OpenRaiser/Intern-Atlas huggingface dataset:
Phonetically-Oriented Word Error Alignment for Speech Recognition Error Analysis in Speech Translation
Venue: arXiv cs.CL 2019
Year: 2019

We propose a variation to the commonly used Word Error Rate (WER) metric for speech recognition evaluation which incorporates the alignment of phonemes, in the absence of time boundary information. After computing the Levenshtein alignment on words in the reference and hypothesis transcripts, spans of adjacent errors are converted into phonemes with word and syllable boundaries and a phonetic Levenshtein alignment is performed. The aligned phonemes are recombined into aligned words that adjust the word alignment labels in each error region. We demonstrate that our Phonetically-Oriented Word Error Rate (POWER) yields similar scores to WER with the added advantages of better word alignments and the ability to capture one-to-many word alignments corresponding to homophonic errors in speech recognition hypotheses. These improved alignments allow us to better trace the impact of Levenshtein error types on downstream tasks such as speech translation.
Optimal Downlink Transmission for Cell Free SWIPT Massive MIMO Systems with Active Eavesdropping
Venue: arXiv cs.IT 2019
Year: 2019

This paper considers secure simultaneous wireless information and power transfer (SWIPT) in cell-free massive multiple-input multiple-output (MIMO) systems. The system consists of a large number of randomly (Poisson-distributed) located access points (APs) serving multiple information users (IUs) and an information-untrusted dual-antenna active energy harvester (EH). The active EH uses one antenna to legitimately harvest energy and the other antenna to eavesdrop information. The APs are networked by a centralized infinite backhaul which allows the APs to synchronize and cooperate via a central processing unit (CPU). Closed-form expressions for the average harvested energy (AHE) and a tight lower bound on the ergodic secrecy rate (ESR) are derived. The obtained lower bound on the ESR takes into account the IUs' knowledge attained by downlink effective precoded-channel training. Since the transmit power constraint is per AP, the ESR is nonlinear in terms of the transmit power elements of the APs and that imposes new challenges in formulating a convex power control problem for the downlink transmission. To deal with these nonlinearities, a new method of balancing the transmit power among the APs via relaxed semidefinite programming (SDP) which is proved to be rank-one globally optimal is derived. A fair comparison between the proposed cell-free and the colocated massive MIMO systems shows that the cell-free MIMO outperforms the colocated MIMO over the interval in which the AHE constraint is low and vice versa. Also, the cell-free MIMO is found to be more immune to the increase in the active eavesdropping power than the colocated MIMO.
Multi-Scale Body-Part Mask Guided Attention for Person Re-identification
Venue: arXiv cs.CV 2019
Year: 2019

Person re-identification becomes a more and more important task due to its wide applications. In practice, person re-identification still remains challenging due to the variation of person pose, different lighting, occlusion, misalignment, background clutter, etc. In this paper, we propose a multi-scale body-part mask guided attention network (MMGA), which jointly learns whole-body and part body attention to help extract global and local features simultaneously. In MMGA, body-part masks are used to guide the training of corresponding attention. Experiments show that our proposed method can reduce the negative influence of variation of person pose, misalignment and background clutter. Our method achieves rank-1/mAP of 95.0%/87.2% on the Market1501 dataset, 89.5%/78.1% on the DukeMTMC-reID dataset, outperforming current state-of-the-art methods.
Characterization of Glue Variables in CDCL SAT Solving
Venue: arXiv cs.AI 2019
Year: 2019

A state-of-the-art criterion to evaluate the importance of a given learned clause is called Literal Block Distance (LBD) score. It measures the number of distinct decision levels in a given learned clause. The lower the LBD score of a learned clause, the better is its quality. The learned clauses with LBD score of 2, called glue clauses, are known to possess high pruning power which are never deleted from the clause databases of the modern CDCL SAT solvers. In this work, we relate glue clauses to decision variables. We call the variables that appeared in at least one glue clause up to the current search state Glue Variables. We first show experimentally, by running the state-of-the-art CDCL SAT solver MapleLCMDist on benchmarks from SAT Competition-2017 and 2018, that branching decisions with glue variables are categorically more inference and conflict efficient than nonglue variables. Based on this observation, we develop a structure aware CDCL variable bumping scheme, which bumps the activity score of a glue variable based on its appearance count in the glue clauses that are learned so far by the search. Empirical evaluation shows effectiveness of the new method over the main track instances from SAT Competition 2017 and 2018.
Web Stereo Video Supervision for Depth Prediction from Dynamic Scenes
Venue: arXiv cs.CV 2019
Year: 2019

