Miracle-dz/UniFAR-FTUs huggingface dataset:
CANTO - Covert AutheNtication With Timing Channels Over Optimized Traffic Flows for CAN

Previous research works have endorsed the use of delays and clock skews for detecting intrusions or fingerprinting controllers that communicate on the CAN bus. Recently, timing characteristics of CAN frames have been also used for establishing a covert channel for cryptographic authentication, in this way cleverly removing the need for cryptographic material inside the short payload of data frames. However, the main drawback of this approach is the limited security level that can be achieved over existing CAN bus traffic. In this work we significantly improve on this by relying on optimization algorithms for scheduling CAN frames and deploy the covert channel on optimized CAN traffic. Under practical bus allocations, we are able to extract 3–5 bits of authentication data from each frame which leads to an efficient intrusion detection and authentication mechanism. By accumulating covert channel data over several consecutive frames, we can achieve higher security levels that are in line with current real-world demands. To prove the correctness of our approach, we present experiments on automotive-grade controllers, i.e., Infineon Aurix, and bus measurements with the use of industry standard tools, i.e., CANoe.
Time-Aware Gradient Attack on Dynamic Network Link Prediction

In network link prediction, it is possible to hide a target link from being predicted with a small perturbation on network structure. This observation may be exploited in many real world scenarios, for example, to preserve privacy, or to exploit financial security. There have been many recent studies to generate adversarial examples to mislead deep learning models on graph data. However, none of the previous work has considered the dynamic nature of real-world systems. In this work, we present the first study of adversarial attack on dynamic network link prediction (DNLP). The proposed attack method, namely time-aware gradient attack (TGA), utilizes the gradient information generated by deep dynamic network embedding (DDNE) across different snapshots to rewire a few links, so as to make DDNE fail to predict target links. We implement TGA in two ways: One is based on traversal search, namely TGA-Tra; and the other is simplified with greedy search for efficiency, namely TGA-Gre. We conduct comprehensive experiments which show the outstanding performance of TGA in attacking DNLP algorithms.
DeepSynth: Program Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning

We propose a method for effective training of deep Reinforcement Learning (RL) agents when the reward is sparse and non-Markovian, but at the same time progress towards the reward requires achieving an unknown sequence of high-level objectives. Our method employs a novel algorithm for synthesis of compact automata to uncover this sequential structure automatically. We synthesise a human-interpretable automaton from trace data generated through exploration of the environment by the deep RL agent. The state space of the environment is then enriched with the synthesised automaton so that generation of an optimal control policy by deep RL is guided by the discovered structure encoded in the automaton. We evaluate performance via a set of experiments including the Atari game Montezuma's Revenge. Compared to existing approaches, we obtain a decrease of two orders of magnitude in the number of iterations required for policy synthesis.
SR-Net: Cooperative Image Segmentation and Restoration in Adverse Environmental Conditions

Most state-of-the-art semantic segmentation or scene parsing approaches only achieve high accuracy rates in good environmental conditions. The performance decrease enormously if images with unknown disturbances occur, which is less discussed but appears more in real applications. Most existing research works cast the handling of the challenging adverse conditions as a post-processing step of signal restoration or enhancement after sensing, then feed the restored images for visual understanding. However, the performance will largely depend on the quality of restoration or enhancement. Whether restoration-based approaches would actually boost the semantic segmentation performance remains questionable. In this paper, we pro-pose a novel net framework to tackle semantic Segmentation and image Restoration in adverse environmental conditions (SR-Net). The proposed approach contains two components: Semantically-Guided Adaptation, which exploits and leverages semantic information from degraded images then help to reﬁne the segmentation; and Exemplar-Guided Synthesis, which synthesizes restored or enhanced images from semantic label maps given speciﬁc degraded exemplars. SR-Net exploits the possibility of building connections of low-level image processing and high level computer vision tasks, achieving image restoration via segmentation reﬁne-ment. Extensive experiments on several datasets demonstrate that our approach can not only improve the accuracy of high-level vision tasks with image adaption, but also boosts the perceptual quality and structural similarity of degraded images with image semantic guidance.
Discovering topics with neural topic models built from PLSA assumptions

In this paper we present a model for unsupervised topic discovery in texts corpora. The proposed model uses documents, words, and topics lookup table embedding as neural network model parameters to build probabilities of words given topics, and probabilities of topics given documents. These probabilities are used to recover by marginalization probabilities of words given documents. For very large corpora where the number of documents can be in the order of billions, using a neural auto-encoder based document embedding is more scalable then using a lookup table embedding as classically done. We thus extended the lookup based document embedding model to continuous auto-encoder based model. Our models are trained using probabilistic latent semantic analysis (PLSA) assumptions. We evaluated our models on six datasets with a rich variety of contents. Conducted experiments demonstrate that the proposed neural topic models are very effective in capturing relevant topics. Furthermore, considering perplexity metric, conducted evaluation benchmarks show that our topic models outperform latent Dirichlet allocation (LDA) model which is classically used to address topic discovery tasks.
Design and Evaluation of a Multi-Domain Trojan Detection Method on Deep Neural Networks

