OpenRaiser/Intern-Atlas huggingface dataset:
The Globally Optimal Reparameterization Algorithm: an Alternative to Fast Dynamic Time Warping for Action Recognition in Video Sequences
Venue: arXiv cs.CV 2018
Year: 2018

Signal alignment has become a popular problem in robotics due in part to its fundamental role in action recognition. Currently, the most successful algorithms for signal alignment are Dynamic Time Warping (DTW) and its variant 'Fast' Dynamic Time Warping (FastDTW). Here we introduce a new framework for signal alignment, namely the Globally Optimal Reparameterization Algorithm (GORA). We review the algorithm's mathematical foundation and provide a numerical verification of its theoretical basis. We compare the performance of GORA with that of the DTW and FastDTW algorithms, in terms of computational efficiency and accuracy in matching signals. Our results show a significant improvement in both speed and accuracy over the DTW and FastDTW algorithms and suggest that GORA has the potential to provide a highly effective framework for signal alignment and action recognition.
Deep neural network ensemble by data augmentation and bagging for skin lesion classification
Venue: arXiv cs.CV 2018
Year: 2018

This work summarizes our submission for the Task 3: Disease Classification of ISIC 2018 challenge in Skin Lesion Analysis Towards Melanoma Detection. We use a novel deep neural network (DNN) ensemble architecture introduced by us that can effectively classify skin lesions by using data-augmentation and bagging to address paucity of data and prevent over-fitting. The ensemble is composed of two DNN architectures: Inception-v4 and Inception-Resnet-v2. The DNN architectures are combined in to an ensemble by using a $1\times1$ convolution for fusion in a meta-learning layer.
Improving the Performance of WLANs by Reducing Unnecessary Active Scans
Venue: arXiv cs.NI 2018
Year: 2018

We consider the problem of excessive and unnecessary active scans in heavily utilized WLANs during which low rate probe requests and responses are broadcast. These management frames severely impact the goodput. Our analysis of two production WLANs reveals that lesser number of non-overlapping channels in $2.4$ GHz makes it more prone to the effects of increased probe frames than $5$ GHz. We find that not only up to $90$% of probe responses carry redundant information but the probe traffic can be as high as $60$\% of the management traffic. Furthermore, active scanning severely impacts real-time applications at a client as it increases the latency by $91$ times. We present a detailed analysis of the impact of active scans on an individual client and the whole network. We discuss three ways to control the probe traffic in production WLANs -- access point configurations, network planning, and client modification. Our proposals for access point configuration are in line with current WLAN deployments, better network planning is device agnostic in nature, and client modification reduces the average number of probe requests per client by up to $50$% without hampering the ongoing WiFi connection.
Learning Probabilistic Logic Programs in Continuous Domains
Venue: arXiv cs.LG 2018
Year: 2018

The field of statistical relational learning aims at unifying logic and probability to reason and learn from data. Perhaps the most successful paradigm in the field is probabilistic logic programming: the enabling of stochastic primitives in logic programming, which is now increasingly seen to provide a declarative background to complex machine learning applications. While many systems offer inference capabilities, the more significant challenge is that of learning meaningful and interpretable symbolic representations from data. In that regard, inductive logic programming and related techniques have paved much of the way for the last few decades. Unfortunately, a major limitation of this exciting landscape is that much of the work is limited to finite-domain discrete probability distributions. Recently, a handful of systems have been extended to represent and perform inference with continuous distributions. The problem, of course, is that classical solutions for inference are either restricted to well-known parametric families (e.g., Gaussians) or resort to sampling strategies that provide correct answers only in the limit. When it comes to learning, moreover, inducing representations remains entirely open, other than "data-fitting" solutions that force-fit points to aforementioned parametric families. In this paper, we take the first steps towards inducing probabilistic logic programs for continuous and mixed discrete-continuous data, without being pigeon-holed to a fixed set of distribution families. Our key insight is to leverage techniques from piecewise polynomial function approximation theory, yielding a principled way to learn and compositionally construct density functions. We test the framework and discuss the learned representations.
Online Submodular Maximization: Beating 1/2 Made Simple
Venue: arXiv cs.DS 2018
Year: 2018

