Miracle-dz/UniFAR-FTUs huggingface dataset:
Anomaly Locality in Video Surveillance

This paper strives for the detection of real-world anomalies such as burglaries and assaults in surveillance videos. Although anomalies are generally local, as they happen in a limited portion of the frame, none of the previous works on the subject has ever studied the contribution of locality. In this work, we explore the impact of considering spatiotemporal tubes instead of whole-frame video segments. For this purpose, we enrich existing surveillance videos with spatial and temporal annotations: it is the first dataset for anomaly detection with bounding box supervision in both its train and test set. Our experiments show that a network trained with spatiotemporal tubes performs better than its analogous model trained with whole-frame videos. In addition, we discover that the locality is robust to different kinds of errors in the tube extraction phase at test time. Finally, we demonstrate that our network can provide spatiotemporal proposals for unseen surveillance videos leveraging only video-level labels. By doing, we enlarge our spatiotemporal anomaly dataset without the need for further human labeling.
Two-sided bounds for cost functionals of time-periodic parabolic optimal control problems

In this paper, a new technique is applied on deriving computable, guaranteed lower bounds of functional type (minorants) for two different cost functionals subject to a parabolic time-periodic boundary value problem. Together with previous results on upper bounds (majorants) for one of the cost functionals, both minorants and majorants lead to two-sided estimates of functional type for the optimal control problem. Moreover, a different cost functional is discussed with the same PDE-constraints. Both upper and lower bounds are derived. The time-periodic optimal control problems are discretized by the multiharmonic finite element method leading to large systems of linear equations having a saddle point structure. The derivation of preconditioners for the minimal residual method for the new optimal control problem is discussed in more detail. Finally, several numerical experiments for both optimal control problems are presented confirming the theoretical results obtained.
Clustered Millimeter-Wave Networks With Non-Orthogonal Multiple Access

We introduce clustered millimeter-wave (mmWave) networks with invoking non-orthogonal multiple access (NOMA) techniques, where the NOMA users are modeled as Poisson cluster processes and each cluster contains a base station (BS) located at the center. To provide realistic directional beamforming, an actual antenna array pattern is deployed at all BSs. We propose three distance-dependent user selection strategies to appraise the path loss impact on the performance of our considered networks. With the aid of such strategies, we derive tractable analytical expressions for the coverage probability and system throughput. Specifically, closed-form expressions are deduced under a sparse network assumption to improve the calculation efficiency. It theoretically demonstrates that the large antenna scale benefits the near user, while such influence for the far user is fluctuant due to the randomness of the beamforming. Moreover, the numerical results illustrate that: 1) the proposed system outperforms traditional orthogonal multiple access techniques and the commonly considered NOMA-mmWave scenarios with the random beamforming; 2) the coverage probability has a negative correlation with the variance of intra-cluster receivers; 3) 73 GHz is the best carrier frequency for the near user, and 28 GHz is the best choice for the far user; and 4) an optimal number of the antenna elements exists for maximizing the system throughput.
Decentralized Likelihood Quantile Networks for Improving Performance in Deep Multi-Agent Reinforcement Learning

Recent successes of value-based multi-agent deep reinforcement learning employ optimism by limiting underestimation updates of value function estimator, through carefully controlled learning rate (Omidshafiei et al., 2017) or reduced update probability (Palmer et al., 2018). To achieve full cooperation when learning independently, an agent must estimate the state values contingent on having optimal teammates; therefore, value overestimation is frequency injected to counteract negative effects caused by unobservable teammate sub-optimal policies and explorations. Aiming to solve this issue through automatic scheduling, this paper introduces a decentralized quantile estimator, which we found empirically to be more stable, sample efficient and more likely to converge to the joint optimal policy.
Divide and Generate: Neural Generation of Complex Sentences

We propose a task to generate a complex sentence from a simple sentence in order to amplify various kinds of responses in the database. We first divide a complex sentence into a main clause and a subordinate clause to learn a generator model of modifiers, and then use the model to generate a modifier clause to create a complex sentence from a simple sentence. We present an automatic evaluation metric to estimate the quality of the models and show that a pipeline model outperforms an end-to-end model.
RED-Attack: Resource Efficient Decision based Attack for Machine Learning

