Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning to Search via Self-Imitation

We study the problem of learning a good search policy. To do so, we propose the self-imitation learning setting, which builds upon imitation learning in two ways. First, self-imitation uses feedback provided by retrospective analysis of demonstrated search traces. Second, the policy can learn from its own decisions and mistakes without requiring repeated feedback from an external expert. Combined, these two properties allow our approach to iteratively scale up to larger problem sizes than the initial problem size for which expert demonstrations were provided. We showcase the effectiveness of our approach on a synthetic maze solving task and the problem of risk-aware path planning.
The JHU Speech LOREHLT 2017 System: Cross-Language Transfer for Situation-Frame Detection

We describe the system our team used during NIST's LoReHLT (Low Resource Human Language Technologies) 2017 Evaluations, which evaluated document topic classification. We present a language agnostic approach combining universal acoustic modeling, evaluation-language-to-English machine translation (MT) and an English-language topic classifier. This combination requires no transcribed speech in the given evaluation language, nor even in a related language. We also examine the benefits of system adaptation from various collected resources. The two evaluation languages (incident languages by the LORELEI terminology) were Tigrinya (IL5) and Oromo (IL6) and for both our system performed well.
The Trusted Server: Concept and reference implementation of a fully autonomous and trustworthy computational server providing an advanced level of Privacy by Design through machine-based trust

Privacy by Design is a core element of the EU General Data Protection Regulation and of similar legislation in other countries. It both proposes and demands the use of appropriate methods to protect personal data and the privacy of individuals whose data is stored and processed in computing environments. These requirements are currently met through anonymization, pseudonymization and encryption. Multi-stage pseudonymization enhances privacy by assuring no single institution or person is in possession of sufficient data to identify an individual. This means privacy is not ensured by design, but rather based on workflow complexity and trust on individuals, especially that of system administrators who have unlimited access to systems and data under their governance. Secure Multiparty Computation using full data encryption is a promising method that has made substantial progress, though real-world cases are rare and highly specific. The Trusted Server is a completely autonomous and generic implementation of Privacy by Design. Once set up and running, it is irrevocably sealed for its whole lifecycle allowing only explicitly imple-mented data input, access, processing and output. Being unlimitedly validatable and verifiable, it supersedes trust built upon human individuals thus acting as trustworthy entity itself. It is strictly standard compliant, easy to set up and use, and is suitable for almost any computational service or processing task other than persistent storage. The Trusted Server allows implementation of straightforward solutions with full privacy protection for otherwise complex problems, as demonstrated in the following examples: first, a reference implementation of a fully-anonymous yet access-controlled web feedback submission server; second, a solution for Yao's Millionaires' Problem is given, which requires just a few simple scripts.
A Fusion of Appearance based CNNs and Temporal evolution of Skeleton with LSTM for Daily Living Action Recognition

In this paper, we propose efficient method which combines skeleton information and appearance features for daily-living action recognition. Many RGB methods focus only on short term temporal information obtained from optical flow. Skeleton based methods on the other hand show that modeling long term skeleton evolution improves action recognition accuracy. In this paper we propose to fuse skeleton based LSTM classifier which models temporal evolution of skeleton with deep CNN which models static appearance. We show that such fusion improves recognition of actions with similar motion and pose footprint, which is especially crucial in daily-living action recognition scenario. We validate our approach on public available CAD60 and MSRDailyActivity3D, achieving state-of-the art results.
Communication Scheduling as a First-Class Citizen in Distributed Machine Learning Systems

State-of-the-art machine learning systems rely on graph-based models, with the distributed training of these models being the norm in AI-powered production pipelines. The performance of these communication-heavy systems depends on the effective overlap of communication and computation. While the overlap challenge has been addressed in systems with simpler model representations, it remains an open problem in graph-based models. In this work, we develop a system for communication scheduling which realizes near-optimal overlap of communication and computation in graph-based models. Our system is implemented over TensorFlow and requires no changes in the model or developer inputs. Our system improves the throughput by up to 82% in inference and 20% in training, while also reducing straggler effect by up to 2.8x. A part of our implementation is already merged with TensorFlow codebase; the rest is publicly available.
Acronym Expansion: A General Approach Using Deep Learning

