OpenRaiser/Intern-Atlas huggingface dataset:
Optimal Decision Lists using SAT
Venue: arXiv cs.AI 2020
Year: 2020

Decision lists are one of the most easily explainable machine learning models. Given the renewed emphasis on explainable machine learning decisions, this machine learning model is increasingly attractive, combining small size and clear explainability. In this paper, we show for the first time how to construct optimal "perfect" decision lists which are perfectly accurate on the training data, and minimal in size, making use of modern SAT solving technology. We also give a new method for determining optimal sparse decision lists, which trade off size and accuracy. We contrast the size and test accuracy of optimal decisions lists versus optimal decision sets, as well as other state-of-the-art methods for determining optimal decision lists. We also examine the size of average explanations generated by decision sets and decision lists.
Hierarchical Paired Channel Fusion Network for Street Scene Change Detection
Venue: arXiv cs.CV 2020
Year: 2020

Street Scene Change Detection (SSCD) aims to locate the changed regions between a given street-view image pair captured at different times, which is an important yet challenging task in the computer vision community. The intuitive way to solve the SSCD task is to fuse the extracted image feature pairs, and then directly measure the dissimilarity parts for producing a change map. Therefore, the key for the SSCD task is to design an effective feature fusion method that can improve the accuracy of the corresponding change maps. To this end, we present a novel Hierarchical Paired Channel Fusion Network (HPCFNet), which utilizes the adaptive fusion of paired feature channels. Specifically, the features of a given image pair are jointly extracted by a Siamese Convolutional Neural Network (SCNN) and hierarchically combined by exploring the fusion of channel pairs at multiple feature levels. In addition, based on the observation that the distribution of scene changes is diverse, we further propose a Multi-Part Feature Learning (MPFL) strategy to detect diverse changes. Based on the MPFL strategy, our framework achieves a novel approach to adapt to the scale and location diversities of the scene change regions. Extensive experiments on three public datasets (i.e., PCD, VL-CMU-CD and CDnet2014) demonstrate that the proposed framework achieves superior performance which outperforms other state-of-the-art methods with a considerable margin.
Unsupervised Selective Manifold Regularized Matrix Factorization
Venue: arXiv cs.LG 2020
Year: 2020

Manifold regularization methods for matrix factorization rely on the cluster assumption, whereby the neighborhood structure of data in the input space is preserved in the factorization space. We argue that using the k-neighborhoods of all data points as regularization constraints can negatively affect the quality of the factorization, and propose an unsupervised and selective regularized matrix factorization algorithm to tackle this problem. Our approach jointly learns a sparse set of representatives and their neighbor affinities, and the data factorization. We further propose a fast approximation of our approach by relaxing the selectivity constraints on the data. Our proposed algorithms are competitive against baselines and state-of-the-art manifold regularization and clustering algorithms.
Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition
Venue: arXiv cs.CV 2020
Year: 2020

One essential problem in skeleton-based action recognition is how to extract discriminative features over all skeleton joints. However, the complexity of the State-Of-The-Art (SOTA) models of this task tends to be exceedingly sophisticated and over-parameterized, where the low efficiency in model training and inference has obstructed the development in the field, especially for large-scale action datasets. In this work, we propose an efficient but strong baseline based on Graph Convolutional Network (GCN), where three main improvements are aggregated, i.e., early fused Multiple Input Branches (MIB), Residual GCN (ResGCN) with bottleneck structure and Part-wise Attention (PartAtt) block. Firstly, an MIB is designed to enrich informative skeleton features and remain compact representations at an early fusion stage. Then, inspired by the success of the ResNet architecture in Convolutional Neural Network (CNN), a ResGCN module is introduced in GCN to alleviate computational costs and reduce learning difficulties in model training while maintain the model accuracy. Finally, a PartAtt block is proposed to discover the most essential body parts over a whole action sequence and obtain more explainable representations for different skeleton action sequences. Extensive experiments on two large-scale datasets, i.e., NTU RGB+D 60 and 120, validate that the proposed baseline slightly outperforms other SOTA models and meanwhile requires much fewer parameters during training and inference procedures, e.g., at most 34 times less than DGNN, which is one of the best SOTA methods.
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Humans can easily recognize actions with only a few examples given, while the existing video recognition models still heavily rely on the large-scale labeled data inputs. This observation has motivated an increasing interest in few-shot video action recognition, which aims at learning new actions with only very few labeled samples. In this paper, we propose a depth guided Adaptive Meta-Fusion Network for few-shot video recognition which is termed as AMeFu-Net. Concretely, we tackle the few-shot recognition problem from three aspects: firstly, we alleviate this extremely data-scarce problem by introducing depth information as a carrier of the scene, which will bring extra visual information to our model; secondly, we fuse the representation of original RGB clips with multiple non-strictly corresponding depth clips sampled by our temporal asynchronization augmentation mechanism, which synthesizes new instances at feature-level; thirdly, a novel Depth Guided Adaptive Instance Normalization (DGAdaIN) fusion module is proposed to fuse the two-stream modalities efficiently. Additionally, to better mimic the few-shot recognition process, our model is trained in the meta-learning way. Extensive experiments on several action recognition benchmarks demonstrate the effectiveness of our model.
Algodynamics: Teaching Algorithms using Interactive Transition Systems
Venue: arXiv cs.CY 2020
Year: 2020

