OpenRaiser/Intern-Atlas huggingface dataset:
Dichotomy for Graph Homomorphisms with Complex Values on Bounded Degree Graphs
Venue: arXiv cs.CC 2020
Year: 2020

The complexity of graph homomorphisms has been a subject of intense study [11, 12, 4, 42, 21, 17, 6, 20]. The partition function $Z_{\mathbf A}(\cdot)$ of graph homomorphism is defined by a symmetric matrix $\mathbf A$ over $\mathbb C$. We prove that the complexity dichotomy of [6] extends to bounded degree graphs. More precisely, we prove that either $G \mapsto Z_{\mathbf A}(G)$ is computable in polynomial-time for every $G$, or for some $Δ> 0$ it is #P-hard over (simple) graphs $G$ with maximum degree $Δ(G) \le Δ$. The tractability criterion on $\mathbf A$ for this dichotomy is explicit, and can be decided in polynomial-time in the size of $\mathbf A$. We also show that the dichotomy is effective in that either a P-time algorithm for, or a reduction from #SAT to, $Z_{\mathbf A}(\cdot)$ can be constructed from $\mathbf A$, in the respective cases.
Weight Poisoning Attacks on Pre-trained Models
Venue: arXiv cs.LG 2020
Year: 2020

Recently, NLP has seen a surge in the usage of large pre-trained models. Users download weights of models pre-trained on large datasets, then fine-tune the weights on a task of their choice. This raises the question of whether downloading untrusted pre-trained weights can pose a security threat. In this paper, we show that it is possible to construct ``weight poisoning'' attacks where pre-trained weights are injected with vulnerabilities that expose ``backdoors'' after fine-tuning, enabling the attacker to manipulate the model prediction simply by injecting an arbitrary keyword. We show that by applying a regularization method, which we call RIPPLe, and an initialization procedure, which we call Embedding Surgery, such attacks are possible even with limited knowledge of the dataset and fine-tuning procedure. Our experiments on sentiment classification, toxicity detection, and spam detection show that this attack is widely applicable and poses a serious threat. Finally, we outline practical defenses against such attacks. Code to reproduce our experiments is available at https://github.com/neulab/RIPPLe.
Fidelity of Statistical Reporting in 10 Years of Cyber Security User Studies
Venue: arXiv cs.CR 2020
Year: 2020

Studies in socio-technical aspects of security often rely on user studies and statistical inferences on investigated relations to make their case. They, thereby, enable practitioners and scientists alike to judge on the validity and reliability of the research undertaken. To ascertain this capacity, we investigated the reporting fidelity of security user studies. Based on a systematic literature review of $114$ user studies in cyber security from selected venues in the 10 years 2006--2016, we evaluated fidelity of the reporting of $1775$ statistical inferences using the \textsf{R} package \textsf{statcheck}. We conducted a systematic classification of incomplete reporting, reporting inconsistencies and decision errors, leading to multinomial logistic regression (MLR) on the impact of publication venue/year as well as a comparison to a compatible field of psychology. We found that half the cyber security user studies considered reported incomplete results, in stark difference to comparable results in a field of psychology. Our MLR on analysis outcomes yielded a slight increase of likelihood of incomplete tests over time, while SOUPS yielded a few percent greater likelihood to report statistics correctly than other venues. In this study, we offer the first fully quantitative analysis of the state-of-play of socio-technical studies in security. While we highlight the impact and prevalence of incomplete reporting, we also offer fine-grained diagnostics and recommendations on how to respond to the situation.
Extending Text Informativeness Measures to Passage Interestingness Evaluation (Language Model vs. Word Embedding)
Venue: arXiv cs.IR 2020
Year: 2020

