OpenRaiser/Intern-Atlas huggingface dataset:
Pible: Battery-Free Mote for Perpetual Indoor BLE Applications
Venue: arXiv cs.NI 2018
Year: 2018

Smart building applications require a large-scale deployment of sensors distributed across the environment. Recent innovations in smart environments are driven by wireless networked sensors as they are easy to deploy. However, replacing these batteries at scale is a non-trivial, labor-intensive task. Energy harvesting has emerged as a potential solution to avoid battery replacement but requires compromises such as application specific design, simplified communication protocol or reduced quality of service. We explore the design space of battery-free sensor nodes using commercial off the shelf components, and present Pible: a Perpetual Indoor BLE sensor node that leverages ambient light and can support numerous smart building applications. We analyze node-lifetime, quality of service and light availability trade-offs and present a predictive algorithm that adapts to changing lighting conditions to maximize node lifetime and application quality of service. Using a 20 node, 15-day deployment in a real building under varying lighting conditions, we show feasible applications that can be implemented using Pible and the boundary conditions under which they can fail.
Generative Adversarial Networks for Recovering Missing Spectral Information
Venue: arXiv cs.LG 2018
Year: 2018

Ultra-wideband (UWB) radar systems nowadays typical operate in the low frequency spectrum to achieve penetration capability. However, this spectrum is also shared by many others communication systems, which causes missing information in the frequency bands. To recover this missing spectral information, we propose a generative adversarial network, called SARGAN, that learns the relationship between original and missing band signals by observing these training pairs in a clever way. Initial results shows that this approach is promising in tackling this challenging missing band problem.
An efficient supervised dictionary learning method for audio signal recognition
Venue: arXiv cs.CV 2018
Year: 2018

Machine hearing or listening represents an emerging area. Conventional approaches rely on the design of handcrafted features specialized to a specific audio task and that can hardly generalized to other audio fields. For example, Mel-Frequency Cepstral Coefficients (MFCCs) and its variants were successfully applied to computational auditory scene recognition while Chroma vectors are good at music chord recognition. Unfortunately, these predefined features may be of variable discrimination power while extended to other tasks or even within the same task due to different nature of clips. Motivated by this need of a principled framework across domain applications for machine listening, we propose a generic and data-driven representation learning approach. For this sake, a novel and efficient supervised dictionary learning method is presented. The method learns dissimilar dictionaries, one per each class, in order to extract heterogeneous information for classification. In other words, we are seeking to minimize the intra-class homogeneity and maximize class separability. This is made possible by promoting pairwise orthogonality between class specific dictionaries and controlling the sparsity structure of the audio clip's decomposition over these dictionaries. The resulting optimization problem is non-convex and solved using a proximal gradient descent method. Experiments are performed on both computational auditory scene (East Anglia and Rouen) and synthetic music chord recognition datasets. Obtained results show that our method is capable to reach state-of-the-art hand-crafted features for both applications.
Considering Race a Problem of Transfer Learning
Venue: arXiv cs.CV 2018
Year: 2018

As biometric applications are fielded to serve large population groups, issues of performance differences between individual sub-groups are becoming increasingly important. In this paper we examine cases where we believe race is one such factor. We look in particular at two forms of problem; facial classification and image synthesis. We take the novel approach of considering race as a boundary for transfer learning in both the task (facial classification) and the domain (synthesis over distinct datasets). We demonstrate a series of techniques to improve transfer learning of facial classification; outperforming similar models trained in the target's own domain. We conduct a study to evaluate the performance drop of Generative Adversarial Networks trained to conduct image synthesis, in this process, we produce a new annotation for the Celeb-A dataset by race. These networks are trained solely on one race and tested on another - demonstrating the subsets of the CelebA to be distinct domains for this task.
Iris-GAN: Learning to Generate Realistic Iris Images Using Convolutional GAN
Venue: arXiv cs.CV 2018
Year: 2018

Generating iris images which look realistic is both an interesting and challenging problem. Most of the classical statistical models are not powerful enough to capture the complicated texture representation in iris images, and therefore fail to generate iris images which look realistic. In this work, we present a machine learning framework based on generative adversarial network (GAN), which is able to generate iris images sampled from a prior distribution (learned from a set of training images). We apply this framework to two popular iris databases, and generate images which look very realistic, and similar to the image distribution in those databases. Through experimental results, we show that the generated iris images have a good diversity, and are able to capture different part of the prior distribution.
MorpheuS: generating structured music with constrained patterns and tension
Venue: arXiv cs.SD 2018
Year: 2018

