OpenRaiser/Intern-Atlas huggingface dataset:
Meta-Learned Feature Critics for Domain Generalized Semantic Segmentation
Venue: arXiv cs.CV 2021
Year: 2021

How to handle domain shifts when recognizing or segmenting visual data across domains has been studied by learning and vision communities. In this paper, we address domain generalized semantic segmentation, in which the segmentation model is trained on multiple source domains and is expected to generalize to unseen data domains. We propose a novel meta-learning scheme with feature disentanglement ability, which derives domain-invariant features for semantic segmentation with domain generalization guarantees. In particular, we introduce a class-specific feature critic module in our framework, enforcing the disentangled visual features with domain generalization guarantees. Finally, our quantitative results on benchmark datasets confirm the effectiveness and robustness of our proposed model, performing favorably against state-of-the-art domain adaptation and generalization methods in segmentation.
Few-Shot Classification in Unseen Domains by Episodic Meta-Learning Across Visual Domains
Venue: arXiv cs.CV 2021
Year: 2021

Few-shot classification aims to carry out classification given only few labeled examples for the categories of interest. Though several approaches have been proposed, most existing few-shot learning (FSL) models assume that base and novel classes are drawn from the same data domain. When it comes to recognizing novel-class data in an unseen domain, this becomes an even more challenging task of domain generalized few-shot classification. In this paper, we present a unique learning framework for domain-generalized few-shot classification, where base classes are from homogeneous multiple source domains, while novel classes to be recognized are from target domains which are not seen during training. By advancing meta-learning strategies, our learning framework exploits data across multiple source domains to capture domain-invariant features, with FSL ability introduced by metric-learning based mechanisms across support and query data. We conduct extensive experiments to verify the effectiveness of our proposed learning framework and show learning from small yet homogeneous source data is able to perform preferably against learning from large-scale one. Moreover, we provide insights into choices of backbone models for domain-generalized few-shot classification.
VibEmoji: Exploring User-authoring Multi-modal Emoticons in Social Communication
Venue: arXiv cs.HC 2021
Year: 2021

Emoticons are indispensable in online communications. With users' growing needs for more customized and expressive emoticons, recent messaging applications begin to support (limited) multi-modal emoticons: e.g., enhancing emoticons with animations or vibrotactile feedback. However, little empirical knowledge has been accumulated concerning how people create, share and experience multi-modal emoticons in everyday communication, and how to better support them through design. To tackle this, we developed VibEmoji, a user-authoring multi-modal emoticon interface for mobile messaging. Extending existing designs, VibEmoji grants users greater flexibility to combine various emoticons, vibrations, and animations on-the-fly, and offers non-aggressive recommendations based on these components' emotional relevance. Using VibEmoji as a probe, we conducted a four-week field study with 20 participants, to gain new understandings from in-the-wild usage and experience, and extract implications for design. We thereby contribute both a novel system and various insights for supporting users' creation and communication of multi-modal emoticons.
Hard Example Guided Hashing for Image Retrieval
Venue: arXiv cs.CV 2021
Year: 2021

Compared with the traditional hashing methods, deep hashing methods generate hash codes with rich semantic information and greatly improves the performances in the image retrieval field. However, it is unsatisfied for current deep hashing methods to predict the similarity of hard examples. It exists two main factors affecting the ability of learning hard examples, which are weak key features extraction and the shortage of hard examples. In this paper, we give a novel end-to-end model to extract the key feature from hard examples and obtain hash code with the accurate semantic information. In addition, we redesign a hard pair-wise loss function to assess the hard degree and update penalty weights of examples. It effectively alleviates the shortage problem in hard examples. Experimental results on CIFAR-10 and NUS-WIDE demonstrate that our model outperformances the mainstream hashing-based image retrieval methods.
Wholesale Electricity Price Forecasting using Integrated Long-term Recurrent Convolutional Network Model
Venue: arXiv cs.LG 2021
Year: 2021

Electricity price is a key factor affecting the decision-making for all market participants. Accurate forecasting of electricity prices is very important and is also very challenging since electricity price is highly volatile due to various factors. This paper proposes an integrated long-term recurrent convolutional network (ILRCN) model to predict electricity prices considering the majority contributing attributes to the market price as input. The proposed ILRCN model combines the functionalities of convolutional neural network and long short-term memory (LSTM) algorithm along with the proposed novel conditional error correction term. The combined ILRCN model can identify the linear and non-linear behavior within the input data. We have used ERCOT wholesale market price data along with load profile, temperature, and other factors for the Houston region to illustrate the proposed model. The performance of the proposed ILRCN electricity price forecasting model is verified using performance/evaluation metrics like mean absolute error and accuracy. Case studies reveal that the proposed ILRCN model is accurate and efficient in electricity price forecasting as compared to the support vector machine (SVM) model, fully-connected neural network model, LSTM model and the LRCN model without the conditional error correction stage.
Augmenting Convolutional networks with attention-based aggregation
Venue: arXiv cs.CV 2021
Year: 2021

