Reviewerly/exHarmony huggingface dataset:
Competitive Gradient Descent

We introduce a new algorithm for the numerical computation of Nash equilibria of competitive two-player games. Our method is a natural generalization of gradient descent to the two-player setting where the update is given by the Nash equilibrium of a regularized bilinear local approximation of the underlying game. It avoids oscillatory and divergent behaviors seen in alternating gradient descent. Using numerical experiments and rigorous analysis, we provide a detailed comparison to methods based on \emph{optimism} and \emph{consensus} and show that our method avoids making any unnecessary changes to the gradient dynamics while achieving exponential (local) convergence for (locally) convex-concave zero sum games. Convergence and stability properties of our method are robust to strong interactions between the players, without adapting the stepsize, which is not the case with previous methods. In our numerical experiments on non-convex-concave problems, existing methods are prone to divergence and instability due to their sensitivity to interactions among the players, whereas we never observe divergence of our algorithm. The ability to choose larger stepsizes furthermore allows our algorithm to achieve faster convergence, as measured by the number of model evaluations.
Label-activating framework for zero-shot learning

Existing zero-shot learning (ZSL) models usually learn mappings between visual space and semantic space. However, few of them take the label information into account. Indirect Attribute Prediction (IAP) learns the posterior probability of each attribute by label space, but labels of seen and unseen classes are defined in different spaces, which is not suitable for Generalized ZSL (GZSL). We propose a Label-Activating Framework (LAF) for semantic-based classification. The purpose of the proposed framework is to activate the label space by learning mappings from vision and semantics to labels. In the training phase, the original label space made up of one-hot vectors is used as common space, on which visual features and semantic information are embedded. After the label space is activated, labels of unseen classes can be regarded as the linear combination of labels of seen classes. In this case, seen and unseen labels are defined in the same space, and the label space has specific meanings rather than only signs of each class. Doing so makes the activated label space become very discriminative, especially for GZSL, which is therefore more challenging and reasonable for real-world tasks. In addition, we develop a specific model based on the framework, which effectively mitigate the projection domain shift problem. Extensive experiments show our framework outperforms state-of-the-art methods and also its suitability for GZSL.
How DNS over HTTPS is Reshaping Privacy, Performance, and Policy in the Internet Ecosystem

Internet communication relies on the Domain Name System (DNS), which maps a human-readable Internet destination to an IP address. A recent proposal for transmitting DNS over HTTPS (DoH) enhances client privacy by tunneling DNS over secure HTTP (HTTPS). In this paper, we explore the policy implications of consolidated DoH by systematically analyzing the marketplace, measure its performance effects, and investigate how it affects the different stakeholders, including consumers. We enumerate the agents in the marketplace as well as their market incentives. We then examine the performance of DoH through client-based measurements compare unencrypted DNS with DoH. As DoH deployments change the competitive landscape of the market, we explore their effect on other operators, ISPs, and broadband access at the last mile, as well as the potential regulatory and policy implications of DoH deployments.
CNN-based superresolution reconstruction of 3D MR images using thick-slice scans

Due to inherent physical and hardware limitations, 3D MR images are often acquired in the form of orthogonal thick slices, resulting in highly anisotropic voxels. This causes the partial volume effect, which introduces blurring of image details, appearance of staircase artifacts and significantly decreases the diagnostic value of images. To restore high resolution isotropic volumes, we propose to use a convolutional neural network (CNN) driven by patches taken from three orthogonal thick-slice images. To assess the validity and efficiency of this postprocessing approach, we used 1x1x1 mm3-voxel brain images of different modalities, available via the well known BrainWeb database. They served as a high resolution reference and were numerically preprocessed to create input images of different slice thickness and anatomical orientation, for CNN training, validation and testing. The visual quality of reconstructed images was indeed superior, compared to images obtained by fusion of interpolated thick-slice images, or to images reconstructed with the CNN using a single input MR scan. The significant increase of objectively computed figures of merit, e.g. the Structural Similarity Image Metric, was also noticed. Keeping in mind that any single value of such quality metrics represents a number of psychophysical effects, we applied the CNN trained on brain images for superresolution reconstruction of synthetic and acquired blood vessel tree images. We then used the restored superresolution volumes for estimation of vessel radii. It was demonstrated that vessel radius values derived from superresolution images of simulated vessel trees are significantly more accurate than those obtained from a standard fusion of interpolated thick-slice orthogonal scans. Superiority of the CNN-based superresolution images was also observed for scanner-acquired MR scans according to the evaluated parameters. These three experiments show the efficiency of CNN-based image reconstruction for qualitative and quantitative improvement of its diagnostic quality, as well as illustrates the practical usefulness of transfer learning - networks trained on example images of one kind can be used to restore superresolution images of physically different objects.
Multiple-image encryption and hiding with an optical diffractive neural network

