OpenRaiser/Intern-Atlas huggingface dataset:
Discriminative Dictionary Learning based on Statistical Methods
Venue: arXiv cs.CV 2021
Year: 2021

Sparse Representation (SR) of signals or data has a well founded theory with rigorous mathematical error bounds and proofs. SR of a signal is given by superposition of very few columns of a matrix called Dictionary, implicitly reducing dimensionality. Training dictionaries such that they represent each class of signals with minimal loss is called Dictionary Learning (DL). Dictionary learning methods like Method of Optimal Directions (MOD) and K-SVD have been successfully used in reconstruction based applications in image processing like image "denoising", "inpainting" and others. Other dictionary learning algorithms such as Discriminative K-SVD and Label Consistent K-SVD are supervised learning methods based on K-SVD. In our experience, one of the drawbacks of current methods is that the classification performance is not impressive on datasets like Telugu OCR datasets, with large number of classes and high dimensionality. There is scope for improvement in this direction and many researchers have used statistical methods to design dictionaries for classification. This chapter presents a review of statistical techniques and their application to learning discriminative dictionaries. The objective of the methods described here is to improve classification using sparse representation. In this chapter a hybrid approach is described, where sparse coefficients of input data are generated. We use a simple three layer Multi Layer Perceptron with back-propagation training as a classifier with those sparse codes as input. The results are quite comparable with other computation intensive methods. Keywords: Statistical modeling, Dictionary Learning, Discriminative Dictionary, Sparse representation, Gaussian prior, Cauchy prior, Entropy, Hidden Markov model, Hybrid Dictionary Learning
Two-Face: Adversarial Audit of Commercial Face Recognition Systems
Venue: arXiv cs.CV 2021
Year: 2021

Computer vision applications like automated face detection are used for a variety of purposes ranging from unlocking smart devices to tracking potential persons of interest for surveillance. Audits of these applications have revealed that they tend to be biased against minority groups which result in unfair and concerning societal and political outcomes. Despite multiple studies over time, these biases have not been mitigated completely and have in fact increased for certain tasks like age prediction. While such systems are audited over benchmark datasets, it becomes necessary to evaluate their robustness for adversarial inputs. In this work, we perform an extensive adversarial audit on multiple systems and datasets, making a number of concerning observations - there has been a drop in accuracy for some tasks on CELEBSET dataset since a previous audit. While there still exists a bias in accuracy against individuals from minority groups for multiple datasets, a more worrying observation is that these biases tend to get exorbitantly pronounced with adversarial inputs toward the minority group. We conclude with a discussion on the broader societal impacts in light of these observations and a few suggestions on how to collectively deal with this issue.
Cross-platform graphics subsystem for an ARINC 653-compatible real-time operating system
Venue: arXiv cs.SE 2021
Year: 2021

In the development of modern cockpits, there is a trend towards the use of large displays that combine information about air navigation and the status of aircraft equipment. Flight and equipment performance information generated by multiple flight control systems should be graphically displayed in an easy-to-read form on widescreen multifunction displays. It is usually generated by independent systems whose output must not interfere with each other in accordance with the requirements of the ARINC 653 standard. This paper presents a solution to the problem of displaying ARINC 653 applications, which further improves security and portability, when running multiple applications on a single screen of one physical device.
Rapping-Singing Voice Synthesis based on Phoneme-level Prosody Control
Venue: arXiv cs.SD 2021
Year: 2021

In this paper, a text-to-rapping/singing system is introduced, which can be adapted to any speaker's voice. It utilizes a Tacotron-based multispeaker acoustic model trained on read-only speech data and which provides prosody control at the phoneme level. Dataset augmentation and additional prosody manipulation based on traditional DSP algorithms are also investigated. The neural TTS model is fine-tuned to an unseen speaker's limited recordings, allowing rapping/singing synthesis with the target's speaker voice. The detailed pipeline of the system is described, which includes the extraction of the target pitch and duration values from an a capella song and their conversion into target speaker's valid range of notes before synthesis. An additional stage of prosodic manipulation of the output via WSOLA is also investigated for better matching the target duration values. The synthesized utterances can be mixed with an instrumental accompaniment track to produce a complete song. The proposed system is evaluated via subjective listening tests as well as in comparison to an available alternate system which also aims to produce synthetic singing voice from read-only training data. Results show that the proposed approach can produce high quality rapping/singing voice with increased naturalness.
An efficient sensitivity analysis for energy performance of a building envelope: a continuous derivative based approach
Venue: arXiv cs.CE 2021
Year: 2021

