Reviewerly/exHarmony huggingface dataset:
Design of virtual reality augmented reality mobile platform and game user behavior monitoring using deep learning

To explore the design of the virtual reality (VR) augmented reality (AR) mobile platform and game decision model based on deep learning (DL), the gesture-based interaction of VR games based on Leap Motion is researched. Based on the interactive features of gestures, a set of general gesture interaction rules in VR games is established. In the meantime, according to the theoretical basis and the characteristics of VR, a set of general models of VR gesture interaction is designed, the factors affecting the efficiency of VR gesture interaction are studied, and reasonable interaction feedback is designed. By using the computer vision and image processing technology, gesture-based interaction can collect natural gestures, extract gesture features, recognize gesture indications, and respond to the user demands. Also, it can extract the basic gestures from gesture-based interaction in VR, analyze the basic features of gestures and gesture-based interaction in VR games, and describes the gesture features by mathematical vectors and sets. The research results show that the application of gesture feature design method in the game can analyze the factors affecting the interaction efficiency. Also, the usability of the gesture-based interaction designed by the gesture design method is verified by tests. Therefore, the AR&amp;DL platform of “AR+DL” establishes a learning platform supported by DL and AR technology. The game decision model is used to describe the process of gesture-based interaction in the game, and the factors affecting the interaction efficiency are reduced, which has certain reference and guidance for VR applications using gesture-based interaction.
Actor-Context-Actor Relation Network for Spatio-Temporal Action Localization

Localizing persons and recognizing their actions from videos is a challenging task towards high-level video understanding. Recent advances have been achieved by modeling direct pairwise relations between entities. In this paper, we take one step further, not only model direct relations between pairs but also take into account indirect higher-order relations established upon multiple elements. We propose to explicitly model the Actor-Context-Actor Relation, which is the relation between two actors based on their interactions with the context. To this end, we design an Actor-Context-Actor Relation Network (ACAR-Net) which builds upon a novel High-order Relation Reasoning Operator and an Actor-Context Feature Bank to enable indirect relation reasoning for spatio-temporal action localization. Experiments on AVA and UCF101-24 datasets show the advantages of modeling actor-context-actor relations, and visualization of attention maps further verifies that our model is capable of finding relevant higher-order relations to support action detection. Notably, our method ranks first in the AVA-Kineticsaction localization task of ActivityNet Challenge 2020, out-performing other entries by a significant margin (+6.71mAP). Training code and models will be available at https://github.com/Siyu-C/ACAR-Net.
Is the Skip Connection Provable to Reform the Neural Network Loss Landscape?

The residual network is now one of the most effective structures in deep learning, which utilizes the skip connections to “guarantee" the performance will not get worse. However, the non-convexity of the neural network makes it unclear whether the skip connections do provably improve the learning ability since the nonlinearity may create many local minima. In some previous works [Freeman and Bruna, 2016], it is shown that despite the non-convexity, the loss landscape of the two-layer ReLU network has good properties when the number m of hidden nodes is very large. In this paper, we follow this line to study the topology (sub-level sets) of the loss landscape of deep ReLU neural networks with a skip connection and theoretically prove that the skip connection network inherits the good properties of the two-layer network and skip connections can help to control the connectedness of the sub-level sets, such that any local minima worse than the global minima of some two-layer ReLU network will be very “shallow". The “depth" of these local minima are at most O(m^(η-1)/n), where n is the input dimension, η&lt;1. This provides a theoretical explanation for the effectiveness of the skip connection in deep learning.
A Convex Variational Model for Restoring SAR Images Corrupted by Multiplicative Noise

This paper studies a new convex variational model for denoising and deblurring images with multiplicative noise. Considering the statistical property of the multiplicative noise following Nakagami distribution, the denoising model consists of a data fidelity term, a quadratic penalty term, and a total variation regularization term. Here, the quadratic penalty term is mainly designed to guarantee the model to be strictly convex under a mild condition. Furthermore, the model is extended for the simultaneous denoising and deblurring case by introducing a blurring operator. We also study some mathematical properties of the proposed model. In addition, the model is solved by applying the primal-dual algorithm. The experimental results show that the proposed method is promising in restoring (blurred) images with multiplicative noise.
Semantic Parsing for English as a Second Language

This paper is concerned with semantic parsing for English as a second language (ESL).Motivated by the theoretical emphasis on the learning challenges that occur at the syntaxsemantics interface during second language acquisition, we formulate the task based on the divergence between literal and intended meanings.We combine the complementary strengths of English Resource Grammar, a linguistically-precise hand-crafted deep grammar, and TLE, an existing manually annotated ESL UD-TreeBank with a novel reranking model.Experiments demonstrate that in comparison to human annotations, our method can obtain a very promising SemBanking quality.By means of the newly created corpus, we evaluate state-of-the-art semantic parsing as well as grammatical error correction models.The evaluation profiles the performance of neural NLP techniques for handling ESL data and suggests some research directions.
From neighbors to strengths - the k-strongest strengths (kSS) classification algorithm

