Reviewerly/exHarmony huggingface dataset:
Camera and LiDAR Fusion for On-road Vehicle Tracking with Reinforcement Learning

We formulate camera and LiDAR fusion tracking as a sequential decision-making process. With our deep reinforcement learning framework, we try to optimize the tracking trajectory to be as accurate, smooth, and long as possible. In contrast to traditional fusion algorithms involving complex feature and strategy design and hyperparameters tuned for different scenarios, our fusion agent can learn the confidence of each input by tracking the results from raw observation in a data-driven fashion. Given the input states of different sensors, our approach chooses one input with a higher expected cumulative reward as the observation of a Kalman filter to iteratively predict the target position. The expected cumulative reward is estimated with a convolutional neural network, trained with a modified DQN algorithm, which takes inputs from both LiDAR and a camera. Through case studies and quantitative result evaluation on our dataset from the 4th Ring Road in Beijing, our algorithm is validated to achieve more accurate and robust tracking performance.
A Modeling Environment for Reified Temporal-Causal Networks: Modeling Plasticity and Metaplasticity in Cognitive Agent Models

Plasticity is a crucial adaptive characteristic of the brain. Relatively recently mechanisms have been found showing that plasticity itself is controlled by what is called metaplasticity. In this paper a modeling environment is introduced to develop and simulate reified temporal-causal network models that can be applied for cognitive agent models. It is shown how this environment is a useful tool to model plasticity combined with metaplasticity.
Visualization of Histopathological Decision Making Using a Roadbook Metaphor

Since pathology is supported by information technology new opportunities and questions have arisen. The digital age enables analyzing histopathological data with artificial intelligence methods to reveal further information and correlations. In this paper existing approaches to visualization of medical decision processes are presented as well as the relevance of explainability in decision making. The first step for implementing decision-paths in systems is to retrace an experienced pathologist's diagnosis finding process. Recording a route through a landscape composed of human tissue in terms of a roadbook is one possible approach to collect information on how diagnoses are found. Choosing the roadbook metaphor provides a simple schema, that holds basic directions enriched with metadata regarding landmarks on a rally - in the context of pathology such landmarks provide information on the decision finding process.
Cumulative ROC curves for discriminating three or more ordinal outcomes with cutpoints on a shared continuous measurement scale

Cumulative receiver operator characteristic (ROC) curve analysis extends classic ROC curve analysis to discriminate three or more ordinal outcome levels on a shared continuous scale. The procedure combines cumulative logit regression with a cumulative extension to the ROC curve and performs as expected with ternary (three-level) ordinal outcomes under a variety of simulated conditions (unbalanced data, proportional and non-proportional odds, areas under the ROC curve [AUCs] from 0.70 to 0.95). Simulations also compared several criteria for selecting cutpoints to discriminate outcome levels: the Youden Index, Matthews Correlation Coefficient, Total Accuracy, and Markedness. Total Accuracy demonstrated the least absolute percent-bias. Cutpoints computed from maximum likelihood regression parameters demonstrated bias that was often negligible. The procedure was also applied to publicly available data related to computer imaging and biomarker exposure science, yielding good to excellent AUCs, as well as cutpoints with sensitivities and specificities of commensurate quality. Implementation of cumulative ROC curve analysis and extension to more than three outcome levels are straightforward. The author's programs for ternary ordinal outcomes are publicly available.
Deep Unsupervised Learning for Simultaneous Visual Odometry and Depth Estimation

Visual odometry and depth estimation are critical to understanding the scene and camera motion, which are particularly helpful to tasks such as scene understanding, autonomous driving, and robotics. Supervised learning methods have been applied in many deep neural network frameworks and demonstrated outstanding results in visual odometry and depth estimation. However, supervised learning requires a significant amount of labeled data for training, which consumes extensive time. In this paper, we explore an unsupervised learning framework that can learn a camera pose regressor from monocular video frames and estimates the scene depth simultaneously. The proposed method is able to perform accurate pose prediction as well as depth estimation, despite the absence of any ground truth data. The effectiveness of our proposed method is demonstrated through experiments on KITTI, Cityscapes, and Make3D benchmark datasets, which shows superb results compared with state-of-the-art methods in both tasks.
Multi-Loss-Aware Channel Pruning of Deep Networks

Channel pruning, which seeks to reduce the model size by removing redundant channels, is a popular solution for deep networks compression. Existing channel pruning methods usually conduct layer-wise channel selection by directly minimizing the reconstruction error of feature maps between the baseline model and the pruned one. However, they ignore the feature and semantic distributions within feature maps and real contribution of channels to the overall performance. In this paper, we propose a new channel pruning method by explicitly using both intermediate outputs of the baseline model and the classification loss of the pruned model to supervise layer-wise channel selection. Particularly, we introduce an additional loss to encode the differences in the feature and semantic distributions within feature maps between the baseline model and the pruned one. By considering the reconstruction error, the additional loss and the classification loss at the same time, our approach can significantly improve the performance of the pruned model. Comprehensive experiments on benchmark datasets demonstrate the effectiveness of the proposed method.
A new foundation for finitary corecursion and iterative algebras