The Submodular Welfare Maximization problem (SWM) captures an important subclass of combinatorial auctions and has been studied extensively from both computational and economic perspectives. In particular, it has been studied in a natural online setting in which items arrive one-by-one and should be allocated irrevocably upon arrival. In this setting, it is well known that the greedy algorithm achieves a competitive ratio of 1/2, and recently Kapralov et al. (2013) showed that this ratio is optimal for the problem. Surprisingly, despite this impossibility result, Korula et al. (2015) were able to show that the same algorithm is 0.5052-competitive when the items arrive in a uniformly random order, but unfortunately, their proof is very long and involved. In this work, we present an (arguably) much simpler analysis that provides a slightly better guarantee of 0.5096-competitiveness for the greedy algorithm in the random-arrival model. Moreover, this analysis applies also to a generalization of online SWM in which the sets defining a (simple) partition matroid arrive online in a uniformly random order, and we would like to maximize a monotone submodular function subject to this matroid. Furthermore, for this more general problem, we prove an upper bound of 0.576 on the competitive ratio of the greedy algorithm, ruling out the possibility that the competitiveness of this natural algorithm matches the optimal offline approximation ratio of 1-1/e.
Ontology-Based Query Expansion with Latently Related Named Entities for Semantic Text Search
Venue: arXiv cs.IR 2018
Year: 2018

Traditional information retrieval systems represent documents and queries by keyword sets. However, the content of a document or a query is mainly defined by both keywords and named entities occurring in it. Named entities have ontological features, namely, their aliases, classes, and identifiers, which are hidden from their textual appearance. Besides, the meaning of a query may imply latent named entities that are related to the apparent ones in the query. We propose an ontology-based generalized vector space model to semantic text search. It exploits ontological features of named entities and their latently related ones to reveal the semantics of documents and queries. We also propose a framework to combine different ontologies to take their complementary advantages for semantic annotation and searching. Experiments on a benchmark dataset show better search quality of our model to other ones.
A High-Accuracy Adaptive Beam Training Algorithm for MmWave Communication
Venue: arXiv cs.IT 2018
Year: 2018

In millimeter wave communications, beam training is an effective way to achieve beam alignment. Traditional beam training method allocates training resources equally to each beam in the pre-designed beam training codebook. The performance of this method is far from satisfactory, because different beams have different beamforming gain, and thus some beams are relatively more difficult to be distinguished from the optimal beam than the others. In this paper, we pro- pose a new beam training algorithm which adaptively allocates training resources to each beam. Specifically, the proposed algorithm allocates more training symbols to the beams with relatively higher beamforming gain, while uses less resources to distinguish the beams with relatively lower beamforming gain. Through theoretical analysis and numerical simulations, we show that in practical situations the proposed adaptive algorithm asymptotically outperforms the traditional method in terms of beam training accuracy. Moreover, simulations also show that this relative performance behavior holds in non-asymptotic regime.
ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs
Venue: arXiv cs.CV 2018
Year: 2018

Recovering structure and motion parameters given a image pair or a sequence of images is a well studied problem in computer vision. This is often achieved by employing Structure from Motion (SfM) or Simultaneous Localization and Mapping (SLAM) algorithms based on the real-time requirements. Recently, with the advent of Convolutional Neural Networks (CNNs) researchers have explored the possibility of using machine learning techniques to reconstruct the 3D structure of a scene and jointly predict the camera pose. In this work, we present a framework that achieves state-of-the-art performance on single image depth prediction for both indoor and outdoor scenes. The depth prediction system is then extended to predict optical flow and ultimately the camera pose and trained end-to-end. Our motion estimation framework outperforms the previous motion prediction systems and we also demonstrate that the state-of-the-art metric depths can be further improved using the knowledge of pose.
Automatic generation of ground truth for the evaluation of obstacle detection and tracking techniques
Venue: arXiv cs.RO 2018
Year: 2018

As automated vehicles are getting closer to becoming a reality, it will become mandatory to be able to characterise the performance of their obstacle detection systems. This validation process requires large amounts of ground-truth data, which is currently generated by manually annotation. In this paper, we propose a novel methodology to generate ground-truth kinematics datasets for specific objects in real-world scenes. Our procedure requires no annotation whatsoever, human intervention being limited to sensors calibration. We present the recording platform which was exploited to acquire the reference data and a detailed and thorough analytical study of the propagation of errors in our procedure. This allows us to provide detailed precision metrics for each and every data item in our datasets. Finally some visualisations of the acquired data are given.
Optional Data Disclosure and the Online Privacy Paradox: A UK Perspective
Venue: arXiv cs.CY 2018
Year: 2018

Opinion polls suggest that the public value their privacy, with majorities calling for greater control of their data. However, individuals continue to use online services which place their personal information at risk, comprising a Privacy Paradox. Previous work has analysed this phenomenon through after-the-fact comparisons, but not studied disclosure behaviour during questioning. We physically surveyed UK cities to study how the British public regard privacy and how perceptions differ between demographic groups. Through analysis of optional data disclosure, we empirically examined whether those who claim to value their privacy act privately with their own data. We found that both opinions and self-reported actions have little effect on disclosure, with over 99\% of individuals revealing private data needlessly. We show that not only do individuals act contrary to their opinions, they disclose information needlessly even whilst describing themselves as private. We believe our findings encourage further analysis of data disclosure, as a means of studying genuine privacy behaviour.
The Perfect Storm: The Privacy Paradox and the Internet-of-Things
Venue: arXiv cs.CY 2018
Year: 2018