A cascaded phase-only mask architecture (or an optical diffractive neural network) can be employed for different optical information processing tasks such as pattern recognition, orbital angular momentum (OAM) mode conversion, image salience detection and image encryption. However, for optical encryption and watermarking applications, such a system usually cannot process multiple pairs of input images and output images simultaneously. In our proposed scheme, multiple input images can be simultaneously fed to an optical diffractive neural network (DNN) system and each corresponding output image will be displayed in a non-overlap sub-region in the output imaging plane. Each input image undergoes a different optical transform in an independent channel within the same system. The multiple cascaded phase masks in the system can be effectively optimized by a wavefront matching algorithm. Similar to recent optical pattern recognition and mode conversion works, the orthogonality property is employed to design a multiplexed DNN.
Persona Perception Scale: Development and Exploratory Validation of an Instrument for Evaluating Individuals’ Perceptions of Personas

Although used in many domains, the evaluation of personas is difficult due to the lack of validated measurement instruments. To tackle this challenge, we propose the Persona Perception Scale (PPS), a survey instrument for evaluating how individuals perceive personas. We develop the scale by reviewing relevant literature from social psychology, persona studies, and Human-Computer Interaction to find relevant constructs and items for measuring persona perceptions. Following initial pilot testing, we conduct an exploratory validation of the scale with 412 respondents and find that the constructs and items of the scale perform satisfactorily for deployment. The research has implications for both academic researchers and persona developers. Using the PPS, researchers and designers can evaluate how different persona designs affect individual perceptions of personas, for example persona users’ (e.g., designers, marketers, software developers) perceived credibility of the persona and their willingness to use it. Because persona perceptions are associated with persona acceptance and adoption, using a perceptual measurement instrument can improve the chances of persona adoption and use in real organizations.
Voting extreme learning machine based distributed denial of service attack detection in cloud computing

Distributed denial of service attack is one of the most dangerous attacks in cloud computing. This attack makes the cloud services inaccessible to the end-users by exhausting resources, resulting in heavy economic and reputation loss. So, developing defensive solutions against these attacks is necessary for the widespread adoption of cloud computing technology. This paper proposes a new system for detecting DDoS attacks in cloud computing environment. The proposed system is built using voting extreme learning machine (V-ELM), a type of artificial neural network. Experiments were performed to evaluate the performance of the proposed system by using two benchmark datasets viz. NSL-KDD dataset and ISCX intrusion detection dataset. It has been shown by experiments that the proposed system detects attacks with an accuracy of 99.18% with the NSL-KDD dataset and 92.11% with the ISCX dataset. Performance of the proposed system has been compared with other systems based on backpropagation artificial neural network, artificial neural network trained with black hole optimization, extreme learning machine, random forest and, adaboost. It performs better than these systems. Experiments have also been performed to analyze the performance of the proposed system under different parameter values viz. number of ELMs in V-ELM and number of hidden layer neurons in a single ELM.
The Role of Coded Side Information in Single-Server Private Information Retrieval

We study the role of coded side information in single-server Private Information Retrieval (PIR). An instance of the single-server PIR problem includes a server that stores a database of K independently and uniformly distributed messages, and a user who wants to retrieve one of these messages from the server. We consider settings in which the user initially has access to a coded side information which includes a linear combination of a subset of M messages in the database. We assume that the identities of the M messages that form the support set of the coded side information as well as the coding coefficients are initially unknown to the server. We consider two different models, depending on whether the support set of the coded side information includes the requested message or not. We also consider the following two privacy requirements: (i) the identities of both the demand and the support set of the coded side information need to be protected, or (ii) only the identity of the demand needs to be protected. For each model and for each of the privacy requirements, we consider the problem of designing a protocol for generating the user's query and the server's answer that enables the user to decode the message they need while satisfying the privacy requirement. We characterize the (scalar-linear) capacity of each setting, defined as the ratio of the number of information bits in a message to the minimum number of information bits downloaded from the server over all (scalar-linear) protocols that satisfy the privacy condition. Our converse proofs rely on new information-theoretic arguments-tailored to the setting of single-server PIR and different from the commonly-used techniques in multi-server PIR settings. We also present novel capacity-achieving scalar-linear protocols for each of the settings being considered.
Defending Deep Learning Models Against Adversarial Attacks