Acronyms are present in usually all documents to express information that is repetitive and well known. But acronyms can be ambiguous because there can be many expansions of the same acronym. In this paper, we propose a general system for acronym expansion that can work on any acronym given some context information it is used in. We present methods for retrieving all the possible expansions of an acronym from Wikipedia and AcronymsFinder.com. We propose to use these expansions to collect the context in which these acronym expansions are used and then score them using a deep learning technique called Doc2Vec. All these things collectively lead to achieving an accuracy of 90.9% in selecting the correct expansion for given acronym on a dataset we scraped from Wikipedia with 707 distinct acronyms and 14,876 disambiguations.
Direction finding of wideband sources in sparse arrays

We consider the problem of detecting and tracking moving sources emitting wideband signals under the severe spatial aliasing condition in sparse passive arrays. We develop direction of arrival(DOA) estimation algorithm based on subband MUSIC(MUltiple SIgnal Classification) method which produces high-resolution estimation and the fact that true bearings are invariant regardless of a change of frequency bands while the aliased false bearings vary. Despite its simplicity, the proposed algorithm overcomes the aliasing effects and improves the localization performance in sparse passive arrays. Simulation results for multiple target tracking are presented and the robustness algorithm with respect to measurement noise is also demonstrated.
Joint Object Category and 3D Pose Estimation from 2D Images

2D object detection is the task of finding (i) what objects are present in an image and (ii) where they are located, while 3D pose estimation is the task of finding the pose of these objects in 3D space. State-of-the-art methods for solving these tasks follow a two-stage approach where a 3D pose estimation system is applied to bounding boxes (with associated category labels) returned by a 2D detection method. This paper addresses the task of joint object category and 3D pose estimation given a 2D bounding box. We design a residual network based architecture to solve these two seemingly orthogonal tasks with new category-dependent pose outputs and loss functions, and show state-of-the-art performance on the challenging Pascal3D+ dataset.
Interpretable R-CNN

This paper presents a method of learning qualitatively interpretable models in object detection using popular two-stage region-based ConvNet detection systems (i.e., R-CNN). R-CNN consists of a region proposal network and a RoI (Region-of-Interest) prediction network.By interpretable models, we focus on weakly-supervised extractive rationale generation, that is learning to unfold latent discriminative part configurations of object instances automatically and simultaneously in detection without using any supervision for part configurations. We utilize a top-down hierarchical and compositional grammar model embedded in a directed acyclic AND-OR Graph (AOG) to explore and unfold the space of latent part configurations of RoIs. We propose an AOGParsing operator to substitute the RoIPooling operator widely used in R-CNN, so the proposed method is applicable to many state-of-the-art ConvNet based detection systems. The AOGParsing operator aims to harness both the explainable rigor of top-down hierarchical and compositional grammar models and the discriminative power of bottom-up deep neural networks through end-to-end training. In detection, a bounding box is interpreted by the best parse tree derived from the AOG on-the-fly, which is treated as the extractive rationale generated for interpreting detection. In learning, we propose a folding-unfolding method to train the AOG and ConvNet end-to-end. In experiments, we build on top of the R-FCN and test the proposed method on the PASCAL VOC 2007 and 2012 datasets with performance comparable to state-of-the-art methods.
Image camouflage based on Generate Model

To protect image contents, most existing encryption algorithms are designed to transform an original image into a texture-like or noise-like image which is, however, an obvious visual sign indicating the presence of an encrypted image and thus results in a significantly large number of attacks. To address this problem, we propose a new image encryption concept that a meaningful normal image that is independent of the original image and the corresponding well-trained generative model can generate the same image as the original image in this paper, instead of transmitting the original image, to achieve the effect of disguising the original image. This image camouflage method not only solves the problem of obviously visual implication, but also guarantees the security of the information.
Query Expansion on Compounds

Compounds constitute a specific issue in search, in particular in languages where they are written in one word, as is the case for Danish and the other Scandinavian languages. For such languages, expansion of the query compound into separate lemmas is a way of finding the often frequent alternative synonymous phrases in which the content of a compound can also be expressed. However, it is crucial to note that the number of irrelevant hits is generally very high when using this expansion strategy. The aim of this paper is to examine how we can obtain better search results on split compounds, partly by looking at the internal structure of the original compound, partly by analyzing the context in which the split compound occurs. We perform an NP analysis and introduce a new, linguistically based threshold for retrieved hits. The results obtained by using this strategy demonstrate that compound splitting combined with a shallow linguistic analysis focusing on the recognition of NPs can improve search by bringing down the number of irrelevant hits.
A Learning from Demonstration Approach fusing Torque Controllers