Privacy is a concept found throughout human history and opinion polls suggest that the public value this principle. However, while many individuals claim to care about privacy, they are often perceived to express behaviour to the contrary. This phenomenon is known as the Privacy Paradox and its existence has been validated through numerous psychological, economic and computer science studies. Several contributory factors have been suggested including user interface design, risk salience, social norms and default configurations. We posit that the further proliferation of the Internet-of-Things (IoT) will aggravate many of these factors, posing even greater risks to individuals' privacy. This paper explores the evolution of both the paradox and the IoT, discusses how privacy risk might alter over the coming years, and suggests further research required to address a reasonable balance. We believe both technological and socio-technical measures are necessary to ensure privacy is protected in a world of ubiquitous technology.
Spatial-Temporal Synergic Residual Learning for Video Person Re-Identification
Venue: arXiv cs.CV 2018
Year: 2018

We tackle the problem of person re-identification in video setting in this paper, which has been viewed as a crucial task in many applications. Meanwhile, it is very challenging since the task requires learning effective representations from video sequences with heterogeneous spatial-temporal information. We present a novel method - Spatial-Temporal Synergic Residual Network (STSRN) for this problem. STSRN contains a spatial residual extractor, a temporal residual processor and a spatial-temporal smooth module. The smoother can alleviate sample noises along the spatial-temporal dimensions thus enable STSRN extracts more robust spatial-temporal features of consecutive frames. Extensive experiments are conducted on several challenging datasets including iLIDS-VID, PRID2011 and MARS. The results demonstrate that the proposed method achieves consistently superior performance over most of state-of-the-art methods.
Subjective and objective experiments on the influence of speaker's gender on the unvoiced segments
Venue: arXiv cs.SD 2018
Year: 2018

Subjective and objective experiments are conducted to understand the extent to which a speaker's gender influences the acoustics of unvoiced (U) sounds. U segments of utterances are replaced by the corresponding segments of a speaker of opposite gender to prepare modified utterances. Humans are asked to judge if the modified utterance is spoken by one or two speakers. The experiments show that human subjects are unable to distinguish the modified from the original. Thus, listeners are able to identify the U segments irrespective of the gender, which may be based on some speaker-independent invariant acoustic cues. To test if this finding is purely a perceptual phenomenon, objective experiments are also conducted. Gender specific HMM based phoneme recognition systems are trained using the TIMIT training set and tested on (a) utterances spoken by the same gender (b) utterances spoken by the opposite gender and (c) the modified utterances of the test set. As expected, the performance is the highest for case (a) and the lowest for case (b). The performance degrades only slightly for case (c). This result shows that the speaker's gender does not as strongly influence the acoustics of U sounds as they do the voiced sounds.
Computationally Efficient Approaches for Image Style Transfer
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we have investigated various style transfer approaches and (i) examined how the stylized reconstruction changes with the change of loss function and (ii) provided a computationally efficient solution for the same. We have used elegant techniques like depth-wise separable convolution in place of convolution and nearest neighbor interpolation in place of transposed convolution. Further, we have also added multiple interpolations in place of transposed convolution. The results obtained are perceptually similar in quality, while being computationally very efficient. The decrease in the computational complexity of our architecture is validated by the decrease in the testing time by 26.1%, 39.1%, and 57.1%, respectively.
Variational Inference: A Unified Framework of Generative Models and Some Revelations
Venue: arXiv cs.LG 2018
Year: 2018

We reinterpreting the variational inference in a new perspective. Via this way, we can easily prove that EM algorithm, VAE, GAN, AAE, ALI(BiGAN) are all special cases of variational inference. The proof also reveals the loss of standard GAN is incomplete and it explains why we need to train GAN cautiously. From that, we find out a regularization term to improve stability of GAN training.
Theme-weighted Ranking of Keywords from Text Documents using Phrase Embeddings
Venue: arXiv cs.CL 2018
Year: 2018

Keyword extraction is a fundamental task in natural language processing that facilitates mapping of documents to a concise set of representative single and multi-word phrases. Keywords from text documents are primarily extracted using supervised and unsupervised approaches. In this paper, we present an unsupervised technique that uses a combination of theme-weighted personalized PageRank algorithm and neural phrase embeddings for extracting and ranking keywords. We also introduce an efficient way of processing text documents and training phrase embeddings using existing techniques. We share an evaluation dataset derived from an existing dataset that is used for choosing the underlying embedding model. The evaluations for ranked keyword extraction are performed on two benchmark datasets comprising of short abstracts (Inspec), and long scientific papers (SemEval 2010), and is shown to produce results better than the state-of-the-art systems.
Pangloss: Fast Entity Linking in Noisy Text Environments
Venue: arXiv cs.IR 2018
Year: 2018

