Reviewerly/exHarmony huggingface dataset:
A ROS-based Simulator for Testing the Enhanced Autonomous Navigation of the Mars 2020 Rover

In order to achieve the ambitious objectives of the Mars 2020 (M2020) mission, in particular the ability to autonomously traverse more challenging terrains more efficiently, new surface mobility software was developed for Enhanced Navigation (ENav). That decision was made early in the project, before most of the new surface flight software (FSW) existed, which created a need for a separate framework where the new navigation algorithms could be quickly prototyped and tested, before more realistic FSW-based testbeds became available. The JPL robotics team chose the Robot Operating System [1] (ROS) as the environment in which to test the new ENav algorithms. This made it possible to write the algorithms in the C language required by the FSW, so they could be directly ported over to the flight module later on, while leveraging all the C++ libraries and tools provided by ROS for simulation and testing. The ENav algorithms were developed as a separate C library, and stubs were used to replace any FSW-specific code, such as Event Reporting (EVRs) and data products (DPs). A ROS simulator was developed to generate a rich set of varied 3D terrains representative of the candidate Mars landing sites and simulate the physics of the rover motion, the point cloud perceived by the rover's stereo vision system, and the new thinking-while-driving (TWD) navigation logic which directs the rover to drive autonomously to user-specified waypoints. To simulate the rover motion and perception, a ROS node was developed that uses a software library called HyperDrive Sim (HDSim), which is a wrapper for the Rover Sequencing and Visualization Program [2] (RSVP). That library provides rover-terrain settling, realistic slip modelling, and camera rendering capability based on the rover's NavCam machine vision models. To simulate the navigation logic, a ROS node was created that initializes and runs the ENav algorithms in a way that mimics the FSW execution, while also providing the capability to load and replay data products, including re-running the recorded inputs through the ENav algorithms for testing. An engineering Graphical User Interface (GUI) was also developed to visualize various elements, such as the rover pose during the drive, the simulated and perceived terrain, the selected local and global paths to the goal, the evaluated candidate paths and the reasons why they were rejected, the keep-in and keep-out zones (KIOZs), etc. Finally, an advanced Monte Carlo (MC) framework that can run many simulations in parallel on the Cloud and automatically generate reports that capture the key ENav performance metrics was developed to evaluate the system in a statistically-meaningful way. This paper provides an overview of the ROS-based simulator used for testing the M2020 ENav algorithms.
On the 2-Adic Complexity of A Class of Binary Sequences of Period 4p with Optimal Autocorrelation Magnitude

Via interleaving Ding-Helleseth-Lam sequences, a class of binary sequences with optimal autocorrelation magnitude was constructed (Des. Codes and Cryptogr., 2018). Later, Sun et al. determined the upper and lower bounds of the 2-adic complexity of such sequences (SETA, 2018). In this paper, we determine the exact value of the 2-adic complexity of this class of sequences. The results show that the 2-adic complexity of this class of binary sequences is close to the maximum.
Community Detection with Secondary Latent Variables

Community detection refers to recovering a (latent) label on which the distribution of the observed graph depends. Recent work has also investigated the impact of additionally knowing the value of another variable at each vertex that is correlated with the vertex label (side information), while assuming side information is independent of the graph edges conditioned on the label. This work extends the scope of community detection in two ways. First, we consider a side information that does not form a Markov chain with the label and graph, and analyze the detection threshold of semidefinite programming subject to knowledge of this side information, which is a non-label latent variable on which the graph edges also depend. In the second part of the work, we consider aside from vertex labels a second latent variable that is unknown both in realization and in distribution. We then investigate the performance of the semidefinite programming community detection as a function of the (unknown) composition of the nuisance latent variable. In both cases, it is shown that semidefinite programming can achieve exact recovery down to the optimal (information theoretic) threshold.
Annotation-Free Learning of Deep Representations for Word Spotting Using Synthetic Data and Self Labeling

Word spotting is a popular tool for supporting the first exploration of historic, handwritten document collections. Today, the best performing methods rely on machine learning techniques, which require a high amount of annotated training material. As training data is usually not available in the application scenario, annotation-free methods aim at solving the retrieval task without representative training samples. In this work, we present an annotation-free method that still employs machine learning techniques and therefore outperforms other annotation-free approaches. The weakly supervised training scheme relies on a lexicon, that does not need to precisely fit the dataset. In combination with a confidence based selection of pseudo-labeled training samples, we achieve state-of-the-art query-by-example performances. Furthermore, our method allows to perform query-by-string, which is usually not the case for other annotation-free methods
Security of Digital Images Based on 3D Arnold Cat Map and Elliptic Curve

