OpenRaiser/Intern-Atlas huggingface dataset:
Assessing Effectiveness of Using Internal Signals for Check-Worthy Claim Identification in Unlabeled Data for Automated Fact-Checking
Venue: arXiv cs.CL 2021
Year: 2021

While recent work on automated fact-checking has focused mainly on verifying and explaining claims, for which the list of claims is readily available, identifying check-worthy claim sentences from a text remains challenging. Current claim identification models rely on manual annotations for each sentence in the text, which is an expensive task and challenging to conduct on a frequent basis across multiple domains. This paper explores methodology to identify check-worthy claim sentences from fake news articles, irrespective of domain, without explicit sentence-level annotations. We leverage two internal supervisory signals - headline and the abstractive summary - to rank the sentences based on semantic similarity. We hypothesize that this ranking directly correlates to the check-worthiness of the sentences. To assess the effectiveness of this hypothesis, we build pipelines that leverage the ranking of sentences based on either the headline or the abstractive summary. The top-ranked sentences are used for the downstream fact-checking tasks of evidence retrieval and the article's veracity prediction by the pipeline. Our findings suggest that the top 3 ranked sentences contain enough information for evidence-based fact-checking of a fake news article. We also show that while the headline has more gisting similarity with how a fact-checking website writes a claim, the summary-based pipeline is the most promising for an end-to-end fact-checking system.
Competitive Algorithms for Online Weighted Bipartite Matching and its Variants
Venue: arXiv cs.DS 2021
Year: 2021

Online bipartite matching has been extensively studied. In the unweighted setting, Karp et al. gave an optimal $(1 - 1/e)$-competitive randomized algorithm. In the weighted setting, optimal algorithms have been achieved only under assumptions on the edge weights. For the general case, little was known beyond the trivial $1/2$-competitive greedy algorithm. Recently, Fahrbach et al. have presented an 0.5086-competitive algorithm (for the problem in a model, namely free-disposal), overcoming the long-standing barrier of 1/2. Besides, in designing competitive algorithms for the online matching problem and its variants, several techniques have been developed, in particular the primal-dual method. Specifically, Devanur et al. gave a primal-dual framework, unifying previous approaches and Devanur and Jain provided another scheme for a generalization of the online matching problem. In this paper, we present competitive algorithms for the online weighted bipartite matching in different models; in particular we achieve the optimal $(1-1/e)$ competitive ratio in the free-disposal model and in other model, namely stochastic reward. Our work also unifies the previous approaches by the mean of the primal-dual technique with configuration linear programs.
Instructive artificial intelligence (AI) for human training, assistance, and explainability
Venue: arXiv cs.AI 2021
Year: 2021

We propose a novel approach to explainable AI (XAI) based on the concept of "instruction" from neural networks. In this case study, we demonstrate how a superhuman neural network might instruct human trainees as an alternative to traditional approaches to XAI. Specifically, an AI examines human actions and calculates variations on the human strategy that lead to better performance. Experiments with a JHU/APL-developed AI player for the cooperative card game Hanabi suggest this technique makes unique contributions to explainability while improving human performance. One area of focus for Instructive AI is in the significant discrepancies that can arise between a human's actual strategy and the strategy they profess to use. This inaccurate self-assessment presents a barrier for XAI, since explanations of an AI's strategy may not be properly understood or implemented by human recipients. We have developed and are testing a novel, Instructive AI approach that estimates human strategy by observing human actions. With neural networks, this allows a direct calculation of the changes in weights needed to improve the human strategy to better emulate a more successful AI. Subjected to constraints (e.g. sparsity) these weight changes can be interpreted as recommended changes to human strategy (e.g. "value A more, and value B less"). Instruction from AI such as this functions both to help humans perform better at tasks, but also to better understand, anticipate, and correct the actions of an AI. Results will be presented on AI instruction's ability to improve human decision-making and human-AI teaming in Hanabi.
Personalized One-Shot Lipreading for an ALS Patient
Venue: arXiv cs.CV 2021
Year: 2021

Lipreading or visually recognizing speech from the mouth movements of a speaker is a challenging and mentally taxing task. Unfortunately, multiple medical conditions force people to depend on this skill in their day-to-day lives for essential communication. Patients suffering from Amyotrophic Lateral Sclerosis (ALS) often lose muscle control, consequently their ability to generate speech and communicate via lip movements. Existing large datasets do not focus on medical patients or curate personalized vocabulary relevant to an individual. Collecting a large-scale dataset of a patient, needed to train mod-ern data-hungry deep learning models is, however, extremely challenging. In this work, we propose a personalized network to lipread an ALS patient using only one-shot examples. We depend on synthetically generated lip movements to augment the one-shot scenario. A Variational Encoder based domain adaptation technique is used to bridge the real-synthetic domain gap. Our approach significantly improves and achieves high top-5accuracy with 83.2% accuracy compared to 62.6% achieved by comparable methods for the patient. Apart from evaluating our approach on the ALS patient, we also extend it to people with hearing impairment relying extensively on lip movements to communicate.
Basis Matters: Better Communication-Efficient Second Order Methods for Federated Learning
Venue: arXiv cs.LG 2021
Year: 2021