We show how to augment any convolutional network with an attention-based global map to achieve non-local reasoning. We replace the final average pooling by an attention-based aggregation layer akin to a single transformer block, that weights how the patches are involved in the classification decision. We plug this learned aggregation layer with a simplistic patch-based convolutional network parametrized by 2 parameters (width and depth). In contrast with a pyramidal design, this architecture family maintains the input patch resolution across all the layers. It yields surprisingly competitive trade-offs between accuracy and complexity, in particular in terms of memory consumption, as shown by our experiments on various computer vision tasks: object classification, image segmentation and detection.
Casr-Cluster: Crash Clustering for Linux Applications
Venue: arXiv cs.CR 2021
Year: 2021

Crash report analysis is a necessary step before developers begin fixing errors. Fuzzing or hybrid (with dynamic symbolic execution) fuzzing is often used in the secure development lifecycle. Modern fuzzers could produce many crashes and developers do not have enough time to fix them till release date. There are two approaches that could reduce developers' effort on crash analysis: crash clustering and crash severity estimation. Crash severity estimation could help developers to prioritize crashes and close security issues first. Crash clustering puts similar crash reports in one cluster what could speed up the analyzing time for all crash reports. In this paper, we focus on crash clustering. We propose an approach for clustering and deduplicating of crashes that occurred in Linux applications. We implement this approach as a tool that could cluster Casr~\cite{fedotov2020casr} crash reports. We evaluated our tool on a set of crash reports that was collected from fuzzing results.
Trajectory Planning for Hybrid Unmanned Aerial Underwater Vehicles with Smooth Media Transition
Venue: arXiv cs.RO 2021
Year: 2021

In the last decade, a great effort has been employed in the study of Hybrid Unmanned Aerial Underwater Vehicles, robots that can easily fly and dive into the water with different levels of mechanical adaptation. However, most of this literature is concentrated on physical design, practical issues of construction, and, more recently, low-level control strategies. Little has been done in the context of high-level intelligence, such as motion planning and interactions with the real world. Therefore, we proposed in this paper a trajectory planning approach that allows collision avoidance against unknown obstacles and smooth transitions between aerial and aquatic media. Our method is based on a variant of the classic Rapidly-exploring Random Tree, whose main advantages are the capability to deal with obstacles, complex nonlinear dynamics, model uncertainties, and external disturbances. The approach uses the dynamic model of the \hydrone, a hybrid vehicle proposed with high underwater performance, but we believe it can be easily generalized to other types of aerial/aquatic platforms. In the experimental section, we present simulated results in environments filled with obstacles, where the robot is commanded to perform different media movements, demonstrating the applicability of our strategy.
GPU-accelerated Faster Mean Shift with euclidean distance metrics
Venue: arXiv cs.CV 2021
Year: 2021

Handling clustering problems are important in data statistics, pattern recognition and image processing. The mean-shift algorithm, a common unsupervised algorithms, is widely used to solve clustering problems. However, the mean-shift algorithm is restricted by its huge computational resource cost. In previous research[10], we proposed a novel GPU-accelerated Faster Mean-shift algorithm, which greatly speed up the cosine-embedding clustering problem. In this study, we extend and improve the previous algorithm to handle Euclidean distance metrics. Different from conventional GPU-based mean-shift algorithms, our algorithm adopts novel Seed Selection & Early Stopping approaches, which greatly increase computing speed and reduce GPU memory consumption. In the simulation testing, when processing a 200K points clustering problem, our algorithm achieved around 3 times speedup compared to the state-of-the-art GPU-based mean-shift algorithms with optimized GPU memory consumption. Moreover, in this study, we implemented a plug-and-play model for faster mean-shift algorithm, which can be easily deployed. (Plug-and-play model is available: https://github.com/masqm/Faster-Mean-Shift-Euc)
Forensic Issues and Techniques to Improve Security in SSD with Flex Capacity Feature
Venue: arXiv cs.CR 2021
Year: 2021

