OpenRaiser/Intern-Atlas huggingface dataset:
Interpolated Joint Space Adversarial Training for Robust and Generalizable Defenses
Venue: arXiv cs.CV 2021
Year: 2021

Adversarial training (AT) is considered to be one of the most reliable defenses against adversarial attacks. However, models trained with AT sacrifice standard accuracy and do not generalize well to novel attacks. Recent works show generalization improvement with adversarial samples under novel threat models such as on-manifold threat model or neural perceptual threat model. However, the former requires exact manifold information while the latter requires algorithm relaxation. Motivated by these considerations, we exploit the underlying manifold information with Normalizing Flow, ensuring that exact manifold assumption holds. Moreover, we propose a novel threat model called Joint Space Threat Model (JSTM), which can serve as a special case of the neural perceptual threat model that does not require additional relaxation to craft the corresponding adversarial attacks. Under JSTM, we develop novel adversarial attacks and defenses. The mixup strategy improves the standard accuracy of neural networks but sacrifices robustness when combined with AT. To tackle this issue, we propose the Robust Mixup strategy in which we maximize the adversity of the interpolated images and gain robustness and prevent overfitting. Our experiments show that Interpolated Joint Space Adversarial Training (IJSAT) achieves good performance in standard accuracy, robustness, and generalization in CIFAR-10/100, OM-ImageNet, and CIFAR-10-C datasets. IJSAT is also flexible and can be used as a data augmentation method to improve standard accuracy and combine with many existing AT approaches to improve robustness.
Improving Code-switching Language Modeling with Artificially Generated Texts using Cycle-consistent Adversarial Networks
Venue: arXiv cs.CL 2021
Year: 2021

This paper presents our latest effort on improving Code-switching language models that suffer from data scarcity. We investigate methods to augment Code-switching training text data by artificially generating them. Concretely, we propose a cycle-consistent adversarial networks based framework to transfer monolingual text into Code-switching text, considering Code-switching as a speaking style. Our experimental results on the SEAME corpus show that utilising artificially generated Code-switching text data improves consistently the language model as well as the automatic speech recognition performance.
Graph-based hierarchical record clustering for unsupervised entity resolution
Venue: arXiv cs.DB 2021
Year: 2021

Here we study the problem of matched record clustering in unsupervised entity resolution. We build upon a state-of-the-art probabilistic framework named the Data Washing Machine (DWM). We introduce a graph-based hierarchical 2-step record clustering method (GDWM) that first identifies large, connected components or, as we call them, soft clusters in the matched record pairs using a graph-based transitive closure algorithm utilized in the DWM. That is followed by breaking down the discovered soft clusters into more precise entity clusters in a hierarchical manner using an adapted graph-based modularity optimization method. Our approach provides several advantages over the original implementation of the DWM, mainly a significant speed-up, increased precision, and overall increased F1 scores. We demonstrate the efficacy of our approach using experiments on multiple synthetic datasets. Our results also provide evidence of the utility of graph theory-based algorithms despite their sparsity in the literature on unsupervised entity resolution.
Representing Knowledge as Predictions (and State as Knowledge)
Venue: arXiv cs.AI 2021
Year: 2021

This paper shows how a single mechanism allows knowledge to be constructed layer by layer directly from an agent's raw sensorimotor stream. This mechanism, the General Value Function (GVF) or "forecast," captures high-level, abstract knowledge as a set of predictions about existing features and knowledge, based exclusively on the agent's low-level senses and actions. Thus, forecasts provide a representation for organizing raw sensorimotor data into useful abstractions over an unlimited number of layers--a long-sought goal of AI and cognitive science. The heart of this paper is a detailed thought experiment providing a concrete, step-by-step formal illustration of how an artificial agent can build true, useful, abstract knowledge from its raw sensorimotor experience alone. The knowledge is represented as a set of layered predictions (forecasts) about the agent's observed consequences of its actions. This illustration shows twelve separate layers: the lowest consisting of raw pixels, touch and force sensors, and a small number of actions; the higher layers increasing in abstraction, eventually resulting in rich knowledge about the agent's world, corresponding roughly to doorways, walls, rooms, and floor plans. I then argue that this general mechanism may allow the representation of a broad spectrum of everyday human knowledge.
What can Data-Centric AI Learn from Data and ML Engineering?
Venue: arXiv cs.LG 2021
Year: 2021

Data-centric AI is a new and exciting research topic in the AI community, but many organizations already build and maintain various "data-centric" applications whose goal is to produce high quality data. These range from traditional business data processing applications (e.g., "how much should we charge each of our customers this month?") to production ML systems such as recommendation engines. The fields of data and ML engineering have arisen in recent years to manage these applications, and both include many interesting novel tools and processes. In this paper, we discuss several lessons from data and ML engineering that could be interesting to apply in data-centric AI, based on our experience building data and ML platforms that serve thousands of applications at a range of organizations.
Plurality and Quantification in Graph Representation of Meaning
Venue: arXiv cs.CL 2021
Year: 2021

