Miracle-dz/UniFAR-FTUs huggingface dataset:
Inverse Reinforcement Learning with Missing Data

We consider the problem of recovering an expert's reward function with inverse reinforcement learning (IRL) when there are missing/incomplete state-action pairs or observations in the demonstrated trajectories. This issue of missing trajectory data or information occurs in many situations, e.g., GPS signals from vehicles moving on a road network are intermittent. In this paper, we propose a tractable approach to directly compute the log-likelihood of demonstrated trajectories with incomplete/missing data. Our algorithm is efficient in handling a large number of missing segments in the demonstrated trajectories, as it performs the training with incomplete data by solving a sequence of systems of linear equations, and the number of such systems to be solved does not depend on the number of missing segments. Empirical evaluation on a real-world dataset shows that our training algorithm outperforms other conventional techniques.
Adaptive Learning Guidance System (ALGS)

This poster presents the conceptual framework of the Adaptive Learning Guidance System ALGS. The system aims to propose a model for adaptive learning environments where two major concerns arising from past studies are being addressed; the marginal role of the teacher, and the need for a big data approach. Most past studies marginalized the teacher role in adaptive learning system, particularly the online ones. The most notable quality about ALGS is empowering the teacher with the capability of having input in all stages. This is where the hybrid recommendation system plays a crucial role in the 3-stage ALGS architecture. The second issue addressed is the need for big data to enhance the system functionality. The more the data collected by the system, the more efficient its adaptation functionality which makes it difficult for a first-time-run system and (or) a first-time user. Accordingly, collaborative filtering is used at first until adequate data about the user interaction are collected. ALGS architecture consists of a user, content, and 3-stage adaptation models.
Mining Unfollow Behavior in Large-Scale Online Social Networks via Spatial-Temporal Interaction

Online Social Networks (OSNs) evolve through two pervasive behaviors: follow and unfollow, which respectively signify relationship creation and relationship dissolution. Researches on social network evolution mainly focus on the follow behavior, while the unfollow behavior has largely been ignored. Mining unfollow behavior is challenging because user's decision on unfollow is not only affected by the simple combination of user's attributes like informativeness and reciprocity, but also affected by the complex interaction among them. Meanwhile, prior datasets seldom contain sufficient records for inferring such complex interaction. To address these issues, we first construct a large-scale real-world Weibo1 dataset, which records detailed post content and relationship dynamics of 1.8 million Chinese users. Next, we define user's attributes as two categories: spatial attributes (e.g., social role of user) and temporal attributes (e.g., post content of user). Leveraging the constructed dataset, we systematically study how the interaction effects between user's spatial and temporal attributes contribute to the unfollow behavior. Afterwards, we propose a novel unified model with heterogeneous information (UMHI) for unfollow prediction. Specifically, our UMHI model: 1) captures user's spatial attributes through social network structure; 2) infers user's temporal attributes through user-posted content and unfollow history; and 3) models the interaction between spatial and temporal attributes by the nonlinear MLP layers. Comprehensive evaluations on the constructed dataset demonstrate that the proposed UMHI model outperforms baseline methods by 16.44 on average in terms of precision. In addition, factor analyses verify that both spatial attributes and temporal attributes are essential for mining unfollow behavior.
Generalized Maximum Causal Entropy for Inverse Reinforcement Learning

We consider the problem of learning from demonstrated trajectories with inverse reinforcement learning (IRL). Motivated by a limitation of the classical maximum entropy model in capturing the structure of the network of states, we propose an IRL model based on a generalized version of the causal entropy maximization problem, which allows us to generate a class of maximum entropy IRL models. Our generalized model has an advantage of being able to recover, in addition to a reward function, another expert's function that would (partially) capture the impact of the connecting structure of the states on experts' decisions. Empirical evaluation on a real-world dataset and a grid-world dataset shows that our generalized model outperforms the classical ones, in terms of recovering reward functions and demonstrated trajectories.
Surface background rejection technique for liquid argon dark matter detectors using a thin scintillating layer

Future large liquid argon direct dark matter detectors can benefit greatly from an efficient surface background rejection technique. To aid the development of these large scale detectors a test stand, Argon-1, has been constructed at Carleton University, Ottawa, Canada, in the noble liquid detector development lab. It aims to test a novel surface background rejection technique using a thin layer of slow scintillating material at the surface of the vessel. Through pulse-shape discrimination of the slow light from the scintillating layer, events from the surface of the detector can be discriminated from liquid argon events. The detector will be implemented with high-granularity SiPMs for light detection which will be used to accurately identify surface events to characterize the proposed technique. An overview of the technique and the status of the experiment are discussed here.
DeepSat V2: feature augmented convolutional neural nets for satellite image classification

