OpenRaiser/Intern-Atlas huggingface dataset:
One-Shot Object Localization Using Learnt Visual Cues via Siamese Networks
Venue: arXiv cs.CV 2020
Year: 2020

A robot that can operate in novel and unstructured environments must be capable of recognizing new, previously unseen, objects. In this work, a visual cue is used to specify a novel object of interest which must be localized in new environments. An end-to-end neural network equipped with a Siamese network is used to learn the cue, infer the object of interest, and then to localize it in new environments. We show that a simulated robot can pick-and-place novel objects pointed to by a laser pointer. We also evaluate the performance of the proposed approach on a dataset derived from the Omniglot handwritten character dataset and on a small dataset of toys.
Towards Assessing Critical Infrastructures Cyber-Security Culture During Covid-19 Crisis: A Tailor-Made Survey
Venue: arXiv cs.CR 2020
Year: 2020

This paper outlines the design and development of a survey targeting the cyber-security culture assessment of critical infrastructures during the COVID-19 crisis, when living routine was seriously disturbed and working reality fundamentally affected. Its foundations lie on a security culture framework consisted of 10 different security dimensions analysed into 52 domains examined under two different pillars: organizational and individual. In this paper, a detailed questionnaire building analysis is being presented while revealing the aims, goals and expected outcomes of each question. It concludes with the survey implementation and delivery plan following a number of pre-survey stages each serving a specific methodological purpose.
Analogy, Mind, and Life
Venue: arXiv cs.OH 2020
Year: 2020

I'll show that the kind of analogy between life and information [argue for by authors such as Davies (2000), Walker and Davies (2013), Dyson (1979), Gleick (2011), Kurzweil (2012), Ward (2009)], that seems to be central to the effect that artificial mind may represents an expected advance in the life evolution in Universe, is like the design argument and that if the design argument is unfounded and invalid, the argument to the effect that artificial mind may represents an expected advance in the life evolution in Universe is also unfounded and invalid. However, if we are prepared to admit (though we should not do) this method of reasoning as valid, I'll show that the analogy between life and information to the effect that artificial mind may represents an expected advance in the life evolution in Universe seems suggest some type of reductionism of life to information, but biology respectively chemistry or physics are not reductionist, contrary to what seems to be suggested by the analogy between life and information.
Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder
Venue: arXiv cs.LG 2020
Year: 2020

One of the most challenging problems in the field of intrusion detection is anomaly detection for discrete event logs. While most earlier work focused on applying unsupervised learning upon engineered features, most recent work has started to resolve this challenge by applying deep learning methodology to abstraction of discrete event entries. Inspired by natural language processing, LSTM-based anomaly detection models were proposed. They try to predict upcoming events, and raise an anomaly alert when a prediction fails to meet a certain criterion. However, such a predict-next-event methodology has a fundamental limitation: event predictions may not be able to fully exploit the distinctive characteristics of sequences. This limitation leads to high false positives (FPs) and high false negatives (FNs). It is also critical to examine the structure of sequences and the bi-directional causality among individual events. To this end, we propose a new methodology: Recomposing event sequences as anomaly detection. We propose DabLog, a Deep Autoencoder-Based anomaly detection method for discrete event Logs. The fundamental difference is that, rather than predicting upcoming events, our approach determines whether a sequence is normal or abnormal by analyzing (encoding) and reconstructing (decoding) the given sequence. Our evaluation results show that our new methodology can significantly reduce the numbers of FPs and FNs, hence achieving a higher $F_1$ score.
Analysis of Short Dwell Time in Relation to User Interest in a News Application
Venue: arXiv cs.IR 2020
Year: 2020

Dwell time has been widely used in various fields to evaluate content quality and user engagement. Although many studies shown that content with long dwell time is good quality, contents with short dwell time have not been discussed in detail. We hypothesize that content with short dwell time is not always low quality and does not always have low user engagement, but is instead related to user interest. The purpose of this study is to clarify the meanings of short dwell time browsing in mobile news application. First, we analyze the relation of short dwell time to user interest using large scale user behavior logs from a mobile news application. This analysis was conducted on a vector space based on users click histories and then users and articles were mapped in the same space. The users with short dwell time are concentrated on a specific position in this space; thus, the length of dwell time is related to their interest. Moreover, we also analyze the characteristics of short dwell time browsing by excluding these browses from their click histories. Surprisingly, excluding short dwell time click history, it was found that short dwell time click history included some aspect of user interest in 30.87% of instances where the cluster of users changed. These findings demonstrate that short dwell time does not always indicate a low level of user engagement, but also level of user interest.
Neural document expansion for ad-hoc information retrieval
Venue: arXiv cs.IR 2020
Year: 2020