In this thesis we present a semantic representation formalism based on directed graphs and explore its linguistic adequacy and explanatory benefits in the semantics of plurality and quantification. Our graph language covers the essentials of natural language semantics using only monadic second-order variables. We define its model-theoretical interpretation in terms of graph traversal, where the relative scope of variables arises from their order of valuation. We present a unification-based mechanism for constructing semantic graphs at a simple syntax-semantics interface, where syntax as a partition function on discourse referents is implemented with categorial grammars by establishing a partly deterministic relation between semantics and syntactic distribution. This mechanism is automated to facilitate future exploration. The present graph formalism is applied to linguistic issues in distributive predication, cross-categorial conjunction, and scope permutation of quantificational expressions, including the exceptional scoping behaviors of indefinites.
Comments on "A Privacy-Preserving Online Ride-Hailing System Without Involving a Third Trusted Server"
Venue: arXiv cs.CR 2021
Year: 2021

Recently, Xie et al. (IEEE Transactions on Information Forensics and Security, vol. 16, pp. 3068-3081, 2021) proposed a privacy-preserving Online Ride-Hailing (ORH) protocol that does not make use of a trusted third-party server. The primary goal of such privacy-preserving ORH protocols is to ensure the privacy of riders' and drivers' location data w.r.t. the ORH Service Provider (SP). In this note, we demonstrate a passive attack by the SP in the protocol of Xie et al. that enables it to completely recover the location of the rider as well as that of the responding drivers in each and every ride request query.
Max-Min Energy-Efficiency Fair Optimization in STAR-RIS Assisted System
Venue: arXiv cs.IT 2021
Year: 2021

In this paper, we investigate simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RIS), which enables to communicate with users both sides by transmitting signals to users forward and reflecting signals to users backward simultaneously. We consider a communicatio system with a STAR-RIS, a base station (BS) and many users to maximize the minimum user energy efficiency (EE) by jointly optimizing the active beamforming, transmission and reflection coefficients with BS power consumption limited. To solve this optimization problem efficiently, we divide it into two sub-problems to optimize the transmitting beamforming matrix and phase shifts of STAR-RIS seperately. With the two subproblems fixed, an Alternating Optimization (AO) method is proposed to solve the maximize minmum user EE fair optimization problem. Numerical results can demonstrate that STAR-RIS behaves better than traditional reflecting-only RIS, and the algorithm we desigend can maximize the minum EE problem efficienty to ensure user fairness.
Proof of Steak
Venue: arXiv cs.CR 2021
Year: 2021

We introduce Proof-of-Steak (PoS) as a fundamental net-zero block generation technique, often accompanied by Non-Frangipane Tokens. Genesis cut is gradually heated and minted (using the appropriate sauce), enabling the miners to redirect the extracted gold and the dissipated heat into the furnace, hence enabling the first fully-circular economy ever built using blockchain technology, utilising tamper-evident steak haché. In this paper we present the basic ingredients for building Proof-of-Steak, assessing its global impact, and opportunities to save the world and beyond!
Ex-Model: Continual Learning from a Stream of Trained Models
Venue: arXiv cs.LG 2021
Year: 2021

Learning continually from non-stationary data streams is a challenging research topic of growing popularity in the last few years. Being able to learn, adapt, and generalize continually in an efficient, effective, and scalable way is fundamental for a sustainable development of Artificial Intelligent systems. However, an agent-centric view of continual learning requires learning directly from raw data, which limits the interaction between independent agents, the efficiency, and the privacy of current approaches. Instead, we argue that continual learning systems should exploit the availability of compressed information in the form of trained models. In this paper, we introduce and formalize a new paradigm named "Ex-Model Continual Learning" (ExML), where an agent learns from a sequence of previously trained models instead of raw data. We further contribute with three ex-model continual learning algorithms and an empirical setting comprising three datasets (MNIST, CIFAR-10 and CORe50), and eight scenarios, where the proposed algorithms are extensively tested. Finally, we highlight the peculiarities of the ex-model paradigm and we point out interesting future research directions.
A Study on Token Pruning for ColBERT
Venue: arXiv cs.IR 2021
Year: 2021