Standard informativeness measures used to evaluate Automatic Text Summarization mostly rely on n-gram overlapping between the automatic summary and the reference summaries. These measures differ from the metric they use (cosine, ROUGE, Kullback-Leibler, Logarithm Similarity, etc.) and the bag of terms they consider (single words, word n-grams, entities, nuggets, etc.). Recent word embedding approaches offer a continuous alternative to discrete approaches based on the presence/absence of a text unit. Informativeness measures have been extended to Focus Information Retrieval evaluation involving a user's information need represented by short queries. In particular for the task of CLEF-INEX Tweet Contextualization, tweet contents have been considered as queries. In this paper we define the concept of Interestingness as a generalization of Informativeness, whereby the information need is diverse and formalized as an unknown set of implicit queries. We then study the ability of state of the art Informativeness measures to cope with this generalization. Lately we show that with this new framework, standard word embeddings outperforms discrete measures only on uni-grams, however bi-grams seems to be a key point of interestingness evaluation. Lastly we prove that the CLEF-INEX Tweet Contextualization 2012 Logarithm Similarity measure provides best results.
A Simple Yet Strong Pipeline for HotpotQA
Venue: arXiv cs.CL 2020
Year: 2020

State-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition. However, does their strong performance on popular multi-hop datasets really justify this added design complexity? Our results suggest that the answer may be no, because even our simple pipeline based on BERT, named Quark, performs surprisingly well. Specifically, on HotpotQA, Quark outperforms these models on both question answering and support identification (and achieves performance very close to a RoBERTa model). Our pipeline has three steps: 1) use BERT to identify potentially relevant sentences independently of each other; 2) feed the set of selected sentences as context into a standard BERT span prediction model to choose an answer; and 3) use the sentence selection model, now with the chosen answer, to produce supporting sentences. The strong performance of Quark resurfaces the importance of carefully exploring simple model designs before using popular benchmarks to justify the value of complex techniques.
Cyclic and convolutional codes with locality
Venue: arXiv cs.IT 2020
Year: 2020

Locally recoverable (LRC) codes and their variants have been extensively studied in recent years. In this paper we focus on cyclic constructions of LRC codes and derive conditions on the zeros of the code that support the property of hierarchical locality. As a result, we obtain a general family of hierarchical LRC codes for a new range of code parameters. We also observe that our approach enables one to represent an LRC code in quasicyclic form, and use this representation to construct tail-biting convolutional LRC codes with locality. Among other results, we extend the general approach to cyclic codes with locality to multidimensional cyclic codes, yielding new families of LRC codes with availability, and construct a family of q-ary cyclic hierarchical LRC codes of unbounded length.
Privacy-Preserving Contact Tracing: current solutions and open questions
Venue: arXiv cs.CR 2020
Year: 2020

The COVID-19 pandemic has posed a unique challenge for the world to find solutions, ranging from vaccines to ICT solutions to slow down the virus spreading. Due to the highly contagious nature of the virus, social distancing is one fundamental measure which has already adopted by many countries. At the technical level, this prioritises contact tracing solutions, which can alert the users who have been in close contact with the infected persons and meanwhile allow heath authorities to take proper actions. In this paper, we examine several existing privacy-aware contact tracing solutions and analyse their (dis)advantages. At the end, we describe several major observations and outline an interdisciplinary research agenda towards more comprehensive and effective privacy-aware contact tracing solutions.
Differentially Private Assouad, Fano, and Le Cam
Venue: arXiv cs.LG 2020
Year: 2020

Le Cam's method, Fano's inequality, and Assouad's lemma are three widely used techniques to prove lower bounds for statistical estimation tasks. We propose their analogues under central differential privacy. Our results are simple, easy to apply and we use them to establish sample complexity bounds in several estimation tasks. We establish the optimal sample complexity of discrete distribution estimation under total variation distance and $\ell_2$ distance. We also provide lower bounds for several other distribution classes, including product distributions and Gaussian mixtures that are tight up to logarithmic factors. The technical component of our paper relates coupling between distributions to the sample complexity of estimation under differential privacy.
Bayesian differential programming for robust systems identification under uncertainty
Venue: arXiv cs.LG 2020
Year: 2020