Torque controllers have become commonplace in the new generation of robots, allowing for complex robot motions involving physical contact with the surroundings in addition to task constraints at Cartesian and joint levels. When learning such skills from demonstrations, one is often required to think in advance about the appropriate task representation (usually either operational or configuration space). We here propose a probabilistic approach for simultaneously learning and synthesizing control commands which take into account task, joint space and force constraints. We treat the problem by considering different torque controllers acting on the robot, whose relevance is learned from demonstrations. This information is used to combine the controllers by exploiting the properties of Gaussian distributions, generating torque commands that satisfy the important features of the task. We validate the approach in two experimental scenarios using 7-DoF torque-controlled manipulators, with tasks requiring the fusion of multiple controllers to be properly executed.
Lp-Regularized Least Squares (0

The least squares problem is formulated in terms of Lp quasi-norm regularization (0<p<1). Two formulations are considered: (i) an Lp-constrained optimization and (ii) an Lp-penalized (unconstrained) optimization. Due to the nonconvexity of the Lp quasi-norm, the solution paths of the regularized least squares problem are not ensured to be continuous. A critical path, which is a maximal continuous curve consisting of critical points, is therefore considered separately. The critical paths are piecewise smooth, as can be seen from the viewpoint of the variational method, and generally contain non-optimal points such as saddle points and local maxima as well as global/local minima. Along each critical path, the correspondence between the regularization parameters (which govern the 'strength' of regularization in the two formulations) is non-monotonic and, more specifically, it has multiplicity. Two paths of critical points connecting the origin and an ordinary least squares (OLS) solution are highlighted. One is a main path starting at an OLS solution, and the other is a greedy path starting at the origin. Part of the greedy path can be constructed with a generalized Minkowskian gradient. The breakpoints of the greedy path coincide with the step-by-step solutions generated by using orthogonal matching pursuit (OMP), thereby establishing a direct link between OMP and Lp-regularized least squares.
Pilot study on optimal task scheduling in learning

Living in an information era where various online learning contents are rapidly available, students often learn with a combination of multiple learning tasks. In this work we explore the possibilities of using optimization theory to find the optimal trade-off between the time invested in two different completing learning tasks for each individual student. We show that the problem can be formulated as a linear programming problem, which can be efficiently solved to determine the optimal amount of time for each task. We also report our ongoing attempts to apply this theory to our Facebook Messenger chatbot software that can optimize the trade-off between learning and self-assessing in form of MCQs on the chatbot platform.
Deep Mean Field Games for Learning Optimal Behavior Policy of Large Populations

We consider the problem of representing a large population's behavior policy that drives the evolution of the population distribution over a discrete state space. A discrete time mean field game (MFG) is motivated as an interpretable model founded on game theory for understanding the aggregate effect of individual actions and predicting the temporal evolution of population distributions. We achieve a synthesis of MFG and Markov decision processes (MDP) by showing that a special MFG is reducible to an MDP. This enables us to broaden the scope of mean field game theory and infer MFG models of large real-world systems via deep inverse reinforcement learning. Our method learns both the reward function and forward dynamics of an MFG from real data, and we report the first empirical test of a mean field game model of a real-world social media population.
Generalized Neural Graph Embedding with Matrix Factorization

Recent advances in language modeling such as word2vec motivate a number of graph embedding approaches by treating random walk sequences as sentences to encode structural proximity in a graph. However, most of the existing principles of neural graph embedding do not incorporate auxiliary information such as node content flexibly. In this paper we take a matrix factorization perspective of graph embedding which generalizes to structural embedding as well as content embedding in a natural way. For structure embedding, we validate that the matrix we construct and factorize preserves the high-order proximities of the graph. Label information can be further integrated into the matrix via the process of random walk sampling to enhance the quality of embedding. In addition, we generalize the Skip-Gram Negative Sampling model to integrate the content of the graph in a matrix factorization framework. As a consequence, graph embedding can be learned in a unified framework integrating graph structure and node content as well as label information simultaneously. We demonstrate the efficacy of the proposed model with the tasks of semi-supervised node classification and link prediction on a variety of real-world benchmark network datasets.
Some Difficulties of Learning to Program

This article is a brief introduction to some of the issues that teachers of programming may find helpful. It starts by presenting a fairly idiosyncratic view of teaching programming which makes use of mechanistic analogies and points out some of the pitfalls. The article goes on to examine certain errors based on the misapplication of analogies as well as certain interaction errors. The main emphasis is on the notional machine both at the general level of understanding (and misunderstanding) the relationship of the terminal to the computer as such, as well as at the more specific level of understanding assignment. Notation and mistakes that poorly-designed languages can induce novices to commit are discussed.
Multi-Level ResNets with Stacked SRUs for Action Recognition

