Reviewerly/exHarmony huggingface dataset:
Regularizer to Mitigate Gradient Masking Effect During Single-Step Adversarial Training

Neural networks are susceptible to adversarial samples: samples with imperceptible noise, crafted to manipulate network's prediction. In order to learn robust models, a training procedure, called Adversarial Training has been introduced. During adversarial training, models are trained with mini-batch containing adversarial samples. In order to scale adversarial training for large datasets and networks, fast and simple methods (e.g., FGSM:Fast Gradient Sign Method) of generating adversarial samples are used while training. It has been shown that models trained using single-step adversarial training methods (i.e., adversarial samples generated using non-iterative methods such as FGSM) are not robust, instead they learn to generate weaker adversaries by masking the gradients. In this work, we propose a regularization term in the training loss, to mitigate the effect of gradient masking during single-step adversarial training. The proposed regularization term causes training loss to increase when the distance between logits (i.e., pre-softmax output of a classifier) for FGSM and R-FGSM (small random noise is added to the clean sample before computing its FGSM sample) adversaries of a clean sample becomes large. The proposed single-step adversarial training is faster than computationally expensive state-of-the-art PGD adversarial training method, and also achieves on par results.
Absolutely maximally entangled states, quantum-maximum-distance-separable codes, and quantum repeaters

We address the relation between absolutely maximally entangled (AME) states and quantum- maximum-distance-separable (QMDS) codes by constructing whole families of QMDS codes from AME states that have stabilizer representations. We introduce a generic reduction-friendly form for the generator set of the stabilizer representation of an AME state, from which the stabilizer form for children codes, all QMDS, can be obtained. Our method would work for relevant high-dimensional codes as well as qubit-based codes. We then relate this to optimal codes for one-way quantum repeaters, by minimizing the short-term infrastructure cost as well as the long-term running cost of such quantum repeaters. This would allow us to obtain the optimal QMDS code, derived from an AME parent state, that can be used in such quantum repeaters.
From Old Ciphers to Modern Communications

The security of the information transmitted, irrespective of the era and the channel selected, has always been one of the main concerns of those using various communication systems. This is particularly the case nowadays, when the Internet has made the mass distribution of any kind of information possible. Codes can be used to transmit data through various noisy channels and any errors that might occur can be corrected; it is therefore very important to design gappropriate codes with adequate parameters. This survey briefly outlines the evolution of the ways in which information has been collected, transmitted and used from the past to the present.
Quantum Security of Hash Functions and Property-Preservation of Iterated Hashing

This work contains two major parts: comprehensively studying the security notions of cryptographic hash functions against quantum attacks and the relationships between them; and revisiting whether Merkle-Damgård and related iterated hash constructions preserve the security properties of the compression function in the quantum setting. Specifically, we adapt the seven notions in Rogaway and Shrimpton (FSE’04) to the quantum setting and prove that the seemingly stronger attack model where an adversary accesses a challenger in quantum superposition does not make a difference. We confirm the implications and separations between the seven properties in the quantum setting, and in addition we construct explicit examples separating an inherently quantum notion called collapsing from several proposed properties. Finally, we pin down the properties that are preserved under several iterated hash schemes. In particular, we prove that the ROX construction in Andreeva et al. (Asiacrypt’07) preserves the seven properties in the quantum random oracle model.
Learning to Play the Chess Variant Crazyhouse Above World Champion Level With Deep Neural Networks and Human Data

Deep neural networks have been successfully applied in learning the board games Go, chess and shogi without prior knowledge by making use of reinforcement learning. Although starting from zero knowledge has been shown to yield impressive results, it is associated with high computationally costs especially for complex games. With this paper, we present CrazyAra which is a neural network based engine solely trained in supervised manner for the chess variant crazyhouse. Crazyhouse is a game with a higher branching factor than chess and there is only limited data of lower quality available compared to AlphaGo. Therefore, we focus on improving efficiency in multiple aspects while relying on low computational resources. These improvements include modifications in the neural network design and training configuration, the introduction of a data normalization step and a more sample efficient Monte-Carlo tree search which has a lower chance to blunder. After training on 569,537 human games for 1.5 days we achieve a move prediction accuracy of 60.4 %. During development, versions of CrazyAra played professional human players. Most notably, CrazyAra achieved a four to one win over 2017 crazyhouse world champion Justin Tan (aka LM Jann Lee) who is more than 400 Elo higher rated compared to the average player in our training set. Furthermore, we test the playing strength of CrazyAra on CPU against all participants of the second Crazyhouse Computer Championships 2017, winning against twelve of the thirteen participants. Finally, for CrazyAraFish we continue training our model on generated engine games. In ten long-time control matches playing Stockfish 10, CrazyAraFish wins three games and draws one out of ten matches.
Remapping a Third Arm in Virtual Reality

