Reviewerly/exHarmony huggingface dataset:
Multi-agent Communication meets Natural Language: Synergies between Functional and Structural Language Learning

We present a method for combining multiagent communication and traditional datadriven approaches to natural language learning, with an end goal of teaching agents to communicate with humans in natural language.Our starting point is a language model that has been trained on generic, not task-specific language data.We then place this model in a multi-agent self-play environment that generates task-specific rewards used to adapt or modulate the model, turning it into a taskconditional language model.We introduce a new way for combining the two types of learning based on the idea of reranking language model samples, and show that this method outperforms others in communicating with humans in a visual referential communication task.Finally, we present a taxonomy of different types of language drift that can occur alongside a set of measures to detect them.
Learning to Complement Humans

A rising vision for AI in the open world centers on the development of systems that can complement humans for perceptual, diagnostic, and reasoning tasks. To date, systems aimed at complementing the skills of people have employed models trained to be as accurate as possible in isolation. We demonstrate how an end-to-end learning strategy can be harnessed to optimize the combined performance of human-machine teams by considering the distinct abilities of people and machines. The goal is to focus machine learning on problem instances that are difficult for humans, while recognizing instances that are difficult for the machine and seeking human input on them. We demonstrate in two real-world domains (scientific discovery and medical diagnosis) that human-machine teams built via these methods outperform the individual performance of machines and people. We then analyze conditions under which this complementarity is strongest, and which training methods amplify it. Taken together, our work provides the first systematic investigation of how machine learning systems can be trained to complement human reasoning.
Neural Graph Matching Networks for Chinese Short Text Matching

Chinese short text matching usually employs word sequences rather than character sequences to get better performance.However, Chinese word segmentation can be erroneous, ambiguous or inconsistent, which consequently hurts the final matching performance.To address this problem, we propose neural graph matching networks, a novel sentence matching framework capable of dealing with multi-granular input information.Instead of a character sequence or a single word sequence, paired word lattices formed from multiple word segmentation hypotheses are used as input and the model learns a graph representation according to an attentive graph matching mechanism.Experiments on two Chinese datasets show that our models outperform the state-of-the-art short text matching models.
Trolling as a Collective Form of Harassment: An Inductive Study of How Online Users Understand Trolling

Rising media and academic concerns for the social implications of online trolling require that scholars understand how everyday users conceptualize trolling. The validity of survey instruments may potentially be at risk if online users and academics, who subsequently advocate for interventions, have competing understandings of trolling. Surveying 120 US-based online users, I find that the spectrum of behaviors classified in the literature as “trolling” do not resonate with respondents. Instead, respondents report that harassment on the basis of race and gender is indicative of trolling. These results suggest a disconnect between academic definitions of trolling that focus on prosocial or humor-based forms of trolling and lay definitions which foreground identity-based harassment and harm. Current instruments that assess trolling behaviors and experiences do not attend to this definition of trolling as identity-based harassment, calling into question construct validity. Furthermore, sociologists identify identity-based harassment as a form of discrimination in other domains of social life, but present measures of discrimination do not consider the online domain. This suggests that studies of everyday discrimination and the implications of mistreatment should extend to online spaces, where racism and sexism are reproduced.
Deep Fusion for Radar Jamming Signal Classification Based on CNN

The accurate classification of radar jamming signal is a core step of anti-jamming.Recently, convolutional neural network (CNN) based methods have shown their powerfulness in signal processing.In this paper, a deep fusion method based on CNN is proposed to classify jamming signal acting on pulse compression radar.The proposed method consists of three subnetworks (i.e., 1D-CNN, 2D-CNN, and fusion network).1D-CNN is used to extract deep features of original radar jamming signal.Meanwhile, in order to extract the time-frequency features, short time Fourier transform (STFT) is applied to jamming signal to obtain time-frequency spectrograms.Then, 2D-CNN is used to extract deep time-frequency features, which are useful for further features fusion processing.Fusion network is used to deeply fuse the extracted features of the aforementioned CNNs and softmax is used to finish the task of radar jamming signal classification.In addition, in order to alleviate the problem of overfitting and improve the generalization ability of proposed model, soft label smoothing is proposed.The experimental results reveal that the proposed method provides competitive results in term of classification accuracy.
A Novel Grayscale Image Steganography Scheme Based on Chaos Encryption and Generative Adversarial Networks