This paper presents a machine learning framework for Bayesian systems identification from noisy, sparse and irregular observations of nonlinear dynamical systems. The proposed method takes advantage of recent developments in differentiable programming to propagate gradient information through ordinary differential equation solvers and perform Bayesian inference with respect to unknown model parameters using Hamiltonian Monte Carlo. This allows us to efficiently infer posterior distributions over plausible models with quantified uncertainty, while the use of sparsity-promoting priors enables the discovery of interpretable and parsimonious representations for the underlying latent dynamics. A series of numerical studies is presented to demonstrate the effectiveness of the proposed methods including nonlinear oscillators, predator-prey systems, chaotic dynamics and systems biology. Taken all together, our findings put forth a novel, flexible and robust workflow for data-driven model discovery under uncertainty.
A Practical Blockchain Framework using Image Hashing for Image Authentication
Venue: arXiv cs.CV 2020
Year: 2020

Blockchain is a relatively new technology that can be seen as a decentralised database. Blockchain systems heavily rely on cryptographic hash functions to store their data, which makes it difficult to tamper with any data stored in the system. A topic that was researched along with blockchain is image authentication. Image authentication focuses on investigating and maintaining the integrity of images. As a blockchain system can be useful for maintaining data integrity, image authentication has the potential to be enhanced by blockchain. There are many techniques that can be used to authenticate images; the technique investigated by this work is image hashing. Image hashing is a technique used to calculate how similar two different images are. This is done by converting the images into hashes and then comparing them using a distance formula. To investigate the topic, an experiment involving a simulated blockchain was created. The blockchain acted as a database for images. This blockchain was made up of devices which contained their own unique image hashing algorithms. The blockchain was tested by creating modified copies of the images contained in the database, and then submitting them to the blockchain to see if it will return the original image. Through this experiment it was discovered that it is plausible to create an image authentication system using blockchain and image hashing. However, the design proposed by this work requires refinement, as it appears to struggle in some situations. This work shows that blockchain can be a suitable approach for authenticating images, particularly via image hashing. Other observations include that using multiple image hash algorithms at the same time can increase performance in some cases, as well as that each type of test done to the blockchain has its own unique pattern to its data.
HODET: Hybrid Object DEtection and Tracking using mmWave Radar and Visual Sensors
Venue: arXiv cs.DC 2020
Year: 2020

Image sensors have been explored heavily in automotive applications for collision avoidance and varying levels of autonomy. It requires a degree of brightness, therefore, the use of an image sensor in nighttime operation or dark conditions can be problematic along with challenging weather such as fog. Radar sensors have been employed to help cover the various environmental challenges with visible spectrum cameras. Edge computing technology has the potential to address a number of issues such as real-time processing requirements, off-loading of processing from congested servers, and size, weight, power, and cost (SWaP-C) constraints. This paper proposes a novel Hybrid Object DEtection and Tracking (HODET) using mmWave Radar and Visual Sensors at the edge. The HODET is a computing application of low SWaP-C electronics performing object detection, tracking and identification algorithms with the simultaneous use of image and radar sensors. While the machine vision camera alone could estimate the distance of an object, the radar sensor will provide an accurate distance and vector of movement. This additional data accuracy can be leveraged to further discriminate a detected object to protect against spoofing attacks. A real-world smart community public safety monitoring scenario is selected to verify the effectiveness of HODET, which detects, tracks objects of interests and identify suspicious activities. The experimental results demonstrate the feasibility of the approach.
Coreferential Reasoning Learning for Language Representation
Venue: arXiv cs.CL 2020
Year: 2020