Over-provisioning technology is typically introduced as a means to improve the performance of storage systems, such as databases. The over-provisioning area is both hidden and difficult for normal users to access. This paper focuses on attack models for such hidden areas. Malicious hackers use advanced over-provisioning techniques that vary capacity according to workload, and as such, our focus is on attack models that use variable over-provisioning technology. According to these attack models, it is possible to scan for invalid blocks containing original data or malware code that is hidden in the over-provisioning area. In this paper, we outline the different forensic processes performed for each memory cell type of the over-provisioning area and disclose security enhancement techniques that increase immunity to these attack models. This leads to a discussion of forensic possibilities and countermeasures for SSDs that can change the over-provisioning area. We also present information-hiding attacks and information-exposing attacks on the invalidation area of the SSD. Our research provides a good foundation upon which the performance and security of SSD-based databases can be further improved.
Multiagent Model-based Credit Assignment for Continuous Control
Venue: arXiv cs.AI 2021
Year: 2021

Deep reinforcement learning (RL) has recently shown great promise in robotic continuous control tasks. Nevertheless, prior research in this vein center around the centralized learning setting that largely relies on the communication availability among all the components of a robot. However, agents in the real world often operate in a decentralised fashion without communication due to latency requirements, limited power budgets and safety concerns. By formulating robotic components as a system of decentralised agents, this work presents a decentralised multiagent reinforcement learning framework for continuous control. To this end, we first develop a cooperative multiagent PPO framework that allows for centralized optimisation during training and decentralised operation during execution. However, the system only receives a global reward signal which is not attributed towards each agent. To address this challenge, we further propose a generic game-theoretic credit assignment framework which computes agent-specific reward signals. Last but not least, we also incorporate a model-based RL module into our credit assignment framework, which leads to significant improvement in sample efficiency. We demonstrate the effectiveness of our framework on experimental results on Mujoco locomotion control tasks. For a demo video please visit: https://youtu.be/gFyVPm4svEY.
Pretty Princess vs. Successful Leader: Gender Roles in Greeting Card Messages
Venue: arXiv cs.HC 2021
Year: 2021

People write personalized greeting cards on various occasions. While prior work has studied gender roles in greeting card messages, systematic analysis at scale and tools for raising the awareness of gender stereotyping remain under-investigated. To this end, we collect a large greeting card message corpus covering three different occasions (birthday, Valentine's Day and wedding) from three sources (exemplars from greeting message websites, real-life greetings from social media and language model generated ones). We uncover a wide range of gender stereotypes in this corpus via topic modeling, odds ratio and Word Embedding Association Test (WEAT). We further conduct a survey to understand people's perception of gender roles in messages from this corpus and if gender stereotyping is a concern. The results show that people want to be aware of gender roles in the messages, but remain unconcerned unless the perceived gender roles conflict with the recipient's true personality. In response, we developed GreetA, an interactive visualization and writing assistant tool to visualize fine-grained topics in greeting card messages drafted by the users and the associated gender perception scores, but without suggesting text changes as an intervention.
Two Guarded Recursive Powerdomains for Applicative Simulation
Venue: arXiv cs.LO 2021
Year: 2021

Clocked Cubical Type Theory is a new type theory combining the power of guarded recursion with univalence and higher inductive types (HITs). This type theory can be used as a metalanguage for synthetic guarded domain theory in which one can solve guarded recursive type equations, also with negative variable occurrences, and use these to construct models for reasoning about programming languages. Combining this with HITs allows for the use of type constructors familiar from set-theory based approaches to semantics, such as quotients and finite powersets in these models. In this paper we show how to reason about the combination of finite non-determinism and recursion in this type theory. Unlike traditional domain theory which takes an ordering of programs as primitive, synthetic guarded domain theory takes the notion of computation step as primitive in the form of a modal operator. We use this extra intensional information to define two guarded recursive (finite) powerdomain constructions differing in the way non-determinism interacts with the computation steps. As an example application of these we show how to prove applicative similarity a congruence in the cases of may- and must-convergence for the untyped lambda calculus with finite non-determinism. Such results are usually proved using operational reasoning and Howe's method. Here we use an adaptation of a denotational method developed by Pitts in the context of domain theory.
Inductive and Coinductive Predicate Liftings for Effectful Programs
Venue: arXiv cs.LO 2021
Year: 2021

We formulate a framework for describing behaviour of effectful higher-order recursive programs. Examples of effects are implemented using effect operations, and include: execution cost, nondeterminism, global store and interaction with a user. The denotational semantics of a program is given by a coinductive tree in a monad, which combines potential return values of the program in terms of effect operations. Using a simple test logic, we construct two sorts of predicate liftings, which lift predicates on a result type to predicates on computations that produce results of that type, each capturing a facet of program behaviour. Firstly, we study inductive predicate liftings which can be used to describe effectful notions of total correctness. Secondly, we study coinductive predicate liftings, which describe effectful notions of partial correctness. The two constructions are dual in the sense that one can be used to disprove the other. The predicate liftings are used as a basis for an endogenous logic of behavioural properties for higher-order programs. The program logic has a derivable notion of negation, arising from the duality of the two sorts of predicate liftings, and it generates a program equivalence which subsumes a notion of bisimilarity. Appropriate definitions of inductive and coinductive predicate liftings are given for a multitude of effect examples. The whole development has been fully formalized in the Agda proof assistant.
Investigating Shifts in GAN Output-Distributions
Venue: arXiv cs.LG 2021
Year: 2021