Entity linking is the task of mapping potentially ambiguous terms in text to their constituent entities in a knowledge base like Wikipedia. This is useful for organizing content, extracting structured data from textual documents, and in machine learning relevance applications like semantic search, knowledge graph construction, and question answering. Traditionally, this work has focused on text that has been well-formed, like news articles, but in common real world datasets such as messaging, resumes, or short-form social media, non-grammatical, loosely-structured text adds a new dimension to this problem. This paper presents Pangloss, a production system for entity disambiguation on noisy text. Pangloss combines a probabilistic linear-time key phrase identification algorithm with a semantic similarity engine based on context-dependent document embeddings to achieve better than state-of-the-art results (>5% in F1) compared to other research or commercially available systems. In addition, Pangloss leverages a local embedded database with a tiered architecture to house its statistics and metadata, which allows rapid disambiguation in streaming contexts and on-device disambiguation in low-memory environments such as mobile phones.
Street Sense: Learning from Google Street View
Venue: arXiv cs.CY 2018
Year: 2018

How good are the public services and the public infrastructure? Does their quality vary by income? These are vital questions---they shed light on how well the government is doing its job, the consequences of disparities in local funding, etc. But there is little good data on many of these questions. We fill this gap by describing a scalable method of getting data on one crucial piece of public infrastructure: roads. We assess the quality of roads and sidewalks by exploiting data from Google Street View. We randomly sample locations on major roads, query Google Street View images for those locations and code the images using Amazon's Mechanical Turk. We apply this method to assess the quality of roads in Bangkok, Jakarta, Lagos, and Wayne County, Michigan. Jakarta's roads have nearly four times the potholes than roads of any other city. Surprisingly, the proportion of road segments with potholes in Bangkok, Lagos, and Wayne is about the same, between .06 and .07. Using the data, we also estimate the relation between the condition of the roads and local income in Wayne, MI. We find that roads in more affluent census tracts have somewhat fewer potholes.
Privacy, Security and Trust in the Internet of Neurons
Venue: arXiv cs.CY 2018
Year: 2018

Arpanet, Internet, Internet of Services, Internet of Things, Internet of Skills. What next? We conjecture that in 15-20 years from now we will have the Internet of Neurons, a new Internet paradigm in which humans will be able to connect bi-directionally to the net using only their brain. The Internet of Neurons will provide new, tremendous opportunities thanks to constant access to unlimited information. It will empower all those outside of the technical industry, actually it will empower all human beings, to access and use technological products and services as everybody will be able to connect, even without possessing a laptop, a tablet or a smartphone. The Internet of Neurons will thus ultimately complete the currently still immature democratization of knowledge and technology. But it will also bring along several enormous challenges, especially concerning security (as well as privacy and trust). In this paper we speculate on the worldwide deployment of the Internet of Neurons by 2038 and brainstorm about its disruptive impact, discussing the main technological (and neurological) breakthroughs required to enable it, the new opportunities it provides and the security challenges it raises. We also elaborate on the novel system models, threat models and security properties that are required to reason about privacy, security and trust in the Internet of Neurons.
A Model for Evaluating Algorithmic Systems Accountability
Venue: arXiv cs.CY 2018
Year: 2018

Algorithmic systems make decisions that have a great impact in our lives. As our dependency on them is growing so does the need for transparency and holding them accountable. This paper presents a model for evaluating how transparent these systems are by focusing on their algorithmic part as well as the maturity of the organizations that utilize them. We applied this model on a classification algorithm created and utilized by a large financial institution. The results of our analysis indicated that the organization was only partially in control of their algorithm and they lacked the necessary benchmark to interpret the deducted results and assess the validity of its inferencing.
Towards a Theory of Software Development Expertise
Venue: arXiv cs.SE 2018
Year: 2018

Software development includes diverse tasks such as implementing new features, analyzing requirements, and fixing bugs. Being an expert in those tasks requires a certain set of skills, knowledge, and experience. Several studies investigated individual aspects of software development expertise, but what is missing is a comprehensive theory. We present a first conceptual theory of software development expertise that is grounded in data from a mixed-methods survey with 335 software developers and in literature on expertise and expert performance. Our theory currently focuses on programming, but already provides valuable insights for researchers, developers, and employers. The theory describes important properties of software development expertise and which factors foster or hinder its formation, including how developers' performance may decline over time. Moreover, our quantitative results show that developers' expertise self-assessments are context-dependent and that experience is not necessarily related to expertise.