This paper presents development on a conceptual method to remap supernumerary limbs using Virtual Reality (VR) as a platform for experimentation. Our VR system allows users to control a third arm through their own limbs such as their head, arms, and feet with the ability to switch between them. To realize and experiment with our remapping method, we used the Oculus Rift in conjunction with OptiTrack to track users in a room-scaled virtual environment. We present some initial findings from a small pilot study and conclude with suggestions for future work.
Impacts of Retina-Related Zones on Quality Perception of Omnidirectional Image

Virtual Reality (VR), which brings immersive experiences to viewers, has been gaining popularity in recent years. A key feature in VR systems is the use of omnidirectional content, which provides 360-degree views of scenes. In this work, we study the human quality perception of omnidirectional images, focusing on different zones surrounding the foveation point. For that purpose, an extensive subjective experiment is carried out to assess the perceptual quality of omnidirectional images with non-uniform quality. Through experimental results, the impacts of different zones are analyzed. Moreover, twenty-five objective quality metrics, including foveal quality metrics, are evaluated using our database. It is quantitatively shown that the zones corresponding to the fovea and parafovea of human eyes are extremely important for quality perception, while the impacts of the other zones corresponding to the perifovea and periphery are small. Besides, most of the investigated metrics are found to be not effective enough to reflect the quality perceived by viewers. Our database has been made available to the public.
The Heading Weight Function: A Novel LiDAR-Based Local Planner for Nonholonomic Mobile Robots

In this paper, we present a novel method for obstacle avoidance designed for a nonholonomic mobile robot. The method relies on light detection and ranging (LiDAR) readings, which are mapped into a polar coordinate system. Obstacles are taken into consideration when they are within a predefined radius from the robot. A central part of the approach is a new Heading Weight Function (HWF), in which the beams within the aperture angle of the LiDAR are virtually weighted in order to generate the best trajectory candidate for the robot. The HWF is designed to find a solution also in the case of a local-minima situation. The function is coupled with the robot’s controller in order to provide both linear and angular velocities. We tested the method both by simulations in a digital environment with a range of different static obstacles, and in a real, experimental environment including static and dynamic obstacles. The results showed that when utilizing the novel HWF, the robot was able to navigate safely toward the target while avoiding all obstacles included in the tests. Our findings thus show that it is possible for a robot to navigate safely in a populated environment using this method, and that sufficient efficiency in navigation may be obtained without basing the method on a global planner. This is particularly promising for navigation challenges occurring in unknown environments where models of the world cannot be obtained.
Automatic Fraud Detection In Call Center Conversations

In this paper, a machine learning system that automatically detects fraudulent call center conversations is presented. The system first transcribes the call center telephone conversations into text using a speech recognition engine and then it automatically detects the fraudulent conversations by a text-categorization algorithm using the transcribed texts. Several classifiers that use different document vectorizers are trained, tested and their performances are compared. The best results are obtained by using deep convolutional neural networks that use word embedding vectors as their inputs. With these networks, 43% of fraudulent calls can be automatically detected with 62% precision.
An Un-Supervised Approach for Backorder Prediction Using Deep Autoencoder

Background: Backorders are an accepted abnormality affecting accumulation alternation and logistics, sales, chump service, and manufacturing, which generally leads to low sales and low chump satisfaction. A predictive archetypal can analyze which articles are best acceptable to acquaintance backorders giving the alignment advice and time to adjust, thereby demography accomplishes to aerate their profit. Objective: To address the issue of predicting backorders, this paper has proposed an un-supervised approach to backorder prediction using Deep Autoencoder. Methods: In this paper, artificial intelligence paradigms are researched in order to introduce a predictive model for the present unbalanced data issues, where the number of products going on backorder is rare. Results: Un-supervised anomaly detection using deep auto encoders has shown better Area under the Receiver Operating Characteristic and precision-recall curves than supervised classification techniques employed with resampling techniques for imbalanced data problems. Conclusion: We demonstrated that Un-supervised anomaly detection methods specifically deep auto- encoders can be used to learn a good representation of the data. The method can be used as a predictive model for inventory management and help to reduce the bullwhip effect, raise customer satisfaction as well as improve operational management in the organization. This technology is expected to create the sentient supply chain of the future - able to feel, perceive and react to situations at an extraordinarily granular level.
Sequential Recommendation Based on Long-Term and Short-Term User Behavior with Self-attention

