OpenRaiser/Intern-Atlas huggingface dataset:
Quantifying Interpretability and Trust in Machine Learning Systems
Venue: arXiv cs.LG 2019
Year: 2019

Decisions by Machine Learning (ML) models have become ubiquitous. Trusting these decisions requires understanding how algorithms take them. Hence interpretability methods for ML are an active focus of research. A central problem in this context is that both the quality of interpretability methods as well as trust in ML predictions are difficult to measure. Yet evaluations, comparisons and improvements of trust and interpretability require quantifiable measures. Here we propose a quantitative measure for the quality of interpretability methods. Based on that we derive a quantitative measure of trust in ML decisions. Building on previous work we propose to measure intuitive understanding of algorithmic decisions using the information transfer rate at which humans replicate ML model predictions. We provide empirical evidence from crowdsourcing experiments that the proposed metric robustly differentiates interpretability methods. The proposed metric also demonstrates the value of interpretability for ML assisted human decision making: in our experiments providing explanations more than doubled productivity in annotation tasks. However unbiased human judgement is critical for doctors, judges, policy makers and others. Here we derive a trust metric that identifies when human decisions are overly biased towards ML predictions. Our results complement existing qualitative work on trust and interpretability by quantifiable measures that can serve as objectives for further improving methods in this field of research.
End-to-End Optimized Transmission over Dispersive Intensity-Modulated Channels Using Bidirectional Recurrent Neural Networks
Venue: arXiv cs.IT 2019
Year: 2019

We propose an autoencoding sequence-based transceiver for communication over dispersive channels with intensity modulation and direct detection (IM/DD), designed as a bidirectional deep recurrent neural network (BRNN). The receiver uses a sliding window technique to allow for efficient data stream estimation. We find that this sliding window BRNN (SBRNN), based on end-to-end deep learning of the communication system, achieves a significant bit-error-rate reduction at all examined distances in comparison to previous block-based autoencoders implemented as feed-forward neural networks (FFNNs), leading to an increase of the transmission distance. We also compare the end-to-end SBRNN with a state-of-the-art IM/DD solution based on two level pulse amplitude modulation with an FFNN receiver, simultaneously processing multiple received symbols and approximating nonlinear Volterra equalization. Our results show that the SBRNN outperforms such systems at both 42 and 84\,Gb/s, while training fewer parameters. Our novel SBRNN design aims at tailoring the end-to-end deep learning-based systems for communication over nonlinear channels with memory, such as the optical IM/DD fiber channel.
Graph heat mixture model learning
Venue: arXiv cs.LG 2019
Year: 2019

Graph inference methods have recently attracted a great interest from the scientific community, due to the large value they bring in data interpretation and analysis. However, most of the available state-of-the-art methods focus on scenarios where all available data can be explained through the same graph, or groups corresponding to each graph are known a priori. In this paper, we argue that this is not always realistic and we introduce a generative model for mixed signals following a heat diffusion process on multiple graphs. We propose an expectation-maximisation algorithm that can successfully separate signals into corresponding groups, and infer multiple graphs that govern their behaviour. We demonstrate the benefits of our method on both synthetic and real data.
Readership Data and Research Impact
Venue: arXiv cs.DL 2019
Year: 2019

Reading academic publications is a key scholarly activity. Scholars accessing and recording academic publications online are producing new types of readership data. These include publisher, repository, and academic social network download statistics as well as online reference manager records. This chapter discusses the use of download and reference manager data for research evaluation and library collection development. The focus is on the validity and application of readership data as an impact indicator for academic publications across different disciplines. Mendeley is particularly promising in this regard, although all data sources are not subjected to rigorous quality control and can be manipulated.
Automatic Parallel Corpus Creation for Hindi-English News Translation Task
Venue: arXiv cs.CL 2019
Year: 2019

The parallel corpus for multilingual NLP tasks, deep learning applications like Statistical Machine Translation Systems is very important. The parallel corpus of Hindi-English language pair available for news translation task till date is of very limited size as per the requirement of the systems are concerned. In this work we have developed an automatic parallel corpus generation system prototype, which creates Hindi-English parallel corpus for news translation task. Further to verify the quality of generated parallel corpus we have experimented by taking various performance metrics and the results are quite interesting.
One-Class Convolutional Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

We present a novel Convolutional Neural Network (CNN) based approach for one class classification. The idea is to use a zero centered Gaussian noise in the latent space as the pseudo-negative class and train the network using the cross-entropy loss to learn a good representation as well as the decision boundary for the given class. A key feature of the proposed approach is that any pre-trained CNN can be used as the base network for one class classification. The proposed One Class CNN (OC-CNN) is evaluated on the UMDAA-02 Face, Abnormality-1001, FounderType-200 datasets. These datasets are related to a variety of one class application problems such as user authentication, abnormality detection and novelty detection. Extensive experiments demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art methods. The source code is available at : github.com/otkupjnoz/oc-cnn.
An Optimized Pattern Recognition Algorithm for Anomaly Detection in IoT Environment
Venue: arXiv cs.CR 2019
Year: 2019