This article proposes a novel grayscale image steganography scheme that is capable of hiding an encrypted secret image into a cover image of the same size. The secret image is encrypted using the chaos encryption technology before being hidden. The encrypted image and cover image are then transformed into the steg image by a convolutional neural network (CNN). Also, a generative adversarial network (GAN) is adopted to produce a more realistic steg image, whose appearance is difficult to distinguish from the cover image. The steganography scheme is composed of three CNNs which are regarded as hiding network, discriminative network, and extracting network, respectively. Additionally, a new weight allocation mechanism is introduced to ensure the balanced training procedure of hiding-extracting networks. The obtained experimental results show that the proposed scheme not only solved the problems of secret information leakage and color distortion of the current mainstream methods but also maintain high embedding capacity. Compared with other methods, the average values of the PSNR and SSIM of our steg images can reach 0.987 and 42.3, respectively. And the quality of the reconstructed secret images is also advantageous.
Impact and Key Challenges of Insider Threats on Organizations and Critical Businesses

The insider threat has consistently been identified as a key threat to organizations and governments. Understanding the nature of insider threats and the related threat landscape can help in forming mitigation strategies, including non-technical means. In this paper, we survey and highlight challenges associated with the identification and detection of insider threats in both public and private sector organizations, especially those part of a nation’s critical infrastructure. We explore the utility of the cyber kill chain to understand insider threats, as well as understanding the underpinning human behavior and psychological factors. The existing defense techniques are discussed and critically analyzed, and improvements are suggested, in line with the current state-of-the-art cyber security requirements. Finally, open problems related to the insider threat are identified and future research directions are discussed.
Hardware Private Circuits: From Trivial Composition to Full Verification

The design of glitch-resistant higher-order masking schemes is an important challenge in cryptographic engineering. A recent work by Moos <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (CHES 2019) showed that most published schemes (and all efficient ones) exhibit local or composability flaws at high security orders, leaving a critical gap in the literature on hardware masking. In this article, we first extend the simulatability framework of Belaïd <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> (EUROCRYPT 2016) and prove that a compositional strategy that is correct without glitches remains valid with glitches. We then use this extended framework to prove the first masked gadgets that enable trivial composition with glitches at arbitrary orders. We show that the resulting “Hardware Private Circuits” approach the implementation efficiency of previous (flawed) schemes. We finally investigate how trivial composition can serve as a basis for a tool that allows verifying full masked hardware implementations (e.g., of complete block ciphers) at any security order from their HDL code. As side products, we improve the randomness complexity of the best published refreshing gadgets, show that some S-box representations allow latency reductions and confirm practical claims based on implementation results.
Quantum dot single-photon emission coupled into single-mode fibers with 3D printed micro-objectives

User-friendly single-photon sources with high photon-extraction efficiency are crucial building blocks for photonic quantum applications. For many of these applications, such as long-distance quantum key distribution, the use of single-mode optical fibers is mandatory, which leads to stringent requirements regarding the device design and fabrication. We report on the on-chip integration of a quantum dot microlens with a 3D-printed micro-objective in combination with a single-mode on-chip fiber coupler. The practical quantum device is realized by deterministic fabrication of the QD-microlens via in-situ electron-beam lithography and 3D two-photon laser writing of the on-chip micro-objective and fiber-holder. The QD with microlens is an efficient single-photon source, whose emission is collimated by the on-chip micro-objective. A second polymer microlens is located at the end facet of the single-mode fiber and ensures that the collimated light is efficiently coupled into the fiber core. For this purpose, the fiber is placed in the on-chip fiber chuck, which is precisely aligned to the QD-microlens thanks to the sub-$\mu$m processing accuracy of high-resolution two-photon direct laser writing. This way, we obtain a fully integrated high-quality quantum device with broadband photon extraction efficiency, a single-mode fiber-coupling efficiency of 26%, a single-photon flux of 1.5 MHz at single-mode fibre output and a multi-photon probability of 13 % under pulsed optical excitation. In addition, the stable design of the developed fiber-coupled quantum device makes it highly attractive for integration into user-friendly plug-and-play quantum applications.
A Semantics-Assisted Video Captioning Model Trained With Scheduled Sampling