Recently, Nogueira et al. [2019] proposed a new approach to document expansion based on a neural Seq2Seq model, showing significant improvement on short text retrieval task. However, this approach needs a large amount of in-domain training data. In this paper, we show that this neural document expansion approach can be effectively adapted to standard IR tasks, where labels are scarce and many long documents are present.
Convolutional Neural Networks in Multi-Class Classification of Medical Data
Venue: arXiv cs.LG 2020
Year: 2020

We report applications of Convolutional Neural Networks (CNN) to multi-classification classification of a large medical data set. We discuss in detail how changes in the CNN model and the data pre-processing impact the classification results. In the end, we introduce an ensemble model that consists of both deep learning (CNN) and shallow learning models (Gradient Boosting). The method achieves Accuracy of 64.93, the highest three-class classification accuracy we achieved in this study. Our results also show that CNN and the ensemble consistently obtain a higher Recall than Precision. The highest Recall is 68.87, whereas the highest Precision is 65.04.
A 3/4 Differential Approximation Algorithm for Traveling Salesman Problem
Venue: arXiv cs.DS 2020
Year: 2020

In this paper, we consider differential approximability of the traveling salesman problem (TSP). We show that TSP is $3/4$-differential approximable, which improves the currently best known bound $3/4 -O(1/n)$ due to Escoffier and Monnot in 2008, where $n$ denotes the number of vertices in the given graph.
Selfish Caching Games on Directed Graphs
Venue: arXiv cs.GT 2020
Year: 2020

Caching networks can reduce the routing costs of accessing contents by caching contents closer to users. However, cache nodes may belong to different entities and behave selfishly to maximize their own benefits, which often lead to performance degradation for the overall network. While there has been extensive literature on allocating contents to caches to maximize the social welfare, the analysis of selfish caching behaviors remains largely unexplored. In this paper, we model the selfish behaviors of cache nodes as selfish caching games on arbitrary directed graphs with heterogeneous content popularity. We study the existence of a pure strategy Nash equilibrium (PSNE) in selfish caching games, and analyze its efficiency in terms of social welfare. We show that a PSNE does not always exist in arbitrary-topology caching networks. However, if the network does not have a mixed request loop, i.e., a directed loop in which each edge is traversed by at least one content request, we show that a PSNE always exists and can be found in polynomial time. Furthermore, we can avoid mixed request loops by properly choosing request forwarding paths. We then show that the efficiency of Nash equilibria, captured by the price of anarchy (PoA), can be arbitrarily poor if we allow arbitrary content request patterns, and adding extra cache nodes can make the PoA worse, i.e., cache paradox happens. However, when cache nodes have homogeneous request patterns, we show that the PoA is bounded even allowing arbitrary topologies. We further analyze the selfish caching games for cache nodes with limited computational capabilities, and show that an approximate PSNE exists with bounded PoA in certain cases of interest. Simulation results show that increasing the cache capacity in the network improves the efficiency of Nash equilibria, while adding extra cache nodes can degrade the efficiency of Nash equilibria.
A Comprehensive Empirical Evaluation of Generating Test Suites for Mobile Applications with Diversity
Venue: arXiv cs.SE 2020
Year: 2020

Context: In search-based software engineering we often use popular heuristics with default configurations, which typically lead to suboptimal results, or we perform experiments to identify configurations on a trial-and-error basis, which may lead to better results for a specific problem. We consider the problem of generating test suites for mobile applications (apps) and rely on \Sapienz, a state-of-the-art approach to this problem that uses a popular heuristic (NSGA-II) with a default configuration. Objective: We want to achieve better results in generating test suites with \Sapienz while avoiding trial-and-error experiments to identify a more suitable configuration of \Sapienz. Method: We conducted a fitness landscape analysis of \Sapienz to analytically understand the search problem, which allowed us to make informed decisions about the heuristic and configuration of \Sapienz when developing \SapienzDiv. We comprehensively evaluated \SapienzDiv in a head-to-head comparison with \Sapienz on 34 apps. Results: Analyzing the fitness landscape of \Sapienz, we observed a lack of diversity of the evolved test suites and a stagnation of the search after 25 generations. \SapienzDiv realizes mechanisms that preserve the diversity of the test suites being evolved. The evaluation showed that \SapienzDiv achieves better or at least similar test results than \Sapienz concerning coverage and the number of revealed faults. However, \SapienzDiv typically produces longer test sequences and requires more execution time than \Sapienz. Conclusions: The understanding of the search problem obtained by the fitness landscape analysis helped us to find a more suitable configuration of \Sapienz without trial-and-error experiments. By promoting diversity of test suites during the search, improved or at least similar test results in terms of faults and coverage can be achieved.
New Insights into the Structure of Equilibria for the Network Creation Game
Venue: arXiv cs.GT 2020
Year: 2020

