Miracle-dz/UniFAR-FTUs huggingface dataset:
A Pseudo-Deterministic RNC Algorithm for General Graph Perfect Matching

We give an NC reduction from search to decision for the problem of finding a minimum weight perfect matching, provided edge weights are polynomially bounded. As a consequence, for settling the long-standing open problem of obtaining an NC perfect matching algorithm, it suffices to obtain an NC algorithm for the decision problem. We believe this new fact has qualitatively changed the nature of this open problem. The difficulty of obtaining an NC perfect matching algorithm led researchers to study matching vis-a-vis clever relaxations of the class NC. In this vein, recently Goldwasser and Grossman [GG15] gave a pseudo-deterministic RNC algorithm for finding a perfect matching in a bipartite graph, i.e., an RNC algorithm with the additional requirement that on the same graph, it should return the same (i.e., unique) perfect matching for almost all choices of random bits. A corollary of our reduction is an analogous algorithm for general graphs. Our reduction and algorithm build on Anari and Vazirani [AV18], whose result used planarity of the input graph critically; in fact, in three different ways. The main challenge was to adapt these steps to general graphs by exploiting the leeway that we are seeking a pseudodeterministic RNC, rather than an NC, algorithm.
A Semantic Community Detection Algorithm Based on Quantizing Progress

The semantic social network is a kind of network that contains enormous nodes and complex semantic information, and the traditional community detection algorithms could not give the ideal cogent communities instead. To solve the issue of detecting semantic social network, we present a clustering community detection algorithm based on the PSO-LDA model. As the semantic model is LDA model, we use the Gibbs sampling method that can make quantitative parameters map from semantic information to semantic space. Then, we present a PSO strategy with the semantic relation to solve the overlapping community detection. Finally, we establish semantic modularity (SimQ) for evaluating the detected semantic communities. The validity and feasibility of the PSO-LDA model and the semantic modularity are verified by experimental analysis.
Beware of the App! On the Vulnerability Surface of Smart Devices through their Companion Apps

Internet of Things (IoT) devices are becoming increasingly important. These devices are often resource-limited, hindering rigorous enforcement of security policies. Assessing the vulnerability of IoT devices is an important problem, but analyzing their firmware is difficult for a variety of reasons, including requiring the purchase of devices. This paper finds that analyzing companion apps to these devices for clues to security vulnerabilities can be an effective strategy. Compared to device hardware and firmware, these apps are easy to download and analyze. A key finding of this study is that the communication between an IoT device and its app is often not properly encrypted and authenticated and these issues enable the construction of exploits to remotely control the devices. To confirm the vulnerabilities found, we created exploits against five popular IoT devices from Amazon by using a combination of static and dynamic analyses. We also did a larger study, finding that analyzing 96 popular IoT devices only required analyzing 32 companion apps. Among the conservative findings, 50% of the apps corresponding to 38% of the devices did not use proper encryption techniques to secure device to companion app communication. Finally, we discuss defense strategies that developers can adapt to address the lessons from our work.
StocHy: automated verification and synthesis of stochastic processes

StocHy is a software tool for the quantitative analysis of discrete-time stochastic hybrid systems (SHS). StocHy accepts a high-level description of stochastic models and constructs an equivalent SHS model. The tool allows to (i) simulate the SHS evolution over a given time horizon; and to automatically construct formal abstractions of the SHS. Abstractions are then employed for (ii) formal verification or (iii) control (policy, strategy) synthesis. StocHy allows for modular modelling, and has separate simulation, verification and synthesis engines, which are implemented as independent libraries. This allows for libraries to be easily used and for extensions to be easily built. The tool is implemented in C++ and employs manipulations based on vector calculus, the use of sparse matrices, the symbolic construction of probabilistic kernels, and multi-threading. Experiments show StocHy's markedly improved performance when compared to existing abstraction-based approaches: in particular, StocHy beats state-of-the-art tools in terms of precision (abstraction error) and computational effort, and finally attains scalability to large-sized models (12 continuous dimensions). StocHy is available at www.gitlab. com/natchi92/StocHy.
Revisiting Sample Selection Approach to Positive-Unlabeled Learning: Turning Unlabeled Data into Positive rather than Negative

In the early history of positive-unlabeled (PU) learning, the sample selection approach, which heuristically selects negative (N) data from U data, was explored extensively. However, this approach was later dominated by the importance reweighting approach, which carefully treats all U data as N data. May there be a new sample selection method that can outperform the latest importance reweighting method in the deep learning age? This paper is devoted to answering this question affirmatively---we propose to label large-loss U data as P, based on the memorization properties of deep networks. Since P data selected in such a way are biased, we develop a novel learning objective that can handle such biased P data properly. Experiments confirm the superiority of the proposed method.
Harnessing GANs for Addition of New Classes in VSR