Due to data dependency and model leakage properties, Deep Neural Networks (DNNs) exhibit several security vulnerabilities. Several security attacks exploited them but most of them require the output probability vector. These attacks can be mitigated by concealing the output probability vector. To address this limitation, decision-based attacks have been proposed which can estimate the model but they require several thousand queries to generate a single untargeted attack image. However, in real-time attacks, resources and attack time are very crucial parameters. Therefore, in resource-constrained systems, e.g., autonomous vehicles where an untargeted attack can have a catastrophic effect, these attacks may not work efficiently. To address this limitation, we propose a resource efficient decision-based methodology which generates the imperceptible attack, i.e., the RED-Attack, for a given black-box model. The proposed methodology follows two main steps to generate the imperceptible attack, i.e., classification boundary estimation and adversarial noise optimization. Firstly, we propose a half-interval search-based algorithm for estimating a sample on the classification boundary using a target image and a randomly selected image from another class. Secondly, we propose an optimization algorithm which first, introduces a small perturbation in some randomly selected pixels of the estimated sample. Then to ensure imperceptibility, it optimizes the distance between the perturbed and target samples. For illustration, we evaluate it for CFAR-10 and German Traffic Sign Recognition (GTSR) using state-of-the-art networks.
Bayes Imbalance Impact Index: A Measure of Class Imbalanced Data Set for Classification Problem

Recent studies of imbalanced data classification have shown that the imbalance ratio (IR) is not the only cause of performance loss in a classifier, as other data factors, such as small disjuncts, noise, and overlapping, can also make the problem difficult. The relationship between the IR and other data factors has been demonstrated, but to the best of our knowledge, there is no measurement of the extent to which class imbalance influences the classification performance of imbalanced data. In addition, it is also unknown which data factor serves as the main barrier for classification in a data set. In this article, we focus on the Bayes optimal classifier and examine the influence of class imbalance from a theoretical perspective. We propose an instance measure called the Individual Bayes Imbalance Impact Index (IBI3) and a data measure called the Bayes Imbalance Impact Index (BI3). IBI3 and BI3 reflect the extent of influence using only the imbalance factor, in terms of each minority class sample and the whole data set, respectively. Therefore, IBI3 can be used as an instance complexity measure of imbalance and BI3 as a criterion to demonstrate the degree to which imbalance deteriorates the classification of a data set. We can, therefore, use BI3 to access whether it is worth using imbalance recovery methods, such as sampling or cost-sensitive methods, to recover the performance loss of a classifier. The experiments show that IBI3 is highly consistent with the increase of the prediction score obtained by the imbalance recovery methods and that BI3 is highly consistent with the improvement in the F1 score obtained by the imbalance recovery methods on both synthetic and real benchmark data sets.
An Arabic Dependency Treebank in the Travel Domain

In this paper we present a dependency treebank of travel domain sentences in Modern Standard Arabic. The text comes from a translation of the English equivalent sentences in the Basic Traveling Expressions Corpus. The treebank dependency representation is in the style of the Columbia Arabic Treebank. The paper motivates the effort and discusses the construction process and guidelines. We also present parsing results and discuss the effect of domain and genre difference on parsing.
Gravitational Wave Polarization Modes in $f(R)$ Theories

Many studies have been carried out in the literature to evaluate the number of polarization modes of gravitational waves in modified theories, in particular in $f(R)$ theories. In the latter ones, besides the usual two transverse-traceless tensor modes present in general relativity, there are two additional scalar ones: a massive longitudinal mode and a massless transverse mode (the so-called breathing mode). This last mode has often been overlooked in the literature, due to the assumption that the application of the Lorenz gauge implies transverse-traceless wave solutions. We however show that this is in general not possible and, in particular, that the traceless condition cannot be imposed due to the fact that we no longer have a Minkowski background metric. Our findings are in agreement with the results found using the Newman-Penrose formalism and thus clarify the inconsistencies found so far in the literature.
Multimodal Deep Feature Fusion (MMDFF) for RGB-D Tracking

Visual tracking is still a challenging task due to occlusion, appearance changes, complex motion, etc. We propose a novel RGB-D tracker based on multimodal deep feature fusion (MMDFF) in this paper. MMDFF model consists of four deep Convolutional Neural Networks (CNNs): Motion-specific CNN, RGB- specific CNN, Depth-specific CNN, and RGB-Depth correlated CNN. The depth image is encoded into three channels which are sent into depth-specific CNN to extract deep depth features. The optical flow image is calculated for every frame and then is fed to motion-specific CNN to learn deep motion features. Deep RGB, depth, and motion information can be effectively fused at multiple layers via MMDFF model. Finally, multimodal fusion deep features are sent into the C-COT tracker to obtain the tracking result. For evaluation, experiments are conducted on two recent large-scale RGB-D datasets and results demonstrate that our proposed RGB-D tracking method achieves better performance than other state-of-art RGB-D trackers.
The Intertwining Impact of Intraorganizational and Routine Networks on Routine Replication Dynamics: An Agent-Based Model

