OpenRaiser/Intern-Atlas huggingface dataset:
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods
Venue: arXiv cs.CV 2022
Year: 2022

In many object recognition applications, the set of possible categories is an open set, and the deployed recognition system will encounter novel objects belonging to categories unseen during training. Detecting such "novel category" objects is usually formulated as an anomaly detection problem. Anomaly detection algorithms for feature-vector data identify anomalies as outliers, but outlier detection has not worked well in deep learning. Instead, methods based on the computed logits of visual object classifiers give state-of-the-art performance. This paper proposes the Familiarity Hypothesis that these methods succeed because they are detecting the absence of familiar learned features rather than the presence of novelty. This distinction is important, because familiarity-based detection will fail in many situations where novelty is present. For example when an image contains both a novel object and a familiar one, the familiarity score will be high, so the novel object will not be noticed. The paper reviews evidence from the literature and presents additional evidence from our own experiments that provide strong support for this hypothesis. The paper concludes with a discussion of whether familiarity-based detection is an inevitable consequence of representation learning.
Behavioural Curves Analysis Using Near-Infrared-Iris Image Sequences
Venue: arXiv cs.CV 2022
Year: 2022

This paper proposes a new method to estimate behavioural curves from a stream of Near-Infra-Red (NIR) iris video frames. This method can be used in a Fitness For Duty system (FFD). The research focuses on determining the effect of external factors such as alcohol, drugs, and sleepiness on the Central Nervous System (CNS). The aim is to analyse how this behaviour is represented on iris and pupil movements and if it is possible to capture these changes with a standard NIR camera. The behaviour analysis showed essential differences in pupil and iris behaviour to classify the workers in "Fit" or "Unfit" conditions. The best results can distinguish subjects robustly under alcohol, drug consumption, and sleep conditions. The Multi-Layer-Perceptron and Gradient Boosted Machine reached the best results in all groups with an overall accuracy for Fit and Unfit classes of 74.0% and 75.5%, respectively. These results open a new application for iris capture devices.
Machine Learning for CUDA+MPI Design Rules
Venue: arXiv cs.PF 2022
Year: 2022

We present a new strategy for automatically exploring the design space of key CUDA+MPI programs and providing design rules that discriminate slow from fast implementations. In such programs, the order of operations (e.g., GPU kernels, MPI communication) and assignment of operations to resources (e.g., GPU streams) makes the space of possible designs enormous. Systems experts have the task of redesigning and reoptimizing these programs to effectively utilize each new platform. This work provides a prototype tool to reduce that burden. In our approach, a directed acyclic graph of CUDA and MPI operations defines the design space for the program. Monte-Carlo tree search discovers regions of the design space that have large impact on the program's performance. A sequence-to-vector transformation defines features for each explored implementation, and each implementation is assigned a class label according to its relative performance. A decision tree is trained on the features and labels to produce design rules for each class; these rules can be used by systems experts to guide their implementations. We demonstrate our strategy using a key kernel from scientific computing -- sparse-matrix vector multiplication -- on a platform with multiple MPI ranks and GPU streams.
Machine Learning Simulates Agent-Based Model Towards Policy
Venue: arXiv cs.MA 2022
Year: 2022

Public Policies are not intrinsically positive or negative. Rather, policies provide varying levels of effects across different recipients. Methodologically, computational modeling enables the application of multiple influences on empirical data, thus allowing for heterogeneous response to policies. We use a random forest machine learning algorithm to emulate an agent-based model (ABM) and evaluate competing policies across 46 Metropolitan Regions (MRs) in Brazil. In doing so, we use input parameters and output indicators of 11,076 actual simulation runs and one million emulated runs. As a result, we obtain the optimal (and non-optimal) performance of each region over the policies. Optimum is defined as a combination of GDP production and the Gini coefficient inequality indicator for the full ensemble of Metropolitan Regions. Results suggest that MRs already have embedded structures that favor optimal or non-optimal results, but they also illustrate which policy is more beneficial to each place. In addition to providing MR-specific policies' results, the use of machine learning to simulate an ABM reduces the computational burden, whereas allowing for a much larger variation among model parameters. The coherence of results within the context of larger uncertainty--vis-à-vis those of the original ABM--reinforces robustness of the model. At the same time the exercise indicates which parameters should policymakers intervene on, in order to work towards precise policy optimal instruments.
Network Services Anomalies in NFV: Survey, Taxonomy, and Verification Methods
Venue: arXiv cs.NI 2022
Year: 2022