Trojan attacks on deep neural networks (DNNs) exploit a backdoor embedded in a DNN model that can hijack any input with an attacker’s chosen signature trigger. Emerging defence mechanisms are mainly designed and validated on vision domain tasks (e.g., image classification) on 2D Convolutional Neural Network (CNN) model architectures; a defence mechanism that is general across vision, text, and audio domain tasks is demanded. This work designs and evaluates a run-time Trojan detection method exploiting STRong Intentional Perturbation of inputs that is a multi-domain input-agnostic Trojan detection defence across Vision, Text and Audio domains—thus termed as STRIP-ViTA. Specifically, STRIP-ViTA is demonstratively independent of not only task domain but also model architectures. Most importantly, unlike other detection mechanisms, it requires neither machine learning expertise nor expensive computational resource, which are the reason behind DNN model outsourcing scenario—one main attack surface of Trojan attack. We have extensively evaluated the performance of STRIP-ViTA over: i) CIFAR10 and GTSRB datasets using 2D CNNs for vision tasks; ii) IMDB and consumer complaint datasets using both LSTM and 1D CNNs for text tasks; and iii) speech command dataset using both 1D CNNs and 2D CNNs for audio tasks. Experimental results based on more than 30 tested Trojaned models (including publicly Trojaned model) corroborate that STRIP-ViTA performs well across all nine architectures and five datasets. Overall, STRIP-ViTA can effectively detect trigger inputs with small false acceptance rate (FAR) with an acceptable preset false rejection rate (FRR). In particular, for vision tasks, we can always achieve a 0 percent FRR and FAR given strong attack success rate always preferred by the attacker. By setting FRR to be 3 percent, average FAR of 1.1 and 3.55 percent are achieved for text and audio tasks, respectively. Moreover, we have evaluated STRIP-ViTA against a number of advanced backdoor attacks and compare its effectiveness with other recent state-of-the-arts.
Intermittent Demand Forecasting with Deep Renewal Processes

Intermittency is a common and challenging problem in demand forecasting. We introduce a new, unified framework for building intermittent demand forecasting models, which incorporates and allows to generalize existing methods in several directions. Our framework is based on extensions of well-established model-based methods to discrete-time renewal processes, which can parsimoniously account for patterns such as aging, clustering and quasi-periodicity in demand arrivals. The connection to discrete-time renewal processes allows not only for a principled extension of Croston-type models, but also for an natural inclusion of neural network based models---by replacing exponential smoothing with a recurrent neural network. We also demonstrate that modeling continuous-time demand arrivals, i.e., with a temporal point process, is possible via a trivial extension of our framework. This leads to more flexible modeling in scenarios where data of individual purchase orders are directly available with granular timestamps. Complementing this theoretical advancement, we demonstrate the efficacy of our framework for forecasting practice via an extensive empirical study on standard intermittent demand data sets, in which we report predictive accuracy in a variety of scenarios that compares favorably to the state of the art.
Roles of Dysfunctional Attitudes and Self-Esteem in the Relationship between Social Support and Depression in Late Adolescence

Adhere to the necessity for stimulating the psychological well-being amongst late adolescents, the current inspection has been orchestrated to investigate the moderating and mediating roles of dysfunctional attitudes and self-esteem in the predictive relationship between social support and depression in late adolescence. In light of this aspiration, a total of 522 late adolescent respondents have been taken from two local secondary schools and a university in Klang Valley, Malaysia, and being assessed with self-report Interpersonal Support Evaluation List, Dysfunctional Attitudes Scale, Rosenberg’s Self-Esteem Scale and Beck Depression Inventory for their perceived social support, dysfunctional attitudes, self-esteem as well as depression. Subsequent to a series of regression analyses, the research findings revealed that self-esteem has meaningfully moderated the predictive relationship between social support and depression which mediated by dysfunctional attitudes in the samples, yet, dysfunctional attitudes has purely and constantly performed its role of mediating the relationship between social support and depression with its insignificant moderating role in the investigation model. Key-Words: Late Adolescents, Depression, Social Support, Self-Esteem, Dysfunctional Attitudes, Mediation, Moderation.
Unsupervised Keyword Extraction for Full-Sentence VQA

