Reviewerly/exHarmony huggingface dataset:
Normalization by Evaluation for Call-By-Push-Value and Polarized Lambda Calculus

We observe that normalization by evaluation for simply-typed lambda-calculus with weak coproducts can be carried out in a weak bi-cartesian closed category of presheaves equipped with a monad that allows us to perform case distinction on neutral terms of sum type. The placement of the monad influences the normal forms we obtain: for instance, placing the monad on coproducts gives us eta-long beta-pi normal forms where pi refers to permutation of case distinctions out of elimination positions. We further observe that placing the monad on every coproduct is rather wasteful, and an optimal placement of the monad can be determined by considering polarized simple types inspired by focalization. Polarization classifies types into positive and negative, and it is sufficient to place the monad at the embedding of positive types into negative ones. We consider two calculi based on polarized types: pure call-by-push-value (CBPV) and polarized lambda-calculus, the natural deduction calculus corresponding to focalized sequent calculus. For these two calculi, we present algorithms for normalization by evaluation. We further discuss different implementations of the monad and their relation to existing normalization proofs for lambda-calculus with sums. Our developments have been partially formalized in the Agda proof assistant.
Parallel Nested Rollout Policy Adaptation

Nested Rollout Policy Adaptation (NRPA) is a Monte Carlo tree search algorithm that excels in the domain of single agent optimization problems. It was applied to a wide class of problems, including vehicle routing, DNA alignment, 3D packing, travelling salesman problem, combinatorial puzzles and more. We develop a parallel version of NRPA that replicates results of the sequential version. The parallelization allows us to run deeper calculations. The experimental data shows that depth of the calculation is a deciding factor in the result quality. Earlier parallelization attempts used parallel architecture to run wider, but not deeper, calculations. We applied the parallel version to the Morpion Solitaire benchmark. To aid parallelization, we used a different best result replacement rule. Using the new rule, on a distributed architecture with 768 cores we obtained an average speedup over a single core computation by a factor 547.48. For the first time a level 6 NRPA computation with 80 iterations per level was finished. It was completed in less than 20 hours of wall-clock time. The different replacement rule was effective. In each run, the record 178-move sequence was found.
Comparing Randomization Strategies for Search-Control Parameters in Monte-Carlo Tree Search

Monte-Carlo Tree Search (MCTS) has been applied successfully in many domains. Previous research has shown that adding randomization to certain components of MCTS might increase the diversification of the search and improve the performance. In a domain that tackles many games with different characteristics, like General Game Playing (GGP), trying to diversify the search might be a good strategy. This paper investigates the effect of randomizing search-control parameters for MCTS in GGP. Four different randomization strategies are compared and results show that randomizing parameter values before each simulation has a positive effect on the search in some of the tested games. Moreover, parameter randomization is compared with on-line parameter tuning.
Co-Located vs. Remote Gameplay: The Role of Physical Co-Presence in Multiplayer Room-Scale VR

Our research investigates the role of physical co-presence for the experience of multiplayer cooperation in virtual reality (VR). We developed and compared a shared-room (co-located) and separated-room (remote) version of a cooperative two-player VR game. In an extensive study (N=92) we assessed measures of task awareness and spatial presence, as well as player communication, interaction, and performance. Our results indicate that players sharing the same physical space tend to neglect the cooperative task. Moreover, our results indicate that being physically separated can beneficially impact the perceived cooperative social presence, quality of communication, and performance in contrast to sharing a physical space. The role of physical co-location in room-scale VR and potential confounding effects caused by it, as well as implications for further research, are discussed.
Automated Scoring of Chatbot Responses in Conversational Dialogue

Rapid advancementYuwono, Steven Kester inWu, Biao naturalD’Haro, Luis Fernando language processing (NLP) and machine learning has led to the recent development of many chatbot systems using various algorithms. However, in a conversational dialogue setting, creating a system to communicate with humans in a meaningful and coherent manner remains a challenging task. Furthermore, it is very difficult even for humans to evaluate the responses of a chatbot system given the context of the conversation. In this paper, we will focus on the problem of automatically evaluating and scoring the quality of chatbot responses in human-chatbot dialogue settings. We propose a novel approach of combining the word representations of human and chatbot responses, and using machine learning algorithms, such as support vector machines (SVM), random forests (RF), and neural networks (NN) to learn the quality of the chatbot responses. Our experimental results show that our proposed approach is able to perform well.
Cross-Domain Transfer Learning for Dependency Parsing