It is an easy task for humans to learn and generalize a problem, perhaps it is due to their ability to visualize and imagine unseen objects and concepts. The power of imagination comes handy especially when interpolating learnt experience (like seen examples) over new classes of a problem. For a machine learning system, acquiring such powers of imagination are still a hard task. We present a novel approach to low-shot learning that uses the idea of imagination over unseen classes in a classification problem setting. We combine a classifier with a `visionary' (i.e., a GAN model) that teaches the classifier to generalize itself over new and unseen classes. This approach can be incorporated into a variety of problem settings where we need a classifier to learn and generalize itself to new and unseen classes. We compare the performance of classifiers with and without the visionary GAN model helping them.
Committee Selection with Attribute Level Preferences

We consider the problem of committee selection from a fixed set of candidates where each candidate has multiple quantifiable attributes. To select the best possible committee, instead of voting for a candidate, a voter is allowed to approve the preferred attributes of a given candidate. Though attribute based preference is addressed in several contexts, committee selection problem with attribute approval of voters has not been attempted earlier. A committee formed on attribute preferences is more likely to be a better representative of the qualities desired by the voters and is less likely to be susceptible to collusion or manipulation. In this work, we provide a formal study of the different aspects of this problem and define properties of weak unanimity, strong unanimity, simple justified representations and compound justified representation, that are required to be satisfied by the selected committee. We show that none of the existing vote/approval aggregation rules satisfy these new properties for attribute aggregation. We describe a greedy approach for attribute aggregation that satisfies the first three properties, but not the fourth, i.e., compound justified representation, which we prove to be NP-complete. Furthermore, we prove that finding a committee with justified representation and the highest approval voting score is NP-complete.
Consensus of Multi-Integral Fractional-Order Multiagent Systems with Nonuniform Time-Delays

Consensus of fractional-order multiagent systems (FOMASs) with single integral has been wildly studied. However, the dynamics with multiple integral (especially double integral to sextuple integral) also exist in FOMASs, and they are rarely studied at present. In this paper, consensus problems for multi-integral fractional-order multiagent systems (MIFOMASs) with nonuniform time-delays are addressed. The consensus conditions for MIFOMASs are obtained by a novel frequency-domain method which properly eliminates consensus problems of the systems associated with nonuniform time-delays. Besides, the method revealed in this paper is applicable to classical high-order multiagent systems which is a special case of MIFOMASs. Finally, several numerical simulations with different parameters are performed to validate the correctness of the results.
Dynamic Manipulation of Flexible Objects with Torque Sequence Using a Deep Neural Network

For dynamic manipulation of flexible objects, we propose an acquisition method of a flexible object motion equation model using a deep neural network and a control method to realize a target state by calculating an optimized time-series joint torque command. By using the proposed method, any physics model of a target object is not needed, and the object can be controlled as intended. We applied this method to manipulations of a rigid object, a flexible object with and without environmental contact, and a cloth, and verified its effectiveness.
On the Expressive Power of Deep Fully Circulant Neural Networks

In this paper, we study deep fully circulant neural networks, that is deep neural networks in which all weight matrices are circulant ones. We show that these networks outperform the recently introduced deep networks with other types of structured layers. Besides introducing principled techniques for training these models, we provide theoretical guarantees regarding their expressivity. Indeed, we prove that the function space spanned by circulant networks of bounded depth includes the one spanned by dense networks with specific properties on their rank. We conduct a thorough experimental study to compare the performance of deep fully circulant networks with state of the art models based on structured matrices and with dense models. We show that our models achieve better accuracy than their structured alternatives while required 2x fewer weights as the next best approach. Finally we train deep fully circulant networks to build a compact and accurate models on a real world video classification dataset with over 3.8 million training examples.
Numerical implementation of dynamical mean field theory for disordered systems: application to the Lotka–Volterra model of ecosystems

Dynamical mean field theory (DMFT) is a tool that allows one to analyze the stochastic dynamics of N interacting degrees of freedom in terms of a self-consistent 1-body problem. In this work, focusing on models of ecosystems, we present the derivation of DMFT through the dynamical cavity method, and we develop a method for solving it numerically. Our numerical procedure can be applied to a large variety of systems for which DMFT holds. We implement and test it for the generalized random Lotka–Volterra model, and show that complex dynamical regimes characterized by chaos and aging can be captured and studied by this framework.
Reconstruction of 3D Porous Media From 2D Slices

