Reviewerly/exHarmony huggingface dataset:
Do Multilingual Users Prefer Chat-bots that Code-mix? Let's Nudge and Find Out!

Despite their pervasiveness, current text-based conversational agents (chatbots) are predominantly monolingual, while users are often multilingual. It is well-known that multilingual users mix languages while interacting with others, as well as in their interactions with computer systems (such as query formulation in text-/voice-based search interfaces and digital assistants). Linguists refer to this phenomenon as code-mixing or code-switching. Do multilingual users also prefer chatbots that can respond in a code-mixed language over those which cannot? In order to inform the design of chatbots for multilingual users, we conduct a mixed-method user-study (N=91) where we examine how conversational agents, that code-mix and reciprocate the users' mixing choices over multiple conversation turns, are evaluated and perceived by bilingual users. We design a human-in-the-loop chatbot with two different code-mixing policies -- (a) always code-mix irrespective of user behavior, and (b) nudge with subtle code-mixed cues and reciprocate only if the user, in turn, code-mixes. These two are contrasted with a monolingual chatbot that never code-mixed. Users are asked to interact with the bots, and provide ratings on perceived naturalness and personal preference. They are also asked open-ended questions around what they (dis)liked about the bots. Analysis of the chat logs, users' ratings, and qualitative responses reveal that multilingual users strongly prefer chatbots that can code-mix. We find that self-reported language proficiency is the strongest predictor of user preferences. Compared to the Always code-mix policy, Nudging emerges as a low-risk low-gain policy which is equally acceptable to all users. Nudging as a policy is further supported by the observation that users who rate the code-mixing bot higher typically tend to reciprocate the language mixing pattern of the bot. These findings present a first step towards developing conversational systems that are more human-like and engaging by virtue of adapting to the users' linguistic style.
Examining Citations of Natural Language Processing Literature

We extracted information from the ACL Anthology (AA) and Google Scholar (GS) to examine trends in citations of NLP papers.We explore questions such as: how well cited are papers of different types (journal articles, conference papers, demo papers, etc.)? how well cited are papers from different areas of within NLP? etc. Notably, we show that only about 56% of the papers in AA are cited ten or more times.CL Journal has the most cited papers, but its citation dominance has lessened in recent years.On average, long papers get almost three times as many citations as short papers; and papers on sentiment classification, anaphora resolution, and entity recognition have the highest median citations.The analyses presented here, and the associated dataset of NLP papers mapped to citations, have a number of uses including: understanding how the field is growing and quantifying the impact of different types of papers.
Real-time infrared image detail enhancement based on fast guided image filter and plateau equalization

Image detail enhancement is critical to the performance of infrared imaging systems because the original images generally suffer from low contrast and a low signal-to-noise ratio. Although conventional decomposition-based methods have advantages in enhancing image details, they also have clear disadvantages, which include intensive computations, over-enhanced noise, and gradient reversal artifacts. In this paper, we propose to accelerate enhancement processing by using a fast guided filter and plateau equalization. Our method consists of image decomposition, base and detail layers processing, and projection of the enhanced image to an 8-bit dynamic range. Experimental results demonstrated that our proposed method achieves a good balance among detail enhancement performance, noise and gradient reversal artifacts suppression, and computational cost, with a frame rate around 30 fps for <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>640</mml:mn> </mml:mrow> </mml:mrow> <mml:mo>×<!-- × --></mml:mo> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mn>512</mml:mn> </mml:mrow> </mml:mrow> </mml:math> infrared images.
PRACTICAL-ORIENTED CONCEPT AS A PRINCIPLE OF PROFESSIONAL EDUCATION OF THE FUTURE PROFESSIONALS

The article considers the essence of the process of practice-oriented concept as a principle of the professional training quality of the future specialists. Socialization is singled out and considered through the interdisciplinary concept in professional education. Socialization is defined as a successful component of the future specialists' professional education. One of the ways of professional education of the future specialists is covered — participation in social projects of the higher education institutions. Emphasis is placed on the peculiarities of the socialization and professional education of the future specialists through the social project's organization. The motivational and innovative components of socialization in the professional education of future specialists are studied. The results of the experimental study are described.
After Libra, Digital Yuan and COVID-19: Central Bank Digital Currencies and the New World of Money and Payment Systems

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Residual Policy Learning for Shared Autonomy