We present a fully data-driven method to compute depth from diverse monocular video sequences that contain large amounts of non-rigid objects, e.g., people. In order to learn reconstruction cues for non-rigid scenes, we introduce a new dataset consisting of stereo videos scraped in-the-wild. This dataset has a wide variety of scene types, and features large amounts of nonrigid objects, especially people. From this, we compute disparity maps to be used as supervision to train our approach. We propose a loss function that allows us to generate a depth prediction even with unknown camera intrinsics and stereo baselines in the dataset. We validate the use of large amounts of Internet video by evaluating our method on existing video datasets with depth supervision, including SINTEL, and KITTI, and show that our approach generalizes better to natural scenes.
Declarative Recursive Computation on an RDBMS, or, Why You Should Use a Database For Distributed Machine Learning
Venue: arXiv cs.DB 2019
Year: 2019

A number of popular systems, most notably Google's TensorFlow, have been implemented from the ground up to support machine learning tasks. We consider how to make a very small set of changes to a modern relational database management system (RDBMS) to make it suitable for distributed learning computations. Changes include adding better support for recursion, and optimization and execution of very large compute plans. We also show that there are key advantages to using an RDBMS as a machine learning platform. In particular, learning based on a database management system allows for trivial scaling to large data sets and especially large models, where different computational units operate on different parts of a model that may be too large to fit into RAM.
Out of the Box: A combined approach for handling occlusion in Human Pose Estimation
Venue: arXiv cs.CV 2019
Year: 2019

Human Pose estimation is a challenging problem, especially in the case of 3D pose estimation from 2D images due to many different factors like occlusion, depth ambiguities, intertwining of people, and in general crowds. 2D multi-person human pose estimation in the wild also suffers from the same problems - occlusion, ambiguities, and disentanglement of people's body parts. Being a fundamental problem with loads of applications, including but not limited to surveillance, economical motion capture for video games and movies, and physiotherapy, this is an interesting problem to be solved both from a practical perspective and from an intellectual perspective as well. Although there are cases where no pose estimation can ever predict with 100% accuracy (cases where even humans would fail), there are several algorithms that have brought new state-of-the-art performance in human pose estimation in the wild. We look at a few algorithms with different approaches and also formulate our own approach to tackle a consistently bugging problem, i.e. occlusions.
Abstract Interpretation under Speculative Execution
Venue: arXiv cs.PL 2019
Year: 2019

Analyzing the behavior of a program running on a processor that supports speculative execution is crucial for applications such as execution time estimation and side channel detection. Unfortunately, existing static analysis techniques based on abstract interpretation do not model speculative execution since they focus on functional properties of a program while speculative execution does not change the functionality. To fill the gap, we propose a method to make abstract interpretation sound under speculative execution. There are two contributions. First, we introduce the notion of virtual control flow to augment instructions that may be speculatively executed and thus affect subsequent instructions. Second, to make the analysis efficient, we propose optimizations to handle merges and loops and to safely bound the speculative execution depth. We have implemented and evaluated the proposed method in a static cache analysis for execution time estimation and side channel detection. Our experiments show that the new method, while guaranteed to be sound under speculative execution, outperforms state-of-the-art abstract interpretation techniques that may be unsound.
Indoor dense depth map at drone hovering
Venue: arXiv cs.CV 2019
Year: 2019

Autonomous Micro Aerial Vehicles (MAVs) gained tremendous attention in recent years. Autonomous flight in indoor requires a dense depth map for navigable space detection which is the fundamental component for autonomous navigation. In this paper, we address the problem of reconstructing dense depth while a drone is hovering (small camera motion) in indoor scenes using already estimated cameras and sparse point cloud obtained from a vSLAM. We start by segmenting the scene based on sudden depth variation using sparse 3D points and introduce a patch-based local plane fitting via energy minimization which combines photometric consistency and co-planarity with neighbouring patches. The method also combines a plane sweep technique for image segments having almost no sparse point for initialization. Experiments show, the proposed method produces better depth for indoor in artificial lighting condition, low-textured environment compared to earlier literature in small motion.
Optimal Approach for Image Recognition using Deep Convolutional Architecture
Venue: arXiv cs.CV 2019
Year: 2019

In the recent time deep learning has achieved huge popularity due to its performance in various machine learning algorithms. Deep learning as hierarchical or structured learning attempts to model high level abstractions in data by using a group of processing layers. The foundation of deep learning architectures is inspired by the understanding of information processing and neural responses in human brain. The architectures are created by stacking multiple linear or non-linear operations. The article mainly focuses on the state-of-art deep learning models and various real world applications specific training methods. Selecting optimal architecture for specific problem is a challenging task, at a closing stage of the article we proposed optimal approach to deep convolutional architecture for the application of image recognition.
Neuromorphic Computing through Time-Multiplexing with a Spin-Torque Nano-Oscillator
Venue: arXiv cs.ET 2019
Year: 2019