Language representation models such as BERT could effectively capture contextual semantic information from plain text, and have been proved to achieve promising results in lots of downstream NLP tasks with appropriate fine-tuning. However, most existing language representation models cannot explicitly handle coreference, which is essential to the coherent understanding of the whole discourse. To address this issue, we present CorefBERT, a novel language representation model that can capture the coreferential relations in context. The experimental results show that, compared with existing baseline models, CorefBERT can achieve significant improvements consistently on various downstream NLP tasks that require coreferential reasoning, while maintaining comparable performance to previous models on other common NLP tasks. The source code and experiment details of this paper can be obtained from https://github.com/thunlp/CorefBERT.
Human Evaluation of Interpretability: The Case of AI-Generated Music Knowledge
Venue: arXiv cs.HC 2020
Year: 2020

Interpretability of machine learning models has gained more and more attention among researchers in the artificial intelligence (AI) and human-computer interaction (HCI) communities. Most existing work focuses on decision making, whereas we consider knowledge discovery. In particular, we focus on evaluating AI-discovered knowledge/rules in the arts and humanities. From a specific scenario, we present an experimental procedure to collect and assess human-generated verbal interpretations of AI-generated music theory/rules rendered as sophisticated symbolic/numeric objects. Our goal is to reveal both the possibilities and the challenges in such a process of decoding expressive messages from AI sources. We treat this as a first step towards 1) better design of AI representations that are human interpretable and 2) a general methodology to evaluate interpretability of AI-discovered knowledge representations.
Combining Visible Light and Infrared Imaging for Efficient Detection of Respiratory Infections such as COVID-19 on Portable Device
Venue: arXiv cs.CV 2020
Year: 2020

Coronavirus Disease 2019 (COVID-19) has become a serious global epidemic in the past few months and caused huge loss to human society worldwide. For such a large-scale epidemic, early detection and isolation of potential virus carriers is essential to curb the spread of the epidemic. Recent studies have shown that one important feature of COVID-19 is the abnormal respiratory status caused by viral infections. During the epidemic, many people tend to wear masks to reduce the risk of getting sick. Therefore, in this paper, we propose a portable non-contact method to screen the health condition of people wearing masks through analysis of the respiratory characteristics. The device mainly consists of a FLIR one thermal camera and an Android phone. This may help identify those potential patients of COVID-19 under practical scenarios such as pre-inspection in schools and hospitals. In this work, we perform the health screening through the combination of the RGB and thermal videos obtained from the dual-mode camera and deep learning architecture.We first accomplish a respiratory data capture technique for people wearing masks by using face recognition. Then, a bidirectional GRU neural network with attention mechanism is applied to the respiratory data to obtain the health screening result. The results of validation experiments show that our model can identify the health status on respiratory with the accuracy of 83.7\% on the real-world dataset. The abnormal respiratory data and part of normal respiratory data are collected from Ruijin Hospital Affiliated to The Shanghai Jiao Tong University Medical School. Other normal respiratory data are obtained from healthy people around our researchers. This work demonstrates that the proposed portable and intelligent health screening device can be used as a pre-scan method for respiratory infections, which may help fight the current COVID-19 epidemic.
NN-based Transformation of Any SQL Cardinality Estimator for Handling DISTINCT, AND, OR and NOT
Venue: arXiv cs.DB 2020
Year: 2020

SQL queries, with the AND, OR, and NOT operators, constitute a broad class of highly used queries. Thus, their cardinality estimation is important for query optimization. In addition, a query planner requires the set-theoretic cardinality (i.e., without duplicates) for queries with DISTINCT as well as in planning; for example, when considering sorting options. Yet, despite the importance of estimating query cardinalities in the presence of DISTINCT, AND, OR, and NOT, many cardinality estimation methods are limited to estimating cardinalities of only conjunctive queries with duplicates counted. The focus of this work is on two methods for handling this deficiency that can be applied to any limited cardinality estimation model. First, we describe a specialized deep learning scheme, PUNQ, which is tailored to representing conjunctive SQL queries and predicting the percentage of unique rows in the query's result with duplicate rows. Using the predicted percentages obtained via PUNQ, we are able to transform any cardinality estimation method that only estimates for conjunctive queries, and which estimates cardinalities with duplicates (e.g., MSCN), to a method that estimates queries cardinalities without duplicates. This enables estimating cardinalities of queries with the DISTINCT keyword. In addition, we describe a recursive algorithm, GenCrd, for extending any cardinality estimation method M that only handles conjunctive queries to one that estimates cardinalities for more general queries (that include AND, OR, and NOT), without changing the method M itself. Our evaluation is carried out on a challenging, real-world database with general queries that include either the DISTINCT keyword or the AND, OR, and NOT operators. Experimentally, we show that the proposed methods obtain accurate cardinality estimates with the same level of accuracy as that of the original transformed methods.
Code-Aligned Autoencoders for Unsupervised Change Detection in Multimodal Remote Sensing Images
Venue: arXiv cs.CV 2020
Year: 2020

