Miracle-dz/UniFAR-FTUs huggingface dataset:
Attention-Based End-to-End Speech Recognition in Mandarin

Recently, there has been an increasing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments. In this paper, we explore the use of attention-based encoder-decoder model for Mandarin speech recognition and to the best of our knowledge, achieve the first promising result. We reduce the source sequence length by skipping frames and regularize the weights for better generalization and convergence. Moreover, we investigate the impact of varying attention mechanism (convolutional attention and attention smoothing) and the correlation between the performance of the model and the width of beam search. On the MiTV dataset, we achieve a character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% without using any lexicon or language model. While together with a trigram language model, we reach 2.81% CER and 5.77% SER.
Incremental View Maintenance for Nested-Relational Databases

Incremental view maintenance is an essential tool for speeding up the processing of large, locally changing workloads. Its fundamental challenge is to ensure that changes are propagated from input to output more efficiently than via recomputation. We formalize this requirement for positive nested relational algebra (NRA+) on bags and we propose a transformation deriving deltas for any expression in the language. The main difficulty in maintaining nested queries lies in the inability to express within NRA+ the efficient updating of inner bags, i.e., without completely replacing the tuples that contain them. To address this problem, we first show how to efficiently incrementalize IncNRA+, a large fragment of NRA+ whose deltas never generate inner bag updates. We then provide a semantics-preserving transformation that takes any nested query into a collection of IncNRA+ queries. This constitutes the first static solution for the efficient incremental processing of languages with nested collections. Furthermore, we show that the state-of-the-art technique of recursive IVM, originally developed for positive relational algebra with aggregation, also extends to nested queries. Finally, we generalize our static approach for the efficient incrementalization of NRA+ to a family of simply-typed lambda calculi, given that its primitives are themselves efficiently incrementalizable.
Stochastic Planning with Conflicting Temporal Logic Goals

We propose algorithms for agents in Markov Decision Processes that plan to maximally satisfy multiple, potentially conflicting, temporal logic goals. These agents (1) satisfy as many goals as possible; (2) prioritize more important goals; and (3) violate goals, if necessary, for the minimum possible duration. We demonstrate the behaviors of such agents in two simple domains.
Differentially Private Histograms for Range-Sum Queries: A Modular Approach

We focus on the problem of differentially private histogram publication, for range-sum query answering. Specifically, we derive a histogram from a given dataset, such that (i) it satisfies $\epsilon$-differential privacy, and (ii) it achieves high utility for queries that request the sum of contiguous histogram bins. Existing schemes are distinguished into two categories: fast but oblivious to utility optimizations that exploit the data characteristics, and data-aware but slow. We are the first to address this problem with emphasis on both efficiency and utility. Towards this goal, we formulate a principled approach, which defines a small set of simple modules, based on which we can devise a variety of more complex schemes. We first express the state-of-the-art methods in terms of these modules, which allows us to identify the performance bottlenecks. Next, we design novel efficient and effective schemes based on non-trivial module combinations. We experimentally evaluate all mechanisms on three real datasets with diverse characteristics, and demonstrate the benefits of our proposals over previous work.
Multiple Object Tracking: A Review

Multiple Object Tracking (MOT) is an important computer vision task which has gained increasing attention due to its academic and commercial potential. Although different kinds of approaches have been proposed to tackle this problem, it still has many issues unsolved. For example, factors such as continuous appearance changes and severe occlusions result in difficulties for the task. In order to help the readers understand and learn this topic, we contribute a comprehensive and systematic review. We review the recent advances in various aspects about this topic and propose some interesting directions for future research. To our best knowledge, there has not been any review about this topic in the community. The main contribution of this review is threefold: 1) All key aspects in the multiple object tracking system, including what scenarios the researchers are working on, how their work can be categorized, what needs to be considered when developing a MOT system and how to evaluate a MOT system, are discussed in a clear structure. This review work could not only provide researchers, especially new comers to the topic of MOT, a general understanding of the state-of-the-arts, but also help them to comprehend the aspects of a MOT system and the inter-connected aspects. 2) Instead of listing and summarizing individual publications, we categorize the approaches in the key aspects involved in a MOT system. In each aspect, the methods are divided into different groups and each group is discussed in details for the principles, advances and drawbacks. 3) We provide some potential directions with insights for MOT, which are still open issues and need more research efforts. This would be helpful for researchers to identify further interesting problems.
Handling Occlusions and Sparse Textures in a Deformable Surface Tracking Framework