With the advent of large-scale heterogeneous search engines comes the problem of unified search control resulting in mismatches that could have otherwise avoided. A mechanism is needed to determine exact patterns in web mining and ubiquitous device searching. In this paper we demonstrate the use of an optimized string searching algorithm to recognize exact patterns from a large database. The underlying principle in designing the algorithm is that each letter that maps to a fixed real values and some arithmetic operations which are applied to compute corresponding pattern and substring values. We have implemented this algorithm in C. We have tested the algorithm using a large dataset. We created our own dataset using DNA sequences. The experimental result shows the number of mismatch occurred in string search from a large database. Furthermore, some of the inherent weaknesses in the use of this algorithm are highlighted.
Ask less - Scale Market Research without Annoying Your Customers
Venue: arXiv cs.LG 2019
Year: 2019

Market research is generally performed by surveying a representative sample of customers with questions that includes contexts such as psycho-graphics, demographics, attitude and product preferences. Survey responses are used to segment the customers into various groups that are useful for targeted marketing and communication. Reducing the number of questions asked to the customer has utility for businesses to scale the market research to a large number of customers. In this work, we model this task using Bayesian networks. We demonstrate the effectiveness of our approach using an example market segmentation of broadband customers.
Misleading Metadata Detection on YouTube
Venue: arXiv cs.CL 2019
Year: 2019

YouTube is the leading social media platform for sharing videos. As a result, it is plagued with misleading content that includes staged videos presented as real footages from an incident, videos with misrepresented context and videos where audio/video content is morphed. We tackle the problem of detecting such misleading videos as a supervised classification task. We develop UCNet - a deep network to detect fake videos and perform our experiments on two datasets - VAVD created by us and publicly available FVC [8]. We achieve a macro averaged F-score of 0.82 while training and testing on a 70:30 split of FVC, while the baseline model scores 0.36. We find that the proposed model generalizes well when trained on one dataset and tested on the other.
Robust Transceiver Design for MIMO Decode-and-Forward Full-Duplex Relay
Venue: arXiv cs.IT 2019
Year: 2019

Robust transceiver design against unresolvable system uncertainties is of crucial importance for reliable communication. For instance, full-duplex communication suffers from such uncertainties when canceling the self-interference, since the residual self-interference (RSI) remains uncanceled due to imperfect channel knowledge. We consider a MIMO multi-hop system, where the source, the relay and the destination are equipped with multiple antennas. We allow multi-stream beamforming granted by MIMO technique, without restricting the transmissions to single streaming. The relay can operate in either half-duplex or full-duplex mode, and it changes the mode depending on the RSI strength. Furthermore, the relay is assumed to perform a decode-and-forward (DF) strategy. We investigate a robust transceiver design problem, which maximizes the throughput rate of the worst-case RSI under RSI channel uncertainty bound constraint. The problem turns out to be a non-convex optimization problem. We propose an efficient algorithm to obtain a local optimal solution iteratively. Eventually, we obtain insights on the optimal antenna allocation at the relay input-frontend and output-frontend, for relay reception and transmission, respectively. Interestingly, with less number of antennas at the source than that at the destination, more number of antennas should be used at the relay input-frontend than the relay output-frontend.
Partitioned Paxos via the Network Data Plane
Venue: arXiv cs.DC 2019
Year: 2019

Consensus protocols are the foundation for building fault-tolerant, distributed systems, and services. They are also widely acknowledged as performance bottlenecks. Several recent systems have proposed accelerating these protocols using the network data plane. But, while network-accelerated consensus shows great promise, current systems suffer from an important limitation: they assume that the network hardware also accelerates the application itself. Consequently, they provide a specialized replicated service, rather than providing a general-purpose high-performance consensus that fits any off-the-shelf application. To address this problem, this paper proposes Partitioned Paxos, a novel approach to network-accelerated consensus. The key insight behind Partitioned Paxos is to separate the two aspects of Paxos, agreement, and execution, and optimize them separately. First, Partitioned Paxos uses the network forwarding plane to accelerate agreement. Then, it uses state partitioning and parallelization to accelerate execution at the replicas. Our experiments show that using this combination of data plane acceleration and parallelization, Partitioned Paxos is able to provide at least x3 latency improvement and x11 throughput improvement for a replicated instance of a RocksDB key-value store.
Unsupervised speech representation learning using WaveNet autoencoders
Venue: arXiv cs.LG 2019
Year: 2019