Given the features of a video, recurrent neural networks can be used to automatically generate a caption for the video. Existing methods for video captioning have at least three limitations. First, semantic information has been widely applied to boost the performance of video captioning models, but existing networks often fail to provide meaningful semantic features. Second, the Teacher Forcing algorithm is often utilized to optimize video captioning models, but during training and inference, different strategies are applied to guide word generation, leading to poor performance. Third, current video captioning models are prone to generate relatively short captions that express video contents inappropriately. Toward resolving these three problems, we suggest three corresponding improvements. First of all, we propose a metric to compare the quality of semantic features, and utilize appropriate features as input for a semantic detection network (SDN) with adequate complexity in order to generate meaningful semantic features for videos. Then, we apply a scheduled sampling strategy that gradually transfers the training phase from a teacher-guided manner toward a more self-teaching manner. Finally, the ordinary logarithm probability loss function is leveraged by sentence length so that the inclination of generating short sentences is alleviated. Our model achieves better results than previous models on the YouTube2Text dataset and is competitive with the previous best model on the MSR-VTT dataset.
Random Matrix Based Extended Target Tracking With Orientation: A New Model and Inference

In this study, we propose a novel extended target tracking algorithm which is capable of representing the extent of dynamic objects as an ellipsoid with a time-varying orientation angle. A diagonal positive semi-definite matrix is defined to model objects' extent within the random matrix framework where the diagonal elements have inverse-Gamma priors. The resulting measurement equation is non-linear in the state variables, and it is not possible to find a closed-form analytical expression for the true posterior because of the absence of conjugacy. We use the variational Bayes technique to perform approximate inference, where the Kullback-Leibler divergence between the true and the approximate posterior is minimized by performing fixed-point iterations. The update equations are easy to implement, and the algorithm can be used in real-time tracking applications. We illustrate the performance of the method in simulations and experiments with real data. The proposed method outperforms the state-of-the-art methods when compared with respect to accuracy and robustness.
Integration of ICTS and Digital Skills in Times of the Pandemic Covid-19

In these times of global tragedy due to the pandemic that caused COVID-19, distance learning relies on the resources of the digital field, as well as on the management of ICT and the development of digital skills. Therefore, this research has been aimed at corroborating the existing links between the integration of ICT and digital skills pandemic times. A study with a quantitative, non-experimental, cross-sectional, correlational approach was developed. The sample consisted of 168 students from a public university in Lima, Peru. Two tools were adapted: 1) integration of ICT, 18 items and 2) digital skills, 30 items, with reliability coefficients by Cronbach's Alpha of 0.976 and 0.889, respectively. The questionnaires were developed and taken through Google forms. The results showed that the level of integration of ICT was high (89.9%) as well as digital skills (86.9%). Spearman's Rho correlation analysis concluded that there was a positive and high relationship between integration of ICT and digital skills (0.761, p &lt; 0.05). Finally, discussions were raised about the development of aspects related to ICT during the current pandemic.
Measurement-Based Variational Quantum Eigensolver

Variational quantum eigensolvers (VQEs) combine classical optimization with efficient cost function evaluations on quantum computers. We propose a new approach to VQEs using the principles of measurement-based quantum computation. This strategy uses entangled resource states and local measurements. We present two measurement-based VQE schemes. The first introduces a new approach for constructing variational families. The second provides a translation of circuit- to measurement-based schemes. Both schemes offer problem-specific advantages in terms of the required resources and coherence times.Received 5 November 2020Revised 8 March 2021Accepted 9 March 2021DOI:https://doi.org/10.1103/PhysRevLett.126.220501© 2021 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMeasurement-based quantum computingQuantum algorithmsQuantum computationQuantum simulationQuantum Information
Memory-efficient GAN-based domain translation of high resolution 3D medical images

Generative adversarial networks (GANs) are currently rarely applied on 3D medical images of large size, due to their immense computational demand. The present work proposes a multi-scale patch-based GAN approach for establishing unpaired domain translation by generating 3D medical image volumes of high resolution in a memory-efficient way. The key idea to enable memory-efficient image generation is to first generate a low-resolution version of the image followed by the generation of patches of constant sizes but successively growing resolutions. To avoid patch artifacts and incorporate global information, the patch generation is conditioned on patches from previous resolution scales. Those multi-scale GANs are trained to generate realistically looking images from image sketches in order to perform an unpaired domain translation. This allows to preserve the topology of the test data and generate the appearance of the training domain data. The evaluation of the domain translation scenarios is performed on brain MRIs of size 155 × 240 × 240 and thorax CTs of size up to 5123. Compared to common patch-based approaches, the multi-resolution scheme enables better image quality and prevents patch artifacts. Also, it ensures constant GPU memory demand independent from the image size, allowing for the generation of arbitrarily large images.
Quantum Codes of Maximal Distance and Highly Entangled Subspaces

