OpenRaiser/Intern-Atlas huggingface dataset:
Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages
Venue: arXiv cs.PL 2021
Year: 2021

Automatic differentiation (AD) aims to compute derivatives of user-defined functions, but in Turing-complete languages, this simple specification does not fully capture AD's behavior: AD sometimes disagrees with the true derivative of a differentiable program, and when AD is applied to non-differentiable or effectful programs, it is unclear what guarantees (if any) hold of the resulting code. We study an expressive differentiable programming language, with piecewise-analytic primitives, higher-order functions, and general recursion. Our main result is that even in this general setting, a version of Lee et al. [2020]'s correctness theorem (originally proven for a first-order language without partiality or recursion) holds: all programs denote so-called $ω$PAP functions, and AD computes correct intensional derivatives of them. Mazza and Pagani [2021]'s recent theorem, that AD disagrees with the true derivative of a differentiable recursive program at a measure-zero set of inputs, can be derived as a straightforward corollary of this fact. We also apply the framework to study probabilistic programs, and recover a recent result from Mak et al. [2021] via a novel denotational argument.
Consensus Synergizes with Memory: A Simple Approach for Anomaly Segmentation in Urban Scenes
Venue: arXiv cs.CV 2021
Year: 2021

Anomaly segmentation is a crucial task for safety-critical applications, such as autonomous driving in urban scenes, where the goal is to detect out-of-distribution (OOD) objects with categories which are unseen during training. The core challenge of this task is how to distinguish hard in-distribution samples from OOD samples, which has not been explicitly discussed yet. In this paper, we propose a novel and simple approach named Consensus Synergizes with Memory (CosMe) to address this challenge, inspired by the psychology finding that groups perform better than individuals on memory tasks. The main idea is 1) building a memory bank which consists of seen prototypes extracted from multiple layers of the pre-trained segmentation model and 2) training an auxiliary model that mimics the behavior of the pre-trained model, and then measuring the consensus of their mid-level features as complementary cues that synergize with the memory bank. CosMe is good at distinguishing between hard in-distribution examples and OOD samples. Experimental results on several urban scene anomaly segmentation datasets show that CosMe outperforms previous approaches by large margins.
Car drivers' privacy concerns and trust perceptions
Venue: arXiv cs.CR 2021
Year: 2021

Modern cars are evolving in many ways. Technologies such as infotainment systems and companion mobile applications collect a variety of personal data from drivers to enhance the user experience. This paper investigates the extent to which car drivers understand the implications for their privacy, including that car manufacturers must treat that data in compliance with the relevant regulations. It does so by distilling out drivers' concerns on privacy and relating them to their perceptions of trust on car cyber-security. A questionnaire is designed for such purposes to collect answers from a set of 1101 participants, so that the results are statistically relevant. In short, privacy concerns are modest, perhaps because there still is insufficient general awareness on the personal data that are involved, both for in-vehicle treatment and for transmission over the Internet. Trust perceptions on cyber-security are modest too (lower than those on car safety), a surprising contradiction to our research hypothesis that privacy concerns and trust perceptions on car cyber-security are opponent. We interpret this as a clear demand for information and awareness-building campaigns for car drivers, as well as for technical cyber-security and privacy measures that are truly considerate of the human factor.
Energy-Efficient Inference on the Edge Exploiting TinyML Capabilities for UAVs
Venue: arXiv cs.LG 2021
Year: 2021

In recent years, the proliferation of unmanned aerial vehicles (UAVs) has increased dramatically. UAVs can accomplish complex or dangerous tasks in a reliable and cost-effective way but are still limited by power consumption problems, which pose serious constraints on the flight duration and completion of energy-demanding tasks. The possibility of providing UAVs with advanced decision-making capabilities in an energy-effective way would be extremely beneficial. In this paper, we propose a practical solution to this problem that exploits deep learning on the edge. The developed system integrates an OpenMV microcontroller into a DJI Tello Micro Aerial Vehicle (MAV). The microcontroller hosts a set of machine learning-enabled inference tools that cooperate to control the navigation of the drone and complete a given mission objective. The goal of this approach is to leverage the new opportunistic features of TinyML through OpenMV including offline inference, low latency, energy efficiency, and data security. The approach is successfully validated on a practical application consisting of the onboard detection of people wearing protection masks in a crowded environment.
Semi-Local Convolutions for LiDAR Scan Processing
Venue: arXiv cs.CV 2021
Year: 2021