We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high level semantic content from the signal, e.g.\ phoneme identities, while being invariant to confounding low level details in the signal such as the underlying pitch contour or background noise. Since the learned representation is tuned to contain only phonetic content, we resort to using a high capacity WaveNet decoder to infer information discarded by the encoder from previous samples. Moreover, the behavior of autoencoder models depends on the kind of constraint that is applied to the latent representation. We compare three variants: a simple dimensionality reduction bottleneck, a Gaussian Variational Autoencoder (VAE), and a discrete Vector Quantized VAE (VQ-VAE). We analyze the quality of learned representations in terms of speaker independence, the ability to predict phonetic content, and the ability to accurately reconstruct individual spectrogram frames. Moreover, for discrete encodings extracted using the VQ-VAE, we measure the ease of mapping them to phonemes. We introduce a regularization scheme that forces the representations to focus on the phonetic content of the utterance and report performance comparable with the top entries in the ZeroSpeech 2017 unsupervised acoustic unit discovery task.
Joint shape learning and segmentation for medical images using a minimalistic deep network
Venue: arXiv cs.CV 2019
Year: 2019

Recently, state-of-the-art results have been achieved in semantic segmentation using fully convolutional networks (FCNs). Most of these networks employ encoder-decoder style architecture similar to U-Net and are trained with images and the corresponding segmentation maps as a pixel-wise classification task. Such frameworks only exploit class information by using the ground truth segmentation maps. In this paper, we propose a multi-task learning framework with the main aim of exploiting structural and spatial information along with the class information. We modify the decoder part of the FCN to exploit class information and the structural information as well. We intend to do this while also keeping the parameters of the network as low as possible. We obtain the structural information using either of the two ways: i) using the contour map and ii) using the distance map, both of which can be obtained from ground truth segmentation maps with no additional annotation costs. We also explore different ways in which distance maps can be computed and study the effects of different distance maps on the segmentation performance. We also experiment extensively on two different medical image segmentation applications: i.e i) using color fundus images for optic disc and cup segmentation and ii) using endoscopic images for polyp segmentation. Through our experiments, we report results comparable to, and in some cases performing better than the current state-of-the-art architectures and with an order of 2x reduction in the number of parameters.
Bayes metaclassifier and Soft-confusion-matrix classifier in the task of multi-label classification
Venue: arXiv cs.LG 2019
Year: 2019

The aim of this paper was to compare soft confusion matrix approach and Bayes metaclassifier under the multi-label classification framework. Although the methods were successfully applied under the multi-label classification framework, they have not been compared directly thus far. Such comparison is of vital importance because both methods are quite similar as they are both based on the concept of randomized reference classifier. Since both algorithms were designed to deal with single-label problems, they are combined with the problem-transformation approach to multi-label classification. Present study included 29 benchmark datasets and four different base classifiers. The algorithms were compared in terms of 11 quality criteria and the results were subjected to statistical analysis.
New Lower Bounds for Permutation Codes using Linear Block Codes
Venue: arXiv cs.IT 2019
Year: 2019

In this paper we prove new lower bounds for the maximal size of permutation codes by connecting the theory of permutation codes with the theory of linear block codes. More specifically, using the columns of a parity check matrix of an $[n,k,d]_q$ linear block code, we are able to prove the existence of a permutation code in the symmetric group of degree $n$, having minimum distance at least $d$ and large cardinality. With our technique, we obtain new lower bounds for permutation codes that enhance the ones in the literature and provide asymptotic improvements in certain regimes of length and distance of the permutation code.
Scalable Realistic Recommendation Datasets through Fractal Expansions
Venue: arXiv cs.IR 2019
Year: 2019

Recommender System research suffers currently from a disconnect between the size of academic data sets and the scale of industrial production systems. In order to bridge that gap we propose to generate more massive user/item interaction data sets by expanding pre-existing public data sets. User/item incidence matrices record interactions between users and items on a given platform as a large sparse matrix whose rows correspond to users and whose columns correspond to items. Our technique expands such matrices to larger numbers of rows (users), columns (items) and non zero values (interactions) while preserving key higher order statistical properties. We adapt the Kronecker Graph Theory to user/item incidence matrices and show that the corresponding fractal expansions preserve the fat-tailed distributions of user engagements, item popularity and singular value spectra of user/item interaction matrices. Preserving such properties is key to building large realistic synthetic data sets which in turn can be employed reliably to benchmark Recommender Systems and the systems employed to train them. We provide algorithms to produce such expansions and apply them to the MovieLens 20 million data set comprising 20 million ratings of 27K movies by 138K users. The resulting expanded data set has 10 billion ratings, 864K items and 2 million users in its smaller version and can be scaled up or down. A larger version features 655 billion ratings, 7 million items and 17 million users.
LOCATA challenge: speaker localization with a planar array
Venue: arXiv cs.SD 2019
Year: 2019