Recent advances in distributed optimization have shown that Newton-type methods with proper communication compression mechanisms can guarantee fast local rates and low communication cost compared to first order methods. We discover that the communication cost of these methods can be further reduced, sometimes dramatically so, with a surprisingly simple trick: {\em Basis Learn (BL)}. The idea is to transform the usual representation of the local Hessians via a change of basis in the space of matrices and apply compression tools to the new representation. To demonstrate the potential of using custom bases, we design a new Newton-type method (BL1), which reduces communication cost via both {\em BL} technique and bidirectional compression mechanism. Furthermore, we present two alternative extensions (BL2 and BL3) to partial participation to accommodate federated learning applications. We prove local linear and superlinear rates independent of the condition number. Finally, we support our claims with numerical experiments by comparing several first and second~order~methods.
A dataset for multi-sensor drone detection
Venue: arXiv cs.CV 2021
Year: 2021

The use of small and remotely controlled unmanned aerial vehicles (UAVs), or drones, has increased in recent years. This goes in parallel with misuse episodes, with an evident threat to the safety of people or facilities. As a result, the detection of UAV has also emerged as a research topic. Most studies on drone detection fail to specify the type of acquisition device, the drone type, the detection range, or the dataset. The lack of proper UAV detection studies employing thermal infrared cameras is also an issue, despite its success with other targets. Besides, we have not found any previous study that addresses the detection task as a function of distance to the target. Sensor fusion is indicated as an open research issue as well, although research in this direction is scarce too. To counteract the mentioned issues and allow fundamental studies with a common public benchmark, we contribute with an annotated multi-sensor database for drone detection that includes infrared and visible videos and audio files. The database includes three different drones, of different sizes and other flying objects that can be mistakenly detected as drones, such as birds, airplanes or helicopters. In addition to using several different sensors, the number of classes is higher than in previous studies. To allow studies as a function of the sensor-to-target distance, the dataset is divided into three categories (Close, Medium, Distant) according to the industry-standard Detect, Recognize and Identify (DRI) requirements, built on the Johnson criteria. Given that the drones must be flown within visual range due to regulations, the largest sensor-to-target distance for a drone is 200 m, and acquisitions are made in daylight. The data has been obtained at three airports in Sweden: Halmstad Airport (IATA code: HAD/ICAO code: ESMT), Gothenburg City Airport (GSE/ESGP) and Malmö Airport (MMX/ESMS).
A high performance fingerprint liveness detection method based on quality related features
Venue: arXiv cs.CV 2021
Year: 2021

A new software-based liveness detection approach using a novel fingerprint parameterization based on quality related features is proposed. The system is tested on a highly challenging database comprising over 10,500 real and fake images acquired with five sensors of different technologies and covering a wide range of direct attack scenarios in terms of materials and procedures followed to generate the gummy fingers. The proposed solution proves to be robust to the multi-scenario dataset, and presents an overall rate of 90% correctly classified samples. Furthermore, the liveness detection method presented has the added advantage over previously studied techniques of needing just one image from a finger to decide whether it is real or fake. This last characteristic provides the method with very valuable features as it makes it less intrusive, more user friendly, faster and reduces its implementation costs.
Parameterized Explanations for Investor / Company Matching
Venue: arXiv cs.IR 2021
Year: 2021

Matching companies and investors is usually considered a highly specialized decision making process. Building an AI agent that can automate such recommendation process can significantly help reduce costs, and eliminate human biases and errors. However, limited sample size of financial data-sets and the need for not only good recommendations, but also explaining why a particular recommendation is being made, makes this a challenging problem. In this work we propose a representation learning based recommendation engine that works extremely well with small datasets and demonstrate how it can be coupled with a parameterized explanation generation engine to build an explainable recommendation system for investor-company matching. We compare the performance of our system with human generated recommendations and demonstrate the ability of our algorithm to perform extremely well on this task. We also highlight how explainability helps with real-life adoption of our system.
An Evaluation of WebAssembly and eBPF as Offloading Mechanisms in the Context of Computational Storage
Venue: arXiv cs.AR 2021
Year: 2021

As the volume of data that needs to be processed continues to increase, we also see renewed interests in near-data processing in the form of computational storage, with eBPF (extended Berkeley Packet Filter) being proposed as a vehicle for computation offloading. However, discussions in this regard have so far ignored viable alternatives, and no convincing analysis has been provided. As such, we qualitatively and quantitatively evaluated eBPF against WebAssembly, a seemingly similar technology, in the context of computation offloading. This report presents our findings.
Getting a Grip: in Materio Evolution of Membrane Morphology for Soft Robotic Jamming Grippers
Venue: arXiv cs.RO 2021
Year: 2021

