OpenRaiser/Intern-Atlas huggingface dataset:
Characterization of migrated seismic volumes using texture attributes: a comparative study
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we examine several typical texture attributes developed in the image processing community in recent years with respect to their capability of characterizing a migrated seismic volume. These attributes are generated in either frequency or space domain, including steerable pyramid, curvelet, local binary pattern, and local radius index. The comparative study is performed within an image retrieval framework. We evaluate these attributes in terms of retrieval accuracy. It is our hope that this comparative study will help acquaint the seismic interpretation community with the many available powerful image texture analysis techniques, providing more alternative attributes for their seismic exploration.
Safe Compilation for Hidden Deterministic Hardware Aliasing and Encrypted Computing
Venue: arXiv cs.CR 2019
Year: 2019

Hardware aliasing occurs when the same logical address sporadically accesses different physical memory locations and is a problem encountered by systems programmers (the opposite, software aliasing, when different addresses access the same location, is more familiar to application programmers). This paper shows how to compile so code works in the presence of {\em hidden deterministic} hardware aliasing. That means that a copy of an address always accesses the same location, and recalculating it exactly the same way also always gives the same access, but otherwise access appears arbitrary and unpredictable. The technique is extended to cover the emerging technology of encrypted computing too.
A QUBO Model for Gaussian Process Variance Reduction
Venue: arXiv cs.ET 2019
Year: 2019

Gaussian Processes are used in many applications to model spatial phenomena. Within this context, a key issue is to decide the set of locations where to take measurements so as to obtain a better approximation of the underlying function. Current state of the art techniques select such set to minimize the posterior variance of the Gaussian process. We explore the feasibility of solving this problem by proposing a novel Quadratic Unconstrained Binary Optimization (QUBO) model. In recent years this QUBO formulation has gained increasing attention since it represents the input for the specialized quantum annealer D-Wave machines. Hence, our contribution takes an important first step towards the sampling optimization of Gaussian processes in the context of quantum computation. Results of our empirical evaluation shows that the optimum of the QUBO objective function we derived represents a good solution for the above mentioned problem. In fact we are able to obtain comparable and in some cases better results than the widely used submodular technique.
Spatial Soft-Core Caching
Venue: arXiv cs.IT 2019
Year: 2019

We propose a decentralized spatial soft-core cache placement (SSCC) policy for wireless networks. SSCC yields a spatially balanced sampling via negative dependence across caches, and can be tuned to satisfy cache size constraints with high probability. Given a desired cache hit probability, we compare the 95% confidence intervals of the required cache sizes for independent placement, hard-core placement and SSCC policies. We demonstrate that in terms of the required cache storage size, SSCC can provide up to more than 180% and 100% gains with respect to the independent and hard-core placement policies, respectively. SSCC can be used to enable proximity-based applications such as device-to-device communications and peer-to-peer networking as it promotes the item diversity and reciprocation among the nodes.
Code Farming: A Process for Creating Generic Computational Building Blocks
Venue: arXiv cs.NE 2019
Year: 2019

Motivated by a desire to improve on the current state of the art in genetic programming, and aided by recent progress in understanding the computational aspects of evolutionary systems, we describe a process that creates a set of generic computational building blocks for the purpose of seeding initial populations of programs in any genetic programming system. This provides an advantage over the standard approach of initializing the population purely randomly in that it avoids the need to constantly rediscover such building blocks. It is also better than seeding the initial population with hand-coded building blocks, since it lessens the amount of human intervention required by the system.
Peer-to-peer Federated Learning on Graphs
Venue: arXiv cs.LG 2019
Year: 2019

We consider the problem of training a machine learning model over a network of nodes in a fully decentralized framework. The nodes take a Bayesian-like approach via the introduction of a belief over the model parameter space. We propose a distributed learning algorithm in which nodes update their belief by aggregate information from their one-hop neighbors to learn a model that best fits the observations over the entire network. In addition, we also obtain sufficient conditions to ensure that the probability of error is small for every node in the network. We discuss approximations required for applying this algorithm to train Deep Neural Networks (DNNs). Experiments on training linear regression model and on training a DNN show that the proposed learning rule algorithm provides a significant improvement in the accuracy compared to the case where nodes learn without cooperation.
High Performance Algorithms for Counting Collisions and Pairwise Interactions
Venue: arXiv cs.DC 2019
Year: 2019