This document describes our submission to the 2018 LOCalization And TrAcking (LOCATA) challenge (Tasks 1, 3, 5). We estimate the 3D position of a speaker using the Global Coherence Field (GCF) computed from multiple microphone pairs of a DICIT planar array. One of the main challenges when using such an array with omnidirectional microphones is the front-back ambiguity, which is particularly evident in Task 5. We address this challenge by post-processing the peaks of the GCF and exploiting the attenuation introduced by the frame of the array. Moreover, the intermittent nature of speech and the changing orientation of the speaker make localization difficult. For Tasks 3 and 5, we also employ a Particle Filter (PF) that favors the spatio-temporal continuity of the localization results.
Optimal Design of SWIPT-Aware Fog Computing Networks
Venue: arXiv cs.CY 2019
Year: 2019

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing network, where a multiple antenna fog function integrated hybrid access point (F-HAP) transfers information and energy to multiple heterogeneous single-antenna sensors and also helps some of them fulfill computing tasks. By jointly optimizing energy and information beamforming designs at the F-HAP, the bandwidth allocation and the computation offloading distribution, an optimization problem is formulated to minimize the required energy under communication and computation requirements, as well as energy harvesting constraints. Two optimal designs, i.e., fixed offloading time (FOT) and optimized offloading time (OOT) designs, are proposed. As both designs get involved in solving non-convex problems, there are no known solutions to them. Therefore, for the FOT design, the semidefinite relaxation (SDR) is adopted to solve it. It is theoretically proved that the rank-one constraints are always satisfied, so the global optimal solution is guaranteed. For the OOT design, since its non-convexity is hard to deal with, a penalty dual decomposition (PDD)-based algorithm is proposed, which is able to achieve a suboptimal solution. The computational complexity for two designs are analyzed. Numerical results show that the partial offloading mode is superior to binary benchmark modes. It is also shown that if the system is with strong enough computing capability, the OOT design is suggested to achieve lower required energy; Otherwise, the FOT design is preferred to achieve a relatively low computation complexity.
Deep Learning on Small Datasets without Pre-Training using Cosine Loss
Venue: arXiv cs.LG 2019
Year: 2019

Two things seem to be indisputable in the contemporary deep learning discourse: 1. The categorical cross-entropy loss after softmax activation is the method of choice for classification. 2. Training a CNN classifier from scratch on small datasets does not work well. In contrast to this, we show that the cosine loss function provides significantly better performance than cross-entropy on datasets with only a handful of samples per class. For example, the accuracy achieved on the CUB-200-2011 dataset without pre-training is by 30% higher than with the cross-entropy loss. Further experiments on other popular datasets confirm our findings. Moreover, we demonstrate that integrating prior knowledge in the form of class hierarchies is straightforward with the cosine loss and improves classification performance further.
A Kalman filtering induced heuristic optimization based partitional data clustering
Venue: arXiv cs.LG 2019
Year: 2019

Clustering algorithms have regained momentum with recent popularity of data mining and knowledge discovery approaches. To obtain good clustering in reasonable amount of time, various meta-heuristic approaches and their hybridization, sometimes with K-Means technique, have been employed. A Kalman Filtering based heuristic approach called Heuristic Kalman Algorithm (HKA) has been proposed a few years ago, which may be used for optimizing an objective function in data/feature space. In this paper at first HKA is employed in partitional data clustering. Then an improved approach named HKA-K is proposed, which combines the benefits of global exploration of HKA and the fast convergence of K-Means method. Implemented and tested on several datasets from UCI machine learning repository, the results obtained by HKA-K were compared with other hybrid meta-heuristic clustering approaches. It is shown that HKA-K is atleast as good as and often better than the other compared algorithms.
Optimal $k$-Coverage Charging Problem
Venue: arXiv cs.NI 2019
Year: 2019

Wireless rechargeable sensor networks, consisting of sensor nodes with rechargeable batteries and mobile chargers to replenish their batteries, have gradually become a promising solution to the bottleneck of energy limitation that hinders the wide deployment of wireless sensor networks (WSN). In this paper, we focus on the mobile charger scheduling and path optimization scenario in which the $k$-coverage ability of a network system needs to be maintained. We formulate the optimal $k$-coverage charging problem of finding a feasible path for a mobile charger to charge a set of sensor nodes within their estimated charging deadlines under the constraint of maintaining the $k$-coverage ability of the network system, with an objective of minimizing the energy consumption on traveling per tour. We show the hardness of the problem that even finding a feasible path for the trivial case of the problem is an NP-complete one. We model the problem and apply dynamic programming to design an algorithm that finds an exact solution to the optimal $k$-coverage charging problem. However, the computational complexity is still prohibitive for large size networks. We then introduce Deep Q-learning, a reinforcement learning algorithm to tackle the problem.