The importance of algorithms and data structures in computer science curricula has been amply recognized. For many students, however, gaining a good understanding of algorithms remains a challenge. Because of the automated nature of sequential algorithms. there is an inherent tension in directly applying the `learning by doing' approach. This partly explains the limitations of efforts like algorithm animation and code tracing. Algodynamics, the approach we propose and advocate, situates algorithms within the framework of transition systems and their dynamics and offers an attractive approach for teaching algorithms. Algodynamics starts with the premise that the key ideas underlying an algorithm can be identified and packaged into interactive transition systems. The algorithm when `opened up', reveals a transition system, shorn of most control aspects, enriched instead with interaction. The design of an algorithm can be carried out by constructing a series of interactive systems, progressively trading interactivity with automation. These transition systems constitute a family of notional machines. We illustrate the algodynamics approach by considering Bubblesort. A sequence of five interactive transition systems culminate in the classic Bubblesort algorithm. The exercise of constructing the individual systems also pays off when coding Bubblesort: a highly modular implementation whose primitives are borrowed from the transition systems. The transition systems used for Bubblesort have been implemented as interactive experiments. These web based implementations are easy to build. The simplicity and flexibility afforded by the algodynamics framework makes it an attractive option to teach algorithms in an interactive way.
Video Reconstruction by Spatio-Temporal Fusion of Blurred-Coded Image Pair
Venue: arXiv cs.CV 2020
Year: 2020

Learning-based methods have enabled the recovery of a video sequence from a single motion-blurred image or a single coded exposure image. Recovering video from a single motion-blurred image is a very ill-posed problem and the recovered video usually has many artifacts. In addition to this, the direction of motion is lost and it results in motion ambiguity. However, it has the advantage of fully preserving the information in the static parts of the scene. The traditional coded exposure framework is better-posed but it only samples a fraction of the space-time volume, which is at best 50% of the space-time volume. Here, we propose to use the complementary information present in the fully-exposed (blurred) image along with the coded exposure image to recover a high fidelity video without any motion ambiguity. Our framework consists of a shared encoder followed by an attention module to selectively combine the spatial information from the fully-exposed image with the temporal information from the coded image, which is then super-resolved to recover a non-ambiguous high-quality video. The input to our algorithm is a fully-exposed and coded image pair. Such an acquisition system already exists in the form of a Coded-two-bucket (C2B) camera. We demonstrate that our proposed deep learning approach using blurred-coded image pair produces much better results than those from just a blurred image or just a coded image.
Bias-Resistant Social News Aggregator Based on Blockchain
Venue: arXiv cs.DC 2020
Year: 2020

In today's world, social networks have become one of the primary sources for creation and propagation of news. Social news aggregators are one of the actors in this area in which users post news items and use positive or negative votes to indicate their preference toward a news item. News items will be ordered and displayed according to their aggregated votes. This approach suffers from several problems raging from being prone to the dominance of the majority to difficulty in discerning between correct and fake news, and lack of incentive for honest behaviors. In this paper, we propose a graph-based news aggregator in which instead of voting on the news items, users submit their votes on the relations between pairs of news items. More precisely, if a user believes two news items support each other, he will submit a positive vote on the link between the two items, and if he believes that two news items undermine each other, he will submit a negative vote on the corresponding link. This approach has mainly two desirable features: (1) mitigating the effect of personal preferences on voting, (2) connection of new items to endorsing and disputing evidence. This approach helps the newsreaders to understand different aspects of a news item better. We also introduce an incentive layer that uses blockchain as a distributed transparent manager to encourages users to behave honestly and abstain from adversary behaviors. The incentive layer takes into account that users can have different viewpoints toward news, enabling users from a wide range of viewpoints to contribute to the network and benefit from its rewards. In addition, we introduce a protocol that enables us to prove fraud in computations of the incentive layer model on the blockchain. Ultimately, we will analyze the fraud proof protocol and examine our incentive layer on a wide range of synthesized datasets.
Polarized Skylight Navigation System (PSNS)/ GNSS/SINS Integrated Navigation
Venue: arXiv cs.RO 2020
Year: 2020