Shared autonomy provides an effective framework for human-robot collaboration that takes advantage of the complementary strengths of humans and robots to achieve common goals.Many existing approaches to shared autonomy make restrictive assumptions that the goal space, environment dynamics, or human policy are known a priori, or are limited to discrete action spaces, preventing those methods from scaling to complicated real world environments.We propose a modelfree, residual policy learning algorithm for shared autonomy that alleviates the need for these assumptions.Our agents are trained to minimally adjust the human's actions such that a set of goal-agnostic constraints are satisfied.We test our method in two continuous control environments: Lunar Lander, a 2D flight control domain, and a 6-DOF quadrotor reaching task.In experiments with human and surrogate pilots, our method significantly improves task performance without any knowledge of the human's goal beyond the constraints.These results highlight the ability of model-free deep reinforcement learning to realize assistive agents suited to continuous control settings with little knowledge of user intent.
Defining what we study: The contribution of machine automation in archaeological research

In the 21st century, advances in computer science have impacted archaeology, most recently in the development of automated algorithms. Like most technology, these methods have been the source of ongoing debate, particularly in their utility for archaeology. Here, I focus on a contribution of automation and machine learning in archaeology that is often overlooked: the ability of computer algorithms to codify unambiguous, semantically consistent definitions. Archaeology has long struggled with establishing consistent characterizations of the phenomena it studies. As such, I argue that the procedures used for automated methods are useful for archaeologists – even outside of automated analyses – by allowing for the creation of consistent definitions which permit for reproducible research designs.
Smartphone- versus smartglasses-based augmented reality (AR) for percutaneous needle interventions: system accuracy and feasibility study

To compare the system accuracy and needle placement performance of smartphone- and smartglasses-based augmented reality (AR) for percutaneous needle interventions.An AR platform was developed to enable the superimposition of annotated anatomy and a planned needle trajectory onto a patient in real time. The system accuracy of the AR display on smartphone (iPhone7) and smartglasses (HoloLens1) devices was evaluated on a 3D-printed phantom. The target overlay error was measured as the distance between actual and virtual targets (n = 336) on the AR display, derived from preprocedural CT. The needle overlay angle was measured as the angular difference between actual and virtual needles (n = 12) on the AR display. Three operators each used the iPhone (n = 8), HoloLens (n = 8) and CT-guided freehand (n = 8) to guide needles into targets in a phantom. Needle placement error was measured with post-placement CT. Needle placement time was recorded from needle puncture to navigation completion.The target overlay error of the iPhone was comparable to the HoloLens (1.75 ± 0.59 mm, 1.74 ± 0.86 mm, respectively, p = 0.9). The needle overlay angle of the iPhone and HoloLens was similar (0.28 ± 0.32°, 0.41 ± 0.23°, respectively, p = 0.26). The iPhone-guided needle placements showed reduced error compared to the HoloLens (2.58 ± 1.04 mm, 3.61 ± 2.25 mm, respectively, p = 0.05) and increased time (87 ± 17 s, 71 ± 27 s, respectively, p = 0.02). Both AR devices reduced placement error compared to CT-guided freehand (15.92 ± 8.06 mm, both p < 0.001).An augmented reality platform employed on smartphone and smartglasses devices may provide accurate display and navigation guidance for percutaneous needle-based interventions.
Non-linear MPC based Navigation for Micro Aerial Vehicles in Constrained Environments

This article establishes a novel Non linear Model Predictive Control (MPC) scheme for the navigation of a MAV (Micro Aerial Vehicle) in constrained environments, such as narrow passages, multi-obstacle populated spaces and tight openings. The proposed NMPC optimization framework is based on the Proximal Averaged Newton type method for Optimal Control (PANOC) and has the merit to employ a penalty method for the proper consideration of the obstacles and other environmental constraints during the navigation. The proposed scheme has the ability to be a fast solution for the navigation of MAVs that can be directly applied online and thus it is creating a powerful navigation framework for demanding flights. For achieving such an agile and fast aerial navigation, the article will also present the proposed penalty creation methodology for dealing with the obstacle avoidance and the space constraint navigation. Finally, the efficacy of the proposed scheme will be demonstrated by multiple simulation results under constrained and demanding environments.
Traffic Congestion Avoidance System Using Foreground Estimation and Cascade Classifier

In recent decades, the traffic on road increased in a huge number. It is very important to manage the safety of the humans as well as to make an efficient flow of the traffic. To manage the traffic flow and to overcome from the situation of the traffic congestion the vehicle detection and counting needs a greater amount of accuracy. In this work, two different techniques are proposed that provides better performance in terms of F-Measure score and Error Ratio. The first technique is based on the foreground estimation while the second proposed technique is based on the training of dataset using a cascade classifier which is based on the Histogram of Oriented Gradients (HOG). Furthermore, four images are provided at once to the proposed system to count the vehicles and generate a signal that shows a greater number of vehicles in that image. The priority of each image will be set on the basics of greater number of vehicles present. The proposed techniques showed outstanding performance on a sunny and a cloudy day which is verified from the experimental results.
Estimating Depth and Global Atmospheric Light for Image Dehazing Using Type-2 Fuzzy Approach