Within the framework of building energy assessment, this article proposes to use a derivative based sensitivity analysis of heat transfer models in a building envelope. Two, global and local, estimators are obtained at low computational cost, to evaluate the influence of the parameters on the model outputs. Ranking of these estimators values allows to reduce the number of model unknown parameters by excluding non-significant parameters. A comparison with variance and regression-based methods is carried out and the results highlight the satisfactory accuracy of the continuous-based approach. Moreover, for the carried investigations the approach is $100$ times faster compared to the variance-based methods. A case study applies the method to a real-world building wall. The sensitivity of the thermal loads to local or global variations of the wall thermal is investigated. Additionally, a case study of wall with window is analyzed.
Contracts with Private Cost per Unit-of-Effort
Venue: arXiv cs.GT 2021
Year: 2021

Economic theory distinguishes between principal-agent settings in which the agent has a private type and settings in which the agent takes a hidden action. Many practical problems, however, involve aspects of both. For example, brand X may seek to hire an influencer Y to create sponsored content to be posted on social media platform Z. This problem has a hidden action component (the brand may not be able or willing to observe the amount of effort exerted by the influencer), but also a private type component (influencers may have different costs per unit-of-effort). This "effort" and "cost per unit-of-effort" perspective naturally leads to a principal-agent problem with hidden action and single-dimensional private type, which generalizes both the classic principal-agent hidden action model of contract theory à la Grossman and Hart [1983] and the (procurement version) of single-dimensional mechanism design à la Myerson [1981]. A natural goal in this model is to design an incentive-compatible contract, which consist of an allocation rule that maps types to actions, and a payment rule that maps types to payments for the stochastic outcomes of the chosen action. Our main contribution is a linear programming (LP) duality based characterization of implementable allocation rules for this model, which applies to both discrete and continuous types. This characterization shares important features of Myerson's celebrated characterization result, but also departs from it in significant ways. We present several applications, including a polynomial-time algorithm for finding the optimal contract with a constant number of actions. This is in sharp contrast to recent work on hidden action problems with multi-dimensional private information, which has shown that the problem of computing an optimal contract for constant numbers of actions is APX-hard.
Are automated static analysis tools worth it? An investigation into relative warning density and external software quality
Venue: arXiv cs.SE 2021
Year: 2021

Automated Static Analysis Tools (ASATs) are part of software development best practices. ASATs are able to warn developers about potential problems in the code. On the one hand, ASATs are based on best practices so there should be a noticeable effect on software quality. On the other hand, ASATs suffer from false positive warnings, which developers have to inspect and then ignore or mark as invalid. In this article, we ask the question if ASATs have a measurable impact on external software quality, using the example of PMD for Java. We investigate the relationship between ASAT warnings emitted by PMD on defects per change and per file. Our case study includes data for the history of each file as well as the differences between changed files and the project in which they are contained. We investigate whether files that induce a defect have more static analysis warnings than the rest of the project. Moreover, we investigate the impact of two different sets of ASAT rules. We find that, bug inducing files contain less static analysis warnings than other files of the project at that point in time. However, this can be explained by the overall decreasing warning density. When compared with all other changes, we find a statistically significant difference in one metric for all rules and two metrics for a subset of rules. However, the effect size is negligible in all cases, showing that the actual difference in warning density between bug inducing changes and other changes is small at best.
IV-GNN : Interval Valued Data Handling Using Graph Neural Network
Venue: arXiv cs.LG 2021
Year: 2021

Graph Neural Network (GNN) is a powerful tool to perform standard machine learning on graphs. To have a Euclidean representation of every node in the Non-Euclidean graph-like data, GNN follows neighbourhood aggregation and combination of information recursively along the edges of the graph. Despite having many GNN variants in the literature, no model can deal with graphs having nodes with interval-valued features. This article proposes an Interval-ValuedGraph Neural Network, a novel GNN model where, for the first time, we relax the restriction of the feature space being countable. Our model is much more general than existing models as any countable set is always a subset of the universal set $R^{n}$, which is uncountable. Here, to deal with interval-valued feature vectors, we propose a new aggregation scheme of intervals and show its expressive power to capture different interval structures. We validate our theoretical findings about our model for graph classification tasks by comparing its performance with those of the state-of-the-art models on several benchmark network and synthetic datasets.
Optimal-Horizon Model-Predictive Control with Differential Dynamic Programming
Venue: arXiv cs.RO 2021
Year: 2021

We present an algorithm, based on the Differential Dynamic Programming framework, to handle trajectory optimization problems in which the horizon is determined online rather than fixed a priori. This algorithm exhibits exact one-step convergence for linear, quadratic, time-invariant problems and is fast enough for real-time nonlinear model-predictive control. We show derivations for the nonlinear algorithm in the discrete-time case, and apply this algorithm to a variety of nonlinear problems. Finally, we show the efficacy of the optimal-horizon model-predictive control scheme compared to a standard MPC controller, on an obstacle-avoidance problem with planar robots.
A Discussion and Comparative Study on Security and Privacy of Smart Meter Data
Venue: arXiv cs.CR 2021
Year: 2021