Deformable surface tracking from monocular images is a well-known under-constrained problem. Occlusions often make the task even more challenging, and can lead current methods to fail if the surface is not sufficiently textured. In this work, we explicitly address the problem of 3D reconstruction of poorly textured, occluded surfaces, proposing a framework based on a template-matching approach that scales dense robust features by a relevancy score. Our approach is extensively compared to current methods employing both local feature matching and dense template alignment. We test on standard datasets as well as on a new dataset (that will be made publicly available) of a sparsely textured, occluded surface. Our framework achieves state-of-the-art results for both well and poorly textured, occluded surfaces.
Persisting RB-Tree into NVM in a Consistency Perspective

Byte-addressable non-volatile memory (NVM) is going to reshape conventional computer systems. With advantages of low latency, byte-addressability, and non-volatility, NVM can be directly put on the memory bus to replace DRAM. As a result, both system and application softwares have to be adjusted to perceive the fact that the persistent layer moves up to the memory. However, most of the current in-memory data structures will be problematic with consistency issues if not well tuned with NVM. This article places emphasis on an important in-memory structure that is widely used in computer systems, i.e., the Red/Black-tree (RB-tree). Since it has a long and complicated update process, the RB-tree is prone to inconsistency problems with NVM. This article presents an NVM-compatible consistent RB-tree with a new technique named cascade-versioning. The proposed RB-tree (i) is all-time consistent and scalable and (ii) needs no recovery procedure after system crashes. Experiment results show that the RB-tree for NVM not only achieves the aim of consistency with insignificant spatial overhead but also yields comparable performance to an ordinary volatile RB-tree.
Phase Retrieval Using Structured Sparsity: A Sample Efficient Algorithmic Framework

We consider the problem of recovering a signal $\mathbf{x}^* \in \mathbf{R}^n$, from magnitude-only measurements, $y_i = |\left\langle\mathbf{a}_i,\mathbf{x}^*\right\rangle|$ for $i=\{1,2,\ldots,m\}$. This is a stylized version of the classical phase retrieval problem, and is a fundamental challenge in bio-imaging systems, astronomical imaging, and speech processing. It is well known that the above problem is ill-posed, and therefore some additional assumptions on the signal and/or the measurements are necessary. In this paper, we first study the case where the underlying signal $\mathbf{x}^*$ is $s$-sparse. We develop a novel recovery algorithm that we call Compressive Phase Retrieval with Alternating Minimization, or CoPRAM. Our algorithm is simple and be obtained via a natural combination of the classical alternating minimization approach for phase retrieval with the CoSaMP algorithm for sparse recovery. Despite its simplicity, we prove that our algorithm achieves a sample complexity of $O(s^2 \log n)$ with Gaussian measurements $\mathbf{a}_i$, which matches the best known existing results; moreover, it also demonstrates linear convergence in theory and practice. Additionally, it requires no extra tuning parameters other than the signal sparsity level $s$. We then consider the case where the underlying signal $\mathbf{x}^*$ arises from structured sparsity models. We specifically examine the case of block-sparse signals with uniform block size of $b$ and block sparsity $k=s/b$. For this problem, we design a recovery algorithm that we call Block CoPRAM that further reduces the sample complexity to $O(ks \log n)$. For sufficiently large block lengths of $b=\Theta(s)$, this bound equates to $O(s \log n)$. To our knowledge, this constitutes the first end-to-end algorithm for phase retrieval where the Gaussian sample complexity has a sub-quadratic dependence on the signal sparsity level.
Infinite Choices of Data Aggregations with Linear Number of Keys

Privacy-preserving data aggregation has long been a hot research issue. It is becoming increasingly important due to the widespread data collection for various analysis purposes. In this paper, we present a novel arithmetic protocol which computes sum and product of n individuals’ input values without disclosing them, which is in turn used to develop an efficient accurate model to aggregate the data in a privacy-preserving manner. Unlike other approaches, our model initiates from an environment without secure communication channel but is robust to the eavesdrop attacks, and it does not rely on a trusted third party either. After the keys are prepared, only 1 communication round is needed to conduct each aggregation while some approaches require O(n) rounds. Notably, we allow any subset of n participants to privately conduct accurate data aggregation with only θ(n) keys while similar works let every participant generate and hold θ(2) keys or more.
Local Monotonic Attention Mechanism for End-to-End Speech Recognition