A number of applications, such as mobile robots or automated vehicles, use LiDAR sensors to obtain detailed information about their three-dimensional surroundings. Many methods use image-like projections to efficiently process these LiDAR measurements and use deep convolutional neural networks to predict semantic classes for each point in the scan. The spatial stationary assumption enables the usage of convolutions. However, LiDAR scans exhibit large differences in appearance over the vertical axis. Therefore, we propose semi local convolution (SLC), a convolution layer with reduced amount of weight-sharing along the vertical dimension. We are first to investigate the usage of such a layer independent of any other model changes. Our experiments did not show any improvement over traditional convolution layers in terms of segmentation IoU or accuracy.
DiPD: Disruptive event Prediction Dataset from Twitter
Venue: arXiv cs.SI 2021
Year: 2021

Riots and protests, if gone out of control, can cause havoc in a country. We have seen examples of this, such as the BLM movement, climate strikes, CAA Movement, and many more, which caused disruption to a large extent. Our motive behind creating this dataset was to use it to develop machine learning systems that can give its users insight into the trending events going on and alert them about the events that could lead to disruption in the nation. If any event starts going out of control, it can be handled and mitigated by monitoring it before the matter escalates. This dataset collects tweets of past or ongoing events known to have caused disruption and labels these tweets as 1. We also collect tweets that are considered non-eventful and label them as 0 so that they can also be used to train a classification system. The dataset contains 94855 records of unique events and 168706 records of unique non-events, thus giving the total dataset 263561 records. We extract multiple features from the tweets, such as the user's follower count and the user's location, to understand the impact and reach of the tweets. This dataset might be useful in various event related machine learning problems such as event classification, event recognition, and so on.
DeDUCE: Generating Counterfactual Explanations Efficiently
Venue: arXiv cs.CV 2021
Year: 2021

When an image classifier outputs a wrong class label, it can be helpful to see what changes in the image would lead to a correct classification. This is the aim of algorithms generating counterfactual explanations. However, there is no easily scalable method to generate such counterfactuals. We develop a new algorithm providing counterfactual explanations for large image classifiers trained with spectral normalisation at low computational cost. We empirically compare this algorithm against baselines from the literature; our novel algorithm consistently finds counterfactuals that are much closer to the original inputs. At the same time, the realism of these counterfactuals is comparable to the baselines. The code for all experiments is available at https://github.com/benedikthoeltgen/DeDUCE.
Open Data and Quantitative Techniques for Anthropology of Road Traffic
Venue: arXiv cs.CY 2021
Year: 2021

What kind of questions about human mobility can computational analysis help answer? How to translate the findings into anthropology? We analyzed a publicly available data set of road traffic counters in Slovenia to answer these questions. The data reveals interesting information on how a nation drives, how it travels for tourism, which locations it prefers, what it does during the week and the weekend, and how its habits change during the year. We conducted the empirical analysis in two parts. First, we defined interesting traffic spots and designed computational methods to find them in a large data set. As shown in the paper, traffic counters hint at potential causes and effects in driving practices that we can interpret anthropologically. Second, we used clustering to find groups of similar traffic counters as described by their daily profiles. Clustering revealed the main features of road traffic in Slovenia. Using the two quantitative approaches, we outline the general properties of road traffic in the country and identify and explain interesting outliers. We show that quantitative data analysis only partially answers anthropological questions, but it can be a valuable tool for preliminary research. We conclude that open data are a useful component in an anthropological analysis and that quantitative discovery of small local events can help us pinpoint future fieldwork sites.
Towards algorithm-free physical equilibrium model of computing
Venue: arXiv cs.CC 2021
Year: 2021

Our computers today, from sophisticated servers to small smartphones, operate based on the same computing model, which requires running a sequence of discrete instructions, specified as an algorithm. This sequential computing paradigm has not yet led to a fast algorithm for an NP-complete problem despite numerous attempts over the past half a century. Unfortunately, even after the introduction of quantum mechanics to the world of computing, we still followed a similar sequential paradigm, which has not yet helped us obtain such an algorithm either. Here a completely different model of computing is proposed to replace the sequential paradigm of algorithms with inherent parallelism of physical processes. Using the proposed model, instead of writing algorithms to solve NP-complete problems, we construct physical systems whose equilibrium states correspond to the desired solutions and let them evolve to search for the solutions. The main requirements of the model are identified and quantum circuits are proposed for its potential implementation.
Predicting Poverty Level from Satellite Imagery using Deep Neural Networks
Venue: arXiv cs.CV 2021
Year: 2021