Automatic music generation systems have gained in popularity and sophistication as advances in cloud computing have enabled large-scale complex computations such as deep models and optimization algorithms on personal devices. Yet, they still face an important challenge, that of long-term structure, which is key to conveying a sense of musical coherence. We present the MorpheuS music generation system designed to tackle this problem. MorpheuS' novel framework has the ability to generate polyphonic pieces with a given tension profile and long- and short-term repeated pattern structures. A mathematical model for tonal tension quantifies the tension profile and state-of-the-art pattern detection algorithms extract repeated patterns in a template piece. An efficient optimization metaheuristic, variable neighborhood search, generates music by assigning pitches that best fit the prescribed tension profile to the template rhythm while hard constraining long-term structure through the detected patterns. This ability to generate affective music with specific tension profile and long-term structure is particularly useful in a game or film music context. Music generated by the MorpheuS system has been performed live in concerts.
Breaking the borders: an investigation of cross-ecosystem software packages
Venue: arXiv cs.SE 2018
Year: 2018

Software ecosystems are collections of projects that are developed and evolve together in the same environment. Existing literature investigates software ecosystems as isolated entities whose boundaries do not overlap and assumes they are self-contained. However, a number of software projects are distributed in more than one ecosystem. As different aspects, e.g., success, security vulnerabilities, bugs, etc., of such cross-ecosystem packages can affect multiple ecosystems, we investigate the presence and characteristics of these cross-ecosystem packages in 12 large software distributions. We found a small number of packages distributed in multiple packaging ecosystems and that such packages are usually distributed in two ecosystems. These packages tend to better support with new releases certain ecosystems, while their evolution can impact a multitude of packages in other ecosystems. Finally, such packages appear to be popular with large developer communities.
SMT vs NMT: A Comparison over Hindi & Bengali Simple Sentences
Venue: arXiv cs.CL 2018
Year: 2018

In the present article, we identified the qualitative differences between Statistical Machine Translation (SMT) and Neural Machine Translation (NMT) outputs. We have tried to answer two important questions: 1. Does NMT perform equivalently well with respect to SMT and 2. Does it add extra flavor in improving the quality of MT output by employing simple sentences as training units. In order to obtain insights, we have developed three core models viz., SMT model based on Moses toolkit, followed by character and word level NMT models. All of the systems use English-Hindi and English-Bengali language pairs containing simple sentences as well as sentences of other complexity. In order to preserve the translations semantics with respect to the target words of a sentence, we have employed soft-attention into our word level NMT model. We have further evaluated all the systems with respect to the scenarios where they succeed and fail. Finally, the quality of translation has been validated using BLEU and TER metrics along with manual parameters like fluency, adequacy etc. We observed that NMT outperforms SMT in case of simple sentences whereas SMT outperforms in case of all types of sentence.
Nanotechnology: The New Features
Venue: arXiv cs.ET 2018
Year: 2018

Nanotechnologies are attracting increasing investments from both governments and industries around the world, which offers great opportunities to explore the new emerging nanodevices, such as the Carbon Nanotube and Nanosensors. This technique exploits the specific properties which arise from structure at a scale characterized by the interplay of classical physics and quantum mechanics. It is difficult to predict these properties a priori according to traditional technologies. Nanotechnologies will be one of the next promising trends after MOS technologies. However, there has been much hype around nanotechnology, both by those who want to promote it and those who have fears about its potentials. This paper gives a deep survey regarding different aspects of the new nanotechnologies, such as materials, physics, and semiconductors respectively, followed by an introduction of several state-of-the-art nanodevices and then new nanotechnology features. Since little research has been carried out on the toxicity of manufactured nanoparticles and nanotubes, this paper also discusses several problems in the nanotechnology area and gives constructive suggestions and predictions.
Fission: A Provably Fast, Scalable, and Secure Permissionless Blockchain
Venue: arXiv cs.CR 2018
Year: 2018