In the majority of the existing Visual Question Answering (VQA) research, the answers consist of short, often single words, as per instructions given to the annotators during dataset construction. This study envisions a VQA task for natural situations, where the answers are more likely to be sentences rather than single words. To bridge the gap between this natural VQA and existing VQA approaches, a novel unsupervised keyword extraction method is proposed. The method is based on the principle that the full-sentence answers can be decomposed into two parts: one that contains new information answering the question (i.e. keywords), and one that contains information already included in the question. Discriminative decoders were designed to achieve such decomposition, and the method was experimentally implemented on VQA datasets containing full-sentence answers. The results show that the proposed model can accurately extract the keywords without being given explicit annotations describing them.
Finite-N corrections to the superconformal index of toric quiver gauge theories

The superconformal index of quiver gauge theories realized on D3-branes in toric Calabi–Yau cones is investigated. We use the AdS/CFT correspondence and study D3-branes wrapped on supersymmetric cycles. We focus on brane configurations in which a single D3-brane is wrapped on a cycle, and we do not take account of branes with multiple wrapping. We propose a formula that gives finite-$N$ corrections to the index caused by such brane configurations. We compare the predictions of the formula for several examples with the results on the gauge theory side obtained by using localization for small sizes of gauge groups, and confirm that the formula correctly reproduces the finite-$N$ corrections up to the expected order.
An End-to-End Framework for Cold Question Routing in Community Question Answering Services

Routing newly posted questions (a.k.a cold questions) to potential answerers with suitable expertise in Community Question Answering sites (CQAs) is an important and challenging task. The existing methods either focus only on embedding the graph structural information and are less effective for newly posted questions, or adopt manually engineered feature vectors that are not as representative as the graph embedding methods. Therefore, we propose to address the challenge of leveraging heterogeneous graph and textual information for cold question routing by designing an end-to-end framework that jointly learns CQA node embeddings and finds best answerers for cold questions. We conducted extensive experiments to confirm the usefulness of incorporating the textual information from question tags and demonstrate that an end-2-end framework can achieve promising performances on routing newly posted questions asked by both existing users and newly registered users.
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationship to the question. This paper introduces a new graph-based recurrent retrieval approach that learns to retrieve reasoning paths over the Wikipedia graph to answer multi-hop open-domain questions. Our retriever model trains a recurrent neural network that learns to sequentially retrieve evidence paragraphs in the reasoning path by conditioning on the previously retrieved documents. Our reader model ranks the reasoning paths and extracts the answer span included in the best reasoning path. Experimental results show state-of-the-art results in three open-domain QA datasets, showcasing the effectiveness and robustness of our method. Notably, our method achieves significant improvement in HotpotQA, outperforming the previous best model by more than 14 points.
Differentially Private Federated Variational Inference

In many real-world applications of machine learning, data are distributed across many clients and cannot leave the devices they are stored on. Furthermore, each client's data, computational resources and communication constraints may be very different. This setting is known as federated learning, in which privacy is a key concern. Differential privacy is commonly used to provide mathematical privacy guarantees. This work, to the best of our knowledge, is the first to consider federated, differentially private, Bayesian learning. We build on Partitioned Variational Inference (PVI) which was recently developed to support approximate Bayesian inference in the federated setting. We modify the client-side optimisation of PVI to provide an (${\epsilon}$, ${\delta}$)-DP guarantee. We show that it is possible to learn moderately private logistic regression models in the federated setting that achieve similar performance to models trained non-privately on centralised data.
Corpus Wide Argument Mining - a Working Solution

One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such content. This line of research yielded moderate success, which is of limited use in a real-world system. Furthermore, for such a system to yield a comprehensive set of relevant arguments, over a wide range of topics, it requires leveraging a large and diverse corpus in an appropriate manner. Here we present a first end-to-end high-precision, corpus-wide argument mining system. This is made possible by combining sentence-level queries over an appropriate indexing of a very large corpus of newspaper articles, with an iterative annotation scheme. This scheme addresses the inherent label bias in the data and pinpoints the regions of the sample space whose manual labeling is required to obtain high-precision among top-ranked candidates.
Subexponential LPs Approximate Max-Cut

