OpenRaiser/Intern-Atlas huggingface dataset:
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning
Venue: arXiv cs.LG 2019
Year: 2019

We assume that we are given a time series of data from a dynamical system and our task is to learn the flow map of the dynamical system. We present a collection of results on how to enforce constraints coming from the dynamical system in order to accelerate the training of deep neural networks to represent the flow map of the system as well as increase their predictive ability. In particular, we provide ways to enforce constraints during training for all three major modes of learning, namely supervised, unsupervised and reinforcement learning. In general, the dynamic constraints need to include terms which are analogous to memory terms in model reduction formalisms. Such memory terms act as a restoring force which corrects the errors committed by the learned flow map during prediction. For supervised learning, the constraints are added to the objective function. For the case of unsupervised learning, in particular generative adversarial networks, the constraints are introduced by augmenting the input of the discriminator. Finally, for the case of reinforcement learning and in particular actor-critic methods, the constraints are added to the reward function. In addition, for the reinforcement learning case, we present a novel approach based on homotopy of the action-value function in order to stabilize and accelerate training. We use numerical results for the Lorenz system to illustrate the various constructions.
A Structure-Tensor Approach to Integer Matrix Completion with Applications to Differentiated Energy Services
Venue: arXiv cs.SY 2019
Year: 2019

Efficient resource allocation is one of the main driving forces of human civilizations. Of the many existing approaches to resource allocation, matrix completion is one that is frequently applied. In this paper, we investigate a special type of matrix completion problem concerning the class of $(0,1)$-matrices with given row/column sums and certain zeros prespecified. We provide a necessary and sufficient condition under which such a class is nonempty. The condition is stated in the form of the nonnegativity of a structure tensor constructed from the information regarding the given row/column sums and fixed zeros. Moreover, we show that a more general matrix completion problem can be studied in a similar manner, namely that involving the class of nonnegative integer matrices with prescribed row/column sums, predetermined zeros, and different bounds across the rows. To illustrate the utility of our results, we apply them to demand response applications in smart grids. Specifically, we address two adequacy problems in differentiated energy services, namely, the problems of supply/demand matching and minimum purchase profile.
Discrete Logarithmic Fuzzy Vault Scheme
Venue: arXiv cs.CR 2019
Year: 2019

In this paper a three fuzzy vault schemes which integrated with discrete logarithmic encryption scheme are proposed. In the first scheme, the message m is encoded with discrete logarithmic encryption scheme using randomly generated identity key \k{appa} for every message and then divided into non-overlapping segments. In the second scheme, the message is divided into non-overlapping segments and each segment is encoded with discrete logarithmic encryption scheme using the randomly generated identity key \k{appa}. In the third scheme, the message is divided into non-overlapping segments where even segments are encoded with identity key \k{appa}_even and odd segments are encoded with identity key \k{appa}_odd. Identity keys \k{appa}_even and \k{appa}_odd are randomly generated for every message. Finally, the encoded segments are declared as coefficients of a polynomial of specific degree. In all proposed schemes, elements of locking set A are used as X-coordinate values to compute evaluations of the polynomial by projecting elements of A onto points lying on the polynomial. A large number of random chaff points that do not lie on the polynomial are added to create noise to hide the encoded segments. Security analysis has shown the proposed scheme enjoys provable security over classical fuzzy vaults.
Developing secure Bitcoin contracts with BitML
Venue: arXiv cs.PL 2019
Year: 2019

We present a toolchain for developing and verifying smart contracts that can be executed on Bitcoin. The toolchain is based on BitML, a recent domain-specific language for smart contracts with a computationally sound embedding into Bitcoin. Our toolchain automatically verifies relevant properties of contracts, among which liquidity, ensuring that funds do not remain frozen within a contract forever. A compiler is provided to translate BitML contracts into sets of standard Bitcoin transactions: executing a contract corresponds to appending these transactions to the blockchain. We assess our toolchain through a benchmark of representative contracts.
Methodically Defeating Nintendo Switch Security
Venue: arXiv cs.CR 2019
Year: 2019

We explain, step by step, how we strategically circumvented the Nintendo Switch's system security, from basic userland code execution, to undermining and exposing the secrets of the security co-processor. To this end, we've identified and utilized two distinct analysis procedures. The software-based analysis suffices for reverse-engineering the userland and operating system services, and is necessary for a general architectural understanding of the software systems in the Nintendo Switch. While this method is extremely powerful and provides significant leverage over the control of the system and its software security, a hardware-based method was devised, which employs analysis of the trusted bootstrap code in ROM. This strategy was essential for the goal of defeating the hardware root of trust. Together, these two vectors provide essential insight required to instance a chain of attacks, in order to gain ROP code execution from the context of a high-security mode of a secure co-processor of a running system, thus allowing us to demonstrate a multi-faceted approach on attacking secure, embedded devices in an unfamiliar and novel environment.
SAWNet: A Spatially Aware Deep Neural Network for 3D Point Cloud Processing
Venue: arXiv cs.CV 2019
Year: 2019