The problem of counting collisions or interactions is common in areas as computer graphics and scientific simulations. Since it is a major bottleneck in applications of these areas, a lot of research has been carried out on such subject, mainly focused on techniques that allow calculations to be performed within pruned sets of objects. This paper focuses on how interaction calculation (such as collisions) within these sets can be done more efficiently than existing approaches. Two algorithms are proposed: a sequential algorithm that has linear complexity at the cost of high memory usage; and a parallel algorithm, mathematically proved to be correct, that manages to use GPU resources more efficiently than existing approaches. The proposed and existing algorithms were implemented, and experiments show a speedup of 21.7 for the sequential algorithm (on small problem size), and 1.12 for the parallel proposal (large problem size). By improving interaction calculation, this work contributes to research areas that promote interconnection in the modern world, such as computer graphics and robotics.
AnomiGAN: Generative adversarial networks for anonymizing private medical data
Venue: arXiv cs.CR 2019
Year: 2019

Typical personal medical data contains sensitive information about individuals. Storing or sharing the personal medical data is thus often risky. For example, a short DNA sequence can provide information that can not only identify an individual, but also his or her relatives. Nonetheless, most countries and researchers agree on the necessity of collecting personal medical data. This stems from the fact that medical data, including genomic data, are an indispensable resource for further research and development regarding disease prevention and treatment. To prevent personal medical data from being misused, techniques to reliably preserve sensitive information should be developed for real world application. In this paper, we propose a framework called anonymized generative adversarial networks (AnomiGAN), to improve the maintenance of privacy of personal medical data, while also maintaining high prediction performance. We compared our method to state-of-the-art techniques and observed that our method preserves the same level of privacy as differential privacy (DP), but had better prediction results. We also observed that there is a trade-off between privacy and performance results depending on the degree of preservation of the original data. Here, we provide a mathematical overview of our proposed model and demonstrate its validation using UCI machine learning repository datasets in order to highlight its utility in practice. Experimentally, our approach delivers a better performance compared to that of the DP approach.
Aggressive Fragmentation Strategy for Enhanced Network Performance in Dense LPWANs
Venue: arXiv cs.NI 2019
Year: 2019

Low Power Wide Area Networks (LPWANs) are gaining ground in the IoT landscape and, in particular, for Industrial IoT applications. However, given the strict duty cycle restrictions (e.g. 1% in SubGHz bands) and the limited power supply of devices, requirements of some applications can not always be met. This paper analyzes the potential of the combination of packet fragmentation -in the direction of the IETF LPWAN working group- and negative group acknowledgement (NACK) in LoRaWAN networks, a widespread LPWAN technology. Results show that the proposed strategy can lead to significant gains in terms of goodput and energy efficiency under congested situations.
Optimization of the Area Under the ROC Curve using Neural Network Supervectors for Text-Dependent Speaker Verification
Venue: arXiv cs.SD 2019
Year: 2019

This paper explores two techniques to improve the performance of text-dependent speaker verification systems based on deep neural networks. Firstly, we propose a general alignment mechanism to keep the temporal structure of each phrase and obtain a supervector with the speaker and phrase information, since both are relevant for a text-dependent verification. As we show, it is possible to use different alignment techniques to replace the global average pooling providing significant gains in performance. Moreover, we also present a novel back-end approach to train a neural network for detection tasks by optimizing the Area Under the Curve (AUC) as an alternative to the usual triplet loss function, so the system is end-to-end, with a cost function close to our desired measure of performance. As we can see in the experimental section, this approach improves the system performance, since our triplet neural network based on an approximation of the AUC (aAUC) learns how to discriminate between pairs of examples from the same identity and pairs of different identities. The different alignment techniques to produce supervectors in addition to the new back-end approach were tested on the RSR2015-Part I database for text-dependent speaker verification, providing competitive results compared to similar size networks using the global average pooling to extract supervectors and using a simple back-end or triplet loss training.
Application of TAM model to the use of information technology
Venue: arXiv cs.HC 2019
Year: 2019

The purpose of this research is to see the application of modified TAM model by entering the experiential variable as a moderation variable to see one's intention in the use of technology especially internet banking. Data obtained through the distribution of questionnaires to customers. The study population is bank customers registered as users of internet banking services. The sample selection used a simple random sampling technique. Hypothesis testing using Partial Least Square (PLS) method through AMOS program. The results showed that the proposed five hypotheses, two significant and three insignificant. Perceived ease of use is significantly related to perceived usefulness. Per-ceived usefulness is not significantly related to intention to use. Perceived ease of use is significantly related to intention to use moderated by experience and not significantly correlated with intention to use moderated by the Experience.
Adding Interpretable Attention to Neural Translation Models Improves Word Alignment
Venue: arXiv cs.CL 2019
Year: 2019