Security of digital data is an important task in the present era. In this paper, we propose a new scheme of digital image encryption and decryption method based on three-dimensional (3D) Arnold cat map (ACM) and elliptic curve. In this proposed encryption method, we have applied 3D ACM on the digital color image which performs the dual encryption first, it performs the permutation and second, it performs the substitution of image pixels. After that, elliptic curve cryptography (ECC) is used to encrypt the image, for this a mapping method is proposed to convert the pixels of the image as points on the elliptic curve. Further, a mapping inverting method is proposed for decryption and then 3D inverse Arnold cat map (iACM) is applied to get the original image. The statistical and security analyses are done on various images and the experimental results show the robustness of the proposed method.
Deep Learning for Search and Recommender Systems in Practice

In this talk, we will go over the components of personalized search and recommender systems and demonstrate the applications of various deep learning techniques along the way.
Privacy Amplification of Iterative Algorithms via Contraction Coefficients

We investigate the framework of privacy amplification by iteration, recently proposed by Feldman et al., from an information-theoretic lens. We demonstrate that differential privacy guarantees of iterative mappings can be determined by a direct application of contraction coefficients derived from strong data processing inequalities for f-divergences. In particular, by generalizing the Dobrushin's contraction coefficient for total variation distance to an f-divergence known as E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">γ</sub> -divergence, we derive tighter bounds on the differential privacy parameters of the projected noisy stochastic gradient descent algorithm with hidden intermediate updates.
Semantic Segmentation of Clouds in Satellite Imagery Using Deep Pre-trained U-Nets

Earth observation and remote sensing technologies are widely used in various application areas. Because the abundance of collected data requires automated analytics, many communities are utilizing deep convolutional neural networks for such tasks. Automating cloud detection in remote sensing and earth observation imagery is a useful prerequisite for providing quality imagery for further analysis. In this paper, we train a model that uses a deep convolutional U-Net architecture, utilizing transfer learning to perform semantic segmentation of clouds in satellite imagery. Our proposed model outperforms state-of-the-art networks on a benchmark dataset based on several relevant segmentation metrics, including Jaccard Index (+7.69%), precision (+6.21%), and specificity (+0.37%). Moreover, we demonstrate that transfer learning utilizing a 4-channel input into a U-Net architecture is possible and highly performant by using a deep ResNet-style architecture pre-trained on ImageNet for the initialization of weights in three channels (red, green, and blue bands) and random initialization of weights in the fourth channel (near infrared band) of the first convolutional layer of the network.
Towards Building an Intelligent Chatbot for Customer Service

In recent years, intelligent chatbots have been widely used in the field of customer service. One of the key challenges for chatbots to maintain fluent dialogues with customers is how to respond at the appropriate time. However, most of the state-of-the-art chatbots follow the turn-by-turn interaction scheme. Such chatbots respond after each time when a customer sends an utterance, which in some cases leads to inappropriate responses and misleads the process of the dialogues. In this paper, we propose a multi-turn response triggering model (MRTM) to address this problem. MRTM is learned from large-scale human-human dialogues between the customers and the agents with a self-supervised learning scheme. It leverages the semantic matching relationships between the context and the response to train a semantic matching model and obtains the weights of the co-occurring utterances in the context through an asymmetrical self-attention mechanism. The weights are then used to determine whether the given context should be responded to. We conduct extensive experiments on two dialogue datasets collected from the real-world online customer service systems. Results show that MRTM outperforms the baselines by a large margin. Furthermore, we incorporate MRTM into DiDi's customer service chatbot. Based on the ability to identify the appropriate time to respond, the chatbot can incrementally aggregate the information across multiple utterances and make more intelligent responses at the appropriate time.
Capacity of Quantum Private Information Retrieval with Colluding Servers

Quantum private information retrieval (QPIR) is a protocol that a user retrieves one of f files from non-communicating n servers by downloading quantum systems without revealing the identity of the target file. As variants of the QPIR with stronger security requirements, the symmetric QPIR is that the files except for the target file are not leaked to the user, and the t-private QPIR is that the identity of the target file is kept secret even if at most t servers may collude to reveal the identity. The QPIR capacity is the maximum ratio of the one file size to the size of downloaded quantum systems, and we prove that the symmetric t-private QPIR capacity is min {1, 2(n - t)/n} for any 1 ≤ t <; n. We construct a capacity-achieving QPIR protocol by the stabilizer formalism and prove the optimality of our protocol. The proposed capacity is greater than the classical counterpart.
Tweet Stance Detection: A Two-stage DC-BILSTM Model Based on Semantic Attention