Bioinspired polarized skylight navigation system (PSNS) is a new navigation method, which imitates insects using polarized skylight for navigation. However, the latest research shows that the current PSNS is difficult to obtain the three-dimensional (3D) attitude in real time only relying on polarized skylight. This causes not only that three-dimensional (3D) cannot be estimated only by PSNS in real time, but also when polarized light is integrated with other navigation methods, 3D attitude cannot be directly used for integrated navigation. Therefore, an integrated navigation algorithm based on bi-direction solar vector is proposed, and a DSP+FPGA (digital signal processor + field programmable gate array) dual-core architecture PSNS/GNSS/SINS (global navigation satellite system/strapdown inertial navigation system) integrated navigation system was constructed.
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks
Venue: arXiv cs.NI 2020
Year: 2020

A mega-constellation of low-altitude earth orbit (LEO) satellites (SATs) and burgeoning unmanned aerial vehicles (UAVs) are promising enablers for high-speed and long-distance communications in beyond fifth-generation (5G) systems. Integrating SATs and UAVs within a non-terrestrial network (NTN), in this article we investigate the problem of forwarding packets between two faraway ground terminals through SAT and UAV relays using either millimeter-wave (mmWave) radio-frequency (RF) or free-space optical (FSO) link. Towards maximizing the communication efficiency, the real-time associations with orbiting SATs and the moving trajectories of UAVs should be optimized with suitable FSO/RF links, which is challenging due to the time-varying network topology and a huge number of possible control actions. To overcome the difficulty, we lift this problem to multi-agent deep reinforcement learning (MARL) with a novel action dimensionality reduction technique. Simulation results corroborate that our proposed SAT-UAV integrated scheme achieves 1.99x higher end-to-end sum throughput compared to a benchmark scheme with fixed ground relays. While improving the throughput, our proposed scheme also aims to reduce the UAV control energy, yielding 2.25x higher energy efficiency than a baseline method only maximizing the throughput. Lastly, thanks to utilizing hybrid FSO/RF links, the proposed scheme achieves up to 62.56x higher peak throughput and 21.09x higher worst-case throughput than the cases utilizing either RF or FSO links, highlighting the importance of co-designing SAT-UAV associations, UAV trajectories, and hybrid FSO/RF links in beyond-5G NTNs.
Language Inference with Multi-head Automata through Reinforcement Learning
Venue: arXiv cs.LG 2020
Year: 2020

The purpose of this paper is to use reinforcement learning to model learning agents which can recognize formal languages. Agents are modeled as simple multi-head automaton, a new model of finite automaton that uses multiple heads, and six different languages are formulated as reinforcement learning problems. Two different algorithms are used for optimization. First algorithm is Q-learning which trains gated recurrent units to learn optimal policies. The second one is genetic algorithm which searches for the optimal solution by using evolution inspired operations. The results show that genetic algorithm performs better than Q-learning algorithm in general but Q-learning algorithm finds solutions faster for regular languages.
Individual corpora predict fast memory retrieval during reading
Venue: arXiv cs.CL 2020
Year: 2020

The corpus, from which a predictive language model is trained, can be considered the experience of a semantic system. We recorded everyday reading of two participants for two months on a tablet, generating individual corpus samples of 300/500K tokens. Then we trained word2vec models from individual corpora and a 70 million-sentence newspaper corpus to obtain individual and norm-based long-term memory structure. To test whether individual corpora can make better predictions for a cognitive task of long-term memory retrieval, we generated stimulus materials consisting of 134 sentences with uncorrelated individual and norm-based word probabilities. For the subsequent eye tracking study 1-2 months later, our regression analyses revealed that individual, but not norm-corpus-based word probabilities can account for first-fixation duration and first-pass gaze duration. Word length additionally affected gaze duration and total viewing duration. The results suggest that corpora representative for an individual's longterm memory structure can better explain reading performance than a norm corpus, and that recently acquired information is lexically accessed rapidly.
On Location Relevance and Diversity in Human Mobility Data
Venue: arXiv cs.DB 2020
Year: 2020