ABSTRACT Satellite image classification is a challenging problem that lies at the crossroads of remote sensing, computer vision, and machine learning. Due to the high variability inherent in satellite data, most of the current object classification approaches are not suitable for handling satellite datasets. The progress of satellite image analytics has also been inhibited by the lack of a single labeled high-resolution dataset with multiple class labels. In a preliminary version of this work, we introduced two new high resolution satellite imagery datasets (SAT-4 and SAT-6) and proposed DeepSat framework for classification based on “handcrafted” features and a deep belief network (DBN). The present paper is an extended version, we present an end-to-end framework leveraging an improved architecture that augments a convolutional neural network (CNN) with handcrafted features (instead of using DBN-based architecture) for classification. Our framework, having access to fused spatial information obtained from handcrafted features as well as CNN feature maps, have achieved accuracies of 99.90\% and 99.84\% respectively, on SAT-4 and SAT-6, surpassing all the other state-of-the-art results. A statistical analysis based on Distribution Separability Criterion substantiates the robustness of our approach in learning better representations for satellite imagery.
N-HANS: Introducing the Augsburg Neuro-Holistic Audio-eNhancement System

N-HANS is a Python toolkit for in-the-wild audio enhancement, including speech, music, and general audio denoising, separation, and selective noise or source suppression. The functionalities are realised based on two neural network models sharing the same architecture, but trained separately. The models are comprised of stacks of residual blocks, each conditioned on additional speech or environmental noise recordings for adapting to different unseen speakers or environments in real life. In addition to a Python API, a command line interface is provided to researchers and developers, both of which are documented at this https URL. Experimental results indicate that N-HANS achieves outstanding performance, and ensure its reliable usage in real-life audio and speech-related tasks, reaching very high audio and speech quality.
Defensive Few-Shot Learning

This article investigates a new challenging problem called defensive few-shot learning in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem. This is because the commonly assumed sample-level distribution consistency between the training and test sets can no longer be met in the few-shot setting. To address this situation, we develop a general defensive few-shot learning (DFSL) framework to answer the following two key questions: (1) how to transfer adversarial defense knowledge from one sample distribution to another? (2) how to narrow the distribution gap between clean and adversarial examples under the few-shot setting? To answer the first question, we propose an episode-based adversarial training mechanism by assuming a task-level distribution consistency to better transfer the adversarial defense knowledge. As for the second question, within each few-shot task, we design two kinds of distribution consistency criteria to narrow the distribution gap between clean and adversarial examples from the feature-wise and prediction-wise perspectives, respectively. Extensive experiments demonstrate that the proposed framework can effectively make the existing few-shot models robust against adversarial attacks. Code is available at https://github.com/WenbinLee/DefensiveFSL.git.
The geopolitics behind the routes data travels: a case study of Iran

In November 2019, in the wake of political demonstrations against the regime, Iran managed to selectively cut off most traffic from the global Internet while fully operating its own domestic network. It seemingly confirmed the main hypothesis our research had led us to, based on prior observation of data routing: Iran’s architecture of connectivity enables selective censorship of international traffic. This paper examines, through the case of Iran, how states can leverage the Border Gateway Protocol (BGP) as a tool of geopolitical control and what are the trade-offs they face. This question raises a methodological question that we also address: how the analysis of BGP can infer and document these strategies of territorialization of cyberspace. The Internet is a network of networks where each network is an autonomous system. Autonomous systems (ASes) are independent administrative entities controlled by a variety of actors such as governments, companies and universities. Their administrators have to agree and communicate on the path followed by packets travelling across the Internet, which is made possible by BGP. Agreements between ASes are often confidential but BGP requires neighbouring ASes to interact with each other in order to coordinate routing through the constant release of connectivity update messages. These messages announce the availability (or withdrawal) of a sequence of ASes that can be followed to reach an IP address prefix. In our study, we inferred the structure of Iran's connectivity through the capture and analysis of these BGP announcements. We show how the particularities of Iran's BGP and connectivity structure can enable active measures, such as censorship, both internally and externally throughout the network. We argue that Iran has found a way to reconcile a priori conflicting strategic goals: developing a self-sustaining and resilient domestic Internet, but with tight control at its borders. It thus enables the regime to leverage connectivity as a tool of censorship in the face of social instability and as a tool of regional influence in the context of strategic competition.
'FILLING GAPS' OR 'JUMPING HOOPS': TRAINEE PRIMARY TEACHERS' VIEWS OF A SUBJECT KNOWLEDGE AUDIT IN MATHEMATICS