In this study we introduce the k-Strongest Strengths (kSS) Classification Algorithm, a novel approach for classification problems based on the well-known k-Nearest Neighbor (kNN) classifier. The proposed kSS method is motivated by an analogy to the Law of Universal Gravitation. The novelty of kSS resides in that instead of only using the neighbors’ labels to classify an unseen object it uses gravitation forces or strengths exerted by training objects. To incorporate this Newtonian concept into kSS, mass to training objects needs to be assigned. Following this idea we propose novel mass functions that exploit object’s topology properties within 11 data sets, which comprise binary and multi-class problems that present high and low imbalance ratio. Experiments show that kSS obtains an average f1 score of 0.87, outperforming other popular Machine Learning methods such as Artificial Neural Networks (0.80), Decision Trees (0.75), other kNN variants (0.79), naïve Bayes (0.72) and Support Vector Machines (0.82). All these differences being statistically significant.
Self-dual DeepBKZ for finding short lattice vectors

Abstract In recent years, the block Korkine-Zolotarev (BKZ) and its variants such as BKZ 2.0 have been used as de facto algorithms to estimate the security of a lattice-based cryptosystem. In 2017, DeepBKZ was proposed as a mathematical improvement of BKZ, which calls LLL with deep insertions (DeepLLL) as a subroutine alternative to LLL. DeepBKZ can find a short lattice vector by smaller blocksizes than BKZ. In this paper, we develop a self-dual variant of DeepBKZ, as in the work of Micciancio and Walter for self-dual BKZ. Like DeepBKZ, our self-dual DeepBKZ calls both DeepLLL and its dual variant as main subroutines in order to accelerate to find a very short lattice vector. We also report experimental results of DeepBKZ and our self-dual DeepBKZ for random bases on the Darmstadt SVP challenge.
Visual Analysis of Computer Game Output Video Stream for Gameplay Metrics

This work contains a solution for game metrics analysis based on a visual data stream dedicated for the player. The solution does not require interference in the programming code of the analyzed game and it is only based on image processing. It is possible to analyze several aspects of the game simultaneously, for example health/energy bars, current weapon used, number of objects worn (aid kits, ammunition). There have been presented methods using cascading classifiers and their training to detect the desired objects on the screen and to prepare data for other stages of processing, e.g. OCR. The effect of the methods is a gameplay chart that allows a thorough analysis of the player’s actions in the game world and his or her advancement. The solution is fast enough that it can be used not only in previously recorded gameplay analysis, but also in real time during simultaneous gameplay.
Automatic recognition of the digital modulation types using the artificial neural networks

As digital communication technologies continue to grow and evolve, applications for this steady development are also growing. This growth has generated a growing need to look for automated methods for recognizing and classifying the digital modulation type used in the communication system, which has an important effect on many civil and military applications. This paper suggests a recognizing system capable of classifying multiple and different types of digital modulation methods (64QAM, 2PSK, 4PSK, 8PSK, 4ASK, 2FSK, 4FSK, 8FSK). This paper focuses on trying to recognize the type of digital modulation using the artificial neural network (ANN) with its complex algorithm to boost the performance and increase the noise immunity of the system.&#x0D; This system succeeded in recognizing all the digital modulation types under the current study without any prior information. The proposed system used 8 signal features that were used to classify these 8 modulation methods. The system succeeded in achieving a recognition ratio of at least 68% for experimental signals on a signal to noise ratio (SNR = 5dB) and 89.1% for experimental signals at (SNR = 10dB) and 91% for experimental signals at (SNR = 15dB) for a channel with Additive White Gaussian Noise (AWGN).
Fast Mixing of Multi-Scale Langevin Dynamics under the Manifold Hypothesis

Recently, the task of image generation has attracted much attention. In particular, the recent empirical successes of the Markov Chain Monte Carlo (MCMC) technique of Langevin Dynamics have prompted a number of theoretical advances; despite this, several outstanding problems remain. First, the Langevin Dynamics is run in very high dimension on a nonconvex landscape; in the worst case, due to the NP-hardness of nonconvex optimization, it is thought that Langevin Dynamics mixes only in time exponential in the dimension. In this work, we demonstrate how the manifold hypothesis allows for the considerable reduction of mixing time, from exponential in the ambient dimension to depending only on the (much smaller) intrinsic dimension of the data. Second, the high dimension of the sampling space significantly hurts the performance of Langevin Dynamics; we leverage a multi-scale approach to help ameliorate this issue and observe that this multi-resolution algorithm allows for a trade-off between image quality and computational expense in generation.
Design and Analysis of Energy Efficient Reversible Logic based Full Adder