Organizational routines are means through which organizations can reutilize best practices and so their replication, i.e., duplicating beneficial routines across context, is a key value-creating strategy. However, it is difficult to map network effects on routine replication. Here, we investigated routine replicating dynamics considering two types of network contexts, namely, (1) connections between different (geographically distributed) units in a decentralized organization and (2) the coupling relation between routines, i.e., a bundle of different routines involved in each unit. By considering routine replication as one kind of template-based activities between different units, we examined interrelations between routines with a NK-based fitness landscape model. Our results show that when there is an appropriate level of absorptive capacities (i.e., when organizations are capable of identifying and acquiring externally generated knowledge), there is an optimal combination of these two types of networks, which is beneficial to routine replicating practices and organization adaptation. Furthermore, we also found that intraorganizational variations, including template-duplicating errors and innovative activities, are instrumental to enhance adaptive changes. Our findings suggest measures to control and manage best practice diffusion across organizations.
Benchmarking Aggression Identification in Social Media

In this paper, we present the report and findings of the Shared Task on Aggression Identification organised as part of the First Workshop on Trolling, Aggression and Cyberbullying (TRAC - 1) at COLING 2018. The task was to develop a classifier that could discriminate between Overtly Aggressive, Covertly Aggressive, and Non-aggressive texts. For this task, the participants were provided with a dataset of 15,000 aggression-annotated Facebook Posts and Comments each in Hindi (in both Roman and Devanagari script) and English for training and validation. For testing, two different sets - one from Facebook and another from a different social media - were provided. A total of 130 teams registered to participate in the task, 30 teams submitted their test runs, and finally 20 teams also sent their system description paper which are included in the TRAC workshop proceedings. The best system obtained a weighted F-score of 0.64 for both Hindi and English on the Facebook test sets, while the best scores on the surprise set were 0.60 and 0.50 for English and Hindi respectively. The results presented in this report depict how challenging the task is. The positive response from the community and the great levels of participation in the first edition of this shared task also highlights the interest in this topic.
Duality of Complex Systems Built from Higher-Order Elements

The duality of nonlinear systems built from higher-order two-terminal Chua’s elements and independent voltage and current sources is analyzed. Two different approaches are now being generalized for circuits with higher-order elements: the classical duality principle, hitherto restricted to circuits built from R-C-L elements, and Chua’s duality of memristive circuits. The so-called storeyed structure of fundamental elements is used as an integrating platform of both approaches. It is shown that the combination of associated flip-type and shift-type transformations of the circuit elements can generate dual networks with interesting features. The regularities of the duality can be used for modeling, hardware emulation, or synthesis of systems built from elements that are not commonly available, such as memristors, via classical dual elements.
Combining Human and Machine Transcriptions on the Zooniverse Platform

Transcribing handwritten documents to create fully searchable texts is an essential part of the archival process. Traditional text recognition methods, such as optical character recognition (OCR), do not work on handwritten documents due to their frequent noisiness and OCR’s need for individually segmented letters. Crowdsourcing and improved machine models are two modern methods for transcribing handwritten documents.
Paths to Polarization: How Extreme Views, Miscommunication, and Random Chance Drive Opinion Dynamics

Understanding the social conditions that tend to increase or decrease polarization is important for many reasons. We study a network-structured agent-based model of opinion dynamics, extending a model previously introduced by Flache and Macy (2011), who found that polarization appeared to increase with the introduction of long-range ties but decrease with the number of salient opinions, which they called the population’s “cultural complexity. ” We find the following. First, polarization is strongly path dependent and sensitive to stochastic variation. Second, polarization depends strongly on the initial distribution of opinions in the population. In the absence of extremists, polarization may be mitigated. Third, noisy communication can drive a population toward more extreme opinions and even cause acute polarization. Finally, the apparent reduction in polarization under increased “cultural complexity” arises via a particular property of the polarization measurement, under which a population containing a wider diversity of extreme views is deemed less polarized. This work has implications for understanding the population dynamics of beliefs, opinions, and polarization as well as broader implications for the analysis of agent-based models of social phenomena.
Complexities in Financial Network Topological Dynamics: Modeling of Emerging and Developed Stock Markets