Deep learning (DL) has been used globally in almost every sector of technology and society. Despite its huge success, DL models and applications have been susceptible to adversarial attacks, impacting the accuracy and integrity of these models. Many state-of-the-art models are vulnerable to attacks by well-crafted adversarial examples, which are perturbed versions of clean data with a small amount of noise added, imperceptible to the human eyes, and can quite easily fool the targeted model. This paper introduces six most effective gradient-based adversarial attacks on the ResNet image recognition model, and demonstrates the limitations of traditional adversarial retraining technique. The authors then present a novel ensemble defense strategy based on adversarial retraining technique. The proposed method is capable of withstanding the six adversarial attacks on cifar10 dataset with accuracy greater than 89.31% and as high as 96.24%. The authors believe the design methodologies and experiments demonstrated are widely applicable to other domains of machine learning, DL, and computation intelligence securities.
What is being transferred in transfer learning

One desired capability for machines is the ability to transfer their knowledge of one domain to another where data is (usually) scarce. Despite ample adaptation of transfer learning in various deep learning applications, we yet do not understand what enables a successful transfer and which part of the network is responsible for that. In this paper, we provide new tools and analyses to address these fundamental questions. Through a series of analyses on transferring to block-shuffled images, we separate the effect of feature reuse from learning low-level statistics of data and show that some benefit of transfer learning comes from the latter. We present that when training from pre-trained weights, the model stays in the same basin in the loss landscape and different instances of such model are similar in feature space and close in parameter space.
Heisenberg-limited single-mode quantum metrology in a superconducting circuit

Abstract Two-mode interferometers lay the foundations for quantum metrology. Instead of exploring quantum entanglement in the two-mode interferometers, a single bosonic mode also promises a measurement precision beyond the shot-noise limit (SNL) by taking advantage of the infinite-dimensional Hilbert space of Fock states. Here, we demonstrate a single-mode phase estimation that approaches the Heisenberg limit (HL) unconditionally. Due to the strong dispersive nonlinearity and long coherence time of a microwave cavity, quantum states of the form $$\left( {\left| 0 \right\rangle + \left| N \right\rangle } \right)/\sqrt 2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mfenced> <mml:mrow> <mml:mfenced> <mml:mrow> <mml:mn>0</mml:mn> </mml:mrow> </mml:mfenced> <mml:mo>+</mml:mo> <mml:mfenced> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:mfenced> </mml:mrow> </mml:mfenced> <mml:mo>∕</mml:mo> <mml:msqrt> <mml:mn>2</mml:mn> </mml:msqrt> </mml:math> can be generated, manipulated and detected with high fidelities, leading to an experimental phase estimation precision scaling as ∼ N −0.94 . A 9.1 dB enhancement of the precision over the SNL at N = 12 is achieved, which is only 1.7 dB away from the HL. Our experimental architecture is hardware efficient and can be combined with quantum error correction techniques to fight against decoherence, and thus promises quantum-enhanced sensing in practical applications.
Comment toxicity detection via a multichannel convolutional bidirectional gated recurrent unit

Recently, toxicity identification has become the most serious problem in online communities and social networking sites. Therefore, an automatic toxic identification system needs to be developed for preventing and limiting users from these online environments. In this paper, we present a multichannel convolutional bidirectional gated recurrent unit (MCBiGRU) for detecting toxic comments in a multilabel environment. The proposed model generates word vectors using pre-trained word embeddings. Moreover, this hybrid model extracts local features with many filters and different kernel sizes to model input words with long term dependency. We then integrate multiple channels with a fully connected layer, normalization layer, and an output layer with a sigmoid activation function for predicting multilabel categories. The experimental results indicate that the proposed MCBiGRU model outperforms in terms of multilabel metrics.
An experimental study on augmented reality assisted manual assembly with occluded components

Augmented Reality (AR) technology has increasingly been applied to facilitate manufacturing tasks such as training, maintenance, and safety management, in which manual assembly frequently occurs. Previously, studies confirmed the values of AR-based assembly systems, but few explored support of spatially restricted assembly where components are visually occluded from operator. In this regard, this research aims to develop new AR functions that assist manual assembly in such situations. The focus is on validating the effectiveness of various assistive information in AR, including assembly interface, operator’s hand movement, and operator held components. Subjective and objective measures are used to evaluate assembly experiments of different degrees of difficulty. Analyses of the experimental data reveal whether or not and how effectively each information offers guidance in occluded condition. In particular, a time reduction in more difficult assembly is realized by showing operator hand movement in AR. The hand model initially offers an operator a visual clue for quickly and roughly locating the assembly interface in a large unseen area, prior to precisely localizing in a smaller region guided by tactile sensing. However, the effectiveness of incorporating the held component is not evident, as positional deviation between real and virtual objects may reduce human’s hand-eye coordination. These findings not only provide preliminary design guidelines of AR assembly functions for occluded components but also demonstrates a novel yet practical application of AR technology in smart manufacturing.
Environmental neuroscience.