Conventional digital systems incur information loss due to erasure of bits during logic operations resulting in remarkable amount of energy/power loss. Reversible computations nullify the information loss by retaining bits at the output. In arithmetic and logic computational structures, adders are the fundamental and performance determining component. In this paper an energy efficient low power reversible full adder is proposed, which is a combination of Feynman gates and a Fredkin gate. This paper proposes a comprehensive analysis and estimation of energy dissipation in reversible circuits. Cadence Virtuoso schematic editor is used to experimentally validate the models. The proposed adder effectively reduces ancilla inputs by 50%, garbage outputs by 50%, quantum cost by 33.33% and transistor count by 16.67% in comparison with full adder architectures present in literature.
Modeling of Anti-tracking Network Based on Convex-Polytope Topology

Anti-tracking network plays an important role in protection of network users’ identities and communication privacy. Confronted with the frequent network attacks or infiltration to anti-tracking network, a robust and destroy-resistant network topology is an important prerequisite to maintain the stability and security of anti-tracking network. From the aspects of network stability, network resilience and destroy-resistance, we propose the convex-polytope topology (CPT) applied in the anti-tracking network. CPT has three main advantages: (1) CPT can easily avoid the threat of key nodes and cut vertices to network structure; (2) Even the nodes could randomly join in or quit the network, CPT can easily keep the network topology in stable structure without the global view of network; (3) CPT can easily achieve the self-optimization of network topology. Anti-tracking network based on CPT can achieve the self-maintenance and self-optimization of its network topology. We compare CPT with other methods of topology optimization. From the experimental results, CPT has better robustness, resilience and destroy-resistance confronted with dynamically changed topology, and performs better in the efficiency of network self-optimization.
Simple and Effective VAE Training with Calibrated Decoders

Variational autoencoders (VAEs) provide an effective and simple method for modeling complex distributions. However, training VAEs often requires considerable hyperparameter tuning to determine the optimal amount of information retained by the latent variable. We study the impact of calibrated decoders, which learn the uncertainty of the decoding distribution and can determine this amount of information automatically, on the VAE performance. While many methods for learning calibrated decoders have been proposed, many of the recent papers that employ VAEs rely on heuristic hyperparameters and ad-hoc modifications instead. We perform the first comprehensive comparative analysis of calibrated decoder and provide recommendations for simple and effective VAE training. Our analysis covers a range of image and video datasets and several single-image and sequential VAE models. We further propose a simple but novel modification to the commonly used Gaussian decoder, which computes the prediction variance analytically. We observe empirically that using heuristic modifications is not necessary with our method. Project website is at https://orybkin.github.io/sigma-vae/
"Equation missing" : A Cross-Platform Programming Framework for Quantum-Accelerated Scientific Computing

This paper introduces a new cross-platform programming framework for developing quantum-accelerated scientific computing applications and executing them on most of today’s cloud-based quantum computers and simulators. It makes use of C++ template meta-programming techniques to implement quantum algorithms as generic, platform-independent expressions, which get automatically synthesized into device-specific compute kernels upon execution. Our software framework supports concurrent and asynchronous execution of multiple quantum kernels via a CUDA-inspired stream concept.
not-MIWAE: Deep Generative Modelling with Missing not at Random Data

When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data. This allows for incorporating prior information about the type of missingness (e.g. self-censoring) into the model. Our inference technique, based on importance-weighted variational inference, involves maximising a lower bound of the joint likelihood. Stochastic gradients of the bound are obtained by using the reparameterisation trick both in latent space and data space. We show on various kinds of data sets and missingness patterns that explicitly modelling the missing process can be invaluable.
MedCo2: Privacy-Preserving Cohort Exploration and Analysis.

Medical studies are usually time consuming, cumbersome and extremely costly to perform, and for exploratory research, their results are also difficult to predict a priori. This is particularly the case for rare diseases, for which finding enough patients is difficult and usually requires an international-scale research. In this case, the process can be even more difficult due to the heterogeneity of data-protection regulations, making the data sharing process particularly hard. In this short paper, we propose MedCo2 (pronounced MedCo square), a distributed system that streamlines the process of a medical study by bridging and enabling both data discovery and data analysis among multiple databases, while protecting data confidentiality and patients' privacy. MedCo2 relies on interactive protocols, homomorphic encryption and differential privacy. It enables the privacy-preserving computations of multiple statistics such as cosine similarity and variance, and the training of machine learning models, on patients that are obliviously selected according to specific criteria among multiple databases.
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks

