OpenRaiser/Intern-Atlas huggingface dataset:
Interaction Methods for Smart Glasses
Venue: arXiv cs.HC 2017
Year: 2017

Since the launch of Google Glass in 2014, smart glasses have mainly been designed to support micro-interactions. The ultimate goal for them to become an augmented reality interface has not yet been attained due to an encumbrance of controls. Augmented reality involves superimposing interactive computer graphics images onto physical objects in the real world. This survey reviews current research issues in the area of human computer interaction for smart glasses. The survey first studies the smart glasses available in the market and afterwards investigates the interaction methods proposed in the wide body of literature. The interaction methods can be classified into hand-held, touch, and touchless input. This paper mainly focuses on the touch and touchless input. Touch input can be further divided into on-device and on-body, while touchless input can be classified into hands-free and freehand. Next, we summarize the existing research efforts and trends, in which touch and touchless input are evaluated by a total of eight interaction goals. Finally, we discuss several key design challenges and the possibility of multi-modal input for smart glasses.
Statistical Modeling of Propagation Channels for Terahertz Band
Venue: arXiv cs.IT 2017
Year: 2017

Digital revolution and recent advances in telecommunications technology enable to design communication systems which operate within the regions close to the theoretical capacity limits. Ever-increasing demand for wireless communications and emerging numerous high-capacity services, data rates and applications mandate providers to employ more bandwidth-oriented solutions to meet the requirements. It is clear that such rates could only be achieved by employing more bandwidth with the state-of-the- art technology. Considering the fact that bands in the range of 275GHz-3000GHz, which are known as Terahertz (THz) bands, are not allocated yet for specific active services around the globe, there is an enormous potential to achieve the desired data rates. Although THz bands look promising to achieve data rates on the order of several tens of Gbps, realization of fully operational THz communications systems obliges to carry out a multi- disciplinary effort including statistical propagation and channel characterizations, adaptive transceiver designs, reconfigurable platforms, advanced signal processing algorithms and techniques along with upper layer protocols equipped with various security and privacy levels. Therefore, in this study, several important statistical parameters for line-of-sight (LOS) channels are measured. High resolution frequency domain measurements are carried out at single-sweep within a span of 60GHz. Impact of antenna misalignment under LOS conditions is also investigated. By validating exponential decay of the received power in both time and frequency domain, path loss exponent is examined for different frequencies along with the frequency-dependent path loss phenomenon. Furthermore, impact of humidity is also tested under LOS scenario. Measurement results are presented along with relevant discussions and future directions are provided as well.
Performance analysis of FSO communications under LOS blockage
Venue: arXiv cs.IT 2017
Year: 2017

We analyze the performance of a free-space optical (FSO) link affected by atmospheric turbulence and line-of-sight (LOS) blockage. For this purpose, the atmospheric turbulence induced fading is modeled by the M-distribution, which includes the Gamma-Gamma distribution as special case. We exploit the fact that the physical interpretation of the M-distribution allows to split the optical energy through the propagation link into three different components: two coherent components and one incoherent scatter component. Based on this separation, we derive novel analytical expressions for the probability density function (PDF), for the cumulative distribution function (CDF) and for the moment generating function (MGF) of the M-distribution under the temporary blockage of the coherent components, hereinafter referred to as LOS blockage. Further, a new closed-form expression for the outage probability (OP) under LOS blockage is derived in terms of the turbulence model parameters and the LOS blockage probability. By means of an asymptotic analysis, this expression is simplified in the high-SNR regime and the OP in terms of the diversity order and diversity gain is then deduced. Obtained results show that the impact of the LOS blockage on the OP strongly depends on the intensity of the turbulence and on the LOS blockage probability.
A comment on the paper Prediction of Kidney Function from Biopsy Images using Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

This letter presente a comment on the paper Prediction of Kidney Function from Biopsy Images using Convolutional Neural Networks by Ledbetter et al. (2017)
Bearing fault diagnosis under varying working condition based on domain adaptation
Venue: arXiv cs.SD 2017
Year: 2017

Traditional intelligent fault diagnosis of rolling bearings work well only under a common assumption that the labeled training data (source domain) and unlabeled testing data (target domain) are drawn from the same distribution. When the distribution changes, most fault diagnosis models need to be rebuilt from scratch using newly recollected labeled training data. However, it is expensive or impossible to annotate huge amount of training data to rebuild such new model. Meanwhile, large amounts of labeled training data have not been fully utilized yet, which is apparently a waste of resources. As one of the important research directions of transfer learning, domain adaptation (DA) typically aims at minimizing the differences between distributions of different domains in order to minimize the cross-domain prediction error by taking full advantage of information coming from both source and target domains. In this paper, we present one of the first studies on unsupervised DA in the field of fault diagnosis of rolling bearings under varying working conditions and a novel diagnosis strategy based on unsupervised DA using subspace alignment (SA) is proposed. After processed by unsupervised DA with SA, the distributions of training data and testing data become close and the classifier trained on training data can be used to classify the testing data. Experimental results on the 60 domain adaptation diagnosis problems under varying working condition in Case Western Reserve benchmark data and 12 domain adaptation diagnosis problems under varying working conditions in our new data are given to demonstrate the effectiveness of the proposed method. The proposed methods can effectively distinguish not only bearing faults categories but also fault severities.
Fashioning with Networks: Neural Style Transfer to Design Clothes
Venue: arXiv cs.CV 2017
Year: 2017