Product recommenders based on users’ interests are becoming increasingly essential in e-commerce. With the continuous development of the recommendation system, the available information is further enriched. In the case, user’s click or purchase behavior could be a visual representation of his or her interest. Due to the rapid update of products, users’ interests are not static, but change over time. In order to cope with the users’ interest changes, we propose a desirable work on the basis of representative recommendation algorithm. The sequence of user interaction behavior is thoroughly utilized, and the items that users interact at different times have different significance for the reflect of users’ interests. By considering the user’s sequential behaviors, this paper focuses on the recent ones to obtains the real interest of user. In this process, user behavior is divided into long-term and short-term, modeled by LSTM and Attention-based model respectively for user’s next click recommendation. We refer this model as LANCR and analyze the model in experiment. The experiment demonstrates that the proposed model has superior improvement compared with standard approaches. We deploy our model on two real datasets to verify the superior performance made in predicting user preferences.
Towards Effective Rebuttal: Listening Comprehension Using Corpus-Wide Claim Mining

Engaging in a live debate requires, among other things, the ability to effectively rebut arguments claimed by your opponent. In particular, this requires identifying these arguments. Here, we suggest doing so by automatically mining claims from a corpus of news articles containing billions of sentences, and searching for them in a given speech. This raises the question of whether such claims indeed correspond to those made in spoken speeches. To this end, we collected a large dataset of 400 speeches in English discussing 200 controversial topics, mined claims for each topic, and asked annotators to identify the mined claims mentioned in each speech. Results show that in the vast majority of speeches debaters indeed make use of such claims. In addition, we present several baselines for the automatic detection of mined claims in speeches, forming the basis for future work. All collected data is freely available for research.
A test for block circular symmetric covariance structure with divergent dimension

The paper considers the likelihood ratio (LR) test on the block circular symmetric covariance structure of a multivariate Gaussian population with divergent dimension. When the sample size n , the dimension of each block p and the number of blocks u satisfy pu &lt; n − 1 and p = p ( n ) → ∞ as n → ∞ , the asymptotic distribution and the moderate deviation principle of the logarithmic LR test statistic under the null hypothesis are established. Some numerical simulations indicate that the proposed LR test method performs well in the divergent-dimensional block circular symmetric covariance structure test.
Strain gauges position based on machine vision positioning

In this article, in order to meet the requirement of automatic positioning before strain gauge grinding, we proposed a new machine vision algorithm based on connected region extraction and minimum circumferential rotation rectangle delineation. At first, based on the location information of the strain gauge, we used algorithms for image binaryzation, and extracted the strain gauge block by using the connected component extraction method, next roughly positioned strain gauges. And then we dispersed the part which connected to the strain gauge, moreover, found the minimum external rotating rectangle of each strain gauge in the pair of strain gauges so as to located the center of the strain gauge and realize the strain gauge positioning in the end. Then the position of other strain gauge was inferred according to the position of strain gauge in the corner. Experimental verification showed that the error of the algorithm in this article is under 0.05mm, and the algorithm met the requirements of project positioning that the error has to below 0.1mm.
New Design of Line Maze Solving Robot with Speed Controller and Short Path Finder Algorithm

Line Maze Solving Robot is a modified line follower robot used to find the shortest path in a maze. The proposed algorithm, called Short Path Finder Algorithm, which consists of three modes, is implemented to the robot. Those modes are the search mode; the short path mode; and the return and path mode. An improved motor speed controller is applied to control the robot’s speed and to correct its movement so that it can follow the line. The controller is designed based on the PID one. The main parts of the robot are line sensors that detect the type and the shape of a line being passed, a minimum system of the Atmega32 Microcontroller which functions as a data processor and robot brain, a motor driver that serves as a supply of voltage to DC motors, and DC motors as a robot drive. Based on experiment results, the designed robot can find the fastest path from the finish to the starting line.
Meta-Learning through Coupled Optimization in Reproducing Kernel Hilbert Spaces

In this paper we consider a problem known as meta-learning, consisting of building policies that achieve good generalization performance and adapt quickly to different tasks. In our novel formulation, which we denote by cross-learning, we introduce meta-learning through the coupled optimization of a set of rewards that are defined for different tasks. This coupling is effected by a projection step that brings the task-specific policies close to a central one which combines the information collected across tasks. Since such a projection is computationally expensive, we derive a relaxed version that can be obtained in closed-form through a geometric rule. While our initial cross-learning formulation is widely general, and connects with state-of-the art strategies, we specialize it for the case of reinforcement learning. In particular, we search for continuous policies in reproducing kernel Hilbert spaces, which are considered in order to avoid discretization and bypass parametric models. Preliminary numerical experiments performed on the classical cartpole system corroborate that the cross-learned control policy performs well in different scenarios.
Simultaneously Learning Architectures and Features of Deep Neural Networks