We study the problem of PAC learning one-hidden-layer ReLU networks with $k$ hidden units on $\mathbb{R}^d$ under Gaussian marginals in the presence of additive label noise. For the case of positive coefficients, we give the first polynomial-time algorithm for this learning problem for $k$ up to $\tilde{O}(\sqrt{\log d})$. Previously, no polynomial time algorithm was known, even for $k=3$. This answers an open question posed by~\cite{Kliv17}. Importantly, our algorithm does not require any assumptions about the rank of the weight matrix and its complexity is independent of its condition number. On the negative side, for the more general task of PAC learning one-hidden-layer ReLU networks with arbitrary real coefficients, we prove a Statistical Query lower bound of $d^{Ω(k)}$. Thus, we provide a separation between the two classes in terms of efficient learnability. Our upper and lower bounds are general, extending to broader families of activation functions.
A subexponential-time, polynomial quantum space algorithm for inverting the CM group action

Abstract We present a quantum algorithm which computes group action inverses of the complex multiplication group action on isogenous ordinary elliptic curves, using subexponential time, but only polynomial quantum space. One application of this algorithm is that it can be used to find the private key from the public key in the isogeny-based CRS and CSIDH cryptosystems. Prior claims by Childs, Jao, and Soukharev of such a polynomial quantum space algorithm for this problem are false; our algorithm (along with contemporaneous, independent work by Biasse, Iezzi, and Jacobson) is the first such result.
Elessar: Ethics in Norm-Aware Agents

We address the problem of designing agents that navigate social norms by selecting ethically appropriate actions. We present Elessar, a framework in which agents aggregate value preferences of users and select ethically appropriate actions through multicriteria decision making in different social contexts. Via simulations, seeded with a survey of user values and attitudes, we find that Elessar agents act ethically and are effective than baseline agents, in terms of (1) exhibiting the Rawlsian property of fairness, and (2) yielding a satisfactory social experience to users. Our results are stable across agent societies of different sizes and connectedness.
Contact tracing: Unearthing key epidemiological features of COVID-19

COVID-19 is an emerging global pandemic with a steady rise in both morbidity and mortality over the past few months. Contact tracing of COVID-19 patients is an essential task to mitigate the spread. The following case was one of the initial patients reported from India and details the importance of contact tracing, timely testing and adequate quarantine/isolation in disease control.
Improving Bilingual Lexicon Induction with Unsupervised Post-Processing of Monolingual Word Vector Spaces

Work on projection-based induction of crosslingual word embedding spaces (CLWEs) predominantly focuses on the improvement of the projection (i.e., mapping) mechanisms.In this work, in contrast, we show that a simple method for post-processing monolingual embedding spaces facilitates learning of the crosslingual alignment and, in turn, substantially improves bilingual lexicon induction (BLI).The post-processing method we examine is grounded in the generalisation of first-and second-order monolingual similarities to the n th -order similarity.By post-processing monolingual spaces before the cross-lingual alignment, the method can be coupled with any projection-based method for inducing CLWE spaces.We demonstrate the effectiveness of this simple monolingual post-processing across a set of 15 typologically diverse languages (i.e., 15×14 BLI setups), and in combination with two different projection methods.
Topic Enhanced Controllable CVAE for Dialogue Generation (Student Abstract)

Neural generation models have shown great potential in conversation generation recently. However, these methods tend to generate uninformative or irrelevant responses. In this paper, we present a novel topic-enhanced controllable CVAE (TEC-CVAE) model to address this issue. On the one hand, the model learns the context-interactive topic knowledge through a novel multi-hop hybrid attention in the encoder. On the other hand, we design a topic-aware controllable decoder to constrain the expression of the stochastic latent variable in the CVAE to reduce irrelevant responses. Experimental results on two public datasets show that the two mechanisms synchronize to improve both relevance and diversity, and the proposed model outperforms other competitive methods.
Memory-bounded Neural Incremental Parsing for Psycholinguistic Prediction

Syntactic surprisal has been shown to have an effect on human sentence processing, and can be calculated from prefix probabilities of generative incremental parsers.Recent state-ofthe-art incremental generative neural parsers are able to produce accurate parses and surprisal values, but have unbounded stack memory, which may be used by the neural parser to maintain explicit in-order representations of all previously parsed words, inconsistent with results of human memory experiments.In contrast, humans seem to have a bounded working memory, demonstrated by inhibited performance on word recall in multi-clause sentences (Bransford and Franks, 1971), and on center-embedded sentences (Miller and Isard, 1964).Bounded statistical parsers exist, but are less accurate than neural parsers in predicting reading times.This paper describes a neural incremental generative parser that is able to provide accurate surprisal estimates and can be constrained to use a bounded stack.Results show that accuracy gains of neural parsers can be reliably extended to psycholinguistic modeling without risk of distortion due to unbounded working memory.