This article proposes a novel single image dehazing method using a Type-2 membership function based similarity function matrix. The proposed method estimates the depth map and global atmospheric light of the observed hazy image. The estimated depth map is further subjected to produce true scene transmission. Finally, the observed hazy image is dehazed by the atmospheric scattering model using scene transmission and global atmospheric light. The qualitative and quantitative comparisons of the proposed method have been presented with benchmarked state-of-the-art methods. The experiments have been extensively performed on benchmarked natural hazy images, MiddleBury Stereo dataset, REalistic Single Image DEhazing (RESIDE) dataset, RESIDE-<inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> dataset, and Stanford ImageNet dataset. The performance metrics used for comparison are peak signal to noise ratio and structural similarity index as quantitative measures; and lightness order error and naturalness image quality evaluator as qualitative measures. Moreover, the detection results using YOLOv2 on RESIDE-<inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> dataset have also been compared in terms of F1-score and area under curve measures. The qualitative and quantitative comparisons show that the proposed method outperforms others and dehazed images are restored effectively maintaining their naturalness.
Defense for Black-Box Attacks on Anti-Spoofing Models by Self-Supervised Learning

High-performance anti-spoofing models for automatic speaker verification (ASV), have been widely used to protect ASV by identifying and filtering spoofing audio that is deliberately generated by text-to-speech, voice conversion, audio replay, etc. However, it has been shown that high-performance anti-spoofing models are vulnerable to adversarial attacks. Adversarial attacks, that are indistinguishable from original data but result in the incorrect predictions, are dangerous for anti-spoofing models and not in dispute we should detect them at any cost. To explore this issue, we proposed to employ Mockingjay, a self-supervised learning based model, to protect anti-spoofing models against adversarial attacks in the black-box scenario. Self-supervised learning models are effective in improving downstream task performance like phone classification or ASR. However, their effect in defense for adversarial attacks has not been explored yet. In this work, we explore the robustness of self-supervised learned high-level representations by using them in the defense against adversarial attacks. A layerwise noise to signal ratio (LNSR) is proposed to quantize and measure the effectiveness of deep models in countering adversarial noise. Experimental results on the ASVspoof 2019 dataset demonstrate that high-level representations extracted by Mockingjay can prevent the transferability of adversarial examples, and successfully counter black-box attacks.
A multiclass classification using one-versus-all approach with the differential partition sampling ensemble

The One-versus-all(OVA) approach is one of the mainstream decomposition methods by which multiple binary classifiers are used to solve multiclass classification tasks. However, it exists the problems of serious class imbalance. This paper proposes a differential partition sampling ensemble method(DPSE) in the OVA framework. The number of majority samples and that of the minority samples in each binary training dataset are used as the upper and lower limits of the sampling interval respectively. Within this range, the construction process of the arithmetic sequence is simulated to generate the set containing multiple different sampling numbers with equal intervals. All samples are divided into safe examples, borderline examples, rare examples, and outliers according to the neighborhood information, then Random undersampling for safe samples(s-Random undersampling) and SMOTE for borderline examples and rare examples (br-SMOTE) are proposed based on the distribution characteristics of the classes. In each iteration, according to the number of differential sampling, the two methods are used to undersample or oversample the majority and minority in each binary training dataset to balance the number of positive and negative samples, which preserves the characteristic of the class structure as much as possible. Balanced training sets are used to train the binary classification model with multiple sub classifiers. The thorough experiments performed on 27 KEEL public multiclass datasets show that DPSE outperforms the typical methods in the OVA scheme, the One-versus-One scheme or direct way in classification performance.
SimPose: Effectively Learning DensePose and Surface Normals of People from Simulated Data

With a proliferation of generic domain-adaptation approaches, we report a simple yet effective technique for learning difficult per-pixel 2.5D and 3D regression representations of articulated people. We obtained strong sim-to-real domain generalization for the 2.5D DensePose estimation task and the 3D human surface normal estimation task. On the multi-person DensePose MSCOCO benchmark, our approach outperforms the state-of-the-art methods which are trained on real images that are densely labelled. This is an important result since obtaining human manifold’s intrinsic uv coordinates on real images is time consuming and prone to labeling noise. Additionally, we present our model’s 3D surface normal predictions on the MSCOCO dataset that lacks any real 3D surface normal labels. The key to our approach is to mitigate the “Inter-domain Covariate Shift” with a carefully selected training batch from a mixture of domain samples, a deep batch-normalized residual network, and a modified multi-task learning objective. Our approach is complementary to existing domain-adaptation techniques and can be applied to other dense per-pixel pose estimation problems.
Completing Multiparticle Representations of the Poincaré Group