Multi-layer models with multiple attention heads per layer provide superior translation quality compared to simpler and shallower models, but determining what source context is most relevant to each target word is more challenging as a result. Therefore, deriving high-accuracy word alignments from the activations of a state-of-the-art neural machine translation model is an open challenge. We propose a simple model extension to the Transformer architecture that makes use of its hidden representations and is restricted to attend solely on encoder information to predict the next word. It can be trained on bilingual data without word-alignment information. We further introduce a novel alignment inference procedure which applies stochastic gradient descent to directly optimize the attention activations towards a given target word. The resulting alignments dramatically outperform the naive approach to interpreting Transformer attention activations, and are comparable to Giza++ on two publicly available data sets.
TUNet: Incorporating segmentation maps to improve classification
Venue: arXiv cs.CV 2019
Year: 2019

Determining the localization of specific protein in human cells is important for understanding cellular functions and biological processes of underlying diseases. Among imaging techniques, high-throughput fluorescence microscopy imaging is an efficient biotechnology to stain the protein of interest in a cell. In this work, we present a novel classification model Twin U-Net (TUNet) for processing and classifying the belonging of protein in the Atlas images. Several notable Deep Learning models including GoogleNet and Resnet have been employed for comparison. Results have shown that our system obtaining competitive performance.
Equivariant Transformer Networks
Venue: arXiv cs.CV 2019
Year: 2019

How can prior knowledge on the transformation invariances of a domain be incorporated into the architecture of a neural network? We propose Equivariant Transformers (ETs), a family of differentiable image-to-image mappings that improve the robustness of models towards pre-defined continuous transformation groups. Through the use of specially-derived canonical coordinate systems, ETs incorporate functions that are equivariant by construction with respect to these transformations. We show empirically that ETs can be flexibly composed to improve model robustness towards more complicated transformation groups in several parameters. On a real-world image classification task, ETs improve the sample efficiency of ResNet classifiers, achieving relative improvements in error rate of up to 15% in the limited data regime while increasing model parameter count by less than 1%.
Building Knowledge Graphs About Political Agents in the Age of Misinformation
Venue: arXiv cs.CY 2019
Year: 2019

This paper presents the construction of a Knowledge Graph about relations between agents in a political system. It discusses the main modeling challenges, with emphasis on the issue of trust and provenance. Implementation decisions are also presented
Semantic Redundancies in Image-Classification Datasets: The 10% You Don't Need
Venue: arXiv cs.CV 2019
Year: 2019

Large datasets have been crucial to the success of deep learning models in the recent years, which keep performing better as they are trained with more labelled data. While there have been sustained efforts to make these models more data-efficient, the potential benefit of understanding the data itself, is largely untapped. Specifically, focusing on object recognition tasks, we wonder if for common benchmark datasets we can do better than random subsets of the data and find a subset that can generalize on par with the full dataset when trained on. To our knowledge, this is the first result that can find notable redundancies in CIFAR-10 and ImageNet datasets (at least 10%). Interestingly, we observe semantic correlations between required and redundant images. We hope that our findings can motivate further research into identifying additional redundancies and exploiting them for more efficient training or data-collection.
Eliom: A Language for Modular Tierless Web Programming
Venue: arXiv cs.PL 2019
Year: 2019

Tierless Web programming languages allow programmers to combine client-side and server-side programming in a single program. Programmers can then define components with both client and server parts and get flexible, efficient and typesafe client-server communications. However, the expressive client-server features found in most tierless languages are not necessarily compatible with functionalities found in many mainstream languages. In particular, we would like to benefit from type safety, an efficient execution, static compilation, modularity and separate compilation. In this paper, we propose Eliom, an industrial-strength tierless functional Web programming language which extends OCaml with support for rich client/server interactions. It allows to build whole applications as a single distributed program, in which it is possible to define modular tierless libraries with both server and client behaviors and combine them effortlessly. Eliom is the only language that combines type-safe and efficient client/server communications with a static compilation model that supports separate compilation and modularity. It also supports excellent integration with OCaml, allowing to transparently leverage its ecosystem. To achieve all these features, Eliom borrows ideas not only from distributed programming languages, but also from meta-programming and modern module systems. We present the design of Eliom, how it can be used in practice and its formalization; including its type system, semantics and compilation scheme. We show that this compilation scheme preserves typing and semantics, and that it supports separate compilation.
Statistics of the Nonlinear Discrete Spectrum of a Noisy Pulse
Venue: arXiv cs.IT 2019
Year: 2019