The application of granular jamming in soft robotics is a recent and promising new technology offer exciting possibilities for creating higher performance robotic devices. Granular jamming is achieved via the application of a vacuum pressure inside a membrane containing particulate matter, and is particularly interesting from a design perspective, as a myriad of design parameters can potentially be exploited to induce a diverse variety of useful behaviours. To date, the effect of variables such as grain shape and size, as well as membrane material, have been studied as a means of inducing bespoke gripping performance, however the other main contributing factor, membrane morphology, has not been studied due to its particular complexities in both accurate modelling and fabrication. This research presents the first study that optimises membrane morphology for granular jamming grippers, combining multi-material 3D printing and an evolutionary algorithm to search through a varied morphology design space in materio. Entire generations are printed in a single run and gripper retention force is tested and used as a fitness measure. Our approach is relatively scalable, circumvents the need for modelling, and guarantees the real-world performance of the grippers considered. Results show that membrane morphology is a key determinant of gripper performance. Common high performance designs are seen to optimise all three of the main identified mechanisms by which granular grippers generate grip force, are significantly different from a standard gripper morphology, and generalise well across a range of test objects.
Adversarially Perturbed Wavelet-based Morphed Face Generation
Venue: arXiv cs.CV 2021
Year: 2021

Morphing is the process of combining two or more subjects in an image in order to create a new identity which contains features of both individuals. Morphed images can fool Facial Recognition Systems (FRS) into falsely accepting multiple people, leading to failures in national security. As morphed image synthesis becomes easier, it is vital to expand the research community's available data to help combat this dilemma. In this paper, we explore combination of two methods for morphed image generation, those of geometric transformation (warping and blending to create morphed images) and photometric perturbation. We leverage both methods to generate high-quality adversarially perturbed morphs from the FERET, FRGC, and FRLL datasets. The final images retain high similarity to both input subjects while resulting in minimal artifacts in the visual domain. Images are synthesized by fusing the wavelet sub-bands from the two look-alike subjects, and then adversarially perturbed to create highly convincing imagery to deceive both humans and deep morph detectors.
Binary classification of proteins by a Machine Learning approach
Venue: arXiv cs.LG 2021
Year: 2021

In this work we present a system based on a Deep Learning approach, by using a Convolutional Neural Network, capable of classifying protein chains of amino acids based on the protein description contained in the Protein Data Bank. Each protein is fully described in its chemical-physical-geometric properties in a file in XML format. The aim of the work is to design a prototypical Deep Learning machinery for the collection and management of a huge amount of data and to validate it through its application to the classification of a sequences of amino acids. We envisage applying the described approach to more general classification problems in biomolecules, related to structural properties and similarities.
Autonomous Magnetic Navigation Framework for Active Wireless Capsule Endoscopy Inspired by Conventional Colonoscopy Procedures
Venue: arXiv cs.RO 2021
Year: 2021

In recent years, simultaneous magnetic actuation and localization (SMAL) for active wireless capsule endoscopy (WCE) has been intensively studied to improve the efficiency and accuracy of the examination. In this paper, we propose an autonomous magnetic navigation framework for active WCE that mimics the "insertion" and "withdrawal" procedures performed by an expert physician in conventional colonoscopy, thereby enabling efficient and accurate navigation of a robotic capsule endoscope in the intestine with minimal user effort. First, the capsule is automatically propelled through the unknown intestinal environment and generate a viable path to represent the environment. Then, the capsule is autonomously navigated towards any point selected on the intestinal trajectory to allow accurate and repeated inspections of suspicious lesions. Moreover, we implement the navigation framework on a robotic system incorporated with advanced SMAL algorithms, and validate it in the navigation in various tubular environments using phantoms and an ex-vivo pig colon. Our results demonstrate that the proposed autonomous navigation framework can effectively navigate the capsule in unknown, complex tubular environments with a satisfactory accuracy, repeatability and efficiency compared with manual operation.
Belief Propagation based Joint Detection and Decoding for Resistive Random Access Memories
Venue: arXiv cs.IT 2021
Year: 2021