Convolutional Neural Networks have been highly successful in performing a host of computer vision tasks such as object recognition, object detection, image segmentation and texture synthesis. In 2015, Gatys et. al [7] show how the style of a painter can be extracted from an image of the painting and applied to another normal photograph, thus recreating the photo in the style of the painter. The method has been successfully applied to a wide range of images and has since spawned multiple applications and mobile apps. In this paper, the neural style transfer algorithm is applied to fashion so as to synthesize new custom clothes. We construct an approach to personalize and generate new custom clothes based on a users preference and by learning the users fashion choices from a limited set of clothes from their closet. The approach is evaluated by analyzing the generated images of clothes and how well they align with the users fashion style.
Temporal Hierarchical Clustering
Venue: arXiv cs.DS 2017
Year: 2017

We study hierarchical clusterings of metric spaces that change over time. This is a natural geometric primitive for the analysis of dynamic data sets. Specifically, we introduce and study the problem of finding a temporally coherent sequence of hierarchical clusterings from a sequence of unlabeled point sets. We encode the clustering objective by embedding each point set into an ultrametric space, which naturally induces a hierarchical clustering of the set of points. We enforce temporal coherence among the embeddings by finding correspondences between successive pairs of ultrametric spaces which exhibit small distortion in the Gromov-Hausdorff sense. We present both upper and lower bounds on the approximability of the resulting optimization problems.
Robust Private Information Retrieval on Coded Data
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of designing PIR scheme on coded data when certain nodes are unresponsive. We provide the construction of $ν$-robust PIR schemes that can tolerate up to $ν$ unresponsive nodes. These schemes are adaptive and universally optimal in the sense of achieving (asymptotically) optimal download cost for any number of unresponsive nodes up to $ν$.
Correction of "Cloud Removal By Fusing Multi-Source and Multi-Temporal Images"
Venue: arXiv cs.CV 2017
Year: 2017

Remote sensing images often suffer from cloud cover. Cloud removal is required in many applications of remote sensing images. Multitemporal-based methods are popular and effective to cope with thick clouds. This paper contributes to a summarization and experimental comparation of the existing multitemporal-based methods. Furthermore, we propose a spatiotemporal-fusion with poisson-adjustment method to fuse multi-sensor and multi-temporal images for cloud removal. The experimental results show that the proposed method has potential to address the problem of accuracy reduction of cloud removal in multi-temporal images with significant changes.
Tuning MPI Collectives by Verifying Performance Guidelines
Venue: arXiv cs.DC 2017
Year: 2017

MPI collective operations provide a standardized interface for performing data movements within a group of processes. The efficiency of collective communication operations depends on the actual algorithm, its implementation, and the specific communication problem (type of communication, message size, number of processes). Many MPI libraries provide numerous algorithms for specific collective operations. The strategy for selecting an efficient algorithm is often times predefined (hard-coded) in MPI libraries, but some of them, such as Open MPI, allow users to change the algorithm manually. Finding the best algorithm for each case is a hard problem, and several approaches to tune these algorithmic parameters have been proposed. We use an orthogonal approach to the parameter-tuning of MPI collectives, that is, instead of testing individual algorithmic choices provided by an MPI library, we compare the latency of a specific MPI collective operation to the latency of semantically equivalent functions, which we call the mock-up implementations. The structure of the mock-up implementations is defined by self-consistent performance guidelines. The advantage of this approach is that tuning using mock-up implementations is always possible, whether or not an MPI library allows users to select a specific algorithm at run-time. We implement this concept in a library called PGMPITuneLib, which is layered between the user code and the actual MPI implementation. This library selects the best-performing algorithmic pattern of an MPI collective by intercepting MPI calls and redirecting them to our mock-up implementations. Experimental results show that PGMPITuneLib can significantly reduce the latency of MPI collectives, and also equally important, that it can help identifying the tuning potential of MPI libraries.
Learning Algorithms for Active Learning
Venue: arXiv cs.LG 2017
Year: 2017