Cloud computing comes with a lot of advanced features along with privacy and security problem. Smart meter data takes the benefit of cloud computing in the smart grid. User's privacy can be compromised by analyzing the smart meter data generated by household electrical appliances. The user loses control over the data while data is shifted to the cloud. This paper describes the issues under the privacy and security of smart meter data in the cloud environment. We also compare the existing approaches for preserving the privacy and security of smart meter data.
A Hitting Set Relaxation for $k$-Server and an Extension to Time-Windows
Venue: arXiv cs.DS 2021
Year: 2021

We study the $k$-server problem with time-windows. In this problem, each request $i$ arrives at some point $v_i$ of an $n$-point metric space at time $b_i$ and comes with a deadline $e_i$. One of the $k$ servers must be moved to $v_i$ at some time in the interval $[b_i, e_i]$ to satisfy this request. We give an online algorithm for this problem with a competitive ratio of ${\rm polylog} (n,Δ)$, where $Δ$ is the aspect ratio of the metric space. Prior to our work, the best competitive ratio known for this problem was $O(k \cdot {\rm polylog}(n))$ given by Azar et al. (STOC 2017). Our algorithm is based on a new covering linear program relaxation for $k$-server on HSTs. This LP naturally corresponds to the min-cost flow formulation of $k$-server, and easily extends to the case of time-windows. We give an online algorithm for obtaining a feasible fractional solution for this LP, and a primal dual analysis framework for accounting the cost of the solution. Together, they yield a new $k$-server algorithm with poly-logarithmic competitive ratio, and extend to the time-windows case as well. Our principal technical contribution lies in thinking of the covering LP as yielding a {\em truncated} covering LP at each internal node of the tree, which allows us to keep account of server movements across subtrees. We hope that this LP relaxation and the algorithm/analysis will be a useful tool for addressing $k$-server and related problems.
Max-3-Lin over Non-Abelian Groups with Universal Factor Graphs
Venue: arXiv cs.CC 2021
Year: 2021

Factor graph of an instance of a constraint satisfaction problem with n variables and m constraints is the bipartite graph between [m] and [n] describing which variable appears in which constraints. Thus, an instance of a CSP is completely defined by its factor graph and the list of predicates. We show inapproximability of Max-3-LIN over non-abelian groups (both in the perfect completeness case and in the imperfect completeness case), with the same inapproximability factor as in the general case, even when the factor graph is fixed. Along the way, we also show that these optimal hardness results hold even when we restrict the linear equations in the Max-3-LIN instances to the form x * y * z = g, where x, y, z are the variables and g is a group element. We use representation theory and Fourier analysis over non-abelian groups to analyze the reductions.
DiverGAN: An Efficient and Effective Single-Stage Framework for Diverse Text-to-Image Generation
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we present an efficient and effective single-stage framework (DiverGAN) to generate diverse, plausible and semantically consistent images according to a natural-language description. DiverGAN adopts two novel word-level attention modules, i.e., a channel-attention module (CAM) and a pixel-attention module (PAM), which model the importance of each word in the given sentence while allowing the network to assign larger weights to the significant channels and pixels semantically aligning with the salient words. After that, Conditional Adaptive Instance-Layer Normalization (CAdaILN) is introduced to enable the linguistic cues from the sentence embedding to flexibly manipulate the amount of change in shape and texture, further improving visual-semantic representation and helping stabilize the training. Also, a dual-residual structure is developed to preserve more original visual features while allowing for deeper networks, resulting in faster convergence speed and more vivid details. Furthermore, we propose to plug a fully-connected layer into the pipeline to address the lack-of-diversity problem, since we observe that a dense layer will remarkably enhance the generative capability of the network, balancing the trade-off between a low-dimensional random latent code contributing to variants and modulation modules that use high-dimensional and textual contexts to strength feature maps. Inserting a linear layer after the second residual block achieves the best variety and quality. Both qualitative and quantitative results on benchmark data sets demonstrate the superiority of our DiverGAN for realizing diversity, without harming quality and semantic consistency.
Sentiment Analysis of Microblogging dataset on Coronavirus Pandemic
Venue: arXiv cs.SI 2021
Year: 2021