Recently, sequence-to-sequence model by using encoder-decoder neural network has gained popularity for automatic speech recognition (ASR). The architecture commonly uses an attentional mechanism which allows the model to learn alignments between source speech sequence and target text sequence. Most attentional mechanisms used today is based on a global attention property which requires a computation of a weighted summarization of the whole input sequence generated by encoder states. However, it is computationally expensive and often produces misalignment on the longer input sequence. Furthermore, it does not fit with monotonous or left-to-right nature in speech recognition task. In this paper, we propose a novel attention mechanism that has local and monotonic properties. Various ways to control those properties are also explored. Experimental results demonstrate that encoder-decoder based ASR with local monotonic attention could achieve significant performance improvements and reduce the computational complexity in comparison with the one that used the standard global attention architecture.
Tensor Product Generation Networks

We present a new tensor product generation network (TPGN) that generates natural language descriptions for images. The model has a novel architecture that instantiates a general framework for encoding and processing symbolic structure through neural network computation. This framework is built on Tensor Product Representations (TPRs). We evaluated the proposed TPGN on the MS COCO image captioning task. The experimental results show that the TPGN outperforms the LSTM based state-of-the-art baseline with a significant margin. Further, we show that our caption generation model can be interpreted as generating sequences of grammatical categories and retrieving words by their categories from a plan encoded as a distributed representation.
The Role of Limited Transmitter Cooperation in Secure Communication over the Interference Channel

This work presents novel achievable schemes and outer bound s for the2-user symmetric interference channel (IC) with limited-rate transmitter cooperation an d secrecy constraints at the receivers. First, a deterministic equivalent model of 2-user Gaussian symmetric IC (GSIC) with transmitter cooper ation is studied. Achievable schemes are proposed for the determi nistic case, which use a combination of interference cancelation, relaying of the other user’s dat a bits, time sharing, and transmission of random bits, depending on the rate of the cooperative link and the re lativ strengths of the signal and the interference. Outer bounds on the secrecy rate are derived, under different assumptions of providing side information to receivers and partitioning the encoded mess age/output depending on the relative strength of the signal and the interference. Using the intuitions gai ned from the deterministic case, achievable schemes and outer bounds are obtained for the Gaussian case. The proposed achievable scheme uses a combination of Marton’s coding scheme and stochastic enco di g along with dummy information transmission. The results illustrate the benefits of limite d transmitter cooperation in facilitating secure communications over 2-user interference channels.
A Survey of Network Traffic Anonymisation Techniques and Implementations

Many networking research activities are dependent on the availability of network captures. Even outside academic research, there is a need for sharing network captures to cooperate on threat assessments or for debugging. However, most network captures cannot be shared due to privacy concerns. Anonymisation of network captures has been a subject of research for quite some time, and many different techniques exist. In this article, we present an overview of the currently available techniques and implementations for network capture anonymisation. There have been many advances in the understanding of anonymisation and cryptographic methods, which have changed the perspective on the effectiveness of many anonymisation techniques. However, these advances, combined with the increase of computational abilities, may have also made it feasible to perform anonymisation in real time. This may make it easier to collect and distribute network captures both for research and for other applications. This article surveys the literature over the period of 1998–2017 on network traffic anonymisation techniques and implementations. The aim is to provide an overview of the current state of the art and to highlight how advances in related fields have shed new light on anonymisation and pseudonimisation methodologies. The few currently maintained implementations are also reviewed. Last, we identify future research directions to enable easier sharing of network traffic, which in turn can enable new insights in network traffic analysis.
Is the Online Discussion Manipulated? Quantifying the Online Discussion Authenticity within Online Social Media

Online social media (OSM) has a great influence in todays' world. Some individuals view OSM as fertile ground for abuse and use it to disseminate misinformation, political propaganda, slander competitors, and spread spam. The crowdturfing industry employs large numbers of bots and human workers to manipulate OSM and misrepresent public opinion. The detection of online discussion topics manipulated by OSM abusers is an emerging problem attracting significant attention. In this paper we propose an approach for quantifying the authenticity of online discussions based on the similarity of OSM accounts participating in the discussion to known abusers and legitimate accounts. Our method uses multiple similarity functions for the analysis and classification of OSM accounts. The proposed methods are demonstrated using Twitter data collected for this study and previously published Arabic Honeypots data. The former includes manually labeled accounts and abusers who participated in crowdturfing platforms. Demonstration of the topic's authenticity, derived from account similarity functions, shows that the suggested approach is effective for discriminating between topics that were strongly promoted by abusers and topics that attracted authentic public interest.
Cost-aware Vulnerability Prediction: the HARMLESS Approach