Determining the poverty levels of various regions throughout the world is crucial in identifying interventions for poverty reduction initiatives and directing resources fairly. However, reliable data on global economic livelihoods is hard to come by, especially for areas in the developing world, hampering efforts to both deploy services and monitor/evaluate progress. This is largely due to the fact that this data is obtained from traditional door-to-door surveys, which are time consuming and expensive. Overhead satellite imagery contain characteristics that make it possible to estimate the region's poverty level. In this work, I develop deep learning computer vision methods that can predict a region's poverty level from an overhead satellite image. I experiment with both daytime and nighttime imagery. Furthermore, because data limitations are often the barrier to entry in poverty prediction from satellite imagery, I explore the impact that data quantity and data augmentation have on the representational power and overall accuracy of the networks. Lastly, to evaluate the robustness of the networks, I evaluate them on data from continents that were absent in the development set.
Scaling Shared-Memory Data Structures as Distributed Global-View Data Structures in the Partitioned Global Address Space model
Venue: arXiv cs.DC 2021
Year: 2021

The Partitioned Global Address Space (PGAS), a memory model in which the global address space is explicitly partitioned across compute nodes in a cluster, strives to bridge the gap between shared-memory and distributed-memory programming. To further bridge this gap, there has been an adoption of global-view distributed data structures, such as 'global arrays' or 'distributed arrays'. This work demonstrates how shared-memory data structures can be modified to scale in distributed memory. Presented in this work is the Distributed Interlocked Hash Table (DIHT), a global-view distributed map data structure inpired by the Interlocked Hash Table (IHT). At 64 nodes with 44 cores per node, DIHT provides upto 110x the performance of the Chapel standard-library HashedDist.
Atos: A Task-Parallel GPU Dynamic Scheduling Framework for Dynamic Irregular Computations
Venue: arXiv cs.DC 2021
Year: 2021

We present Atos, a task-parallel GPU dynamic scheduling framework that is especially suited to dynamic irregular applications. Compared to the dominant Bulk Synchronous Parallel (BSP) frameworks, Atos exposes additional concurrency by supporting task-parallel formulations of applications with relaxed dependencies, achieving higher GPU utilization, which is particularly significant for problems with concurrency bottlenecks. Atos also offers implicit task-parallel load balancing in addition to data-parallel load balancing, providing users the flexibility to balance between them to achieve optimal performance. Finally, Atos allows users to adapt to different use cases by controlling the kernel strategy and task-parallel granularity. We demonstrate that each of these controls is important in practice. We evaluate and analyze the performance of Atos vs. BSP on three applications: breadth-first search, PageRank, and graph coloring. Atos implementations achieve geomean speedups of 3.44x, 2.1x, and 2.77x and peak speedups of 12.8x, 3.2x, and 9.08x across three case studies, compared to a state-of-the-art BSP GPU implementation. Beyond simply quantifying the speedup, we extensively analyze the reasons behind each speedup. This deeper understanding allows us to derive general guidelines for how to select the optimal Atos configuration for different applications. Finally, our analysis provides insights for future dynamic scheduling framework designs.
ZCSD: a Computational Storage Device over Zoned Namespaces (ZNS) SSDs
Venue: arXiv cs.AR 2021
Year: 2021

The Big Data trend is putting strain on modern storage systems, which have to support high-performance I/O accesses for the large quantities of data. With the prevalent Von Neumann computing architecture, this data is constantly moved back and forth between the computing (i.e., CPU) and storage entities (DRAM, Non-Volatile Memory NVM storage). Hence, as the data volume grows, this constant data movement between the CPU and storage devices has emerged as a key performance bottleneck. To improve the situation, researchers have advocated to leverage computational storage devices (CSDs), which offer a programmable interface to run user-defined data processing operations close to the storage without excessive data movement, thus offering performance improvements. However, despite its potential, building CSD-aware applications remains a challenging task due to the lack of exploration and experimentation with the right API and abstraction. This is due to the limited accessibility to latest CSD/NVM devices, emerging device interfaces, and closed-source software internals of the devices. To remedy the situation, in this work we present an open-source CSD prototype over emerging NVMe Zoned Namespaces (ZNS) SSDs and an interface that can be used to explore application designs for CSD/NVM storage devices. In this paper we summarize the current state of the practice with CSD devices, make a case for designing a CSD prototype with the ZNS interface and eBPF (ZCSD), and present our initial findings. The prototype is available at https://github.com/Dantali0n/qemu-csd.
Efficient Big Text Data Clustering Algorithms using Hadoop and Spark
Venue: arXiv cs.DC 2021
Year: 2021