This paper presents a novel method which simultaneously learns the number of filters and network features repeatedly over multiple epochs. We propose a novel pruning loss to explicitly enforces the optimizer to focus on promising candidate filters while suppressing contributions of less relevant ones. In the meanwhile, we further propose to enforce the diversities between filters and this diversity-based regularization term improves the trade-off between model sizes and accuracies. It turns out the interplay between architecture and feature optimizations improves the final compressed models, and the proposed method is compared favorably to existing methods, in terms of both models sizes and accuracies for a wide range of applications including image classification, image compression and audio classification.
Large-Scale Mixed-Bandwidth Deep Neural Network Acoustic Modeling for Automatic Speech Recognition

In automatic speech recognition (ASR), wideband (WB) and narrowband (NB) speech signals with different sampling rates typically use separate acoustic models. Therefore mixed-bandwidth (MB) acoustic modeling has important practical values for ASR system deployment. In this paper, we extensively investigate large-scale MB deep neural network acoustic modeling for ASR using 1,150 hours of WB data and 2,300 hours of NB data. We study various MB strategies including downsampling, upsampling and bandwidth extension for MB acoustic modeling and evaluate their performance on 8 diverse WB and NB test sets from various application domains. To deal with the large amounts of training data, distributed training is carried out on multiple GPUs using synchronous data parallelism.
Physical Intrusion Detection System Using Stereo Video Analytics

Physical Intrusion Detection System (PIDS) detects unwanted elements (person or object) entering into a restricted zone. There are various single-camera solutions for surveillance available in the market. The key problem with video analytics based solution is false alert. With the addition of dimensional information of intruding object, it is possible to reduce false alerts without compromising an increase in true negative. We propose deployment of a pair of cameras in stereo configuration for facilitating dimensional measurements. This method also will be useful to detect and characterize flying objects entering in restricted zone. Experiments were conducted on various scenarios emphasizing the usability of stereo in surveillance.
Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks

With substantial amount of time, resources and human (team) efforts invested to explore and develop successful deep neural networks (DNN), there emerges an urgent need to protect these inventions from being illegally copied, redistributed, or abused without respecting the intellectual properties of legitimate owners. Following recent progresses along this line, we investigate a number of watermark-based DNN ownership verification methods in the face of ambiguity attacks, which aim to cast doubts on the ownership verification by forging counterfeit watermarks. It is shown that ambiguity attacks pose serious threats to existing DNN watermarking methods. As remedies to the above-mentioned loophole, this paper proposes novel passport-based DNN ownership verification schemes which are both robust to network modifications and resilient to ambiguity attacks. The gist of embedding digital passports is to design and train DNN models in a way such that, the DNN inference performance of an original task will be significantly deteriorated due to forged passports. In other words, genuine passports are not only verified by looking for the predefined signatures, but also reasserted by the unyielding DNN model inference performances. Extensive experimental results justify the effectiveness of the proposed passport-based DNN ownership verification schemes. Code and models are available at this https URL
Ellipsoidal Trust Region Methods for Neural Network Training

We investigate the use of ellipsoidal trust region constraints for second-order optimization of neural networks. This approach can be seen as a higher-order counterpart of adaptive gradient methods, which we here show to be interpretable as first-order trust region methods with ellipsoidal constraints. In particular, we show that the preconditioning matrix used in RMSProp and Adam satisfies the necessary conditions for convergence of (first- and) second-order trust region methods and report that this ellipsoidal constraint constantly outperforms its spherical counterpart in practice.
Systematic aware learning

Experimental science often has to cope with systematic errors that coherently bias data. We analyze this issue on the analysis of data produced by experiments of the Large Hadron Collider at CERN as a case of supervised domain adaptation. Systematics-aware learning should create an efficient representation that is insensitive to perturbations induced by the systematic effects. We present an experimental comparison of the adversarial knowledge-free approach and a less data-intensive alternative.
GANCoder: An Automatic Natural Language-to-Programming Language Translation Approach Based on GAN

We propose GANCoder, an automatic programming approach based on Generative Adversarial Networks (GAN), which can generate the same functional and logical programming language codes conditioned on the given natural language utterances. The adversarial training between generator and discriminator helps generator learn distribution of dataset and improve code generation quality. Our experimental results show that GANCoder can achieve comparable accuracy with the state-of-the-art methods and is more stable when programming languages.