Network Function Virtualization (NFV) has emerged as a disruptive networking architecture whose galloping evolution is prompting enterprises to outsource network functions to the cloud and ultimately harvest the fruits of cloud computing, including elasticity, pay-as-you-go billing model, and on-demand services provisioning. However, many reluctant enterprises oppose the benefits of this outsourcing to their critical and pressing concerns about security, trust, and compliance. The latter anticipate possible security and QoS policy violations stemming from dishonest behaviors by cloud providers, attacks by co-resident competitors, misconfiguration by cloud administrators, or implementations flaws by NFV developers. As a result, migrating sensitive workloads to the cloud requires enterprises to first assess risks by gaining knowledge of possible network services' anomalies and second, to build trust in the cloud by designing effective mechanisms to detect such anomalies. This survey provides scrutiny of network services anomalies that may occur in the NFV environments. We first present a taxonomy of network service anomalies and analyze their negative impacts on critical service attributes, including security and performance. Second, we compare and classify the existing anomalies' verification mechanisms from the literature. Finally, we point out the literature gap and identify future research directions for anomalies verification in NFV.
Chance-Constrained Optimization in Contact-Rich Systems for Robust Manipulation
Venue: arXiv cs.RO 2022
Year: 2022

This paper presents a chance-constrained formulation for robust trajectory optimization during manipulation. In particular, we present a chance-constrained optimization for Stochastic Discrete-time Linear Complementarity Systems (SDLCS). To solve the optimization problem, we formulate Mixed-Integer Quadratic Programming with Chance Constraints (MIQPCC). In our formulation, we explicitly consider joint chance constraints for complementarity as well as states to capture the stochastic evolution of dynamics. We evaluate robustness of our optimized trajectories in simulation on several systems. The proposed approach outperforms some recent approaches for robust trajectory optimization for SDLCS.
Cross Language Image Matching for Weakly Supervised Semantic Segmentation
Venue: arXiv cs.CV 2022
Year: 2022

It has been widely known that CAM (Class Activation Map) usually only activates discriminative object regions and falsely includes lots of object-related backgrounds. As only a fixed set of image-level object labels are available to the WSSS (weakly supervised semantic segmentation) model, it could be very difficult to suppress those diverse background regions consisting of open set objects. In this paper, we propose a novel Cross Language Image Matching (CLIMS) framework, based on the recently introduced Contrastive Language-Image Pre-training (CLIP) model, for WSSS. The core idea of our framework is to introduce natural language supervision to activate more complete object regions and suppress closely-related open background regions. In particular, we design object, background region and text label matching losses to guide the model to excite more reasonable object regions for CAM of each category. In addition, we design a co-occurring background suppression loss to prevent the model from activating closely-related background regions, with a predefined set of class-related background text descriptions. These designs enable the proposed CLIMS to generate a more complete and compact activation map for the target objects. Extensive experiments on PASCAL VOC2012 dataset show that our CLIMS significantly outperforms the previous state-of-the-art methods.
Boosting the Learning for Ranking Patterns
Venue: arXiv cs.AI 2022
Year: 2022

Discovering relevant patterns for a particular user remains a challenging tasks in data mining. Several approaches have been proposed to learn user-specific pattern ranking functions. These approaches generalize well, but at the expense of the running time. On the other hand, several measures are often used to evaluate the interestingness of patterns, with the hope to reveal a ranking that is as close as possible to the user-specific ranking. In this paper, we formulate the problem of learning pattern ranking functions as a multicriteria decision making problem. Our approach aggregates different interestingness measures into a single weighted linear ranking function, using an interactive learning procedure that operates in either passive or active modes. A fast learning step is used for eliciting the weights of all the measures by mean of pairwise comparisons. This approach is based on Analytic Hierarchy Process (AHP), and a set of user-ranked patterns to build a preference matrix, which compares the importance of measures according to the user-specific interestingness. A sensitivity based heuristic is proposed for the active learning mode, in order to insure high quality results with few user ranking queries. Experiments conducted on well-known datasets show that our approach significantly reduces the running time and returns precise pattern ranking, while being robust to user-error compared with state-of-the-art approaches.
Meta Mirror Descent: Optimiser Learning for Fast Convergence
Venue: arXiv cs.LG 2022
Year: 2022

Optimisers are an essential component for training machine learning models, and their design influences learning speed and generalisation. Several studies have attempted to learn more effective gradient-descent optimisers via solving a bi-level optimisation problem where generalisation error is minimised with respect to optimiser parameters. However, most existing optimiser learning methods are intuitively motivated, without clear theoretical support. We take a different perspective starting from mirror descent rather than gradient descent, and meta-learning the corresponding Bregman divergence. Within this paradigm, we formalise a novel meta-learning objective of minimising the regret bound of learning. The resulting framework, termed Meta Mirror Descent (MetaMD), learns to accelerate optimisation speed. Unlike many meta-learned optimisers, it also supports convergence and generalisation guarantees and uniquely does so without requiring validation data. We evaluate our framework on a variety of tasks and architectures in terms of convergence rate and generalisation error and demonstrate strong performance.
Enumeration of Maximal Cycles Generated by Orthogonal Cellular Automata
Venue: arXiv cs.CR 2022
Year: 2022