Document clustering is a traditional, efficient and yet quite effective, text mining technique when we need to get a better insight of the documents of a collection that could be grouped together. The K-Means algorithm and the Hierarchical Agglomerative Clustering (HAC) algorithm are two of the most known and commonly used clustering algorithms; the former due to its low time cost and the latter due to its accuracy. However, even the use of K-Means in text clustering over large-scale collections can lead to unacceptable time costs. In this paper we first address some of the most valuable approaches for document clustering over such 'big data' (large-scale) collections. We then present two very promising alternatives: (a) a variation of an existing K-Means-based fast clustering technique (known as BigKClustering - BKC) so that it can be applied in document clustering, and (b) a hybrid clustering approach based on a customized version of the Buckshot algorithm, which first applies a hierarchical clustering procedure on a sample of the input dataset and then it uses the results as the initial centers for a K-Means based assignment of the rest of the documents, with very few iterations. We also give highly efficient adaptations of the proposed techniques in the MapReduce model which are then experimentally tested using Apache Hadoop and Spark over a real cluster environment. As it comes out of the experiments, they both lead to acceptable clustering quality as well as to significant time improvements (compared to K-Means - especially the Buckshot-based algorithm), thus constituting very promising alternatives for big document collections.
3DVNet: Multi-View Depth Prediction and Volumetric Refinement
Venue: arXiv cs.CV 2021
Year: 2021

We present 3DVNet, a novel multi-view stereo (MVS) depth-prediction method that combines the advantages of previous depth-based and volumetric MVS approaches. Our key idea is the use of a 3D scene-modeling network that iteratively updates a set of coarse depth predictions, resulting in highly accurate predictions which agree on the underlying scene geometry. Unlike existing depth-prediction techniques, our method uses a volumetric 3D convolutional neural network (CNN) that operates in world space on all depth maps jointly. The network can therefore learn meaningful scene-level priors. Furthermore, unlike existing volumetric MVS techniques, our 3D CNN operates on a feature-augmented point cloud, allowing for effective aggregation of multi-view information and flexible iterative refinement of depth maps. Experimental results show our method exceeds state-of-the-art accuracy in both depth prediction and 3D reconstruction metrics on the ScanNet dataset, as well as a selection of scenes from the TUM-RGBD and ICL-NUIM datasets. This shows that our method is both effective and generalizes to new settings.
Improved sparse PCA method for face and image recognition
Venue: arXiv cs.CV 2021
Year: 2021

Face recognition is the very significant field in pattern recognition area. It has multiple applications in military and finance, to name a few. In this paper, the combination of the sparse PCA with the nearest-neighbor method (and with the kernel ridge regression method) will be proposed and will be applied to solve the face recognition problem. Experimental results illustrate that the accuracy of the combination of the sparse PCA method (using the proximal gradient method and the FISTA method) and one specific classification system may be lower than the accuracy of the combination of the PCA method and one specific classification system but sometimes the combination of the sparse PCA method (using the proximal gradient method or the FISTA method) and one specific classification system leads to better accuracy. Moreover, we recognize that the process computing the sparse PCA algorithm using the FISTA method is always faster than the process computing the sparse PCA algorithm using the proximal gradient method.
True or False: Does the Deep Learning Model Learn to Detect Rumors?
Venue: arXiv cs.CL 2021
Year: 2021

It is difficult for humans to distinguish the true and false of rumors, but current deep learning models can surpass humans and achieve excellent accuracy on many rumor datasets. In this paper, we investigate whether deep learning models that seem to perform well actually learn to detect rumors. We evaluate models on their generalization ability to out-of-domain examples by fine-tuning BERT-based models on five real-world datasets and evaluating against all test sets. The experimental results indicate that the generalization ability of the models on other unseen datasets are unsatisfactory, even common-sense rumors cannot be detected. Moreover, we found through experiments that models take shortcuts and learn absurd knowledge when the rumor datasets have serious data pitfalls. This means that simple modifications to the rumor text based on specific rules will lead to inconsistent model predictions. To more realistically evaluate rumor detection models, we proposed a new evaluation method called paired test (PairT), which requires models to correctly predict a pair of test samples at the same time. Furthermore, we make recommendations on how to better create rumor dataset and evaluate rumor detection model at the end of this paper.
GLocal: Global Graph Reasoning and Local Structure Transfer for Person Image Generation
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we focus on person image generation, namely, generating person image under various conditions, e.g., corrupted texture or different pose. To address texture occlusion and large pose misalignment in this task, previous works just use the corresponding region's style to infer the occluded area and rely on point-wise alignment to reorganize the context texture information, lacking the ability to globally correlate the region-wise style codes and preserve the local structure of the source. To tackle these problems, we present a GLocal framework to improve the occlusion-aware texture estimation by globally reasoning the style inter-correlations among different semantic regions, which can also be employed to recover the corrupted images in texture inpainting. For local structural information preservation, we further extract the local structure of the source image and regain it in the generated image via local structure transfer. We benchmark our method to fully characterize its performance on DeepFashion dataset and present extensive ablation studies that highlight the novelty of our method.