We show that for every $\varepsilon > 0$, the degree $-n^{\varepsilon}$ Sherali-Adams linear program (with $\exp(\tilde{O}(n^{\varepsilon})$) variables and constraints) approximates the maximum cut problem within a factor of ($\frac{1}{2}+\varepsilon^{\prime}$), for some $\varepsilon^{\prime}(\varepsilon) > 0$. Our result provides a surprising converse to known lower bounds against all linear programming relaxations of Max-Cut [1], [2], and hence resolves the extension complexity of approximate Max-Cut for approximation factors close to $\frac{1}{2}$ (up to the function $\varepsilon^{\prime}(\varepsilon)$). Previously, only semidefinite programs and spectral methods were known to yield approximation factors better than $\frac{1}{2}$ for Max-Cut in time $2^{o(n)}$. We also show that constant-degree Sherali-Adams linear programs (with $\text{poly}(n)$ variables and constraints) can solve Max-Cut with approximation factor close to 1 on graphs of small threshold rank: this is the first connection of which we are aware between threshold rank and linear programming-based algorithms. Our results separate the power of Sherali-Adams versus Lovász-Schrijver hierarchies for approximating Max-Cut, since it is known [3] that ($\frac{1}{2}+\varepsilon$) approximation of Max Cut requires $\Omega_{\varepsilon}(n)$ rounds in the Lovász-Schrijver hierarchy. We also provide a subexponential time approximation for Khot's Unique Games problem [4]: we show that for every $\varepsilon > 0$ the degree-($n^{\varepsilon}\log q$) Sherali-Adams linear program distinguishes instances of Unique Games of value $\geq 1-\varepsilon ^{\prime}$ from instances of value $\leq \varepsilon^{\prime}$, for some $\varepsilon^{\prime}(\varepsilon) > 0$, where $q$ is the alphabet size. Such guarantees are qualitatively similar to those of previous subexponential-time algorithms for Unique Games but our algorithm does not rely on semidefinite programming or subspace enumeration techniques [5]–[7].
Thermal-null medium (TNM): a novel material to achieve feasible thermodynamics devices beyond conventional challenges

Recently, heat manipulation has gained the attention of scientific community due to its several applications. In this letter, based on transformation thermodynamic (TT) methodology, a novel material, which is called thermal-null medium (TNM), is proposed that enables us to design various thermal functionalities such as thermal bending devices, arbitrary shape heat concentrators and omnidirectional thermal cloaks. In contrary to the conventional TT-based conductivities, which are inhomogeneous and anisotropic, TNMs are homogeneous and easy to realize. In addition, the attained TNMs are independent of the device shape. That is if the geometry of the desired device is changed, there is no need to recalculate the necessitating conductivities. This feature of TNM will make it suitable for scenarios where re-configurability is of utmost importance. Several numerical simulations are carried out to demonstrate the TNM capability and its applications in directional bending devices, heat concentrators and thermal cloaks. The proposed TNM could open a new avenue for potential applications in solar thermal panels and thermal-electric devices.
Projective Quadratic Regression for Online Learning

This paper considers online convex optimization (OCO) problems - the paramount framework for online learning algorithm design. The loss function of learning task in OCO setting is based on streaming data so that OCO is a powerful tool to model large scale applications such as online recommender systems. Meanwhile, real-world data are usually of extreme high-dimensional due to modern feature engineering techniques so that the quadratic regression is impractical. Factorization Machine as well as its variants are efficient models for capturing feature interactions with low-rank matrix model but they can't fulfill the OCO setting due to their non-convexity. In this paper, We propose a projective quadratic regression (PQR) model. First, it can capture the import second-order feature information. Second, it is a convex model, so the requirements of OCO are fulfilled and the global optimal solution can be achieved. Moreover, existing modern online optimization methods such as Online Gradient Descent (OGD) or Follow-The-Regularized-Leader (FTRL) can be applied directly. In addition, by choosing a proper hyper-parameter, we show that it has the same order of space and time complexity as the linear model and thus can handle high-dimensional data. Experimental results demonstrate the performance of the proposed PQR model in terms of accuracy and efficiency by comparing with the state-of-the-art methods.
Controllable List-wise Ranking for Universal No-reference Image Quality Assessment

No-reference image quality assessment (NR-IQA) has received increasing attention in the IQA community since reference image is not always available. Real-world images generally suffer from various types of distortion. Unfortunately, existing NR-IQA methods do not work with all types of distortion. It is a challenging task to develop universal NR-IQA that has the ability of evaluating all types of distorted images. In this paper, we propose a universal NR-IQA method based on controllable list-wise ranking (CLRIQA). First, to extend the authentically distorted image dataset, we present an imaging-heuristic approach, in which the over-underexposure is formulated as an inverse of Weber-Fechner law, and fusion strategy and probabilistic compression are adopted, to generate the degraded real-world images. These degraded images are label-free yet associated with quality ranking information. We then design a controllable list-wise ranking function by limiting rank range and introducing an adaptive margin to tune rank interval. Finally, the extended dataset and controllable list-wise ranking function are used to pre-train a CNN. Moreover, in order to obtain an accurate prediction model, we take advantage of the original dataset to further fine-tune the pre-trained network. Experiments evaluated on four benchmark datasets (i.e. LIVE, CSIQ, TID2013, and LIVE-C) show that the proposed CLRIQA improves the state of the art by over 9% in terms of overall performance. The code and model are publicly available at this https URL.