We study the sum classic network creation game introduced by Fabrikant et al. in which $n$ players conform a network buying links at individual price $α$. When studying this model we are mostly interested in \emph{Nash equilibria} (\NE) and the \emph{Price of Anarchy} (\PoA). It is conjectured that the \PoA is constant for any $α$. Up until now, it has been proved constant \PoA for the range $α= O(n^{1-δ_1})$ with $δ_1>0$ a positive constant and it has been proved constant \PoA for the range $α>n(1+δ_2)$ with $δ_2>0$ a positive constant. Our contribution consists in proving that \NE graphs satisfy very restrictive topological properties generalising some properties proved in the literature and providing new insights that might help settling the conjecture that the \PoA is constant for the remaining range of $α$: (i) We show that \emph{every} node has the majority of the other nodes of the \NE graph at the \emph{same} narrow range of distances. (ii) If instead of considering the average degree, we focus on the number of non-bridge links bought for any player, we can prove that there exists a constant $D$ such that the average degree is upper bounded by $\max(R, \frac{2n}α+6)$ for any \NE graph of diameter larger than $D$, where $R$ is a positive constant.
Context-Aware Personality Inference in Dyadic Scenarios: Introducing the UDIVA Dataset
Venue: arXiv cs.CV 2020
Year: 2020

This paper introduces UDIVA, a new non-acted dataset of face-to-face dyadic interactions, where interlocutors perform competitive and collaborative tasks with different behavior elicitation and cognitive workload. The dataset consists of 90.5 hours of dyadic interactions among 147 participants distributed in 188 sessions, recorded using multiple audiovisual and physiological sensors. Currently, it includes sociodemographic, self- and peer-reported personality, internal state, and relationship profiling from participants. As an initial analysis on UDIVA, we propose a transformer-based method for self-reported personality inference in dyadic scenarios, which uses audiovisual data and different sources of context from both interlocutors to regress a target person's personality traits. Preliminary results from an incremental study show consistent improvements when using all available context information.
Low-Cost Maximum Entropy Covariance Matrix Reconstruction Algorithm for Robust Adaptive Beamforming
Venue: arXiv cs.IT 2020
Year: 2020

In this letter, we present a novel low-complexity adaptive beamforming technique using a stochastic gradient algorithm to avoid matrix inversions. The proposed method exploits algorithms based on the maximum entropy power spectrum (MEPS) to estimate the noise-plus-interference covariance matrix (MEPS-NPIC) so that the beamforming weights are updated adaptively, thus greatly reducing the computational complexity. MEPS is further used to reconstruct the desired signal covariance matrix and to improve the estimate of the desired signals's steering vector (SV). Simulations show the superiority of the proposed MEPS-NPIC approach over previously proposed beamformers.
Generalized Quantile Loss for Deep Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

This note presents a simple way to add a count (or quantile) constraint to a regression neural net, such that given $n$ samples in the training set it guarantees that the prediction of $m<n$ samples will be larger than the actual value (the label). Unlike standard quantile regression networks, the presented method can be applied to any loss function and not necessarily to the standard quantile regression loss, which minimizes the mean absolute differences. Since this count constraint has zero gradients almost everywhere, it cannot be optimized using standard gradient descent methods. To overcome this problem, an alternation scheme, which is based on standard neural network optimization procedures, is presented with some theoretical analysis.
Urban volumetrics: spatial complexity and wayfinding, extending space syntax to three dimensional space
Venue: arXiv cs.CY 2020
Year: 2020

Wayfinding behavior and pedestrian movement pattern research relies on objective spatial configuration representation and analysis, such as space syntax, to quantify and control for the difficulty of wayfinding in multi-level buildings and urban built environments. However, the space syntax's representation oversimplifies multi-level vertical connections. The more recent segment and angular approaches to space syntax remain un-operationalizable in three dimensional space. The two dimensional axial-map and segment map line representations are reviewed to determine their extension to a novel three dimensional space line representation. Using an extreme case study research strategy, four representations of a large scale complex multi-level outdoor and indoor built environment are tested against observed pedestrian movement patterns N = 17,307. Association with the movement pattern increases steadily as the representation increases toward high three-dimensional space level of definition and completeness. A novel hybrid angular-Euclidean analysis was used for the objective description of three dimensional built environment complexity. The results suggest that pedestrian wayfinding and movement pattern research in a multi-level built environment should include interdependent outdoor and indoor, and use full three-dimensioanal line representation.
Effective Email Spam Detection System using Extreme Gradient Boosting
Venue: arXiv cs.CR 2020
Year: 2020