The ColBERT model has recently been proposed as an effective BERT based ranker. By adopting a late interaction mechanism, a major advantage of ColBERT is that document representations can be precomputed in advance. However, the big downside of the model is the index size, which scales linearly with the number of tokens in the collection. In this paper, we study various designs for ColBERT models in order to attack this problem. While compression techniques have been explored to reduce the index size, in this paper we study token pruning techniques for ColBERT. We compare simple heuristics, as well as a single layer of attention mechanism to select the tokens to keep at indexing time. Our experiments show that ColBERT indexes can be pruned up to 30\% on the MS MARCO passage collection without a significant drop in performance. Finally, we experiment on MS MARCO documents, which reveal several challenges for such mechanism.
One Size Fits All: A Conceptual Data Model for Any Approach to Terminology
Venue: arXiv cs.DL 2021
Year: 2021

In this paper, we want to speculate about the possibility to model all the currently known/proposed approaches to terminology into a single schema. We will use the Entity-Relationship (ER) diagram as our tool for the conceptual data model of the problem and to express the associations between the objects of the study. We will analyse the onomasiological and semasiological approaches, the ontoterminology paradigm, and the frame-based model, and we will draw the consequences in terms of the conceptual data model. The result of this discussion will be used as the basis of the next step of the data organization in terms of standardized terminological records and Linked Data.
(R)SE challenges in HPC
Venue: arXiv cs.SE 2021
Year: 2021

We discuss some specific software engineering challenges in the field of high-performance computing, and argue that the slow adoption of SE tools and techniques is at least in part caused by the fact that these do not address the HPC challenges `out-of-the-box'. By giving some examples of solutions for designing, testing and benchmarking HPC software, we intend to bring software engineering and HPC closer together.
Lifelong Hyper-Policy Optimization with Multiple Importance Sampling Regularization
Venue: arXiv cs.LG 2021
Year: 2021

Learning in a lifelong setting, where the dynamics continually evolve, is a hard challenge for current reinforcement learning algorithms. Yet this would be a much needed feature for practical applications. In this paper, we propose an approach which learns a hyper-policy, whose input is time, that outputs the parameters of the policy to be queried at that time. This hyper-policy is trained to maximize the estimated future performance, efficiently reusing past data by means of importance sampling, at the cost of introducing a controlled bias. We combine the future performance estimate with the past performance to mitigate catastrophic forgetting. To avoid overfitting the collected data, we derive a differentiable variance bound that we embed as a penalization term. Finally, we empirically validate our approach, in comparison with state-of-the-art algorithms, on realistic environments, including water resource management and trading.
Bounds in the Lee Metric and Optimal Codes
Venue: arXiv cs.IT 2021
Year: 2021

In this paper we investigate known Singleton-like bounds in the Lee metric and characterize optimal codes, which turn out to be very few. We then focus on Plotkin-like bounds in the Lee metric and present a new bound that extends and refines a previously known, and out-performs it in the case of non-free codes. We then compute the density of optimal codes with regard to the new bound. Finally we fill a gap in the characterization of Lee-equidistant codes.
Bayesian Nonparametric View to Spawning
Venue: arXiv cs.LG 2021
Year: 2021

In tracking multiple objects, it is often assumed that each observation (measurement) is originated from one and only one object. However, we may encounter a situation that each measurement may or may not be associated with multiple objects at each time step --spawning. Therefore, the association of each measurement to multiple objects is a crucial task to perform in order to track multiple objects with birth and death. In this paper, we introduce a novel Bayesian nonparametric approach that models a scenario where each observation may be drawn from an unknown number of objects for which it provides a tractable Markov chain Monte Carlo (MCMC) approach to sample from the posterior distribution. The number of objects at each time step, itself, is also assumed to be unknown. We, then, show through experiments the advantage of nonparametric modeling to scenarios with spawning events. Our experiment results also demonstrate the advantages of our framework over the existing methods.
Optimal Rate Adaption in Federated Learning with Compressed Communications
Venue: arXiv cs.LG 2021
Year: 2021

Federated Learning (FL) incurs high communication overhead, which can be greatly alleviated by compression for model updates. Yet the tradeoff between compression and model accuracy in the networked environment remains unclear and, for simplicity, most implementations adopt a fixed compression rate only. In this paper, we for the first time systematically examine this tradeoff, identifying the influence of the compression error on the final model accuracy with respect to the learning rate. Specifically, we factor the compression error of each global iteration into the convergence rate analysis under both strongly convex and non-convex loss functions. We then present an adaptation framework to maximize the final model accuracy by strategically adjusting the compression rate in each iteration. We have discussed the key implementation issues of our framework in practical networks with representative compression algorithms. Experiments over the popular MNIST and CIFAR-10 datasets confirm that our solution effectively reduces network traffic yet maintains high model accuracy in FL.
Anchor Retouching via Model Interaction for Robust Object Detection in Aerial Images
Venue: arXiv cs.CV 2021
Year: 2021