Stance classification in tweet aims at detecting whether the author of the tweet is in FAVOR of, AGAINST, or NONE towards a pre-chosen target entity. Recently proposed Densely Connected BI-LSTM can effectively relieve overfitting and vanishing-gradient problems as well as dealing with long-term dependencies during multi-layer LSTM training. Based on this, we propose a two-stage deep attention neural network(T-DAN) for target-specific stance detection. This model employs densely connected BI-LSTM to encode tweet tokens and traditional bidirectional LSTM to encode target tokens. Besides, we decompose this ternary classification problem into two binary classification problems to mitigating the imbalanced distribution of labels. In the first stage, we find out the tweet is neutral or subjective about the specific target. In the second stage, we classify the stance of a given subjective tweet's stance. Moreover, we propose a novel method of attention calculation based on the semantic similarity of tweet tokens and target tokens which can locate the crucial words related to target. Experimental results on English and Chinese datasets demonstrate that our proposed method surpasses some strong baselines and achieves the stateof-the-art performance.
Privacy-Utility Tradeoff in a Guessing Framework Inspired by Index Coding

This paper studies the tradeoff in privacy and utility in a single-trial multi-terminal guessing (estimation) framework using a system model that is inspired by index coding. There are n independent discrete sources at a data curator. There are m legitimate users and one adversary, each with some side information about the sources. The data curator broadcasts a distorted function of sources to legitimate users, which is also overheard by the adversary. In terms of utility, each legitimate user wishes to perfectly reconstruct some of the unknown sources and attain a certain gain in the estimation correctness for the remaining unknown sources. In terms of privacy, the data curator wishes to minimize the maximal leakage: the worst-case guessing gain of the adversary in estimating any target function of its unknown sources after receiving the broadcast data. Given the system settings, we derive fundamental performance lower bounds on the maximal leakage to the adversary, which are inspired by the notion of confusion graph and performance bounds for the index coding problem. We also detail a greedy privacy enhancing mechanism, which is inspired by the agglomerative clustering algorithms in the information bottleneck and privacy funnel problems.
Context-to-Session Matching: Utilizing Whole Session for Response Selection in Information-Seeking Dialogue Systems

We study the retrieval-based multi-turn information-seeking dialogue systems, which are widely used in many scenarios. Most of the previous works select the response according to the matching degree between the query's context and the candidate responses. Though great progress has been made, existing works ignore the contexts of the responses, which could provide rich information for selecting the most appropriate response. The more similar the query's context and certain response's context are, the more likely they are to indicate the same question, and thus, the more likely this response is to answer the query. In this paper, we consider the response and its context as a whole session and explore the task of matching the query's context with the sessions. More specifically, we propose to match between the query's context and response's context and integrate the context-to-context matching with context-to-response matching. Experiment results prove that our proposed context-to-session method outperforms the strong baselines significantly.
Post-mortem digital forensic artifacts of TikTok Android App

TikTok is a social network known mostly for the creation and sharing of short videos and for its popularity for those under 30 years old. Although it has only appeared as Android and iOS apps in 2017, it has gathered a large user base, being one of the most downloaded and used app. In this paper, we study the digital forensic artifacts of TikTok's app that can be recovered with a post mortem analysis of an Android phone, detailing the databases and XML with data that might be relevant for a digital forensic practitioner. We also provide the module tiktok.py to extract several forensic artifacts of TikTok in a digital forensic analysis of an Android phone. The module runs under Autopsy's Android Analyzer environment. Although TikTok offers a rich set of features, it is very internet-dependent, with a large amount of its inner data kept on the cloud, and thus not easily accessible in a post mortem analysis. Nonetheless, we were able to recover messages exchanged through the app communications channels, the list of TikTok users that have interacted with the TikTok account used at the smartphone, photos linked to the app and in some circumstances, TikTok's videos watched by the smartphone's user.
Information-theoretic analysis for transfer learning

Transfer learning, or domain adaptation, is concerned with machine learning problems in which training and testing data come from possibly different distributions (denoted as μ and μ', respectively). In this work, we give an informationtheoretic analysis on the generalization error and the excess risk of transfer learning algorithms, following a line of work initiated by Russo and Zhou. Our results suggest, perhaps as expected, that the Kullback-Leibler (KL) divergence D(μ||μ') plays an important role in characterizing the generalization error in the settings of domain adaptation. Specifically, we provide generalization error upper bounds for general transfer learning algorithms, and extend the results to a specific empirical risk minimization (ERM) algorithm where data from both distributions are available in the training phase. We further apply the method to iterative, noisy gradient descent algorithms, and obtain upper bounds which can be easily calculated, only using parameters from the learning algorithms. A few illustrative examples are provided to demonstrate the usefulness of the results. In particular, our bound is tighter in specific classification problems than the bound derived using Rademacher complexity.
Latent-variable Private Information Retrieval