Society needs more secure software. But predicting vulnerabilities is difficult and existing methods are not applied in practical use due to various limitations. The goal of this paper is to design a vulnerability prediction method in a cost-aware manner so that it can balance the percentage of vulnerabilities found against the cost of human effort on security review and test. To this purpose, this paper presents HARMLESS, an incremental vulnerability prediction tool. HARMLESS is an active learner that (a) builds a support vector machine on the source code files reviewed to date; then (b) suggests what other source code files might have vulnerabilities and need to be reviewed next. A unique feature of HARMLESS is that HARMLESS can estimate the number of remaining vulnerabilities. To the best of our knowledge, HARMLESS is the first tool providing such estimation in the arena of vulnerability prediction. Using that estimator, HARMLESS can guide the security review and test to any level of desired recall, i.e. percentage of vulnerabilities found. In experiments on a case study of Mozilla Firefox project, HARMLESS found 90, 95, 99% of known vulnerabilities by reviewing and testing 26, 33, 45% of the source code files, respectively.
Monitoring tool usage in cataract surgery videos using boosted convolutional and recurrent neural networks

This paper investigates the automatic monitoring of tool usage during a surgery, with potential applications in report generation, surgical training and real-time decision support. Two surgeries are considered: cataract surgery, the most common surgical procedure, and cholecystectomy, one of the most common digestive surgeries. Tool usage is monitored in videos recorded either through a microscope (cataract surgery) or an endoscope (cholecystectomy). Following state-of-the-art video analysis solutions, each frame of the video is analyzed by convolutional neural networks (CNNs) whose outputs are fed to recurrent neural networks (RNNs) in order to take temporal relationships between events into account. Novelty lies in the way those CNNs and RNNs are trained. Computational complexity prevents the end-to-end training of "CNN+RNN" systems. Therefore, CNNs are usually trained first, independently from the RNNs. This approach is clearly suboptimal for surgical tool analysis: many tools are very similar to one another, but they can generally be differentiated based on past events. CNNs should be trained to extract the most useful visual features in combination with the temporal context. A novel boosting strategy is proposed to achieve this goal: the CNN and RNN parts of the system are simultaneously enriched by progressively adding weak classifiers (either CNNs or RNNs) trained to improve the overall classification accuracy. Experiments were performed in a dataset of 50 cataract surgery videos and a dataset of 80 cholecystectomy videos. Very good classification performance are achieved in both datasets: tool usage could be labeled with an average area under the ROC curve of $A_z = 0.9961$ and $A_z = 0.9939$, respectively, in offline mode (using past, present and future information), and $A_z = 0.9957$ and $A_z = 0.9936$, respectively, in online mode (using past and present information only).
Photo-realistic Face Images Synthesis for Learning-based Fine-scale 3D Face Reconstruction

—With the powerfulness of convolution neural networks (CNN), CNN based detailed face reconstruction has recently shown promising performance in reconstructing detailed face shape from a single image. The key to the success of CNN-based methods lies in a large number of labelled data. However, there is no such dataset available that can provide large-scale face images with their corresponding detailed 3D face geometry. The state-of-the-art learning based face reconstruction method synthesizes the training data by using a coarse morphable face model, where their synthesized face images are not photo-realistic. In this paper, we propose a novel data generation method by rendering a large number of photo-realistic face images with different attributes based on inverse rendering. Furthermore, we also construct a ﬁne-detailed face image dataset by transferring different scales of details from one image to another 1 . With the nicely constructed datasets, we are able to train two cascaded CNNs in a coarse-to-ﬁne manner. Extensive experimental results demonstrate that our method can reconstruct 3D face shapes with geometry details from only one image, and demonstrates the robustness of our method to pose, expression and lighting.
Object Discovery By Generative Adversarial & Ranking Networks

The deep generative adversarial networks (GAN) recently have been shown to be promising for different computer vision applications, like image editing, synthesizing high resolution images, generating videos, etc. These networks and the corresponding learning scheme can handle various visual space mappings. We approach GANs with a novel training method and learning objective, to discover multiple object instances for three cases: 1) synthesizing a picture of a specific object within a cluttered scene; 2) localizing different categories in images for weakly supervised object detection; and 3) improving object discovery in object detection pipelines. A crucial advantage of our method is that it learns a new deep similarity metric, to distinguish multiple objects in one image. We demonstrate that the network can act as an encoder-decoder generating parts of an image which contain an object, or as a modified deep CNN to represent images for object detection and discovery. Our ranking GAN offers a novel way to search through images for visual patterns. We have conducted experiments for different scenarios and demonstrate the method performance using the MS-COCO and PASCAL VOC datasets.