Deep neural networks have established themselves as the state-of-the-art methodology in almost all computer vision tasks to date. But their application to processing data lying on non-Euclidean domains is still a very active area of research. One such area is the analysis of point cloud data which poses a challenge due to its lack of order. Many recent techniques have been proposed, spearheaded by the PointNet architecture. These techniques use either global or local information from the point clouds to extract a latent representation for the points, which is then used for the task at hand (classification/segmentation). In our work, we introduce a neural network layer that combines both global and local information to produce better embeddings of these points. We enhance our architecture with residual connections, to pass information between the layers, which also makes the network easier to train. We achieve state-of-the-art results on the ModelNet40 dataset with our architecture, and our results are also highly competitive with the state-of-the-art on the ShapeNet part segmentation dataset and the indoor scene segmentation dataset. We plan to open source our pre-trained models on github to encourage the research community to test our networks on their data, or simply use them for benchmarking purposes.
Learning to Memorize in Neural Task-Oriented Dialogue Systems
Venue: arXiv cs.CL 2019
Year: 2019

In this thesis, we leverage the neural copy mechanism and memory-augmented neural networks (MANNs) to address existing challenge of neural task-oriented dialogue learning. We show the effectiveness of our strategy by achieving good performance in multi-domain dialogue state tracking, retrieval-based dialogue systems, and generation-based dialogue systems. We first propose a transferable dialogue state generator (TRADE) that leverages its copy mechanism to get rid of dialogue ontology and share knowledge between domains. We also evaluate unseen domain dialogue state tracking and show that TRADE enables zero-shot dialogue state tracking and can adapt to new few-shot domains without forgetting the previous domains. Second, we utilize MANNs to improve retrieval-based dialogue learning. They are able to capture dialogue sequential dependencies and memorize long-term information. We also propose a recorded delexicalization copy strategy to replace real entity values with ordered entity types. Our models are shown to surpass other retrieval baselines, especially when the conversation has a large number of turns. Lastly, we tackle generation-based dialogue learning with two proposed models, the memory-to-sequence (Mem2Seq) and global-to-local memory pointer network (GLMP). Mem2Seq is the first model to combine multi-hop memory attention with the idea of the copy mechanism. GLMP further introduces the concept of response sketching and double pointers copying. We show that GLMP achieves the state-of-the-art performance on human evaluation.
DivGraphPointer: A Graph Pointer Network for Extracting Diverse Keyphrases
Venue: arXiv cs.CL 2019
Year: 2019

Keyphrase extraction from documents is useful to a variety of applications such as information retrieval and document summarization. This paper presents an end-to-end method called DivGraphPointer for extracting a set of diversified keyphrases from a document. DivGraphPointer combines the advantages of traditional graph-based ranking methods and recent neural network-based approaches. Specifically, given a document, a word graph is constructed from the document based on word proximity and is encoded with graph convolutional networks, which effectively capture document-level word salience by modeling long-range dependency between words in the document and aggregating multiple appearances of identical words into one node. Furthermore, we propose a diversified point network to generate a set of diverse keyphrases out of the word graph in the decoding process. Experimental results on five benchmark data sets show that our proposed method significantly outperforms the existing state-of-the-art approaches.
A simple but energy-efficient HVAC control synthesis for data centers
Venue: arXiv cs.SY 2019
Year: 2019

The air conditioning management of data centers, a key question with respect to energy saving, is here tackled via the recent model-free control synthesis. Mathematical modeling becomes useless in this approach. The tuning of the corresponding intelligent proportional controller is straightforward. Computer simulations show excellent tracking performances in various realistic situations, like CPU load or temperature changes.
The teaching complexity of erasing pattern languages with bounded variable frequency
Venue: arXiv cs.FL 2019
Year: 2019

Patterns provide a concise, syntactic way of describing a set of strings, but their expressive power comes at a price: a number of fundamental decision problems concerning (erasing) pattern languages, such as the membership problem and inclusion problem, are known to be NP-complete or even undecidable, while the decidability of the equivalence problem is still open; in learning theory, the class of pattern languages is unlearnable in models such as the distribution-free (PAC) framework (if $\mathcal{P}/poly \neq \mathcal{NP}/poly$). Much work on the algorithmic learning of pattern languages has thus focussed on interesting subclasses of patterns for which positive learnability results may be achieved. A natural restriction on a pattern is a bound on its variable frequency -- the maximum number $m$ such that some variable occurs exactly $m$ times in the pattern. This paper examines the effect of limiting the variable frequency of all patterns belonging to a class $Π$ on the worst-case minimum number of labelled examples needed to uniquely identify any pattern of $Π$ in cooperative teaching-learning models. Two such models, the teaching dimension model as well as the preference-based teaching model, will be considered.
Online reactions to the 2017 'Unite the Right' rally in Charlottesville: Measuring polarization in Twitter networks using media followership
Venue: arXiv cs.SI 2019
Year: 2019