In recent years, the research of dependency parsing focuses on improving the accuracy of in-domain data and has made remarkable progress. However, the real world is different from a single scenario dataset, filled with countless scenarios that are not covered by the dataset, namely, out-of-domain. As a result, parsers that perform well on the in-domain data often suffer significant performance degradation on the out-of-domain data. Therefore, in order to adapt the existing in-domain parsers with substantial performance to the new domain scenario, cross-domain transfer learning techniques are essential to solve the domain problem in parsing. In this paper, we examine two scenarios for cross-domain transfer learning: semi-supervised and unsupervised cross-domain transfer learning. Specifically, we adopt a pretrained language model BERT for training on the source domain (in-domain) data at subword level and introduce two tri-training variant methods for the two scenarios so as to achieve the goal of cross-domain transfer learning. The system based on this paper participated in NLPCC-2019-shared-task on cross-domain dependency parsing and won the first place on the “subtask3-un-open” and “subtask4-semi-open” subtasks, indicating the effectiveness of the approaches adopted.
A mixed methods probe into the direct disclosure of software vulnerabilities

Software vulnerabilities are security-related software bugs. Direct disclosure refers to a practice that is widely used for communicating the confidential information about vulnerabilities between two parties, vulnerability discoverers and software producers. Building on software vulnerability life cycle analysis, this empirical paper observes the qualitative and quantitative characteristics of direct disclosure practices, focusing particularly on the historical problem related to producers’ reluctance to participate in the practices. According to the results, the problem was still present in the 2000s and early 2010s—and likely is still present today. By presenting this empirical result about the under researched phenomenon of direct disclosure of software vulnerabilities, the paper contributes to the research domain of vulnerability life cycle modeling in general and the subdomain of empirical vulnerability disclosure research in particular.
Challenges in Assessing Network Latency Impact on QoE and In-Game Performance in VR First Person Shooter Games

In this paper we present the results of two user studies focused on exploring Virtual Reality (VR) first person shooter games. We observe and analyze the impact of network latency on subjective (Quality of Experience, willingness to continue playing in given network conditions, perceived weapon precision) and objective (in-game survival rates) metrics, leading to the conclusion that VR games potentially display a degree of latency-sensitivity that is comparable to first-person non-VR games. Furthermore, after comparing inconsistencies in the outcomes of conducted studies, we provide a detailed discussion on noise-introducing contextual factors (weapon choice, level of difficulty, social context) that might have influenced the results of our initial study, and highlight the impact of methodology on the overall conclusiveness of user studies focused on multiplayer games. These findings may serve as guidelines for the design of future studies on networked VR applications and multiplayer games in general.
The reference corpus matters

Abstract This study investigates the effect that reference corpora of different registers have on the content of keyword lists. The study focusses on two target corpora and the keyword lists generated for each when using three distinct reference corpora. The two target corpora consist of published research by faculty at two PhD-granting programs in applied linguistics in North America. The reference corpora comprise published research in applied linguistics, newspaper and magazine articles, and fiction texts, respectively. The findings suggest that while common keywords representing each target corpus emerge regardless of the reference corpus used in the analysis, there are also substantial differences. Primarily, using a reference corpus of the same sub-register as the target corpus better highlights content unique to each target corpus while using a reference corpus of a different register better uncovers words that reflect the register that the target corpora represent. Implications for conducting keyword analysis are discussed.
Rain and snow removal using multi-guided filter and anisotropic gradient in the quaternion framework

In many cases the rain and snow on an image significantly degrade the effectiveness of any computer vision algorithm, such as object recognition, tracking, retrieving and so on. The automated detection and removing such degradations in a color image is still a challenging task. This paper presents a new rain and snow removal method using low- and highfrequency parts of a single image. For this purpose, we use a color image multi-guided filter and anisotropic gradient in Hamiltonian quaternions. The quaternion framework is used to represent a color image to take into account all three channels simultaneously when inpainting the RGB image. Our results show that it has good performance in rain removal and snow removal.
Faithful Simulation of Distributed Quantum Measurements with Applications in Distributed Rate-Distortion Theory

We investigate faithful simulation of distributed quantum measurements as an extension of Winter’s measurement compression theorem. We characterize a set of communication and common randomness rates needed to provide faithful simulation of distributed measurements. To achieve this, we introduce binning and mutual packing lemma for distributed quantum measurements. These techniques can be viewed as the quantum counterpart of their classical analogues. Finally, using these results, we develop a distributed quantum-to-classical rate distortion theory and characterize a rate region analogous to Berger-Tung’s in terms of single-letter quantum mutual information quantities.
Isolated Chinese Sign Language Recognition Using Gray-Level Co-occurrence Matrix and Parameter-Optimized Medium Gaussian Support Vector Machine