This paper contributes to a generic theory of behaviour of "finite-state" systems. Systems are coalgebras with a finitely generated carrier for an endofunctor on a locally finitely presentable category. Their behaviour gives rise to the locally finite fixpoint (LFF), a new fixpoint of the endofunctor. The LFF exists provided that the endofunctor is finitary and preserves monomorphisms, is a subcoalgebra of the final coalgebra, i.e. it is fully abstract w.r.t. behavioural equivalence, and it is characterized by two universal properties: as the final locally finitely generated coalgebra, and as the initial fg-iterative algebra. Instances of the LFF are: regular languages, rational streams, rational formal power-series, regular trees etc. Moreover, we obtain e.g. (realtime deterministic resp. non-deterministic) context-free languages, constructively S-algebraic formal power-series (in general, the behaviour of finite coalgebras under the coalgebraic language semantics arising from the generalized powerset construction by Silva, Bonchi, Bonsangue, and Rutten), and the monad of Courcelle's algebraic trees.
Virtual World Bridges the Real Challenge: Automated Data Generation for Autonomous Driving

In autonomous driving research, one of the bottlenecks is the shortage of a well-annotated dataset to train deep neural networks for object detection. Specifically, a dataset focusing on harsh weather conditions is insufficient. The purpose of this research is to explore the power of utilizing synthetic data for training object detection deep neural networks under harsh weather conditions. We introduce a state-of-the-art automated pipeline to collect synthetic images from a high realism video game and generate training data which can be used for training an autonomous driving object detection neural network. We use our synthetic dataset, KITTI, and Cityscapes to train three separate object detection neural networks and employ the PASCAL object detection criteria to evaluate each neural networks' performance. The results from the experiment indicate that the neural network trained by our synthetic dataset outperforms its counterparts and achieves higher average precision (AP) in detecting images under harsh weather conditions. The result sheds a light on employing synthetic data to resolve the challenges in the real world.
Channel-Wise and Feature-Points Reweights Densenet for Image Classification

Recent network research has demonstrated that the performance of convolutional neural networks can be improved by introducing a learning block that capture spatial correlations and channel-wise correlations . In this work, we propose a novel Channel-wise and Feature-points Reweights DenseNet (CAPR-DenseNet) architecture. The CAPR-DenseNet improves the representation power of the DenseNet by adaptively recalibrating the channel-wise feature responses and explicitly modeling the interdependencies between feature-points. First, in order to perform dynamic channel-wise feature recalibration, we construct the Channel-wise Feature Reweight DenseNet (CFR-DenseNet) by introducing the Squeeze-and-Excitation Module (SEM) to DenseNet. Then, we present a novel CAPR-DenseNet by adding a Feature-points Reweight Module (FPRM) to the CFR-DenseNet. Through massive experiments, we demonstrate that by recalibrating the channel-wise feature and the feature-points responses. Our CAPR-DenseNet performs better than DenseNet across challenging datasets CIFAR-10 and CIFAR-100.
Improved Interpolation Attacks on Cryptographic Primitives of Low Algebraic Degree

Symmetric cryptographic primitives with low multiplicative complexity have been proposed to improve the performance of emerging applications such as secure Multi-Party Computation. However, primitives composed of round functions with low algebraic degree require a careful evaluation to assess their security against algebraic cryptanalysis, and in particular interpolation attacks. This paper proposes new low-memory interpolation attacks on symmetric key primitives of low degree. Moreover, we present generic attacks on block ciphers with a simple key schedule; our attacks require either constant memory or constant data complexity. The improved attack is applied to the block cipher MiMC which aims to minimize the number of multiplications in large finite fields. As a result, we can break MiMC-129/129 with 38 rounds with time and data complexity $$2^{65.5}$$ and $$2^{60.2}$$ respectively and with negligible memory; this attack invalidates one of the security claims of the designers. Our attack indicates that for MiMC-129/129 the full 82 rounds are necessary even with restrictions on the memory available to the attacker. For variants of MiMC with larger keys, we present new attacks with reduced complexity. Our results do not affect the security claims of the full round MiMC.
Hierarchical Meta-Embeddings for Code-Switching Named Entity Recognition