Data from one cohort of PGCE trainee primary teachers is used to examine their perceived value of a subject knowledge auditing process in preparation to teach mathematics in primary schools. Questionnaires (n = 96) were used to collect quantitative and qualitative data. Analysis suggests that some trainee teachers see the auditing process as 'filling in gaps' in their subject knowledge, developing their confidence and supporting them in their teaching, whereas other more confident trainee teachers do not see any relevance and may even see the process as ‘jumping hoops’ to fulfil the requirements of the course.
Music Theme Recognition Using CNN and Self-Attention

We present an efficient architecture to detect mood/themes in music tracks on autotagging-moodtheme subset of the MTG-Jamendo dataset. Our approach consists of two blocks, a CNN block based on MobileNetV2 architecture and a self-attention block from Transformer architecture to capture long term temporal characteristics. We show that our proposed model produces a significant improvement over the baseline model. Our model (team name: AMLAG) achieves 4th place on PR-AUC-macro Leaderboard in MediaEval 2019: Emotion and Theme Recognition in Music Using Jamendo.
Open Archive Toulouse Archive Ouverte (OATAO)

Ochratoxin A (OTA) is a potential nephrotoxic, teratogenic, immunogenic, hepatotoxic and carcinogenic mycotoxin, produced by Aspergillus westerdijkiae NRRL 3174. Herein we describe the characterization of a putative OTA-polyketide synthase gene ‘‘ aoks1 ”, cloned by using gene walking approach. The predicted amino acid sequence of the 2 kb clone display 34–60% similarities to different polyketide synthase genes including lovastatine biosynthesis gene ‘‘ lovb ” in A. terreus , compactin biosynthesis gene ‘‘ mlcA ” in Peni- cillium citrinum and OTA biosynthesis gene ‘‘ otapksPN ” in P. nordicum. Based on the reverse transcription PCR and kinetic secondary metabolites production studies, aoks1 expression was found to be associated with OTA biosynthesis. Further a mutant, in which the aoks1 gene was inactivated by Escherichia coli hygromycin B phosphotransferase gene, lost the capacity to produce OTA, but still producing mellein. To our knowledge this report describes for the ﬁrst time characterization of a gene involved in OTA biosynthesis, with the information about mellein which was proposed in the literature to be an intermediate OTA. This study also suggests that aoks1 may be the second polyketide synthase gene required for OTA biosynthesis in A. westerdijkiae NRRL 3174. In this study, we report the characterization of a PKS gene ( aoks1 ) required for the biosynthesis OTA in A. westerdijkiae NRRL 3174. Disruption of this gene stop the biosynthesis of OTA but did not affect the biosynthesis of mellein. This gene is different from the pks gene reported by O’Callaghan et al. (2003).
Study of Non-Uniform Channel Polarization and Design of Polar Codes with Arbitrary Rates

In this paper, we present the concept of non-uniform channel polarization and a scheme for rate-compatible polar code construction for any codeword length using additive white Gaussian noise (AWGN) channels and the successive cancellation (SC) decoder. A Non-Uniform Polarization technique based on the Gaussian Approximation (NUPGA) is developed and an efficient rate-compatible design technique is devised to choose the best channels for transmission by a process of re-polarization of the codeword with the desired length. Simulations illustrate the proposed NUPGA design against existing rate-compatible techniques.
ImmuNeCS: Neural Committee Search by an Artificial Immune System

Current Neural Architecture Search techniques can suffer from a few shortcomings, including high computational cost, excessive bias from the search space, conceptual complexity or uncertain empirical benefits over random search. In this paper, we present ImmuNeCS, an attempt at addressing these issues with a method that offers a simple, flexible, and efficient way of building deep learning models automatically, and we demonstrate its effectiveness in the context of convolutional neural networks. Instead of searching for the 1-best architecture for a given task, we focus on building a population of neural networks that are then ensembled into a neural network committee, an approach we dub 'Neural Committee Search'. To ensure sufficient performance from the committee, our search algorithm is based on an artificial immune system that balances individual performance with population diversity. This allows us to stop the search when accuracy starts to plateau, and to bridge the performance gap through ensembling. In order to justify our method, we first verify that the chosen search space exhibits the locality property. To further improve efficiency, we also combine partial evaluation, weight inheritance, and progressive search. First, experiments are run to verify the validity of these techniques. Then, preliminary experimental results on two popular computer vision benchmarks show that our method consistently outperforms random search and yields promising results within reasonable GPU budgets. An additional experiment also shows that ImmuNeCS's solutions transfer effectively to a more difficult task, where they achieve results comparable to a direct search on the new task. We believe these findings can open the way for new, accessible alternatives to traditional NAS.
Building Fast Fuzzers