In order to recognize Chinese sign language more accurately, we proposed an efficient method using gray-level co-occurrence matrix (GLCM) and parameter-optimized medium Gaussian support vector machine (MGSVM). First, sign language images were acquired by digital camera or picked from video as keyframes, and then the hand shapes were segmented from background. Second, each image was resized to N × N size and converted into gray-level image. The number of intensity values in grayscale image was reduced from 256 to 8, and gray-level co-occurrence matrix was created. Third, the extracted and reduced features were sent to MGSVM; meanwhile, the classification was performed on a tenfold cross-validation. The experimental results of the 450 isolated Chinese sign language images from the 30 categories demonstrated that the GLCM–MGSVM achieved a classification accuracy of 85.3%, which was much higher than GLCM-DT (decision tree). Therefore, the GLCM-MGSVM was seen to be effective in classifying Chinese sign language.
Vulnerability of Covariate Shift Adaptation Against Malicious Poisoning Attacks

Adversarial machine learning has recently risen to prominence due to increased concerns over the vulnerability of machine learning algorithms to malicious attacks. While the impact of malicious poisoning attacks on some popular algorithms, such as deep neural networks, has been well researched, the vulnerability of other approaches has not yet been properly established. In this effort, we explore the vulnerability of unconstrained least squares importance fitting (uLSIF), an algorithm used for computing the importance ratio for covariate shift domain adaptation problems. The uLSIF algorithm is an accurate and efficient technique to compute the importance ratio; however, we show that the approach is susceptible to a poisoning attack, where an intelligent adversary – having full or partial access to the training data – can inject well crafted malicious samples into the training data, resulting in an incorrect estimation of the importance values. Through strategically designed synthetic as well as real world datasets, we demonstrate that importance ratio estimation through uLSIF algorithm can be easily compromised with the insertion of even modest number of attack points into the training data. We also show that incorrect estimation of importance values can then cripple the performance of a subsequent covariate shift adaptation.
Bitcoin's Fatal Flaw: The Limited Adoption Problem

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Efficient Contextual Representation Learning With Continuous Outputs

Contextual representation models have achieved great success in improving various downstream natural language processing tasks. However, these language-model-based encoders are difficult to train due to their large parameter size and high computational complexity. By carefully examining the training procedure, we observe that the softmax layer, which predicts a distribution of the target word, often induces significant overhead, especially when the vocabulary size is large. Therefore, we revisit the design of the output layer and consider directly predicting the pre-trained embedding of the target word for a given context. When applied to ELMo, the proposed approach achieves a 4-fold speedup and eliminates 80% trainable parameters while achieving competitive performance on downstream tasks. Further analysis shows that the approach maintains the speed advantage under various settings, even when the sentence encoder is scaled up.
Identification of Multi-Dimensional Electromagnetic Information Leakage Using CNN

The unintentional electromagnetic (EM) radiation from electronic information equipment will cause the leakage of information. EM information leakage identification is an important research area of EM information security. Previous studies only focused on a particular type of information leakage, ignoring that there are multiple types of information contained in the leaked EM signal. This paper presents a multi-dimensional information leakage model for the first time, which reveals the multiple information leakage risks in an unintentional EM radiation. The EM information leakage of computer monitors is taken as an example to verify the model. Furthermore, a Convolutional Neural Network (CNN) based multi-dimensional information identification method is proposed. This novel method designed a unified neural network architecture to identify multiple information leaked by the same EM radiation from the monitor. It overcomes the limitation of available identification methods that need to definite leakage characteristics for each type of information leakage. The effectiveness of the method is validated by contrast experiments.
A New Perspective on Improving the Lossless Compression Efficiency for Initially Acquired Images

A new lossless compression scheme of compressing the initially-acquired continuous-intensity images with a lossy compression algorithm to obtain higher compression efficiency is proposed. Even if a lossy algorithm is employed, for decoded original images, there is no loss of data in the same sense as the conventional lossless scheme. To realize the new idea, the compression efficiency of the existing lossy subband compression algorithm is improved at high bitrates. For the entropy coding part, a run-length based, symbol-grouping entropy coding method is introduced. For the quantization part, the entropy-constrained scalar quantization is implemented using a novel and simple thresholding method. Coding results show that bit savings of the proposed lossless scheme, which employs a lossy algorithm, over the conventional lossless scheme achieve a maximum of 27.2% and an average of 11.4% in our test.
Symmetries for a high-level neural decoder on the toric code