In many branches of earth sciences, the problem of rock study on the micro-level arises. However, a significant number of representative samples is not always feasible. Thus the problem of the generation of samples with similar properties becomes actual. In this paper, we propose a novel deep learning architecture for three-dimensional porous media reconstruction from two-dimensional slices. We fit a distribution on all possible three-dimensional structures of a specific type based on the given dataset of samples. Then, given partial information (central slices) we recover the three-dimensional structure around such slices as the most probable one according to that constructed distribution. Technically, we implement this in the form of a deep neural network with encoder, generator and discriminator modules. Numerical experiments show that this method provides good reconstruction in terms of Minkowski functionals.
License Plate Detection with Shallow and Deep CNNs in Complex Environments

License plate detection is a challenging problem due to the large visual variations in complex environments, such as motion blur, occlusion, and lighting changes. An advanced discriminative model is needed to accurately segment license plates from the backgrounds. However, effective models for the problem tend to be computationally prohibitive. To address these two conflicting challenges, we propose to detect license plate based on two CNNs, a shallow CNN and a deep CNN. The shallow CNN is used to quickly remove most of the background regions to reduce the computation cost, and the deep CNN is used to detect license plate in the remaining regions. These two CNNs are trained end to end and are complementary to each other to guarantee the detection precision with low computation cost. Experimental results show that the proposed method is promising for license plate detection.
Examining the Intergovernmental and Interorganizational Network of Responding to Major Accidents for Improving the Emergency Management System in China

Since the SARS crisis in 2003, institutionalized emergency management systems have been established in each government level for improving inter-organizational collaboration in China. Major accidents require participation of public organizations affiliated with multiple government levels, and the lack of collaboration and coordination among the involved organizations within the critical time constraints during the response process is an existing problem. In this research, a case study of examining the intergovernmental and cross-sectoral collaboration for responding to a well-known oil pipeline explosion accident in China by a complex network method is conducted. The aim is to obtain managerial insights in improving the existing emergency management system in a centralized political-administrative context, such as China. A mixed method of data collection is applied to identify the participating organizations and to determine the interaction spanning organizational boundaries in both hierarchical and horizontal dimensions. An emergency response network is built and visualized for representing intergovernmental and interorganizational collaboration during the response process of the major accident by social network analysis (SNA) tools. The SNA indicators are used to measure quantitatively the network structure at the levels of the whole network, subnetwork, and node. The obstacles of achieving intergovernmental collaboration are found, and managerial suggestions for improving the existing emergency management system are provided. This research indicates that the Chinese government should pay attention to establishing and sustaining partnerships with private and nonprofit organizations and conduct a blend of hierarchical, market, and network principles in fostering collaboration for addressing major accidents. The public organizations in the local government level are shown to be more active than other participators in coordinating their response operations, and their capability should be emphasized for improvement. Additionally, the interactive relationships among specific emergency function groups and between the affected communities and organizations performing emergency command and coordination function should be strengthened.
Consensus for Mixed-Order Multiagent Systems over Jointly Connected Topologies via Impulse Control

Because of the complexity of the environment, the dynamics of agents in the same system may be different. That is, the dynamics of some agents may be first ordered, and the others may be second ordered, even high ordered. In addition, the network topologies of systems are always varying over time. Because of these facts, this paper studies the consensus problem of the mixed-order multiagent networks over the jointly connected topologies. By adopting the impulse control technique, some control protocols are proposed based on the information of the agents themselves and their neighbors. Several simulation results are given to verify the correctness of the theoretical results.
Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers

Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization. We apply Bayesian network classifiers to the facial biotype classification problem, an important stage during orthodontic treatment planning. For this, we present adaptations of classical Bayesian networks classifiers to handle continuous attributes; also, we propose an incremental tree construction procedure for tree like Bayesian network classifiers. We evaluate the performance of the proposed adaptations and compare them with other continuous Bayesian network classifiers approaches as well as support vector machines. The results under the classification performance measures, accuracy and kappa, showed the effectiveness of the continuous Bayesian network classifiers, especially for the case when a reduced number of attributes were used. Additionally, the resulting networks allowed visualizing the probability relations amongst the attributes under this classification problem, a useful tool for decision-making for orthodontists.
Terrain Adaptive Estimation of Instantaneous Centres of Rotation for Tracked Robots