In countries that speak multiple main languages, mixing up different languages within a conversation is commonly called codeswitching.Previous works addressing this challenge mainly focused on word-level aspects such as word embeddings.However, in many cases, languages share common subwords, especially for closely related languages, but also for languages that are seemingly irrelevant.Therefore, we propose Hierarchical Meta-Embeddings (HME) that learn to combine multiple monolingual word-level and subword-level embeddings to create language-agnostic lexical representations.On the task of Named Entity Recognition for English-Spanish code-switching data, our model achieves the state-of-the-art performance in the multilingual settings.We also show that, in cross-lingual settings, our model not only leverages closely related languages, but also learns from languages with different roots.Finally, we show that combining different subunits are crucial for capturing codeswitching entities.
Learning What and Where from Attributes to Improve Person Re-Identification

Due to high-level semantic cues (what) and spatial properties (where) of person attribute, some recent works try to introduce it into person re-identification. However, jointly learning attributes and identity by directly combining their loss function does not work, because of the significant difference between these two tasks. To address this problem, we propose an Attribute-identity Feature Fusion Network (AFFNet) for person re-ID, which fuses attribute and identity recognition tasks not only on loss level, but also on feature level. Specifically, to learn different features for attribute and identity, we split them into two branches to avoid the interference effects between each other. These two types of features are then concatenated to form the final representation. In the attribute branch, we propose to combine hierarchical features and use a Feature Attention Block (FAB), to mining high-level semantic and spatial information, respectively. The experimental results on two public datasets show that the proposed method performs favorably against state-of-the-art methods.
A Kconfig Translation to Logic with One-Way Validation System

Automated analysis of variability models is crucial for managing software system variants, customized for different market segments or contexts of use. As most approaches for automated analysis are built upon logic engines, they require having a Boolean logic translation of the variability models. However, the translation of some significant languages to Boolean logic is remarkably non-trivial. The contribution of this paper is twofold: first, a translation of the Kconfig language is presented; second, an approach to test the translation for any given model is provided. The proposed translation has been empirically tested with the introduced validation procedure on five open-source projects.
Social Relation Recognition in Egocentric Photostreams

This paper proposes an approach to automatically categorize the social interactions of a user wearing a photo-camera (2fpm), by relying solely on what the camera is seeing. The problem is challenging due to the overwhelming complexity of social life and the extreme intra-class variability of social interactions captured under unconstrained conditions. We adopt the formalization proposed in Bugental's social theory, that groups human relations into five social domains with related categories. Our method is a new deep learning architecture that exploits the hierarchical structure of the label space and relies on a set of social attributes estimated at frame level to provide a semantic representation of social interactions. Experimental results on the new EgoSocialRelation dataset demonstrate the effectiveness of our proposal.
Parallel Temporal Encoder For Sign Language Translation

This paper addresses the sign video interpretation which is a weakly supervised task. Each sign action in videos lacks exact boundaries or labels. We design a Parallel Temporal Encoder (PTEnc) to learn the temporal relation of a sign video from local and global sequential learning views in parallel. PTEnc utilizes the complementarity between the local and global temporal cues. Then, fused encoded feature sequence is fed into a Connectionist Temporal Classification (CTC) based sentence decoder. In addition, in order to enhance the temporal cues in each video, we introduce a reconstruction loss, which performs in an unsupervised way without additional labels. The CTC loss cooperates with the reconstruction loss in an end-to-end training manner. Experimental results on a benchmark dataset demonstrate the effectiveness of the proposed method.
Continuous Hand Gesture Spotting and Classification Using 3D Finger Joints Information

This paper presents a novel approach for continuous dynamic hand gesture recognition for RGB video input. Our approach contains two main modules. Firstly, in the gesture spotting module, the video sequence with continuous gestures are pre-segmented into isolated gestures. Secondly, the gesture classification module classifies the segmented gestures. In the gesture spotting module, the motion of the hand palm and finger movements are fed into Bidirectional Long Short-Term Memory (Bi-LSTM) network for gesture spotting purpose. In the gesture classification module, two 3D Convolution Neural Networks (3D_CNN) and one Long Short-Term Memory (LSTM) network are combined to efficiently utilize the combination of multiple data channels such as RGB, Optical Flow, and 3D key pose positions. The promising performance of our approach is obtained by experiments conducted on three publish datasets - Chalearn LAP ConGD dataset, 20BN-Jester, and NVIDIA Dynamic Hand gesture Dataset. Our approach achieves mean Jaccard Index of 0.5535, which outperforms the state-of-the-art methods on Chalearn LAP ConGD dataset.
Real-time 3D LiDAR Flow for Autonomous Vehicles