A fundamental and still largely unsolved question in the context of Generative Adversarial Networks is whether they are truly able to capture the real data distribution and, consequently, to sample from it. In particular, the multidimensional nature of image distributions leads to a complex evaluation of the diversity of GAN distributions. Existing approaches provide only a partial understanding of this issue, leaving the question unanswered. In this work, we introduce a loop-training scheme for the systematic investigation of observable shifts between the distributions of real training data and GAN generated data. Additionally, we introduce several bounded measures for distribution shifts, which are both easy to compute and to interpret. Overall, the combination of these methods allows an explorative investigation of innate limitations of current GAN algorithms. Our experiments on different data-sets and multiple state-of-the-art GAN architectures show large shifts between input and output distributions, showing that existing theoretical guarantees towards the convergence of output distributions appear not to be holding in practice.
Intelligent Document Processing -- Methods and Tools in the real world
Venue: arXiv cs.IR 2021
Year: 2021

The originality of this publication is to look at the subject of IDP (Intelligent Document Processing) from the perspective of an end-user and industrialist and not that of a Computer Science researcher. This domain is one part of the challenge of information digitalisation that constitutes the Industrial Revolution of the twenty first century (Industry 4.0) and this paper looks specifically at the difficult areas of classifying, extracting information and subsequent integration into business processes with respect to forms and invoices. Since the focus is on practical implementation a brief review is carried out of the market in commercial tools for OCR, document classification and data extraction in so far as this is publicly available together with pricing (if known). Brief definitions of the main terms encountered in Computer Science publications and commercial prospectuses are provided in order to de-mystify the language for the layman. A small number of practical tests are carried out on a few real documents in order to illustrate the capabilities of tools that are commonly available at a reasonable price. The unsolved (so far) issue of tables contained in invoices is raised. The case of a typical large industrial company is evoked where the requirement is to extract 100 per cent of the information with 100 per cent reliability in order to integrate into the back-end Enterprise Resource Planning system. Finally a brief description is given of the state-of-the-art research by the huge corporations who are pushing the boundaries of deep learning techniques further and further with massive computing and financial power - progress that will undoubtedly trickle down into the real world at some later date. The paper finishes by asking the question whether the objectives and timing of the commercial world and the progress of Computer Science are fully aligned.
Do Not Forget the Past: A Buffer-Aided Framework for Relay Based Key Generation
Venue: arXiv cs.IT 2021
Year: 2021

We address relay-assisted key generation wherein two wireless nodes, that have no direct channel between them, seek the assistance of an intermediate relay to generate secret keys. In a celebrated version of the relay-assisted protocol, as applied by Lai et al., Zhou et al., Wang et al. and Waqas et al., the relay node generates pair-wise keys with the two nodes, and then broadcasts an XOR version of the two keys. Although such protocols are simple and effective, we observe that they face reduction in key rates due to two problems. First, for confidentiality, the relay broadcasts an XOR function of the pair-wise keys thereby pruning the length of the shared key to be the minimum of the key lengths of the pair-wise keys. Secondly, the broadcast phase may also experience outages thereby not being able to share the generated key in every round of the protocol. Identifying these issues, we propose a buffer-aided relaying protocol wherein buffer is used at the relay to store unused secret bits generated in the previous rounds of the protocol so as to provide confidentiality in the subsequent rounds of broadcast. On this buffer-aided protocol, we propose a power-allocation strategy between the phases of key generation and broadcast so as to maximize the throughput and key rate. Rigorous analyses show that buffer-aided relay when implemented along with the proposed power-allocation strategy offer remarkable advantages over existing baselines.
Query Suggestion for Click-Absent Queries in Enterprise Search
Venue: arXiv cs.IR 2021
Year: 2021

Creating alternative queries, also known as query suggestion, has been proved to be helpful on improving users' search experience. Owing to the suggestions, users could retrieve their information need more quickly and accurately. In many scenarios, these suggestions could be generated from the click-through logs by establishing a bipartite graph of the clicked query-document pairs. Most of the existing methods focused on click-existing queries which possess clicked information in the search logs, to suggest related queries using the co-clicked documents. In this paper, we propose a simple yet effective query suggestion method particularly for click-absent queries by ensuring semantic consistency without utilising any additional resources. Our experimental results show that the proposed technique generates comparatively good suggestions for click-absent queries on a real bilingual enterprise search log.