Despite the great promises that the resistive random access memory (ReRAM) has shown as the next generation of non-volatile memory technology, its crossbar array structure leads to a severe sneak path interference to the signal read back from the memory cell. In this paper, we first propose a novel belief propagation (BP) based detector for the sneak path interference in ReRAM. Based on the conditions for a sneak path to occur and the dependence of the states of the memory cells that are involved in the sneak path, a Tanner graph for the ReRAM channel is constructed, inside which specific messages are updated iteratively to get a better estimation of the sneak path affected cells. We further combine the graph of the designed BP detector with that of the BP decoder of the polar codes to form a joint detector and decoder. Tailored for the joint detector and decoder over the ReRAM channel, effective polar codes are constructed using the genetic algorithm. Simulation results show that the BP detector can effectively detect the cells affected by the sneak path, and the proposed polar codes and the joint detector and decoder can significantly improve the error rate performance of ReRAM.
Leveraging Advantages of Interactive and Non-Interactive Models for Vector-Based Cross-Lingual Information Retrieval
Venue: arXiv cs.CL 2021
Year: 2021

Interactive and non-interactive model are the two de-facto standard frameworks in vector-based cross-lingual information retrieval (V-CLIR), which embed queries and documents in synchronous and asynchronous fashions, respectively. From the retrieval accuracy and computational efficiency perspectives, each model has its own superiority and shortcoming. In this paper, we propose a novel framework to leverage the advantages of these two paradigms. Concretely, we introduce semi-interactive mechanism, which builds our model upon non-interactive architecture but encodes each document together with its associated multilingual queries. Accordingly, cross-lingual features can be better learned like an interactive model. Besides, we further transfer knowledge from a well-trained interactive model to ours by reusing its word embeddings and adopting knowledge distillation. Our model is initialized from a multilingual pre-trained language model M-BERT, and evaluated on two open-resource CLIR datasets derived from Wikipedia and an in-house dataset collected from a real-world search engine. Extensive analyses reveal that our methods significantly boost the retrieval accuracy while maintaining the computational efficiency.
Privacy-Preserving Energy Storage Sharing with Blockchain and Secure Multi-Party Computation
Venue: arXiv cs.CR 2021
Year: 2021

Energy storage provides an effective way of shifting temporal energy demands and supplies, which enables significant cost reduction under time-of-use energy pricing plans. Despite its promising benefits, the cost of present energy storage remains expensive, presenting a major obstacle to practical deployment. A more viable solution to improve the cost-effectiveness is by sharing energy storage, such as community sharing, cloud energy storage and peer-to-peer sharing. However, revealing private energy demand data to an external energy storage operator may compromise user privacy, and is susceptible to data misuses and breaches. In this paper, we explore a novel approach to support energy storage sharing with privacy protection, based on privacy-preserving blockchain and secure multi-party computation. We present an integrated solution to enable privacy-preserving energy storage sharing, such that energy storage service scheduling and cost-sharing can be attained without the knowledge of individual users' demands. It also supports auditing and verification by the grid operator via blockchain. Furthermore, our privacy-preserving solution can safeguard against a dishonest majority of users, who may collude in cheating, without requiring a trusted third-party. We implemented our solution as a smart contract on real-world Ethereum blockchain platform, and provide empirical evaluation in this paper.
Three-dimensional Cooperative Localization of Commercial-Off-The-Shelf Sensors
Venue: arXiv cs.IR 2021
Year: 2021

Many location-based services use Received Signal Strength (RSS) measurements due to their universal availability. In this paper, we study the association of a large number of low-cost Internet-of-Things (IoT) sensors and their possible installation locations, which can enable various sensing and automation-related applications. We propose an efficient approach to solve the corresponding permutation combinatorial optimization problem, which integrates continuous space cooperative localization and permutation space likelihood ascent search. A convex relaxation-based optimization is designed to estimate the coarse locations of blindfolded devices in continuous 3D spaces, which are then projected to the feasible permutation space. An efficient Cramér-Rao Lower Bound based likelihood ascent search algorithm is proposed to refine the solution. Extensive experiments were conducted to evaluate the performance of the proposed approach, which show that the proposed approach significantly outperforms state-of-the-art combinatorial optimization algorithms and achieves close-to-100% accuracy with affordable execution time.
Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data
Venue: arXiv cs.CV 2021
Year: 2021

Originally developed in fields such as robotics and autonomous driving with image-based navigation in mind, deep learning-based single-image depth estimation (SIDE) has found great interest in the wider image analysis community. Remote sensing is no exception, as the possibility to estimate height maps from single aerial or satellite imagery bears great potential in the context of topographic reconstruction. A few pioneering investigations have demonstrated the general feasibility of single image height prediction from optical remote sensing images and motivate further studies in that direction. With this paper, we present the first-ever demonstration of deep learning-based single image height prediction for the other important sensor modality in remote sensing: synthetic aperture radar (SAR) data. Besides the adaptation of a convolutional neural network (CNN) architecture for SAR intensity images, we present a workflow for the generation of training data, and extensive experimental results for different SAR imaging modes and test sites. Since we put a particular emphasis on transferability, we are able to confirm that deep learning-based single-image height estimation is not only possible, but also transfers quite well to unseen data, even if acquired by different imaging modes and imaging parameters.