Cellular Automata (CA) are an interesting computational model for designing Pseudorandom Number Generators (PRNG), due to the complex dynamical behavior they can exhibit depending on the underlying local rule. Most of the CA-based PRNGs proposed in the literature, however, suffer from poor diffusion since a change in a single cell can propagate only within its neighborhood during a single time step. This might pose a problem especially when such PRNGs are used for cryptographic purposes. In this paper, we consider an alternative approach to generate pseudorandom sequences through \emph{orthogonal CA} (OCA), which guarantees a better amount of diffusion. After defining the related PRNG, we perform an empirical investigation of the maximal cycles in OCA pairs up to diameter $d=8$. Next, we focus on OCA induced by linear rules, giving a characterization of their cycle structure based on the rational canonical form of the associated Sylvester matrix. Finally, we devise an algorithm to enumerate all linear OCA pairs characterized by a single maximal cycle, and apply it up to diameter $d=16$ and $d=13$ for OCA respectively over the binary and ternary alphabets.
Boosting human decision-making with AI-generated decision aids
Venue: arXiv cs.AI 2022
Year: 2022

Human decision-making is plagued by many systematic errors. Many of these errors can be avoided by providing decision aids that guide decision-makers to attend to the important information and integrate it according to a rational decision strategy. Designing such decision aids used to be a tedious manual process. Advances in cognitive science might make it possible to automate this process in the future. We recently introduced machine learning methods for discovering optimal strategies for human decision-making automatically and an automatic method for explaining those strategies to people. Decision aids constructed by this method were able to improve human decision-making. However, following the descriptions generated by this method is very tedious. We hypothesized that this problem can be overcome by conveying the automatically discovered decision strategy as a series of natural language instructions for how to reach a decision. Experiment 1 showed that people do indeed understand such procedural instructions more easily than the decision aids generated by our previous method. Encouraged by this finding, we developed an algorithm for translating the output of our previous method into procedural instructions. We applied the improved method to automatically generate decision aids for a naturalistic planning task (i.e., planning a road trip) and a naturalistic decision task (i.e., choosing a mortgage). Experiment 2 showed that these automatically generated decision-aids significantly improved people's performance in planning a road trip and choosing a mortgage. These findings suggest that AI-powered boosting might have potential for improving human decision-making in the real world.
Deep Q-Learning Based Resource Allocation in Interference Systems With Outage Constraint
Venue: arXiv cs.NI 2022
Year: 2022

This correspondence considers the resource allocation problem in wireless interference channel (IC) under link outage constraints. Since the optimization problem is non-convex in nature, existing approaches to find the optimal power allocation are computationally intensive and thus practically infeasible. Recently, deep reinforcement learning has shown promising outcome in solving non-convex optimization problems with reduced complexity. In this correspondence, we utilize a deep Q-learning (DQL) approach which interacts with the wireless environment and learns the optimal power allocation of a wireless IC while maximizing overall sum-rate of the system and maintaining reliability requirement of each link. We have used two separate deep Q-networks to remove the inherent instability in learning process. Simulation results demonstrate that the proposed DQL approach outperforms existing geometric programming based solution.
On the Error of Random Sampling: Uniformly Distributed Random Points on Parametric Curves
Venue: arXiv cs.CG 2022
Year: 2022

Given a parametric polynomial curve $γ:[a,b]\rightarrow \mathbb{R}^n$, how can we sample a random point $\mathfrak{x}\in \mathrm{im}(γ)$ in such a way that it is distributed uniformly with respect to the arc-length? Unfortunately, we cannot sample exactly such a point-even assuming we can perform exact arithmetic operations. So we end up with the following question: how does the method we choose affect the quality of the approximate sample we obtain? In practice, there are many answers. However, in theory, there are still gaps in our understanding. In this paper, we address this question from the point of view of complexity theory, providing bounds in terms of the size of the desired error.
Region Proposal Rectification Towards Robust Instance Segmentation of Biological Images
Venue: arXiv cs.CV 2022
Year: 2022

Top-down instance segmentation framework has shown its superiority in object detection compared to the bottom-up framework. While it is efficient in addressing over-segmentation, top-down instance segmentation suffers from over-crop problem. However, a complete segmentation mask is crucial for biological image analysis as it delivers important morphological properties such as shapes and volumes. In this paper, we propose a region proposal rectification (RPR) module to address this challenging incomplete segmentation problem. In particular, we offer a progressive ROIAlign module to introduce neighbor information into a series of ROIs gradually. The ROI features are fed into an attentive feed-forward network (FFN) for proposal box regression. With additional neighbor information, the proposed RPR module shows significant improvement in correction of region proposal locations and thereby exhibits favorable instance segmentation performances on three biological image datasets compared to state-of-the-art baseline methods. Experimental results demonstrate that the proposed RPR module is effective in both anchor-based and anchor-free top-down instance segmentation approaches, suggesting the proposed method can be applied to general top-down instance segmentation of biological images.
Towards Self-Supervised Category-Level Object Pose and Size Estimation
Venue: arXiv cs.CV 2022
Year: 2022