We introduce a model that learns active learning algorithms via metalearning. For a distribution of related tasks, our model jointly learns: a data representation, an item selection heuristic, and a method for constructing prediction functions from labeled training sets. Our model uses the item selection heuristic to gather labeled training sets from which to construct prediction functions. Using the Omniglot and MovieLens datasets, we test our model in synthetic and practical settings.
The Code2Text Challenge: Text Generation in Source Code Libraries
Venue: arXiv cs.CL 2017
Year: 2017

We propose a new shared task for tactical data-to-text generation in the domain of source code libraries. Specifically, we focus on text generation of function descriptions from example software projects. Data is drawn from existing resources used for studying the related problem of semantic parser induction (Richardson and Kuhn, 2017b; Richardson and Kuhn, 2017a), and spans a wide variety of both natural languages and programming languages. In this paper, we describe these existing resources, which will serve as training and development data for the task, and discuss plans for building new independent test sets.
Multiple Stakeholders in Music Recommender Systems
Venue: arXiv cs.CY 2017
Year: 2017

Music recommendation services collectively spin billions of songs for millions of listeners on a daily basis. Users can typically listen to a variety of songs tailored to their personal tastes and preferences. Music is not the only type of content encountered in these services, however. Advertisements are generally interspersed throughout the music stream to generate revenue for the business. Additional content may include artist messaging, ticketing, sports, news and weather. In this paper, we discuss issues that arise when multiple content providers are stakeholders in the recommendation process. These stakeholders each have their own objectives and must work in concert to sustain a healthy music recommendation service.
Optimal design of three-planetary-gear power-split hybrid powertrains
Venue: arXiv cs.SY 2017
Year: 2017

Many of today's power-split hybrid electric vehicles (HEVs) utilize planetary gears (PGs) to connect the powertrain elements together. Recent power-split HEVs tend to use two PGs and some of them have multiple modes to achieve better fuel economy and driving performance. Looking to the future, hybrid powertrain technologies must be enhanced to design hybrid light trucks. For light trucks, the need for multi-mode and more PGs is stronger, to achieve the required performance. To systematically explore all the possible designs of multi-mode HEVs with three PGs, an efficient searching and optimization methodology is proposed. All possible clutch topology and modes for one existing configuration that uses three PGs were exhaustively searched. The launching performance is first used to screen out designs that fail to satisfy the required launching performance. A near-optimal and computationally efficient energy management strategy was then employed to identify designs that achieve good fuel economy. The proposed design process successfully identify 8 designs that achieve better launching performance and better fuel economy, while using fewer number of clutches than the benchmark and a patented design.
A Survey on Visual Query Systems in the Web Era (extended version)
Venue: arXiv cs.DB 2017
Year: 2017

As more and more collections of data are becoming available on the web to everyone, non expert users demand easy ways to retrieve data from these collections. One solution is the so called Visual Query Systems (VQS) where queries are represented visually and users do not have to understand query languages such as SQL or XQuery. In 1996, a paper by Catarci reviewed the Visual Query Systems available until that year. In this paper, we review VQSs from 1997 until now and try to determine whether they have been the solution for non expert users. The short answer is no because very few systems have in fact been used in real environments or as commercial tools. We have also gathered basic features of VQSs such as the visual representation adopted to present the reality of interest or the visual representation adopted to express queries.
SenGen: Sentence Generating Neural Variational Topic Model
Venue: arXiv cs.CL 2017
Year: 2017

We present a new topic model that generates documents by sampling a topic for one whole sentence at a time, and generating the words in the sentence using an RNN decoder that is conditioned on the topic of the sentence. We argue that this novel formalism will help us not only visualize and model the topical discourse structure in a document better, but also potentially lead to more interpretable topics since we can now illustrate topics by sampling representative sentences instead of bag of words or phrases. We present a variational auto-encoder approach for learning in which we use a factorized variational encoder that independently models the posterior over topical mixture vectors of documents using a feed-forward network, and the posterior over topic assignments to sentences using an RNN. Our preliminary experiments on two different datasets indicate early promise, but also expose many challenges that remain to be addressed.
A Graph Analytics Framework for Ranking Authors, Papers and Venues
Venue: arXiv cs.DL 2017
Year: 2017

A lot of scientific works are published in different areas of science, technology, engineering and mathematics. It is not easy, even for experts, to judge the quality of authors, papers and venues (conferences and journals). An objective measure to assign scores to these entities and to rank them is very useful. Although, several metrics and indexes have been proposed earlier, they suffer from various problems. In this paper, we propose a graph-based analytics framework to assign scores and to rank authors, papers and venues. Our algorithm considers only the link structures of the underlying graphs. It does not take into account other aspects, such as the associated texts and the reputation of these entities. In the limit of large number of iterations, the solution of the iterative equations gives the unique entity scores. This framework can be easily extended to other interdependent networks.
The Topology-Free Construction of the Universal Type Structure for Conditional Probability Systems
Venue: arXiv cs.LO 2017
Year: 2017