Surface codes are a promising method of quantum error correction and the basis of many proposed quantum computation implementations. However, their efficient decoding is still not fully explored. Recently, approaches based on machine learning techniques have been proposed by Torlai and Melko [Phys. Rev. Lett. 119, 030501 (2017)] as well as Varsamopoulos et al. [Quantum Sci. Technol. 3, 015004 (2017)]. In these approaches, a so-called high-level decoder is used to post-correct an underlying decoder by correcting logical errors. A significant problem is that these methods require large amounts of training data even for relatively small code distances. The above-mentioned methods were tested on the rotated surface code which encodes one logical qubit. Here, we show that they are viable even for the toric surface code which encodes two logical qubits. Furthermore, we explain how symmetries of the toric code can be exploited to reduce the amount of training data that is required to obtain good decoding results. Finally, we compare different underlying decoders and show that the accuracy of high-level decoding noticeably depends on the quality of the underlying decoder in the realistic case of imperfect training.
As-Aligned-As-Possible Image Stitching Based on Deviation-Corrected Warping With Global Similarity Constraints

Handling local misalignment caused by the local warp remains a common and challenging task for image stitching. Moreover, the stitched image is prone to appearing ghosting due to the variations of the image viewpoint between images. To solve the problem of local misalignment, we propose a projection deviation-corrected local warping method with a global similarity constraint for image stitching. Recent warps prove that the warp of the local mesh guide image effectively improves the accuracy of image alignment. Geometric projection deviation is well used to accurately correct pixel offsets in image warping. To correct pixel offsets, we first remove the outliers from matching points by using the normal distribution model. The retained matches are more precise and can improve the accuracy of image alignment. Next, we use the local warping model combining local homography and global similarity for image warping. To further address the misalignment problem caused by local warping, we describe the local projection deviation of the local warping model by adopting a three-dimensional mesh interpolation model. Finally, the warped images are blended by a linear smoothing model. Experimental results show that our method outperforms the state-of-the-arts in alignment accuracy, and also provides better visual effects on challenging images.
Kernel Stochastic Separation Theorems and Separability Characterizations of Kernel Classifiers

In this work we provide generalizations and extensions of stochastic separation theorems to kernel classifiers. A general separability result for two random sets is also established. We show that despite feature maps corresponding to a given kernel function may be infinite-dimensional, kernel separability characterizations can be expressed in terms of finite-dimensional volume integrals. These integrals allow to determine and quantify separability properties of an arbitrary kernel function. The theory is illustrated with numerical examples.
Single Underwater Image Enhancement Based on $L_{P}$ -Norm Decomposition

Integration of ocean monitoring networks with artificial intelligence has become a popular topic for researchers. Artificial intelligence plays an important role in underwater image processing. For optical images captured in an underwater environment, the light scattering and absorption effect caused by the water medium results in poor visibility, such as blur and color casts. A novel approach is proposed herein to enhance the single underwater image with poor visibility. Similar to other image enhancement strategies built on fusion principles, our method also generates two input channels from the original degraded image, and these two channels are modulated by their corresponding weight measures. However, the main innovation of our method is that we propose a new multilevel decomposition approach based on I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm (ρ = 0, 1, 2) decomposition. According to the different sparse representation abilities of I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm to an image's spatial information, our approach decomposes the image into three levels: detail level, structure level, and illuminance level. Thus, these three levels can be manipulated separately. Because this new decomposition approach is based on image structural contents, rather than direct per-pixel downsampling that is utilized in traditional multi-resolution pyramid decomposition, it is more accurate and flexible. Additionally, according to specific underwater imaging conditions, we carefully select two input channels and their three associated global contrast, local contrast, and saliency weight measures. Our method generates output with more accurate details and a better illuminant dynamic range. Generally, we are the first to impose an I <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm-based decomposition strategy on underwater image restoration and enhancement. Extensive qualitative and quantitative evaluations demonstrate that our strategy yields better results than state-of-the-art algorithms.
Cryptocurrency value changes in response to national elections: do they behave like money or commodities?

This study examines changes in the value of Bitcoin after the BREXIT and US Presidential elections in 2016 to determine whether it and other cryptocurrencies experienced value changes associated with the vote outcomes. For comparative purposes, Bitcoin is also compared to currencies such as the Great Britain Pound, Mexican Peso and Special Drawing Rights to determine whether Bitcoin values change like currencies. Some evidence showed changes in the value of Bitcoin were weakly similar to store-of-wealth commodities like gold and silver. Overall, the election-window volatility of cryptocurrencies was much higher than the preceding one-year average for the BREXIT vote but less volatile than the one-year average for the US election vote.