We present Fission, a new permissionless blockchain that achieves scalability in both terms of system throughput and transaction confirmation time, while at the same time, retaining blockchain's core values of equality and decentralization. Fission overcomes the system throughput bottleneck by employing a novel Eager-Lazy pipeling model that achieves very high system throughputs via block pipelining, an adaptive partitioning mechanism that auto-scales to transaction volumes, and a provably secure energy-efficient consensus protocol to ensure security and robustness. Fission applies a hybrid network which consists of a relay network, and a peer-to-peer network. The goal of the relay network is to minimize the transaction confirmation time by minimizing the information propagation latency. To optimize the performance on the relay network in the presence of churn, dynamic network topologies, and network heterogeneity, we propose an ultra-fast game-theoretic relay selection algorithm that achieves near-optimal performance in a fully distributed manner. Fission's peer-to-peer network complements the relay network and provides a very high data availability via enabling users to contribute their storage and bandwidth for information dissemination (with incentive). We propose a distributed online data retrieval strategy that optimally offloads the relay network without degrading the system performance. By re-innovating all the core elements of the blockchain technology - computation, networking, and storage - in a holistic manner, Fission aims to achieve the best balance among scalability, security and decentralization.
Differentially Testing Soundness and Precision of Program Analyzers
Venue: arXiv cs.SE 2018
Year: 2018

In the last decades, numerous program analyzers have been developed both by academia and industry. Despite their abundance however, there is currently no systematic way of comparing the effectiveness of different analyzers on arbitrary code. In this paper, we present the first automated technique for differentially testing soundness and precision of program analyzers. We used our technique to compare six mature, state-of-the art analyzers on tens of thousands of automatically generated benchmarks. Our technique detected soundness and precision issues in most analyzers, and we evaluated the implications of these issues to both designers and users of program analyzers.
Synthesis of High-Quality Visible Faces from Polarimetric Thermal Faces using Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

The large domain discrepancy between faces captured in polarimetric (or conventional) thermal and visible domain makes cross-domain face verification a highly challenging problem for human examiners as well as computer vision algorithms. Previous approaches utilize either a two-step procedure (visible feature estimation and visible image reconstruction) or an input-level fusion technique, where different Stokes images are concatenated and used as a multi-channel input to synthesize the visible image given the corresponding polarimetric signatures. Although these methods have yielded improvements, we argue that input-level fusion alone may not be sufficient to realize the full potential of the available Stokes images. We propose a Generative Adversarial Networks (GAN) based multi-stream feature-level fusion technique to synthesize high-quality visible images from prolarimetric thermal images. The proposed network consists of a generator sub-network, constructed using an encoder-decoder network based on dense residual blocks, and a multi-scale discriminator sub-network. The generator network is trained by optimizing an adversarial loss in addition to a perceptual loss and an identity preserving loss to enable photo realistic generation of visible images while preserving discriminative characteristics. An extended dataset consisting of polarimetric thermal facial signatures of 111 subjects is also introduced. Multiple experiments evaluated on different experimental protocols demonstrate that the proposed method achieves state-of-the-art performance. Code will be made available at https://github.com/hezhangsprinter.
Local Probabilistic Model for Bayesian Classification: a Generalized Local Classification Model
Venue: arXiv cs.LG 2018
Year: 2018

In Bayesian classification, it is important to establish a probabilistic model for each class for likelihood estimation. Most of the previous methods modeled the probability distribution in the whole sample space. However, real-world problems are usually too complex to model in the whole sample space; some fundamental assumptions are required to simplify the global model, for example, the class conditional independence assumption for naive Bayesian classification. In this paper, with the insight that the distribution in a local sample space should be simpler than that in the whole sample space, a local probabilistic model established for a local region is expected much simpler and can relax the fundamental assumptions that may not be true in the whole sample space. Based on these advantages we propose establishing local probabilistic models for Bayesian classification. In addition, a Bayesian classifier adopting a local probabilistic model can even be viewed as a generalized local classification model; by tuning the size of the local region and the corresponding local model assumption, a fitting model can be established for a particular classification problem. The experimental results on several real-world datasets demonstrate the effectiveness of local probabilistic models for Bayesian classification.
When Semi-Supervised Learning Meets Transfer Learning: Training Strategies, Models and Datasets
Venue: arXiv cs.CV 2018
Year: 2018