In the presence of additive Gaussian noise, the statistics of the nonlinear Fourier transform (NFT) of a pulse are not yet completely known in closed form. In this paper, we propose a novel approach to study this problem. Our contributions are twofold: first, we extend the existing Fourier Collocation (FC) method to compute the whole discrete spectrum (eigenvalues and spectral amplitudes). We show numerically that the accuracy of FC is comparable to the state-of-the-art NFT algorithms. Second, we apply perturbation theory of linear operators to derive analytic expressions for the joint statistics of the eigenvalues and the spectral amplitudes when a pulse is contaminated by additive Gaussian noise. Our analytic expressions closely match the empirical statistics obtained through simulations.
The Interference Channel Revisited: Aligning Interference by Adjusting Receive Antenna Separation
Venue: arXiv cs.IT 2019
Year: 2019

It is shown that a receiver equipped with two antennas may null an arbitrary large number of spatial directions to any desired accuracy, while maintaining the interference-free signal-to-noise ratio, by judiciously adjusting the distance between the antenna elements. The main theoretical result builds on ergodic theory. The practicality of the scheme in moderate signal-to-noise systems is demonstrated for a scenario where each transmitter is equipped with a single antenna and each receiver has two receive chains and where the desired spacing between antenna elements is achieved by selecting the appropriate antennas from a large linear antenna array. We further extend the proposed scheme to show that interference can be eliminated also in specular multipath channels as well as multiple-input multiple-output interference channels where a single extra receiver suffices to align all interferers into a one-dimensional subspace. To demonstrate the performance of the scheme, we show significant gains for interference channels with four as well as six users, at low to moderate signal-to-noise ratios (0-20 dB). The robustness of the proposed technique to small channel estimation errors is also explored.
Exploring the context of recurrent neural network based conversational agents
Venue: arXiv cs.CL 2019
Year: 2019

Conversational agents have begun to rise both in the academic (in terms of research) and commercial (in terms of applications) world. This paper investigates the task of building a non-goal driven conversational agent, using neural network generative models and analyzes how the conversation context is handled. It compares a simpler Encoder-Decoder with a Hierarchical Recurrent Encoder-Decoder architecture, which includes an additional module to model the context of the conversation using previous utterances information. We found that the hierarchical model was able to extract relevant context information and include them in the generation of the output. However, it performed worse (35-40%) than the simple Encoder-Decoder model regarding both grammatically correct output and meaningful response. Despite these results, experiments demonstrate how conversations about similar topics appear close to each other in the context space due to the increased frequency of specific topic-related words, thus leaving promising directions for future research and how the context of a conversation can be exploited.
Deep Recurrent Factor Model: Interpretable Non-Linear and Time-Varying Multi-Factor Model
Venue: arXiv cs.LG 2019
Year: 2019

A linear multi-factor model is one of the most important tools in equity portfolio management. The linear multi-factor models are widely used because they can be easily interpreted. However, financial markets are not linear and their accuracy is limited. Recently, deep learning methods were proposed to predict stock return in terms of the multi-factor model. Although these methods perform quite well, they have significant disadvantages such as a lack of transparency and limitations in the interpretability of the prediction. It is thus difficult for institutional investors to use black-box-type machine learning techniques in actual investment practice because they should show accountability to their customers. Consequently, the solution we propose is based on LSTM with LRP. Specifically, we extend the linear multi-factor model to be non-linear and time-varying with LSTM. Then, we approximate and linearize the learned LSTM models by LRP. We call this LSTM+LRP model a deep recurrent factor model. Finally, we perform an empirical analysis of the Japanese stock market and show that our recurrent model has better predictive capability than the traditional linear model and fully-connected deep learning methods.
Relating Voluntary Turnover with Job Characteristics, Satisfaction and Work Exhaustion - An Initial Study with Brazilian Developers
Venue: arXiv cs.SE 2019
Year: 2019

High rates of turnover among software developers remain, involving additional costs of hiring and training. Voluntary turnover may be due to workplace issues or personal career decisions, but it might as well relate to Job Characteristics, or even Job Satisfaction and Work Exhaustion. This paper reports on an initial study which quantitatively measured those constructs among 78 software developers working in Brazil who left their jobs voluntarily. For this, we adapted well-known survey instruments, namely the JDS from Hackman and Oldham's Job Characteristics Model, and Maslach et al.'s Burnout Measurement. In average, developers demonstrated low to moderate autonomy (3.75, on a 1-7 scale) and satisfaction (4.08), in addition to moderate exhaustion (4.2) before leaving their jobs, while experiencing high task significance (5.15). Also, testers reported significantly lower job satisfaction than programmers. These results allow us to raise interesting hypotheses to be addressed by future studies.