Environmental neuroscience is an emerging field devoted to the scientific study of brain-mediated, bidirectional relationships between organisms and their social and physical environments. A key feature of environmental neuroscience is the rigorous quantification of environmental features that affect the brain and subsequent behavior. In addition, environmental neuroscience considers factors that vary across multiple temporal and spatial scales that interact to produce behavior (e.g., synapses, neural circuits, cognition, local social interactions, citywide social interactions, citywide physical structures). Environmental neuroscientists then measure the spatial and temporal dynamics of the interactions between different levels of analysis. For example, we demonstrate through hierarchical systems theory and mathematical modeling how interacting with urban greenspace may reduce psychopathology via improvements in neurocognitive functioning, which, in turn, may increase social interactions. This example illustrates how different levels of analysis (e.g., neurocognitive factors, the physical environment, and the social environment) may be combined to understand behavior in novel ways. In addition, we advocate for the collection of data across these scales and measuring their interactions, which will generate rich data sets that will continue to yield insights as new ways to model these complex multilevel systems are developed. We believe that examining all of these levels of analysis at different temporal and spatial scales in addition to modeling their relationships will lead to advances in understanding behavior. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Accenture/Ponemon Institute: The Cost of Cybercrime

Now in its ninth year, this annual report never makes for comfortable reading. The numbers it places on the consequences to businesses of falling victim to cybercrime are simply eye-watering. And while Accenture, the sponsor of this year's report, tries to put a positive spin on the issue, the overall message is that things are getting worse.
TextNet: Irregular Text Reading from Images with an End-to-End Trainable Network

Reading text from images remains challenging due to multi-orientation, perspective distortion and especially the curved nature of irregular text. Most of existing approaches attempt to solve the problem in two or multiple stages, which is considered to be the bottleneck to optimize the overall performance. To address this issue, we propose an end-to-end trainable network architecture, named TextNet, which is able to simultaneously localize and recognize irregular text from images. Specifically, we develop a scale-aware attention mechanism to learn multi-scale image features as a backbone network, sharing fully convolutional features and computation for localization and recognition. In text detection branch, we directly generate text proposals in quadrangles, covering oriented, perspective and curved text regions. To preserve text features for recognition, we introduce a perspective RoI transform layer, which can align quadrangle proposals into small feature maps. Furthermore, in order to extract effective features for recognition, we propose to encode the aligned RoI features by RNN into context information, combining spatial attention mechanism to generate text sequences. This overall pipeline is capable of handling both regular and irregular cases. Finally, text localization and recognition tasks can be jointly trained in an end-to-end fashion with designed multi-task loss. Experiments on standard benchmarks show that the proposed TextNet can achieve state-of-the-art performance, and outperform existing approaches on irregular datasets by a large margin.
Three-dimensional analysis reveals two major architectural subgroups of prostate cancer growth patterns

The Gleason score is one of the most important parameters for therapeutic decision-making in prostate cancer patients. Gleason growth patterns are defined by their histological features on 4- to 5-µm cross sections, and little is known about their three-dimensional architecture. Our objective was to characterize the three-dimensional architecture of prostate cancer growth patterns. Intact tissue punches (n = 46) of representative Gleason growth patterns from radical prostatectomy specimens were fluorescently stained with antibodies targeting Keratin 8/18 and Keratin 5 for the detection of luminal and basal epithelial cells, respectively. Punches were optically cleared in benzyl alcohol-benzyl benzoate and imaged using a confocal laser scanning microscope up to a depth of 500 µm. Gleason pattern 3, poorly formed pattern 4, and cords pattern 5 all formed a continuum of interconnecting tubules in which the diameter of the structures and the lumen size decreased with higher grades. In fused pattern 4, the interconnections between the tubules were markedly closer together. In these patterns, all tumor cells were in direct contact with the surrounding stroma. In contrast, cribriform Gleason pattern 4 and solid pattern 5 demonstrated a three-dimensional continuum of contiguous tumor cells, in which the vast majority of cells had no contact with the surrounding stroma. Transitions between cribriform pattern 4 and solid pattern 5 were seen. There was a decrease in the number and size of intercellular lumens from cribriform to solid growth pattern. Glomeruloid pattern 4 formed an intermediate structure consisting of a tubular network with intraluminal epithelial protrusions close to the tubule splitting points. In conclusion, three-dimensional microscopy revealed two major architectural subgroups of prostate cancer growth patterns: (1) a tubular interconnecting network including Gleason pattern 3, poorly formed and fused Gleason pattern 4, and cords Gleason pattern 5, and (2) serpentine contiguous epithelial proliferations including cribriform Gleason pattern 4 and solid Gleason pattern 5.