We construct the universal type structure for conditional probability systems without any topological assumption, namely a type structure that is terminal, belief-complete, and non-redundant. In particular, in order to obtain the belief-completeness in a constructive way, we extend the work of Meier [An Infinitary Probability Logic for Type Spaces. Israel Journal of Mathematics, 192, 1-58] by proving strong soundness and strong completeness of an infinitary conditional probability logic with truthful and non-epistemic conditioning events.
Deriving Verb Predicates By Clustering Verbs with Arguments
Venue: arXiv cs.CL 2017
Year: 2017

Hand-built verb clusters such as the widely used Levin classes (Levin, 1993) have proved useful, but have limited coverage. Verb classes automatically induced from corpus data such as those from VerbKB (Wijaya, 2016), on the other hand, can give clusters with much larger coverage, and can be adapted to specific corpora such as Twitter. We present a method for clustering the outputs of VerbKB: verbs with their multiple argument types, e.g. "marry(person, person)", "feel(person, emotion)." We make use of a novel low-dimensional embedding of verbs and their arguments to produce high quality clusters in which the same verb can be in different clusters depending on its argument type. The resulting verb clusters do a better job than hand-built clusters of predicting sarcasm, sentiment, and locus of control in tweets.
Emerging Topics in Internet Technology: A Complex Networks Approach
Venue: arXiv cs.DL 2017
Year: 2017

Communication networks, in general, and internet technology, in particular, is a fast-evolving area of research. While it is important to keep track of emerging trends in this domain, it is such a fast-growing area that it can be very difficult to keep track of literature. The problem is compounded by the fast-growing number of citation databases. While other databases are gradually indexing a large set of reliable content, currently the Web of Science represents one of the most highly valued databases. Research indexed in this database is known to highlight key advancements in any domain. In this paper, we present a Complex Network-based analytical approach to analyze recent data from the Web of Science in communication networks. Taking bibliographic records from the recent period of 2014 to 2017, we model and analyze complex scientometric networks. Using bibliometric coupling applied over complex citation data we present answers to co-citation patterns of documents, co-occurrence patterns of terms, as well as the most influential articles, among others, We also present key pivot points and intellectual turning points. Complex network analysis of the data demonstrates a considerably high level of interest in two key clusters labeled descriptively as "social networks" and "computer networks". In addition, key themes in highly cited literature were clearly identified as "communication networks," "social networks," and "complex networks".
A Software-Defined Multi-Element VLC Architecture
Venue: arXiv cs.NI 2017
Year: 2017

In the modern era of radio frequency (RF) spectrum crunch, visible light communication (VLC) is a recent and promising alternative technology that operates at the visible light spectrum. Thanks to its unlicensed and large bandwidth, VLC can deliver high throughput, better energy efficiency, and low cost data communications. In this article, a hybrid RF/VLC architecture is considered that can simultaneously provide light- ing and communication coverage across a room. Considered architecture involves a novel multi-element hemispherical bulb design, which can transmit multiple data streams over light emitting diode (LED) modules. Simulations considering various VLC transmitter configurations and topologies show that good link quality and high spatial reuse can be maintained in typical indoor communication scenarios.
ProjectionNet: Learning Efficient On-Device Deep Networks Using Neural Projections
Venue: arXiv cs.LG 2017
Year: 2017

Deep neural networks have become ubiquitous for applications related to visual recognition and language understanding tasks. However, it is often prohibitive to use typical neural networks on devices like mobile phones or smart watches since the model sizes are huge and cannot fit in the limited memory available on such devices. While these devices could make use of machine learning models running on high-performance data centers with CPUs or GPUs, this is not feasible for many applications because data can be privacy sensitive and inference needs to be performed directly "on" device. We introduce a new architecture for training compact neural networks using a joint optimization framework. At its core lies a novel objective that jointly trains using two different types of networks--a full trainer neural network (using existing architectures like Feed-forward NNs or LSTM RNNs) combined with a simpler "projection" network that leverages random projections to transform inputs or intermediate representations into bits. The simpler network encodes lightweight and efficient-to-compute operations in bit space with a low memory footprint. The two networks are trained jointly using backpropagation, where the projection network learns from the full network similar to apprenticeship learning. Once trained, the smaller network can be used directly for inference at low memory and computation cost. We demonstrate the effectiveness of the new approach at significantly shrinking the memory requirements of different types of neural networks while preserving good accuracy on visual recognition and text classification tasks. We also study the question "how many neural bits are required to solve a given task?" using the new framework and show empirical results contrasting model predictive capacity (in bits) versus accuracy on several datasets.