The theme of human mobility is transversal to multiple fields of study and applications, from ad-hoc networks to smart cities, from transportation planning to recommendation systems on social networks. Despite the considerable efforts made by a few scientific communities and the relevant results obtained so far, there are still many issues only partially solved, that ask for general and quantitative methodologies to be addressed. A prominent aspect of scientific and practical relevance is how to characterize the mobility behavior of individuals. In this article, we look at the problem from a location-centric perspective: we investigate methods to extract, classify and quantify the symbolic locations specified in telco trajectories, and use such measures to feature user mobility. A major contribution is a novel trajectory summarization technique for the extraction of the locations of interest, i.e. attractive, from symbolic trajectories. The method is built on a density-based trajectory segmentation technique tailored to telco data, which is proven to be robust against noise. To inspect the nature of those locations, we combine the two dimensions of location attractiveness and frequency into a novel location taxonomy, which allows for a more accurate classification of the visited places. Another major contribution is the selection of suitable entropy-based metrics for the characterization of single trajectories, based on the diversity of the locations of interest. All these components are integrated in a framework utilized for the analysis of 100,000+ telco trajectories. The experiments show how the framework manages to dramatically reduce data complexity, provide high-quality information on the mobility behavior of people and finally succeed in grasping the nature of the locations visited by individuals.
Supertagging-based Parsing with Linear Context-free Rewriting Systems
Venue: arXiv cs.CL 2020
Year: 2020

We present the first supertagging-based parser for LCFRS. It utilizes neural classifiers and tremendously outperforms previous LCFRS-based parsers in both accuracy and parsing speed. Moreover, our results keep up with the best (general) discontinuous parsers, particularly the scores for discontinuous constitutents are excellent. The heart of our approach is an efficient lexicalization procedure which induces a lexical LCFRS from any discontinuous treebank. It is an adaptation of previous work by Mörbitz and Ruprecht (2020). We also describe a modification to usual chart-based LCFRS parsing that accounts for supertagging and introduce a procedure for the transformation of lexical LCFRS derivations into equivalent parse trees of the original treebank. Our approach is implemented and evaluated on the English Discontinuous Penn Treebank and the German corpora NeGra and Tiger.
A microsimulation approach for the impact assessment of a Vehicle-to-Infrastructure based Road Hazard Warning system
Venue: arXiv cs.OH 2020
Year: 2020

Cooperative Intelligent Transportation Systems (C-ITS) constitute technologies which enable vehicles to communicate with each other and with road infrastructure. Verification or testing is required for C-ITS applications, in order to assess their impact on traffic operation. In this work, a microscopic traffic simulation approach is used, to evaluate the impact of Vehicle-to-Infrastructure (V2I) technologies in the context of a road traffic accident. Specifically, the methodology is implemented to explicitly models vehicles collisions, Road Hazard Warning (RHW), Emergency Electronic Brake Light (EEBL) warnings and the resulting driver behavior. Moreover, a new gap control mechanism is adopted, to improve safety by advising vehicles in hazard lane to increase their headways with respect to their preceding vehicle, so that they can avoid a collision. Perfect communication links to all vehicles are assumed. The study findings indicate that the proposed V2I hazard warning strategy has a positive impact on traffic flow safety and efficiency.
Anisotropic Graph Convolutional Network for Semi-supervised Learning
Venue: arXiv cs.LG 2020
Year: 2020

Graph convolutional networks learn effective node embeddings that have proven to be useful in achieving high-accuracy prediction results in semi-supervised learning tasks, such as node classification. However, these networks suffer from the issue of over-smoothing and shrinking effect of the graph due in large part to the fact that they diffuse features across the edges of the graph using a linear Laplacian flow. This limitation is especially problematic for the task of node classification, where the goal is to predict the label associated with a graph node. To address this issue, we propose an anisotropic graph convolutional network for semi-supervised node classification by introducing a nonlinear function that captures informative features from nodes, while preventing oversmoothing. The proposed framework is largely motivated by the good performance of anisotropic diffusion in image and geometry processing, and learns nonlinear representations based on local graph structure and node features. The effectiveness of our approach is demonstrated on three citation networks and two image datasets, achieving better or comparable classification accuracy results compared to the standard baseline methods.
Leveraging Technology for Healthcare and Retaining Access to Personal Health Data to Enhance Personal Health and Well-being
Venue: arXiv cs.CR 2020
Year: 2020

Health data is a sensitive category of personal data. It might result in a high risk to individual and health information handling rights and opportunities unless there is a palatable defense. Reasonable security standards are needed to protect electronic health records (EHR). All personal data handling needs adequate explanation. Maintaining access to medical data even in the developing world would favor health and well-being across the world. Unfortunately, there are still countries that hinder the portability of medical records. Numerous occurrences have shown that it still takes weeks for the medical data to be ported from one general physician (GP) to another. Cross border portability is nearly impossible due to the lack of technical infrastructure and standardization. We demonstrate the difficulty of the portability of medical records with some example case studies as a collaborative engagement exercise through a data mapping process to describe how different people and datapoints interact and evaluate EHR portability techniques. We then propose a blockchain-based EHR system that allows secure, and cross border sharing of medical data. The ethical and technical challenges around having such a system have also been discussed in this study.