Object detection has made tremendous strides in computer vision. Small object detection with appearance degradation is a prominent challenge, especially for aerial observations. To collect sufficient positive/negative samples for heuristic training, most object detectors preset region anchors in order to calculate Intersection-over-Union (IoU) against the ground-truthed data. In this case, small objects are frequently abandoned or mislabeled. In this paper, we present an effective Dynamic Enhancement Anchor (DEA) network to construct a novel training sample generator. Different from the other state-of-the-art techniques, the proposed network leverages a sample discriminator to realize interactive sample screening between an anchor-based unit and an anchor-free unit to generate eligible samples. Besides, multi-task joint training with a conservative anchor-based inference scheme enhances the performance of the proposed model while reducing computational complexity. The proposed scheme supports both oriented and horizontal object detection tasks. Extensive experiments on two challenging aerial benchmarks (i.e., DOTA and HRSC2016) indicate that our method achieves state-of-the-art performance in accuracy with moderate inference speed and computational overhead for training. On DOTA, our DEA-Net which integrated with the baseline of RoI-Transformer surpasses the advanced method by 0.40% mean-Average-Precision (mAP) for oriented object detection with a weaker backbone network (ResNet-101 vs ResNet-152) and 3.08% mean-Average-Precision (mAP) for horizontal object detection with the same backbone. Besides, our DEA-Net which integrated with the baseline of ReDet achieves the state-of-the-art performance by 80.37%. On HRSC2016, it surpasses the previous best model by 1.1% using only 3 horizontal anchors.
Characterization of $p$-ary functions in terms of association schemes and its applications
Venue: arXiv cs.IT 2021
Year: 2021

We obtain an explicit criterion for $p$-ary functions to produce association schemes in terms of their Walsh spectrum. Employing this characterization, we explicitly find a correlation between $p$-ary bent functions and association schemes; to be more exact, we prove that a $p$-ary bent function induces a $p$-class association scheme if and only if the function is weakly regular. As applications of our main criterion, we construct many infinite families of few-class association schemes arising from $p$-ary functions. Furthermore, we present four classes of $p$-ary two-weight linear codes, which are constructed from the association schemes produced in this paper.
Safe Linear Leveling Bandits
Venue: arXiv cs.LG 2021
Year: 2021

Multi-armed bandits (MAB) are extensively studied in various settings where the objective is to \textit{maximize} the actions' outcomes (i.e., rewards) over time. Since safety is crucial in many real-world problems, safe versions of MAB algorithms have also garnered considerable interest. In this work, we tackle a different critical task through the lens of \textit{linear stochastic bandits}, where the aim is to keep the actions' outcomes close to a target level while respecting a \textit{two-sided} safety constraint, which we call \textit{leveling}. Such a task is prevalent in numerous domains. Many healthcare problems, for instance, require keeping a physiological variable in a range and preferably close to a target level. The radical change in our objective necessitates a new acquisition strategy, which is at the heart of a MAB algorithm. We propose SALE-LTS: Safe Leveling via Linear Thompson Sampling algorithm, with a novel acquisition strategy to accommodate our task and show that it achieves sublinear regret with the same time and dimension dependence as previous works on the classical reward maximization problem absent any safety constraint. We demonstrate and discuss our algorithm's empirical performance in detail via thorough experiments.
hARMS: A Hardware Acceleration Architecture for Real-Time Event-Based Optical Flow
Venue: arXiv cs.AR 2021
Year: 2021

Event-based vision sensors produce asynchronous event streams with high temporal resolution based on changes in the visual scene. The properties of these sensors allow for accurate and fast calculation of optical flow as events are generated. Existing solutions for calculating optical flow from event data either fail to capture the true direction of motion due to the aperture problem, do not use the high temporal resolution of the sensor, or are too computationally expensive to be run in real time on embedded platforms. In this research, we first present a faster version of our previous algorithm, ARMS (Aperture Robust Multi-Scale flow). The new optimized software version (fARMS) significantly improves throughput on a traditional CPU. Further, we present hARMS, a hardware realization of the fARMS algorithm allowing for real-time computation of true flow on low-power, embedded platforms. The proposed hARMS architecture targets hybrid system-on-chip devices and was designed to maximize configurability and throughput. The hardware architecture and fARMS algorithm were developed with asynchronous neuromorphic processing in mind, abandoning the common use of an event frame and instead operating using only a small history of relevant events, allowing latency to scale independently of the sensor resolution. This change in processing paradigm improved the estimation of flow directions by up to 73% compared to the existing method and yielded a demonstrated hARMS throughput of up to 1.21 Mevent/s on the benchmark configuration selected. This throughput enables real-time performance and makes it the fastest known realization of aperture-robust, event-based optical flow to date.