Fuzzing is one of the key techniques for evaluating the robustness of programs against attacks. Fuzzing has to be effective in producing inputs that cover functionality and find vulnerabilities. But it also has to be efficient in producing such inputs quickly. Random fuzzers are very efficient, as they can quickly generate random inputs; but they are not very effective, as the large majority of inputs generated is syntactically invalid. Grammar-based fuzzers make use of a grammar (or another model for the input language) to produce syntactically correct inputs, and thus can quickly cover input space and associated functionality. Existing grammar-based fuzzers are surprisingly inefficient, though: Even the fastest grammar fuzzer Dharma still produces inputs about a thousand times slower than the fastest random fuzzer. So far, one can have an effective or an efficient fuzzer, but not both. In this paper, we describe how to build fast grammar fuzzers from the ground up, treating the problem of fuzzing from a programming language implementation perspective. Starting with a Python textbook approach, we adopt and adapt optimization techniques from functional programming and virtual machine implementation techniques together with other novel domain-specific optimizations in a step-by-step fashion. In our F1 prototype fuzzer, these improve production speed by a factor of 100--300 over the fastest grammar fuzzer Dharma. As F1 is even 5--8 times faster than a lexical random fuzzer, we can find bugs faster and test with much larger valid inputs than previously possible.
Toward partial Verma functors of $\mathcal{U}^{[r]}(\mathfrak{g})$ and related results

This note extends the Steinberg Tensor product theorem from the Frobenius kernel $G_{(r)}$ to the deformation $\mathcal{U}^{[r]}(\mathfrak{g})$ of its distribution algebra. As a Corollary we proof some conjectures from \cite{Wes}. Further it describes the graded representation theory of $\mathcal{U}^{[r]}(\mathfrak{g})$ at a generic semisimple p-central character as a twist of the category of graded $G_{(r-1)}$-modules. We conclude by explaining this relation for $SL_2$ explicitely as well as computing the center in this case.
Explicit Contextual Semantics for Text Comprehension

Who did what to whom is a major focus in natural language understanding, which is right the aim of semantic role labeling (SRL) task. Despite of sharing a lot of processing characteristics and even task purpose, it is surprisingly that jointly considering these two related tasks was never formally reported in previous work. Thus this paper makes the first attempt to let SRL enhance text comprehension and inference through specifying verbal predicates and their corresponding semantic roles. In terms of deep learning models, our embeddings are enhanced by explicit contextual semantic role labels for more fine-grained semantics. We show that the salient labels can be conveniently added to existing models and significantly improve deep learning models in challenging text comprehension tasks. Extensive experiments on benchmark machine reading comprehension and inference datasets verify that the proposed semantic learning helps our system reach new state-of-the-art over strong baselines which have been enhanced by well pretrained language models from the latest progress.
Reformulating Scalar-Tensor Field Theories as Scalar-Scalar Field Theories Using Lorentzian Cofinsler Spaces

In this paper I shall show how notions of Finsler geometry can be used to construct a new type of geometry using a scalar field, f, on the cotangent bundle of a differentiable manifold, M. This new geometry will be called Lorentzian Cofinsler geometry. This geometry will enable me to use the second vertical derivatives of f, along with the differential of the scalar field, phi on M, to construct a Lorentzian metric tensor on M, that depends upon phi. f will be chosen so that the resultant metric on M has the form of a FLRW metric, with the t equal constant slices being flat. When the Horndeski Lagrangians are evaluated for this choice of geometry the quartic and quintic Lagrangians are of third order, but reduce to non-degenerate second-order Lagrangians plus a divergence. Upon varying phi in these "scalarized" Horndeski Lagrangians, equations will be obtained which admit self-inflating universe solutions, provided that the coefficient functions appearing in the Horndeski Lagrangians are chosen suitably. This approach is also used to study solutions of the most general conformally invariant scalar-tensor field theory which is flat space compatible (i.e., such that the Lagrangians of the field theory are well-defined when either the space is flat or the scalar field is constant). There too the coefficient functions can be chosen to give self-inflating universes. Arguments will be presented to show that it is possible to construct model universes that begin explosively, and then settle down to a period of much quieter acceleration which either continues forever, or stops and collapses to the models original, pre-expansion, state.
SMART: Skeletal Motion Action Recognition aTtack

Adversarial attack has inspired great interest in computer vision, by showing that classification-based solutions are prone to imperceptible attack in many tasks. In this paper, we propose a method, SMART, to attack action recognizers which rely on 3D skeletal motions. Our method involves an innovative perceptual loss which ensures the imperceptibility of the attack. Empirical studies demonstrate that SMART is effective in both white-box and black-box scenarios. Its generalizability is evidenced on a variety of action recognizers and datasets. Its versatility is shown in different attacking strategies. Its deceitfulness is proven in extensive perceptual studies. Finally, SMART shows that adversarial attack on 3D skeletal motion, one type of time-series data, is significantly different from traditional adversarial attack problems.