We study the Twitter conversation following the August 2017 `Unite the Right' rally in Charlottesville, Virginia, using tools from network analysis and data science. We use media followership on Twitter and principal component analysis (PCA) to compute a `Left'/`Right' media score on a one-dimensional axis to characterize nodes. We then use these scores, in concert with retweet relationships, to examine the structure of a retweet network of approximately 300,000 accounts that communicated with the #Charlottesville hashtag. The retweet network is sharply polarized, with an assortativity coefficient of 0.8 with respect to the sign of the media PCA score. Community detection using two approaches, a Louvain method and InfoMap, yields largely homogeneous communities in terms of Left/Right node composition. When comparing tweet content, we find that tweets about `Trump' were widespread in both the Left and Right, though the accompanying language was unsurprisingly different. Nodes with large degrees in communities on the Left include accounts that are associated with disparate areas, including activism, business, arts and entertainment, media, and politics. Support of Donald Trump was a common thread among the Right communities, connecting communities with accounts that reference white-supremacist hate symbols, communities with influential personalities in the alt-right, and the largest Right community (which includes the Twitter account FoxNews).
Sparse Transfer Learning via Winning Lottery Tickets
Venue: arXiv cs.LG 2019
Year: 2019

The recently proposed Lottery Ticket Hypothesis of Frankle and Carbin (2019) suggests that the performance of over-parameterized deep networks is due to the random initialization seeding the network with a small fraction of favorable weights. These weights retain their dominant status throughout training -- in a very real sense, this sub-network "won the lottery" during initialization. The authors find sub-networks via unstructured magnitude pruning with 85-95% of parameters removed that train to the same accuracy as the original network at a similar speed, which they call winning tickets. In this paper, we extend the Lottery Ticket Hypothesis to a variety of transfer learning tasks. We show that sparse sub-networks with approximately 90-95% of weights removed achieve (and often exceed) the accuracy of the original dense network in several realistic settings. We experimentally validate this by transferring the sparse representation found via pruning on CIFAR-10 to SmallNORB and FashionMNIST for object recognition tasks.
U-Net Based Multi-instance Video Object Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Multi-instance video object segmentation is to segment specific instances throughout a video sequence in pixel level, given only an annotated first frame. In this paper, we implement an effective fully convolutional networks with U-Net similar structure built on top of OSVOS fine-tuned layer. We use instance isolation to transform this multi-instance segmentation problem into binary labeling problem, and use weighted cross entropy loss and dice coefficient loss as our loss function. Our best model achieves F mean of 0.467 and J mean of 0.424 on DAVIS dataset, which is a comparable performance with the State-of-the-Art approach. But case analysis shows this model can achieve a smoother contour and better instance coverage, meaning it better for recall focused segmentation scenario. We also did experiments on other convolutional neural networks, including Seg-Net, Mask R-CNN, and provide insightful comparison and discussion.
Improving security and bandwidth efficiency of NewHope using error-correction schemes
Venue: arXiv cs.CR 2019
Year: 2019

Among many submissions to the NIST post-quantum cryptography (PQC) project, NewHope is a promising key encapsulation mechanism (KEM) based on the Ring-Learning with errors (Ring-LWE) problem. Since the most important factors to be considered for PQC are security and cost including bandwidth and time/space complexity, in this paper, by doing exact noise analysis and using Bose Chaudhuri Hocquenghem (BCH) codes, it is shown that the security and bandwidth efficiency of NewHope can be substantially improved. In detail, the decryption failure rate (DFR) of NewHope is recalculated by performing exact noise analysis, and it is shown that the DFR of NewHope has been too conservatively calculated. Since the recalculated DFR is much lower than the required $2^{-128}$, this DFR margin is exploited to improve the security up to 8.5 \% or the bandwidth efficiency up to 5.9 \% without changing the procedure of NewHope. The additive threshold encoding (ATE) used in NewHope is a simple error correcting code (ECC) robust to side channel attack, but its error-correction capability is relatively weak compared with other ECCs. Therefore, if a proper error-correction scheme is applied to NewHope, either security or bandwidth efficiency or both can be improved. Among various ECCs, BCH code has been widely studied for its application to cryptosystems due to its advantages such as no error floor problem. In this paper, the ATE and total noise channel are regarded as a super channel from an information-theoretic viewpoint. Based on this super channel analysis, various concatenated coding schemes of ATE and BCH code for NewHope have been investigated. Through numerical analysis, it is revealed that the security and bandwidth efficiency of NewHope are substantially improved by using the proposed error-correction schemes.
CERTIFAI: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models
Venue: arXiv cs.LG 2019
Year: 2019