The popularity, cost-effectiveness and ease of information exchange that electronic mails offer to electronic device users has been plagued with the rising number of unsolicited or spam emails. Driven by the need to protect email users from this growing menace, research in spam email filtering/detection systems has being increasingly active in the last decade. However, the adaptive nature of spam emails has often rendered most of these systems ineffective. While several spam detection models have been reported in literature, the reported performance on an out of sample test data shows the room for more improvement. Presented in this research is an improved spam detection model based on Extreme Gradient Boosting (XGBoost) which to the best of our knowledge has received little attention spam email detection problems. Experimental results show that the proposed model outperforms earlier approaches across a wide range of evaluation metrics. A thorough analysis of the model results in comparison to the results of earlier works is also presented.
Color Channel Perturbation Attacks for Fooling Convolutional Neural Networks and A Defense Against Such Attacks
Venue: arXiv cs.CV 2020
Year: 2020

The Convolutional Neural Networks (CNNs) have emerged as a very powerful data dependent hierarchical feature extraction method. It is widely used in several computer vision problems. The CNNs learn the important visual features from training samples automatically. It is observed that the network overfits the training samples very easily. Several regularization methods have been proposed to avoid the overfitting. In spite of this, the network is sensitive to the color distribution within the images which is ignored by the existing approaches. In this paper, we discover the color robustness problem of CNN by proposing a Color Channel Perturbation (CCP) attack to fool the CNNs. In CCP attack new images are generated with new channels created by combining the original channels with the stochastic weights. Experiments were carried out over widely used CIFAR10, Caltech256 and TinyImageNet datasets in the image classification framework. The VGG, ResNet and DenseNet models are used to test the impact of the proposed attack. It is observed that the performance of the CNNs degrades drastically under the proposed CCP attack. Result show the effect of the proposed simple CCP attack over the robustness of the CNN trained model. The results are also compared with existing CNN fooling approaches to evaluate the accuracy drop. We also propose a primary defense mechanism to this problem by augmenting the training dataset with the proposed CCP attack. The state-of-the-art performance using the proposed solution in terms of the CNN robustness under CCP attack is observed in the experiments. The code is made publicly available at \url{https://github.com/jayendrakantipudi/Color-Channel-Perturbation-Attack}.
An efficient method for approximating resonance curves of weakly-damped nonlinear mechanical systems
Venue: arXiv cs.CE 2020
Year: 2020

A method is presented for tracing the locus of a specific peak in the frequency response under variation of a parameter. It is applicable to periodic, steady-state vibrations of harmonically forced nonlinear mechanical systems. It operates in the frequency domain and its central idea is to assume a constant phase lag between forcing and response. The method is validated for a two-degree-of-freedom oscillator with cubic spring and a bladed disk with shroud contact. The method provides superior computational efficiency, but is limited to weakly-damped systems. Finally, the capability to reveal isolated solution branches is highlighted.
Disentangled Planning and Control in Vision Based Robotics via Reward Machines
Venue: arXiv cs.RO 2020
Year: 2020

In this work we augment a Deep Q-Learning agent with a Reward Machine (DQRM) to increase speed of learning vision-based policies for robot tasks, and overcome some of the limitations of DQN that prevent it from converging to good-quality policies. A reward machine (RM) is a finite state machine that decomposes a task into a discrete planning graph and equips the agent with a reward function to guide it toward task completion. The reward machine can be used for both reward shaping, and informing the policy what abstract state it is currently at. An abstract state is a high level simplification of the current state, defined in terms of task relevant features. These two supervisory signals of reward shaping and knowledge of current abstract state coming from the reward machine complement each other and can both be used to improve policy performance as demonstrated on several vision based robotic pick and place tasks. Particularly for vision based robotics applications, it is often easier to build a reward machine than to try and get a policy to learn the task without this structure.
Phishing Detection through Email Embeddings
Venue: arXiv cs.CR 2020
Year: 2020

The problem of detecting phishing emails through machine learning techniques has been discussed extensively in the literature. Conventional and state-of-the-art machine learning algorithms have demonstrated the possibility of building classifiers with high accuracy. The existing research studies treat phishing and genuine emails through general indicators and thus it is not exactly clear what phishing features are contributing to variations of the classifiers. In this paper, we crafted a set of phishing and legitimate emails with similar indicators in order to investigate whether these cues are captured or disregarded by email embeddings, i.e., vectorizations. We then fed machine learning classifiers with the carefully crafted emails to find out about the performance of email embeddings developed. Our results show that using these indicators, email embeddings techniques is effective for classifying emails as phishing or legitimate.