We present new bounds on the existence of general quantum maximum distance separable codes (QMDS): the length <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi></mml:math> of all QMDS codes with local dimension <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>D</mml:mi></mml:math> and distance <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>d</mml:mi><mml:mo>≥</mml:mo><mml:mn>3</mml:mn></mml:math> is bounded by <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>n</mml:mi><mml:mo>≤</mml:mo><mml:msup><mml:mi>D</mml:mi><mml:mn>2</mml:mn></mml:msup><mml:mo>+</mml:mo><mml:mi>d</mml:mi><mml:mo>−</mml:mo><mml:mn>2</mml:mn></mml:math>. We obtain their weight distribution and present additional bounds that arise from Rains' shadow inequalities. Our main result can be seen as a generalization of bounds that are known for the two special cases of stabilizer QMDS codes and absolutely maximally entangled states, and confirms the quantum MDS conjecture in the special case of distance-three codes. As the existence of QMDS codes is linked to that of highly entangled subspaces (in which every vector has uniform <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>r</mml:mi></mml:math>-body marginals) of maximal dimension, our methods directly carry over to address questions in multipartite entanglement.
SIMULEVAL: An Evaluation Toolkit for Simultaneous Translation

Simultaneous translation on both text and speech focuses on a real-time and low-latency scenario where the model starts translating before reading the complete source input.Evaluating simultaneous translation models is more complex than offline models because the latency is another factor to consider in addition to translation quality.The research community, despite its growing focus on novel modeling approaches to simultaneous translation, currently lacks a universal evaluation procedure.Therefore, we present SIMULEVAL, an easy-to-use and general evaluation toolkit for both simultaneous text and speech translation.A server-client scheme is introduced to create a simultaneous translation scenario, where the server sends source input and receives predictions for evaluation and the client executes customized policies.Given a policy, it automatically performs simultaneous decoding and collectively reports several popular latency metrics.We also adapt latency metrics from text simultaneous translation to the speech task.Additionally, SIMULEVAL is equipped with a visualization interface to provide better understanding of the simultaneous decoding process of a system.SIMULEVAL has already been extensively used for the IWSLT 2020 shared task on simultaneous speech translation.
Experimental investigation of quantum key distribution over a water channel

Quantum key distribution (QKD) has undergone significant development in recent decades, particularly with respect to free-space (air) and optical fiber channels. Here, we report a proof-of-principle experiment for the BB84 protocol QKD over a water channel. First, we demonstrate again the polarization preservation properties of the water channel in optical transmission according to the measured Mueller matrix, which is close to the unit matrix. The reason for the polarization preservation, revealed by Monte Carlo simulation, is that almost all the received photons are unscattered. Then, we performed the first polarization encoding BB84 protocol QKD over a 2.37 m water channel. The results show that QKD can be performed with a low quantum bit error rate, less than 3.5%, with different attenuation coefficients.
Investigating Transferability in Pretrained Language Models

How does language model pretraining help transfer learning?We consider a simple ablation technique for determining the impact of each pretrained layer on transfer task performance.This method, partial reinitialization, involves replacing different layers of a pretrained model with random weights, then finetuning the entire model on the transfer task and observing the change in performance.This technique reveals that in BERT, layers with high probing performance on downstream GLUE tasks are neither necessary nor sufficient for high accuracy on those tasks.Furthermore, the benefit of using pretrained parameters for a layer varies dramatically with finetuning dataset size: parameters that provide tremendous performance improvement when data is plentiful may provide negligible benefits in data-scarce settings.These results reveal the complexity of the transfer learning process, highlighting the limitations of methods that operate on frozen models or single data samples.
Segment-Based Methods for Facial Attribute Detection from Partial Faces

State-of-the-art methods of attribute detection from faces almost always assume the presence of a full, unoccluded face. Hence, their performance degrades for partially visible and occluded faces. In this paper, we introduce SPLITFACE, a deep convolutional neural network-based method that is explicitly designed to perform attribute detection in partially occluded faces. Taking several facial segments and the full face as input, the proposed method takes a data driven approach to determine which attributes are localized in which facial segments. The unique architecture of the network allows each attribute to be predicted by multiple segments, which permits the implementation of committee machine techniques for combining local and global decisions to boost performance. With access to segment-based predictions, SPLITFACE can predict well those attributes which are localized in the visible parts of the face, without having to rely on the presence of the whole face. We use the CelebA and LFWA facial attribute datasets for standard evaluations. We also modify both datasets, to occlude the faces, so that we can evaluate the performance of attribute detection algorithms on partial faces. Our evaluation shows that SPLITFACE significantly outperforms other recent methods especially for partial faces.