Sentiment analysis can largely influence the people to get the update of the current situation. Coronavirus (COVID-19) is a contagious illness caused by the coronavirus 2 that causes severe respiratory symptoms. The lives of millions have continued to be affected by this pandemic, several countries have resorted to a full lockdown. During this lockdown, people have taken social networks to express their emotions to find a way to calm themselves down. People are spreading their sentiments through microblogging websites as one of the most preventive steps of this disease is the socialization to gain people's awareness to stay home and keep their distance when they are outside home. Twitter is a popular online social media platform for exchanging ideas. People can post their different sentiments, which can be used to aware people. But, some people want to spread fake news to frighten the people. So, it is necessary to identify the positive, negative, and neutral thoughts so that the positive opinions can be delivered to the mass people for spreading awareness to the people. Moreover, a huge volume of data is floating on Twitter. So, it is also important to identify the context of the dataset. In this paper, we have analyzed the Twitter dataset for evaluating the sentiment using several machine learning algorithms. Later, we have found out the context learning of the dataset based on the sentiments.
Learning to Align Sequential Actions in the Wild
Venue: arXiv cs.CV 2021
Year: 2021

State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspondences across videos in time. They either learn frame-to-frame mapping across sequences, which does not leverage temporal information, or assume monotonic alignment between each video pair, which ignores variations in the order of actions. As such, these methods are not able to deal with common real-world scenarios that involve background frames or videos that contain non-monotonic sequence of actions. In this paper, we propose an approach to align sequential actions in the wild that involve diverse temporal variations. To this end, we propose an approach to enforce temporal priors on the optimal transport matrix, which leverages temporal consistency, while allowing for variations in the order of actions. Our model accounts for both monotonic and non-monotonic sequences and handles background frames that should not be aligned. We demonstrate that our approach consistently outperforms the state-of-the-art in self-supervised sequential action representation learning on four different benchmark datasets.
Hypercontractivity on High Dimensional Expanders: Approximate Efron-Stein Decompositions for $\varepsilon$-Product Spaces
Venue: arXiv cs.CC 2021
Year: 2021

We prove hypercontractive inequalities on high dimensional expanders. As in the settings of the p-biased hypercube, the symmetric group, and the Grassmann scheme, our inequalities are effective for global functions, which are functions that are not significantly affected by a restriction of a small set of coordinates. As applications, we obtain Fourier concentration, small-set expansion, and Kruskal-Katona theorems for high dimensional expanders. Our techniques rely on a new approximate Efron-Stein decomposition for high dimensional link expanders.
Fine-Grained Vehicle Classification in Urban Traffic Scenes using Deep Learning
Venue: arXiv cs.CV 2021
Year: 2021

The increasingly dense traffic is becoming a challenge in our local settings, urging the need for a better traffic monitoring and management system. Fine-grained vehicle classification appears to be a challenging task as compared to vehicle coarse classification. Exploring a robust approach for vehicle detection and classification into fine-grained categories is therefore essentially required. Existing Vehicle Make and Model Recognition (VMMR) systems have been developed on synchronized and controlled traffic conditions. Need for robust VMMR in complex, urban, heterogeneous, and unsynchronized traffic conditions still remain an open research area. In this paper, vehicle detection and fine-grained classification are addressed using deep learning. To perform fine-grained classification with related complexities, local dataset THS-10 having high intra-class and low interclass variation is exclusively prepared. The dataset consists of 4250 vehicle images of 10 vehicle models, i.e., Honda City, Honda Civic, Suzuki Alto, Suzuki Bolan, Suzuki Cultus, Suzuki Mehran, Suzuki Ravi, Suzuki Swift, Suzuki Wagon R and Toyota Corolla. This dataset is available online. Two approaches have been explored and analyzed for classification of vehicles i.e, fine-tuning, and feature extraction from deep neural networks. A comparative study is performed, and it is demonstrated that simpler approaches can produce good results in local environment to deal with complex issues such as dense occlusion and lane departures. Hence reducing computational load and time, e.g. fine-tuning Inception-v3 produced highest accuracy of 97.4% with lowest misclassification rate of 2.08%. Fine-tuning MobileNet-v2 and ResNet-18 produced 96.8% and 95.7% accuracies, respectively. Extracting features from fc6 layer of AlexNet produces an accuracy of 93.5% with a misclassification rate of 6.5%.
Distributed Proximal Policy Optimization for Contention-Based Spectrum Access
Venue: arXiv cs.NI 2021
Year: 2021

The increasing number of wireless devices operating in unlicensed spectrum motivates the development of intelligent adaptive approaches to spectrum access that go beyond traditional carrier sensing. We develop a novel distributed implementation of a policy gradient method known as Proximal Policy Optimization modelled on a two stage Markov decision process that enables such an intelligent approach, and still achieves decentralized contention-based medium access. In each time slot, a base station (BS) uses information from spectrum sensing and reception quality to autonomously decide whether or not to transmit on a given resource, with the goal of maximizing proportional fairness network-wide. Empirically, we find the proportional fairness reward accumulated by the policy gradient approach to be significantly higher than even a genie-aided adaptive energy detection threshold. This is further validated by the improved sum and maximum user throughputs achieved by our approach.