Fabricating powerful neuromorphic chips the size of a thumb requires miniaturizing their basic units: synapses and neurons. The challenge for neurons is to scale them down to submicrometer diameters while maintaining the properties that allow for reliable information processing: high signal to noise ratio, endurance, stability, reproducibility. In this work, we show that compact spin-torque nano-oscillators can naturally implement such neurons, and quantify their ability to realize an actual cognitive task. In particular, we show that they can naturally implement reservoir computing with high performance and detail the recipes for this capability.
Deductive Proof of Ethereum Smart Contracts Using Why3
Venue: arXiv cs.PL 2019
Year: 2019

A bug or error is a common problem that any software or computer program may encounter. It can occur from badly writing the program, a typing error or bad memory management. However, errors can become a significant issue if the unsafe program is used for critical systems. Therefore, formal methods for these kinds of systems are greatly required. In this paper, we use a formal language that performs deductive verification on an Ethereum Blockchain application based on smart contracts, which are self-executing digital contracts. Blockchain systems manipulate cryptocurrency and transaction information. Therefore , if a bug occurs in the blockchain, serious consequences such as a loss of money can happen. Thus, the aim of this paper is to propose a language dedicated to deductive verification, called Why3, as a new language for writing formal and verified smart contracts, thereby avoiding attacks exploiting such contract execution vulnerabilities. We first write a Why3 smart contracts program; next we formulate specifications to be proved as absence of RunTime Error properties and functional properties, then we verify the behavior of the program using the Why3 system. Finally we compile the Why3 contracts to the Ethereum Virtual Machine (EVM). Moreover, we give a set of generic mathematical statements that allows verifying functional properties suited to any type of smart contracts holding cryptocurrency, showing that Why3 can be a suitable language to write smart contracts. To illustrate our approach, we describe its application to a realistic industrial use case.
Joint Allocation Strategies of Power and Spreading Factors with Imperfect Orthogonality in LoRa Networks
Venue: arXiv cs.NI 2019
Year: 2019

The LoRa physical layer is one of the most promising Low Power Wide-Area Network (LPWAN) technologies for future Internet of Things (IoT) applications. It provides a flexible adaptation of coverage and data rate by allocating different Spreading Factors (SFs) and transmit powers to end-devices. We focus on improving throughput fairness while reducing energy consumption. Whereas most existing methods assume perfect SF orthogonality and ignore the harmful effects of inter-SF interferences, we formulate a joint SF and power allocation problem to maximize the minimum uplink throughput of end-devices, subject to co-SF and inter-SF interferences, and power constraints. This results into a mixed-integer non-linear optimization, which, for tractability, is split into two sub-problems: firstly, the SF assignment for fixed transmit powers, and secondly, the power allocation given the previously obtained assignment solution. For the first sub-problem, we propose a low-complexity many-to-one matching algorithm between SFs and end-devices. For the second one, given its intractability, we transform it using two types of constraints approximation: a linearized and a quadratic version. Our performance evaluation demonstrates that the proposed joint SF allocation and power optimization enables to drastically enhance various performance objectives such as throughput, fairness and power consumption, and that it outperforms baseline schemes.
Cybertwin: An Origin of Next Generation Network Architecture
Venue: arXiv cs.NI 2019
Year: 2019

With fast development of Internet of Everything (IoE) and its applications, the ever increasing mobile internet traffic and services bring unprecedented challenges including scalability, mobility, availability, and security which cannot be addressed by the current clean-slate network architecture. In this paper, a cybertwin based next generation network architecture is proposed to accomodate the evolution from end-to-end connection to cloud-to-end connection in the future network. As a digital representation of human or things in the virtual cyberspace, cybertwin serves in multiple capacities, such as communications assistant, network data logger, and digital asset owner. The new and unique characteristics of the cybertwin make the proposed network to be flexible, scalable, reliable, and secure. Further, we advocate a new cloud network operating system which can work in a distributed way through a real-time multi-agent trading platform to allocate 3C (computing, caching, communications) resources. We also propose cloud operator, a new operator that can provide and manage the resources to the end users and offer location and authentication services for human and things in the cyberspace. Some promising and open research topics are discussed to envision the challenges and opportunities of the cybertwin in the future network architecture.
Unsupervised Deep Learning for Bayesian Brain MRI Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning to implement segmentation tools that are computationally efficient at test time. However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensitive to the intensity profiles in the training dataset. To develop a deep learning-based segmentation model for a new image dataset (e.g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches. In this paper, we propose an alternative strategy that combines a conventional probabilistic atlas-based segmentation with deep learning, enabling one to train a segmentation model for new MRI scans without the need for any manually segmented images. Our experiments include thousands of brain MRI scans and demonstrate that the proposed method achieves good accuracy for a brain MRI segmentation task for different MRI contrasts, requiring only approximately 15 seconds at test time on a GPU. The code is freely available at http://voxelmorph.mit.edu.
Performance Prediction for Coarse-Grained Locking
Venue: arXiv cs.DC 2019
Year: 2019

