectral sensor at each pinhole position has a unique spectrum pattern corresponding to its conjugate measured depth. A normalized cross-correlation (NCC) algorithm is developed to establish a spectrum-depth response curve with its corresponding spectrum pattern sets for accurate reconstruction of the tested 3D surface profile. With real test on standard targets, the measurement repeatability for a single surface depth is less than 0.6 μm.