In many applications, content accessed by users (movies, videos, news articles, etc.) can leak sensitive latent attributes, such as religious and political views, sexual orientation, ethnicity, gender, and others. To prevent such information leakage, the goal of classical PIR is to hide the identity of the content/message being accessed, which subsequently also hides the latent attributes. This solution, while private, can be too costly, particularly, when perfect (information-theoretic) privacy constraints are imposed. For instance, for a single database holding K messages, privately retrieving one message is possible if and only if the user downloads the entire database of K messages. Retrieving content privately, however, may not be necessary to perfectly hide the latent attributes.Motivated by the above, we formulate and study the problem of latent-variable private information retrieval (LV-PIR), which aims at allowing the user efficiently retrieve one out of K messages (indexed by θ) without revealing any information about the latent variable (modeled by S). We focus on the practically relevant setting of a single database and show that one can significantly reduce the download cost of LV-PIR (compared to the classical PIR) based on the correlation between θ and S. We present a general scheme for LV-PIR as a function of the statistical relationship between θ and S, and also provide new results on the capacity/download cost of LV-PIR. Several open problems and new directions are also discussed.
Designing Dynamic and Personalized Nudges

Nudging is about influencing people to make decisions that are beneficial to society and individuals. We are in particular concerned with using nudges to cause a behavioral change for persons, where healthier or environmentally friendlier behavior may be the goal. As people make more and more decisions in a digital context, digital nudging has steadily become more relevant. With today's technology, it is feasible to dynamically generate highly personalized nudges, using information on the person receiving the nudge, such as their intention and the situation they are in. This paper presents a new nudge model, designed with personalization in mind. We propose to use personal and situational data to generate the most suitable nudge designed from nudge components. The presented nudges should be transparent, helpful and effective to the user.
A Note on the Probability of Rectangles for Correlated Binary Strings

Consider two sequences of n independent and identically distributed fair coin tosses, X = (X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , . . . , X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ) and Y = (Y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> , . . . , Y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sub> ), which are ρ-correlated for each j, i.e. P[X <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> = Y <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">j</sub> ] = 1+ρ/2 .We study the question of how large (small) the probability P[X ∈ A, Y ∈ B] can be among all sets A, B ⊂ {0, 1} <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> of a given cardinality. For sets |A|, |B| = Θ(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> ) it is well known that the largest (smallest) probability is approximately attained by concentric (anti-concentric) Hamming balls, and this can be proved via the hypercontractive inequality (reverse hypercontractivity). Here we consider the case of |A|, |B| = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Θ</sup> (n). By applying a recent extension of the hypercontractive inequality of Polyanskiy-Samorodnitsky (J. Functional Analysis, 2019), we show that Hamming balls of the same size approximately maximize P[X ∈ A, Y ∈ B] in the regime of p → 1. We also prove a similar tight lower bound, i.e. show that for p → 0 the pair of opposite Hamming balls approximately minimizes the probability P[X ∈ A, Y ∈ B].
Geometrically Driven Underground Camera Modeling and Calibration With Coplanarity Constraints for a Boom-Type Roadheader

The conventional calibration methods based on a perspective camera model are not suitable for the underground camera with two-layer glasses, which is specially designed for explosion proof and dust removal in a coal mine. Underground camera modeling and calibration algorithms are urgently needed to improve the precision and reliability of underground visual measurement systems. This article presents a novel geometrically driven underground camera calibration algorithm for a boom-type roadheader. The underground camera model is established under coplanarity constraints, considering explicitly the impact of refraction triggered by the two-layer glasses and deriving the geometrical relationship of equivalent collinearity equations. On this basis, we perform parameters calibration based on a geometrically driven calibration model, which are 2D-2D correspondences between the image points and object coordinates of the planar target. A hybrid Levenberg-Marqurdt (LM) and particle swarm optimization (PSO) algorithm is further proposed in terms of the dynamic combination of the LM and PSO, which optimizes the underground camera calibration results by minimizing the error of the nonlinear underground camera model. The experimental results demonstrate that the pose errors caused by the two-layer glass refraction are well corrected by the proposed method. The accuracy of the cutting-head pose estimation has increased by 55.73%, meeting the requirements of underground excavations.