In this work, we tackle the challenging problem of category-level object pose and size estimation from a single depth image. Although previous fully-supervised works have demonstrated promising performance, collecting ground-truth pose labels is generally time-consuming and labor-intensive. Instead, we propose a label-free method that learns to enforce the geometric consistency between category template mesh and observed object point cloud under a self-supervision manner. Specifically, our method consists of three key components: differentiable shape deformation, registration, and rendering. In particular, shape deformation and registration are applied to the template mesh to eliminate the differences in shape, pose and scale. A differentiable renderer is then deployed to enforce geometric consistency between point clouds lifted from the rendered depth and the observed scene for self-supervision. We evaluate our approach on real-world datasets and find that our approach outperforms the simple traditional baseline by large margins while being competitive with some fully-supervised approaches.
Domain Adaptation with Factorizable Joint Shift
Venue: arXiv cs.LG 2022
Year: 2022

Existing domain adaptation (DA) usually assumes the domain shift comes from either the covariates or the labels. However, in real-world applications, samples selected from different domains could have biases in both the covariates and the labels. In this paper, we propose a new assumption, Factorizable Joint Shift (FJS), to handle the co-existence of sampling bias in covariates and labels. Although allowing for the shift from both sides, FJS assumes the independence of the bias between the two factors. We provide theoretical and empirical understandings about when FJS degenerates to prior assumptions and when it is necessary. We further propose Joint Importance Aligning (JIA), a discriminative learning objective to obtain joint importance estimators for both supervised and unsupervised domain adaptation. Our method can be seamlessly incorporated with existing domain adaptation algorithms for better importance estimation and weighting on the training data. Experiments on a synthetic dataset demonstrate the advantage of our method.
foREST: A Tree-based Approach for Fuzzing RESTful APIs
Venue: arXiv cs.SE 2022
Year: 2022

Representational state transfer (REST) is a widely employed architecture by web applications and cloud. Users can invoke such services according to the specification of their application interfaces, namely RESTful APIs. Existing approaches for fuzzing RESTful APIs are generally based on classic API-dependency graphs. However, such dependencies are inefficient for REST services due to the explosion of dependencies among APIs. In this paper, we propose a novel tree-based approach that can better capture the essential dependencies and largely improve the efficiency of RESTful API fuzzing. In particular, the hierarchical information of the endpoints across multiple APIs enables us to construct an API tree, and the relationships of tree nodes can indicate the priority of resource dependencies, \textit{e.g.,} it's more likely that a node depends on its parent node rather than its offspring or siblings. In the evaluation part, we first confirm that such a tree-based approach is more efficient than traditional graph-based approaches. We then apply our tool to fuzz two real-world RESTful services and compare the performance with two state-of-the-art tools, EvoMaster and RESTler. Our results show that foREST can improve the code coverage in all experiments, ranging from 11.5\% to 82.5\%. Besides, our tool finds 11 new bugs previously unknown.
Intertwining of Complementary Thue-Morse Factors
Venue: arXiv cs.FL 2022
Year: 2022

We consider the positions of occurrences of a factor $x$ and its binary complement $\overline{x}$ in the Thue-Morse word ${\bf t} = {\tt 01101001} \cdots$, and show that these occurrences are "intertwined" in essentially two different ways. Our proof method consists of stating the needed properties as a first-order logic formula $φ$, and then using a theorem-prover to prove $φ$.
Towards a Responsible AI Development Lifecycle: Lessons From Information Security
Venue: arXiv cs.AI 2022
Year: 2022

Legislation and public sentiment throughout the world have promoted fairness metrics, explainability, and interpretability as prescriptions for the responsible development of ethical artificial intelligence systems. Despite the importance of these three pillars in the foundation of the field, they can be challenging to operationalize and attempts to solve the problems in production environments often feel Sisyphean. This difficulty stems from a number of factors: fairness metrics are computationally difficult to incorporate into training and rarely alleviate all of the harms perpetrated by these systems. Interpretability and explainability can be gamed to appear fair, may inadvertently reduce the privacy of personal information contained in training data, and increase user confidence in predictions -- even when the explanations are wrong. In this work, we propose a framework for responsibly developing artificial intelligence systems by incorporating lessons from the field of information security and the secure development lifecycle to overcome challenges associated with protecting users in adversarial settings. In particular, we propose leveraging the concepts of threat modeling, design review, penetration testing, and incident response in the context of developing AI systems as ways to resolve shortcomings in the aforementioned methods.