Autonomous vehicles require an accurate understanding of the underlying motion of their surroundings. Traditionally this understanding is acquired using optical flow algorithms on camera images, RADAR sensors which measure velocity directly or by object tracking through various sensors. We propose a novel method to estimate point-wise 3D motion vectors from LiDAR point clouds using fully convolutional networks trained and evaluated on the KITTI dataset. Besides, we show how this motion information can be used to efficiently estimate odometry. We demonstrate that our approach achieves significant speed ups over the current state of the art.
Local to Global with Multi-Scale Attention Network for Person Re-Identification

Recently, part-based person re-identification methods attract lots of attention and largely improve the accuracy. However, due to the large variations in camera occlusion, pose change and misalignment, the corresponding part regions of different images from a same person may miss the key cues. In this paper, we proposed a local to global with multi-scale attention network (LGMANet), which sufficiently exploits the contextual information and spacial attention information. Our proposed model includes two branches. One is local to global branch. By pooling operation, an image generates the feature maps of different dimensions. Then, we learn local to global descriptors by partitioning these feature maps with the same scale. The other is multi-scale attention branch, which captures the contextual dependencies from different convolution layers and further improves the discriminative ability of the image feature. Experimental results demonstrate that our method achieves the state-of-the-art results on three benchmark datasets, Market-1501, DukeMTMC-reID and CUHK03.
Towards a Character-based Meta Recommender for Movies

Improving user experience through personalization is one of the current research trends in HCI. This includes recommendations that suit the preferences of all users (not only the majority) as dictated by their character, i.e. all aspects that influence human behaviors; including personality traits, affective states, socio-cultural embeddings and individual beliefs to name but a few. The aim of this paper is developing a recommender system for movies, that is adaptive in the way it recommends selections on the basis of the user's character. We present an architecture for a generic module-based recommendation platform that uses the user's character to choose a recommendation algorithm for each user. We deployed a movie recommendation application to determine the relation between recommender algorithm preference and (1) the user's personality, (2) background and (3) gender. Based on the data collected from 84 participants, high correlation between the user's personality (Openness, Extraversion and Conscientiousness) and gender and the recommender algorithm that they prefer.
Comparison of Blockchain-Based Solutions to Mitigate Data Tampering Security Risk

Blockchain-based applications are arising because they ensure integrity, anti-tampering, and traceability. The data tampering risk is one of the main security concerns of data-centric applications. By the nature of the blockchain technology, it is befitting a revolutionary solution to mitigate the tampering risk. But there exists no proper guidance to explain how blockchain-based application could mitigate this risk. In this paper, we consider tampering risk management and discuss how blockchain-based applications could mitigate it. The study includes a comparison of different solutions.
An Image Identification Scheme Of Encrypted Jpeg Images For Privacy-Preserving Photo Sharing Services

We propose an image identification scheme for double-compressed encrypted JPEG images that aims to identify encrypted JPEG images that are generated from an original JPEG image. To store images without any visual sensitive information on photo sharing services, encrypted JPEG images are generated by using a block-scrambling-based encryption method that has been proposed for Encryption-then-Compression systems with JPEG compression. In addition, feature vectors robust against JPEG compression are extracted from encrypted JPEG images. The use of the image encryption and feature vectors allows us to identify encrypted images recompressed multiple times. Moreover, the proposed scheme is designed to identify images re-encrypted with different keys. The results of a simulation show that the identification performance of the scheme is high even when images are recompressed and re-encrypted.
Efficient privacy-preserving recommendations based on social graphs

Many recommender systems use association rules mining, a technique that captures relations between user interests and recommends new probable ones accordingly. Applying association rule mining causes privacy concerns as user interests may contain sensitive personal information (e.g., political views). This potentially even inhibits the user from providing information in the first place. Current distributed privacy-preserving association rules mining (PPARM) approaches use cryptographic primitives that come with high computational and communication costs, rendering PPARM unsuitable for large-scale applications such as social networks. We propose improvements in the efficiency and privacy of PPARM approaches by minimizing the required data. We propose and compare sampling strategies to sample the data based on social graphs in a privacy-preserving manner. The results on real-world datasets show that our sampling-based approach can achieve a high average precision score with as low as 50% sampling rate and, therefore, with a 50% reduction of communication cost.
Cut-resistant links and multipartite entanglement resistant to particle loss

In this work, we explore the space of quantum states composed of $N$ particles. To investigate the entanglement resistant to particles loss, we introduce the notion of $m$-resistant states. A quantum state is $m$-resistant if it remains entangled after losing an arbitrary subset of m particles, but becomes separable after losing a number of particles larger than $m$. We establish an analogy to the problem of designing a topological link consisting of $N$ rings such that, after cutting any $(m + 1)$ of them, the remaining rings become disconnected. We present a constructive solution to this problem, which allows us to exhibit several distinguished $N$-particles states with the desired property of entanglement resistance to a particle loss.