We extend the definition of asymptotic multiparticle states of the $S$-matrix beyond the tensor products of one-particle states. We identify new quantum numbers called pairwise helicities, or ${q}_{ij}$, associated with asymptotically separated pairs of particles. We first treat all single particles and particle pairs independently, allowing us to generalize the Wigner construction, and ultimately projecting onto the physical states. Our states reduce to tensor product states for vanishing ${q}_{ij}$, while for vanishing spins they reproduce Zwanziger's scalar dyon states. This construction yields the correct asymptotic states for the scattering of electric and magnetic charges, with pairwise helicity identified as ${q}_{ij}={e}_{i}{g}_{j}\ensuremath{-}{e}_{j}{g}_{i}$.
Self-Balancing Two-Wheeled Robot Featuring Intelligent End-to-End Deep Visual-Steering

This article proposes a self-balancing wheeled robot named J4.α equipped with intelligent modules for automatic navigation. A mechanism involving dynamic mass was designed to control the linear motion of the robot. RGB-D camera was adopted to monitor the front environment; a deep convolutional neural network was adopted to realize a certain level of socially compliant navigation. In combination with the topological localization scheme, J4.α can navigate between nodal locations without environment modification. To facilitate the work of the manipulator on-board, an auto-balancing module was designed to maintain force equilibrium. To further secure spatial reliability, motorized parking stands are also introduced. Prototypes were made and tests were carried out to examine the system dynamics. The experimental result confirmed the effectiveness of the intelligent navigation modules.
OpenUE: An Open Toolkit of Universal Extraction from Text

Natural language processing covers a wide variety of tasks with token-level or sentencelevel understandings.In this paper, we provide a simple insight that most tasks can be represented in a single universal extraction format.We introduce a prototype model and provide an open-source and extensible toolkit called OpenUE for various extraction tasks.OpenUE allows developers to train custom models to extract information from the text and supports quick model validation for researchers.Besides, OpenUE provides various functional modules to maintain sufficient modularity and extensibility.Except for the toolkit, we also deploy an online demo 1 with restful APIs to support real-time extraction without training and deploying.Additionally, the online system can extract information in various tasks, including relational triple extraction, slot & intent detection, event extraction, and so on.We release the source code, datasets, and pretrained models to promote future researches in
Uncertainty and symmetry bounds for the quantum total detection probability

We investigate a generic discrete quantum system prepared in state |ψin〉 under repeated detection attempts, aimed to find the particle in state |d〉, for example, a quantum walker on a finite graph searching for a node. For the corresponding classical random walk, the total detection probability Pdet is unity. Due to destructive interference, one may find initial states |ψin〉 with Pdet<1. We first obtain an uncertainty relation which yields insight on this deviation from classical behavior, showing the relation between Pdet and energy fluctuations: ΔPVar[Ĥ]d≥|〈d|[Ĥ,D̂]|ψin〉|2, where ΔP=Pdet−|〈ψin|d〉|2 and D̂=|d〉〈d| is the measurement projector. Secondly, exploiting symmetry we show that Pdet≤1/ν, where the integer ν is the number of states equivalent to the initial state. These bounds are compared with the exact solution for small systems, obtained from an analysis of the dark and bright subspaces, showing the usefulness of the approach. The upper bound works well even in large systems, and we show how to tighten the lower bound in this case.Received 20 June 2019Accepted 27 May 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.023392Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum measurementsQuantum theoryQuantum walksTechniquesFirst passage problemsQuantum InformationStatistical PhysicsGeneral Physics
UFO$$^2$$: A Unified Framework Towards Omni-supervised Object Detection

Existing work on object detection often relies on a single form of annotation: the model is trained using either accurate yet costly bounding boxes or cheaper but less expressive image-level tags. However, real-world annotations are often diverse in form, which challenges these existing works. In this paper, we present UFO $$^2$$ , a unified object detection framework that can handle different forms of supervision simultaneously. Specifically, UFO $$^2$$ incorporates strong supervision (e.g., boxes), various forms of partial supervision (e.g., class tags, points, and scribbles), and unlabeled data. Through rigorous evaluations, we demonstrate that each form of label can be utilized to either train a model from scratch or to further improve a pre-trained model. We also use UFO $$^2$$ to investigate budget-aware omni-supervised learning, i.e., various annotation policies are studied under a fixed annotation budget: we show that competitive performance needs no strong labels for all data. Finally, we demonstrate the generalization of UFO $$^2$$ , detecting more than 1,000 different objects without bounding box annotations.