Semi-Supervised Learning (SSL) has been proved to be an effective way to leverage both labeled and unlabeled data at the same time. Recent semi-supervised approaches focus on deep neural networks and have achieved promising results on several benchmarks: CIFAR10, CIFAR100 and SVHN. However, most of their experiments are based on models trained from scratch instead of pre-trained models. On the other hand, transfer learning has demonstrated its value when the target domain has limited labeled data. Here comes the intuitive question: is it possible to incorporate SSL when fine-tuning a pre-trained model? We comprehensively study how SSL methods starting from pretrained models perform under varying conditions, including training strategies, architecture choice and datasets. From this study, we obtain several interesting and useful observations. While practitioners have had an intuitive understanding of these observations, we do a comprehensive emperical analysis and demonstrate that: (1) the gains from SSL techniques over a fully-supervised baseline are smaller when trained from a pre-trained model than when trained from random initialization, (2) when the domain of the source data used to train the pre-trained model differs significantly from the domain of the target task, the gains from SSL are significantly higher and (3) some SSL methods are able to advance fully-supervised baselines (like Pseudo-Label). We hope our studies can deepen the understanding of SSL research and facilitate the process of developing more effective SSL methods to utilize pre-trained models. Code is now available at github.
A Dynamic Tracing Model for Agile Software Product Lines Domain Engineering from Features to Structural Elements: An Approach Based on Dynamic Routing
Venue: arXiv cs.SE 2018
Year: 2018

Even if the benefit of implementing Software Product Lines is well established, adopting such a large system is still a complex choice to make: it is hard to implement, needs a good knowledge of market growth and a clear vision of the enterprise objectives for long term. Therefore, many companies remain unwilling to adopt such an approach, unless they gain flexibility and get guarantees. Flexibility can be achieved by adopting an Agile Software Product Line approach, to make sure changes are rapidly implemented and product adapted to market evolution. Guarantees can be acquired by tracing elements and the relations between them. However, tracing in Agile Software Product Line context still needs to gain maturity as it is costly and therefore rarely adopted. In this paper, we discuss the added value of traceability for Agile Software Product Lines, and present our tracing model inspired from dynamic network routing.
Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry
Venue: arXiv cs.SE 2018
Year: 2018

Deep Neural Networks (DNN) will emerge as a cornerstone in automotive software engineering. However, developing systems with DNNs introduces novel challenges for safety assessments. This paper reviews the state-of-the-art in verification and validation of safety-critical systems that rely on machine learning. Furthermore, we report from a workshop series on DNNs for perception with automotive experts in Sweden, confirming that ISO 26262 largely contravenes the nature of DNNs. We recommend aerospace-to-automotive knowledge transfer and systems-based safety approaches, e.g., safety cage architectures and simulated system test cases.
Security and Privacy Implications of Middlebox Cooperation Protocols
Venue: arXiv cs.NI 2018
Year: 2018

This white paper presents an analysis done by the MAMI project of the privacy and security concerns surrounding middlebox cooperation protocols (MCPs), based on our experimental experience with the Path Layer UDP Substrate (PLUS) proposal. Our key finding is that adding explicit signaling meant for on-path devices presents no significant new attack surface as compared to the status quo in the Internet architecture. While middlebox cooperation can make a passive adversary's job easier, it does not enable entirely new attacks.
Pluralize: a Trustworthy Framework for High-Level Smart Contract-Draft
Venue: arXiv cs.CR 2018
Year: 2018

The paper presents Pluralize a formal logical framework able to extend the execution of blockchain transactions to events coming from external oracles, like external time, sensor data, human-made declarations, etc. These events are by essence non-reliable, since transaction execution can be triggered by information whose veracity cannot be established by the blockchain. To overcome this problem, the language features a first-order logic and an authority algebra to allow formal reasoning and establish accountability of agents for blockchain-enabled transactions. We provide an accountability model that allows to formally prove the accountability of agents by a formal proof locally executable by each agent of the blockchain.
Using Motion and Internal Supervision in Object Recognition
Venue: arXiv cs.CV 2018
Year: 2018

In this thesis we address two related aspects of visual object recognition: the use of motion information, and the use of internal supervision, to help unsupervised learning. These two aspects are inter-related in the current study, since image motion is used for internal supervision, via the detection of spatiotemporal events of active-motion and the use of tracking. Most current work in object recognition deals with static images during both learning and recognition. In contrast, we are interested in a dynamic scene where visual processes, such as detecting motion events and tracking, contribute spatiotemporal information, which is useful for object attention, motion segmentation, 3-D understanding and object interactions. We explore the use of these sources of information in both learning and recognition processes. In the first part of the work, we demonstrate how motion can be used for adaptive detection of object-parts in dynamic environments, while automatically learning new object appearances and poses. In the second and main part of the study we develop methods for using specific types of visual motion to solve two difficult problems in unsupervised visual learning: learning to recognize hands by their appearance and by context, and learning to extract direction of gaze. We use our conclusions in this part to propose a model for several aspects of learning by human infants from their visual environment.