As a type of skid-steering mobile robot, the tracked robot suffers from inevitable slippage, which results in an imprecise kinematics model and a degradation of performance during navigation. Compared with the traditional robot, the kinematics model is able to reflect the influences of slippage through the introduction of instantaneous centres of rotation (ICRs). However, ICRs cannot be measured directly and are time-varying with terrain variation, and thus, here, we aim to develop an online estimation method to acquire the ICRs of a robot by means of data fusion technologies. First, an innovation-based extended Kalman filter (IEKF) is employed to fuse the readings from two incremental encoders and a GPS-compass integrated sensor, to provide a real-time ICR estimation. Second, a decision tree-based learning system is used to classify the terrains that the robot traverses, according to the vibration signals gathered by an accelerometer. The results of this terrain classification are improved via a Bayesian filter, by utilizing temporal correlation in the terrain time series. Third, the performances of the ICR estimation and terrain classification are mutually promoted. On one hand, terrain variation is detected with the aid of the terrain classification, and therefore, the process noise variance of IEKF can be automatically adjusted. Hence, the results of ICR estimation are smooth if the terrain does not change and converge rapidly upon terrain variation. On the other hand, the sudden changes in innovation are used to adjust the state transition probability during the recursive calculation of the Bayesian filter, thus increasing the accuracy of the terrain classification. A real-world experiment was undertaken on a tracked robot to validate the effectiveness of the proposed method. It is also demonstrated that the terrain adaptive odometry outperforms the traditional approach with the knowledge of ICRs.
Multiobjective Personalized Recommendation Algorithm Using Extreme Point Guided Evolutionary Computation

Recommender systems suggest items to users based on their potential interests, and they are important to alleviate the search and selection pressures induced by the increasing item information. Classical recommender systems mainly focus on the accuracy of recommendation. However, with the increase of the diversified demands of users, multiple metrics which may conflict with each other have to be considered in modern recommender systems, especially for the personalized recommender system. In this paper, we design a personalized recommendation system considering the three conflicting objectives, i.e., the accuracy, diversity, and novelty. Then, to let the system provide more comprehensive recommended items, we present a multiobjective personalized recommendation algorithm using extreme point guided evolutionary computation (called MOEA-EPG). The proposed MOEA-EPG is guided by three extreme points and its crossover operator is designed for better satisfying the demands of users. The experimental results validate the effectiveness of MOEA-EPG when compared to some state-of-the-art recommendation algorithms in terms of accuracy, diversity, and novelty on recommendation.
Cyber-aggression Detection using Cross Segment-and-Concatenate Multi-Task Learning from Text

In this paper, we propose a novel deep-learning architecture for text classification, named cross segment-and-concatenate multi-task learning (CSC-MTL). We use CSC-MTL to improve the performance of cyber-aggression detection from text. Our approach provides a robust shared feature representation for multi-task learning by detecting contrasts and similarities among polarity and neutral classes. We participated in the cyber-aggression shared task under the team name uOttawa. We report 59.74% F1 performance for the Facebook test set and 56.9% for the Twitter test set, for detecting aggression from text.
Spread the Joy: How High and Low Bias for Happy Facial Emotions Translate into Different Daily Life Affect Dynamics

There is evidence that people commonly show a bias toward happy facial emotions during laboratory tasks, that is, they identify other people’s happy facial emotions faster than other people’s negative facial emotions. However, not everybody shows this bias. Individuals with a vulnerability for depression, for example, show a low happy bias compared to healthy controls. The main aim of this study was to acquire a better understanding of laboratory measures of happy bias by studying how these translate to people’s daily life. We investigated whether stable high and low happy bias during a laboratory task were associated with different daily life affect dynamics (i.e., effects from one time interval of 6 hours to the next). We compared the daily life affect dynamics of young adults (age 18–24) with a high bias toward happy facial emotions (N=25) to the affect dynamics of young adults with a low bias toward happy emotions (N=25). Affect and related measures were assessed three times per day during 30 days. We used multilevel vector autoregressive (VAR) modelling to estimate lag 1 affect networks for the high and low happy bias groups and used permutation tests to compare the two groups. Compared to their peers with a low happy bias, individuals with a high happy bias more strongly sustained the effects of daily life reward experiences over time. Individuals with a high happy bias may use their reward experiences more optimally in daily life to build resources that promote well-being and mental health. Low reward responsiveness in daily life may be key to why individuals who show a low happy bias during laboratory tasks are vulnerable for depression. This study illustrates the potential benefits of a network approach for unraveling psychological mechanisms.