Image translation with convolutional autoencoders has recently been used as an approach to multimodal change detection in bitemporal satellite images. A main challenge is the alignment of the code spaces by reducing the contribution of change pixels to the learning of the translation function. Many existing approaches train the networks by exploiting supervised information of the change areas, which, however, is not always available. We propose to extract relational pixel information captured by domain-specific affinity matrices at the input and use this to enforce alignment of the code spaces and reduce the impact of change pixels on the learning objective. A change prior is derived in an unsupervised fashion from pixel pair affinities that are comparable across domains. To achieve code space alignment we enforce that pixel with similar affinity relations in the input domains should be correlated also in code space. We demonstrate the utility of this procedure in combination with cycle consistency. The proposed approach are compared with state-of-the-art deep learning algorithms. Experiments conducted on four real datasets show the effectiveness of our methodology.
Contextual Pyramid Attention Network for Building Segmentation in Aerial Imagery
Venue: arXiv cs.CV 2020
Year: 2020

Building extraction from aerial images has several applications in problems such as urban planning, change detection, and disaster management. With the increasing availability of data, Convolutional Neural Networks (CNNs) for semantic segmentation of remote sensing imagery has improved significantly in recent years. However, convolutions operate in local neighborhoods and fail to capture non-local features that are essential in semantic understanding of aerial images. In this work, we propose to improve building segmentation of different sizes by capturing long-range dependencies using contextual pyramid attention (CPA). The pathways process the input at multiple scales efficiently and combine them in a weighted manner, similar to an ensemble model. The proposed method obtains state-of-the-art performance on the Inria Aerial Image Labelling Dataset with minimal computation costs. Our method improves 1.8 points over current state-of-the-art methods and 12.6 points higher than existing baselines on the Intersection over Union (IoU) metric without any post-processing. Code and models will be made publicly available.
Bias in Multimodal AI: Testbed for Fair Automatic Recruitment
Venue: arXiv cs.CV 2020
Year: 2020

The presence of decision-making algorithms in society is rapidly increasing nowadays, while concerns about their transparency and the possibility of these algorithms becoming new sources of discrimination are arising. In fact, many relevant automated systems have been shown to make decisions based on sensitive information or discriminate certain social groups (e.g. certain biometric systems for person recognition). With the aim of studying how current multimodal algorithms based on heterogeneous sources of information are affected by sensitive elements and inner biases in the data, we propose a fictitious automated recruitment testbed: FairCVtest. We train automatic recruitment algorithms using a set of multimodal synthetic profiles consciously scored with gender and racial biases. FairCVtest shows the capacity of the Artificial Intelligence (AI) behind such recruitment tool to extract sensitive information from unstructured data, and exploit it in combination to data biases in undesirable (unfair) ways. Finally, we present a list of recent works developing techniques capable of removing sensitive information from the decision-making process of deep learning architectures. We have used one of these algorithms (SensitiveNets) to experiment discrimination-aware learning for the elimination of sensitive information in our multimodal AI framework. Our methodology and results show how to generate fairer AI-based tools in general, and in particular fairer automated recruitment systems.