Inspiring by the fact that the enormous breakthrough convolutional networks consistently make in image classification while most existing works are either low efficiency or hard to optimized, we propose multiple level residual networks with stacked simple recurrent units(R-SRU) model trained end-to-end that ResNets learn spatial information from frame appearances and stacked SRUs learn temporal dynamics from video sequences, both deep in spatial and temporal. We investigate the effect of diverse hyper-parameter settings aiming at recommending researchers the better choice of hyper-parameters for using SRUs. Additionally, we compare low-, mid-, high-level features produced by ResNets and combine multi-level features to pass it through SRUs with various time pooling manners after that, experimentally demonstrating the extent of contribution of each level features to action recognition. Specifically, we are the first to apply SRU to distinguish actions. A series of experiments is carried out on two standard benchmarks: HMDB-51 and UCF-101 dataset. Experimental results illustrate that R-SRU outperforms the majority of methods which only take RGB data as input and obtain competitive performances with the state-of-the-art, achieving 51.31% on HMDB-51 and 81.38% on UCF-101.
Learning to Follow Language Instructions with Adversarial Reward Induction

Recent work has shown that deep reinforcement-learning agents can learn to follow language-like instructions from infrequent environment rewards. However, for many real-world natural language commands that involve a degree of underspecification or ambiguity, such as tidy the room, it would be challenging or impossible to program an appropriate reward function. To overcome this, we present a method for learning to follow commands from a training set of instructions and corresponding example goal-states, rather than an explicit reward function. Importantly, the example goal-states are not seen at test time. The approach effectively separates the representation of what instructions require from how they can be executed. In a simple grid world, the method enables an agent to learn a range of commands requiring interaction with blocks and understanding of spatial relations and underspecified abstract arrangements. We further show the method allows our agent to adapt to changes in the environment without requiring new training examples.
Low Resource Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers

In this paper, we present a black-box attack against API call based machine learning malware classifiers. We generate adversarial examples combining API call sequences and static features (e.g., printable strings) that will be misclassified by the classifier without affecting the malware functionality. Our attack only requires access to the predicted label of the attacked model (without the confidence level) and minimizes the number of target classifier queries. We evaluate the attack's effectiveness against many classifiers such as RNN variants, DNN, SVM, GBDT, etc. We show that the attack requires fewer queries and less knowledge about the attacked model's architecture than other existing black-box attacks. We also implement BADGER, a software framework to recraft any malware binary so that it won't be detected by classifiers, without access to the malware source code. Finally, we discuss the robustness of this attack to existing defense mechanisms.
Learning Driving Models with a Surround-View Camera System and a Route Planner

For people, having a rear-view mirror and side-view mirrors is vital for safe driving. They deliver a better view of what happens around the car. Human drivers also heavily exploit their mental map for navigation. Nonetheless, several methods have been published that learn driving models with only a front-facing camera and without a route planner. This lack of information renders the self-driving task quite intractable. Hence, we investigate the problem with a more realistic setting, which consists of a surround-view camera system with eight cameras, a route planner, and a CAN bus reader. In particular, we develop a sensor setup that provides data for a 360-degree view of the area surrounding the vehicle, the driving route to the destination, and the low-level driving maneuvers (e.g. steering angle and speed) by human drivers. With such sensor setup we collect a new driving dataset, covering diverse driving scenarios and varying weather/illumination conditions. Finally, we learn a novel driving model by integrating information from the surround-view cameras and the route planner. Two route planners are exploited: one based on OpenStreetMap and the other on TomTom Maps. The route planners are exploited in two ways: 1) by representing the planned routes as a stack of GPS coordinates, and 2) by rendering the planned routes on a map and recording the progression into a video. Our experiments show that: 1) 360-degree surround-view cameras help avoid failures made with a single front-view camera for the driving task; and 2) a route planner helps the driving task significantly. We acknowledge that our method is not the best-ever driving model, but that is not our focus. Rather, it provides a strong basis for further academic research, especially on driving relevant tasks by integrating information from street-view images and the planned driving routes. Code and data will be made available.
On the Complexity of the Weighted Fussed Lasso

The solution path of the 1D fused lasso for an ndimensional input is piecewise linear with O(n) segments [1], [2]. However, existing proofs of this bound do not hold for the weighted fused lasso. At the same time, results for the generalized lasso, of which the weighted fused lasso is a special case, allow Ω(3) segments [3]. In this paper, we prove that the number of segments in the solution path of the weighted fused lasso is O(n), and for some instances Ω(n). We also give a new, very simple, proof of the O(n) bound for the fused lasso.