Policy makings and regulations of financial markets rely on a good understanding of the complexity of financial markets. There have been recent advances in applying data-driven science and network theory into the studies of social and financial systems. Financial assets and institutions are strongly connected and influence each other. It is essential to study how the topological structures of financial networks could potentially influence market behaviors. Network analysis is an innovative method to enhance data mining and knowledge discovery in financial data. With the help of complex network theory, the topological network structures of a market can be extracted to reveal hidden information and relationships among stocks. In this study, two major markets of the most influential economies, China and the United States, are systematically studied from the perspective of financial network analysis. Results suggest that the network properties and hierarchical structures are fundamentally different for the two stock markets. The patterns embedded in the price movements are revealed and shed light on the market dynamics. Financial policymakers and regulators can gain inspiration from these findings for applications in policy making, regulations design, portfolio management, risk management, and trading.
A Users' Awareness Study and Influence of Socio-Demography Perception of Anti-Phishing Security Tips

Security tips are now used as a method of priming online users from falling prey for fraudulent scams. These security tips usually come as email, SMS or online posts where they can be easily accessed by the users. In this work, phishing attacks are simulated with varying cues that are available in such fraudulent email messages, SMS and web pages were used to investigate the effectiveness of the security tips used by Nigerian banks to prime their customers of online threats. A total of 427 respondents, purposively selected from three tertiary institutions in Ogun State, participated in the study. Each respondent was asked to identify five messages with varying phishing cues to evaluate their understanding of the security tips messages. The results which were computed at 95% Confidence Interval, indicated that 58.91% failed on the first attribute, 58.59% failed on the second attribute while 58.73% failed on the third attribute. 74.24% of the participant could not correctly identify a fake email message (fourth attribute) while 76.71% could not correctly identify a phished bank verification number update message (fifth attribute). Using the Mann Whitney Test, the result further showed that overall, those who failed the test are significantly more than those who passed. Moreover, a regression model is proposed to evaluate the influence of the socio demographic factors used in the study. This result indicated that gender, academic qualification and user's computer knowledge significantly influences their ability to recognize phished messages.
Factor That Influence To Acceptance M-Learning among Jordanian Students

The research aims to examine critical factors that influence the acceptance and use of M-learning among Jordanian student. This study integrates technology acceptance model (TAM) with service quality factor. The primary data were collected from 487 valid questionnaires, which were distributed, to random Jordanian students in four cities. The analyses of the gathered data employed the (SPSS). The validity of the final overall model was evaluated using the statistics and acceptable fit of the measurement model to the data has been demonstrated. Based on the outcomes, the factors with the highest direct effect on Intention to use M-learning appeared to be Attitude toward using M-learning, while the factor with the highest indirect effect on Intention to use M-learning appeared to be Compatibility. The main findings of the study are: trust factor has a significant stronger impact on PEOU and PU. PEOU and PU have the stronger impact on customers' attitude, which in turn influences students’ intention to use M - learning services.
Complex Dynamics in One-Dimensional Nonlinear Schrödinger Equations with Stepwise Potential

We prove the existence and multiplicity of periodic solutions as well as solutions presenting a complex behavior for the one-dimensional nonlinear Schrödinger equation -ε2u′′+V(x)u=f(u), where the potential V(x) approximates a two-step function. The term f(u) generalizes the typical p-power nonlinearity considered by several authors in this context. Our approach is based on some recent developments of the theory of topological horseshoes, in connection with a linked twist maps geometry, which are applied to the discrete dynamics of the Poincaré map. We discuss the periodic and the Neumann boundary conditions. The value of the term ε>0, although small, can be explicitly estimated.
Variational Approach for Learning Community Structures

Discovering and modeling community structure exist to be a fundamentally challenging task. In domains such as biology, chemistry, and physics, researchers often rely on community detection algorithms to uncover community structures from complex systems yet no unified definition of community structure exists. Furthermore, existing models tend to be oversimplified leading to a neglect of richer information such as nodal features. Coupled with the surge of user generated information on social networks, a demand for newer techniques beyond traditional approaches is inevitable. Deep learning techniques such as network representation learning have shown tremendous promise. More specifically, supervised and semisupervised learning tasks such as link prediction and node classification have achieved remarkable results. However, unsupervised learning tasks such as community detection remain widely unexplored. In this paper, a novel deep generative model for community detection is proposed. Extensive experiments show that the proposed model, empowered with Bayesian deep learning, can provide insights in terms of uncertainty and exploit nonlinearities which result in better performance in comparison to state-of-the-art community detection methods. Additionally, unlike traditional methods, the proposed model is community structure definition agnostic. Leveraging on low-dimensional embeddings of both network topology and feature similarity, it automatically learns the best model configuration for describing similarities in a community.