As artificial intelligence plays an increasingly important role in our society, there are ethical and moral obligations for both businesses and researchers to ensure that their machine learning models are designed, deployed, and maintained responsibly. These models need to be rigorously audited for fairness, robustness, transparency, and interpretability. A variety of methods have been developed that focus on these issues in isolation, however, managing these methods in conjunction with model development can be cumbersome and timeconsuming. In this paper, we introduce a unified and model-agnostic approach to address these issues: Counterfactual Explanations for Robustness, Transparency, Interpretability, and Fairness of Artificial Intelligence models (CERTIFAI). Unlike previous methods in this domain, CERTIFAI is a general tool that can be applied to any black-box model and any type of input data. Given a model and an input instance, CERTIFAI uses a custom genetic algorithm to generate counterfactuals: instances close to the input that change the prediction of the model. We demonstrate how these counterfactuals can be used to examine issues of robustness, interpretability, transparency, and fairness. Additionally, we introduce CERScore, the first black-box model robustness score that performs comparably to methods that have access to model internals.
Actin droplet machine
Venue: arXiv cs.ET 2019
Year: 2019

The actin droplet machine is a computer model of a three-dimensional network of actin bundles developed in a droplet of a physiological solution, which implements mappings of sets of binary strings. The actin bundle network is conductive to travelling excitations, i.e. impulses. The machine is interfaced with an arbitrary selected set of k electrodes through which stimuli, binary strings of length k represented by impulses generated on the electrodes, are applied and responses are recorded. The responses are recorded in a form of impulses and then converted to binary strings. The machine's state is a binary string of length k: if there is an impulse recorded on the $i$th electrode, there is a `1' in the i-th position of the string, and `0' otherwise. We present a design of the machine and analyse its state transition graphs. We envisage that actin droplet machines could form an elementary processor of future massive parallel computers made from biopolymers.
Not All Parts Are Created Equal: 3D Pose Estimation by Modelling Bi-directional Dependencies of Body Parts
Venue: arXiv cs.CV 2019
Year: 2019

Not all the human body parts have the same~degree of freedom~(DOF) due to the physiological structure. For example, the limbs may move more flexibly and freely than the torso does. Most of the existing 3D pose estimation methods, despite the very promising results achieved, treat the body joints equally and consequently often lead to larger reconstruction errors on the limbs. In this paper, we propose a progressive approach that explicitly accounts for the distinct DOFs among the body parts. We model parts with higher DOFs like the elbows, as dependent components of the corresponding parts with lower DOFs like the torso, of which the 3D locations can be more reliably estimated. Meanwhile, the high-DOF parts may, in turn, impose a constraint on where the low-DOF ones lie. As a result, parts with different DOFs supervise one another, yielding physically constrained and plausible pose-estimation results. To further facilitate the prediction of the high-DOF parts, we introduce a pose-attribute estimation, where the relative location of a limb joint with respect to the torso, which has the least DOF of a human body, is explicitly estimated and further fed to the joint-estimation module. The proposed approach achieves very promising results, outperforming the state of the art on several benchmarks.
Abusive Language Detection in Online Conversations by Combining Content-and Graph-based Features
Venue: arXiv cs.IR 2019
Year: 2019

In recent years, online social networks have allowed worldwide users to meet and discuss. As guarantors of these communities, the administrators of these platforms must prevent users from adopting inappropriate behaviors. This verification task, mainly done by humans, is more and more difficult due to the ever growing amount of messages to check. Methods have been proposed to automatize this moderation process, mainly by providing approaches based on the textual content of the exchanged messages. Recent work has also shown that characteristics derived from the structure of conversations, in the form of conversational graphs, can help detecting these abusive messages. In this paper, we propose to take advantage of both sources of information by proposing fusion methods integrating content-and graph-based features. Our experiments on raw chat logs show that the content of the messages, but also of their dynamics within a conversation contain partially complementary information, allowing performance improvements on an abusive message classification task with a final F-measure of 93.26%.
Skeleton-Based Hand Gesture Recognition by Learning SPD Matrices with Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we propose a new hand gesture recognition method based on skeletal data by learning SPD matrices with neural networks. We model the hand skeleton as a graph and introduce a neural network for SPD matrix learning, taking as input the 3D coordinates of hand joints. The proposed network is based on two newly designed layers that transform a set of SPD matrices into a SPD matrix. For gesture recognition, we train a linear SVM classifier using features extracted from our network. Experimental results on a challenging dataset (Dynamic Hand Gesture dataset from the SHREC 2017 3D Shape Retrieval Contest) show that the proposed method outperforms state-of-the-art methods.