A standard design pattern found in many concurrent data structures, such as hash tables or ordered containers, is an alternation of parallelizable sections that incur no data conflicts and critical sections that must run sequentially and are protected with locks. A lock can be viewed as a queue that arbitrates the order in which the critical sections are executed, and a natural question is whether we can use stochastic analysis to predict the resulting throughput. As a preliminary evidence to the affirmative, we describe a simple model that can be used to predict the throughput of coarse-grained lock-based algorithms. We show that our model works well for CLH lock, and we expect it to work for other popular lock designs such as TTAS, MCS, etc.
Voltage control of DC-DC Three level Boost converter using TS Fuzzy PI controller
Venue: arXiv cs.SY 2019
Year: 2019

Appropriate control contributes essentially in the design of efficient DC-DC converters. With this intention, a study deals with the synthesis of a controller for DC-DC Three-level Boost converter (TLBC), has been addressed. The studied TLBC, known as nonlinear system, has been locally modeled using transfer function models. For instance, PI controllers were designed using the local models, and then they were combined using Takagi-Sugeno fuzzy (TSF) approach to form a TSF-PI controller. Simulation tests show the flexibility of the proposed controller, its rejection capability to different disturbances, and its ability to achieve the performance specification overall the wide operating range of the system.
DTLS Performance - How Expensive is Security?
Venue: arXiv cs.NI 2019
Year: 2019

Secure communication is an integral feature of many Internet services. The widely deployed TLS protects reliable transport protocols. DTLS extends TLS security services to protocols relying on plain UDP packet transport, such as VoIP or IoT applications. In this paper, we construct a model to determine the performance of generic DTLS-enabled applications. Our model considers basic network characteristics, e.g., number of connections, and the chosen security parameters, e.g., the encryption algorithm in use. Measurements are presented demonstrating the applicability of our model. These experiments are performed using a high-performance DTLS-enabled VPN gateway built on top of the well-established libraries DPDK and OpenSSL. This VPN solution represents the most essential parts of DTLS, creating a DTLS performance baseline. Using this baseline the model can be extended to predict even more complex DTLS protocols besides the measured VPN. Code and measured data used in this paper are publicly available at https://git.io/MoonSec and https://git.io/Sdata.
Age of Information in Multicast Networks with Multiple Update Streams
Venue: arXiv cs.IT 2019
Year: 2019

We consider the age of information in a multicast network where there is a single source node that sends time-sensitive updates to $n$ receiver nodes. Each status update is one of two kinds: type I or type II. To study the age of information experienced by the receiver nodes for both types of updates, we consider two cases: update streams are generated by the source node at-will and update streams arrive exogenously to the source node. We show that using an earliest $k_1$ and $k_2$ transmission scheme for type I and type II updates, respectively, the age of information of both update streams at the receiver nodes can be made a constant independent of $n$. In particular, the source node transmits each type I update packet to the earliest $k_1$ and each type II update packet to the earliest $k_2$ of $n$ receiver nodes. We determine the optimum $k_1$ and $k_2$ stopping thresholds for arbitrary shifted exponential link delays to individually and jointly minimize the average age of both update streams and characterize the pareto optimal curve for the two ages.
Face Video Generation from a Single Image and Landmarks
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we are concerned with the challenging problem of producing a full image sequence of a deformable face given only an image and generic facial motions encoded by a set of sparse landmarks. To this end we build upon recent breakthroughs in image-to-image translation such as pix2pix, CycleGAN and StarGAN which learn Deep Convolutional Neural Networks (DCNNs) that learn to map aligned pairs or images between different domains (i.e., having different labels) and propose a new architecture which is not driven any more by labels but by spatial maps, facial landmarks. In particular, we propose the MotionGAN which transforms an input face image into a new one according to a heatmap of target landmarks. We show that it is possible to create very realistic face videos using a single image and a set of target landmarks. Furthermore, our method can be used to edit a facial image with arbitrary motions according to landmarks (e.g., expression, speech, etc.). This provides much more flexibility to face editing, expression transfer, facial video creation, etc. than models based on discrete expressions, audios or action units.
First things first: If software engineering is the solution, then what is the problem?
Venue: arXiv cs.SE 2019
Year: 2019

Software engineering (SE) undergoes an ontological crisis and it lacks of a theory. Why? Among other reasons, because always it succumbed to the pragmatism demanded by the commercial and political interests and abandoned any intention to become a science instead of a professional discipline. For beginning a discussion for define a theory of software, first, is required to know what software is.