OpenRaiser/Intern-Atlas huggingface dataset:
Ergodic capacity evaluation of wireless THz fiber extenders
Venue: arXiv cs.IT 2019
Year: 2019

This paper focuses on delivering quantified results for the evaluation of the aggregated impact of stochastic antenna misalignment, multipath fading and hardware imperfections on the terahertz (THz) wireless fiber extenders. In this line, we present the appropriate signal model that accommodates the different technical and environmental parameters. In particular, it takes into consideration the antenna gains, the central frequency, the transmission range, the environmental conditions, i.e. temperature, humidity and pressure, the spatial jitter between the transmitter and receiver antennas, which results to antenna misalignment, the intensity of hardware imperfections, and the stochastic behavior of the wireless channel. Based on this model, we assess the joint impact of antenna misalignment and multipath fading, by providing Monte Carlo simulation results for the channels ergodic capacity.
SELF: Learning to Filter Noisy Labels with Self-Ensembling
Venue: arXiv cs.CV 2019
Year: 2019

Deep neural networks (DNNs) have been shown to over-fit a dataset when being trained with noisy labels for a long enough time. To overcome this problem, we present a simple and effective method self-ensemble label filtering (SELF) to progressively filter out the wrong labels during training. Our method improves the task performance by gradually allowing supervision only from the potentially non-noisy (clean) labels and stops learning on the filtered noisy labels. For the filtering, we form running averages of predictions over the entire training dataset using the network output at different training epochs. We show that these ensemble estimates yield more accurate identification of inconsistent predictions throughout training than the single estimates of the network at the most recent training epoch. While filtered samples are removed entirely from the supervised training loss, we dynamically leverage them via semi-supervised learning in the unsupervised loss. We demonstrate the positive effect of such an approach on various image classification tasks under both symmetric and asymmetric label noise and at different noise ratios. It substantially outperforms all previous works on noise-aware learning across different datasets and can be applied to a broad set of network architectures.
An Accurate Edge-based FEM for Electromagnetic Analysis with Its Applications to Multiscale Structures
Venue: arXiv cs.CE 2019
Year: 2019

This paper introduces an accurate edge-based smoothed finite element method (ES-FEM) for electromagnetic analysis for both two dimensional cylindrical and three dimensional cartesian systems, which shows much better performance in terms of accuracy and numerical stability for mesh distortion compared with the traditional FEM. Unlike the traditional FEM, the computational domain in ES-FEM is divided into nonoverlapping smoothing domains associated with each edge of elements, triangles in two dimensional domain and tetrahedrons in three dimensional domain. Then, the gradient smoothing technique (GST) is used to smooth the gradient components in the stiff matrix of the FEM. Several numerical experiments are carried out to validate its accuracy and numerical stability. Numerical results show that the ES-FEM can obtain much more accurate results and is almost independent of mesh distortion.
EBBIOT: A Low-complexity Tracking Algorithm for Surveillance in IoVT Using Stationary Neuromorphic Vision Sensors
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we present EBBIOT-a novel paradigm for object tracking using stationary neuromorphic vision sensors in low-power sensor nodes for the Internet of Video Things (IoVT). Different from fully event based tracking or fully frame based approaches, we propose a mixed approach where we create event-based binary images (EBBI) that can use memory efficient noise filtering algorithms. We exploit the motion triggering aspect of neuromorphic sensors to generate region proposals based on event density counts with >1000X less memory and computes compared to frame based approaches. We also propose a simple overlap based tracker (OT) with prediction based handling of occlusion. Our overall approach requires 7X less memory and 3X less computations than conventional noise filtering and event based mean shift (EBMS) tracking. Finally, we show that our approach results in significantly higher precision and recall compared to EBMS approach as well as Kalman Filter tracker when evaluated over 1.1 hours of traffic recordings at two different locations.
An adaptive hybrid algorithm for social networks to choose groups with independent members
Venue: arXiv cs.SI 2019
Year: 2019

Choosing a committee with independent members in social networks can be named as a problem in group selection and independence in the committee is considered as the main criterion of this selection. Independence is calculated based on the social distance between group members. Although there are many solutions to solve the problem of group selection in social networks, such as selection of the target set or community detection, just one solution has been proposed to choose committee members based on their independence as a measure of group performance. In this paper, a new adaptive hybrid algorithm is proposed to select the best committee members to maximize the independence of the committees. This algorithm is a combination of particle swarm optimization (PSO) algorithm with two local search algorithms. The goal of this work is to combine exploration and exploitation to improve the efficiency of the proposed algorithm and obtain the optimal solution. Additionally, to combine local search algorithms with particle swarm optimization, an effective selection mechanism is used to select a suitable local search algorithm to combine with particle swarm optimization during the search process. The results of experimental simulation are compared with the well-known and successful metaheuristic algorithms. This comparison shows that the proposed method improves the group independence by at least 21%.
Analytical Performance Evaluation of THz Wireless Fiber Extenders
Venue: arXiv cs.IT 2019
Year: 2019

This paper presents the theoretical framework for the performance evaluation of terahertz (THz) wireless fiber extender in the presence of misalignment and multipath fading. In more detail, after providing the appropriate system model that incorporates the different operation, design, and environmental parameters, such as the operation frequency, transceivers antenna gains, the level of misalignment as well as the stochastic behavior of the channel, we extract novel closed-form expressions for the ergodic capacity. These expressions are expected to be used as useful tools for the analysis and design of such systems. Moreover, several insightful scenarios are simulated. Their results highlight the importance of taking into account the impact of misalignment fading when analyzing the performance of the THz wireless fiber extender.
On the Effectiveness of OTFS for Joint Radar and Communication
Venue: arXiv cs.IT 2019
Year: 2019

We consider a joint radar estimation and communication system using orthogonal time frequency space (OTFS) modulation. The scenario is motivated by vehicular applications where a vehicle equipped with a mono-static radar wishes to communicate data to its target receiver, while estimating parameters of interest related to this receiver. In a point-to-point communication setting over multi-path time-frequency selective channels, we study the joint radar and communication system from two perspectives, i.e., the radar estimation at the transmitter as well as the symbol detection at the receiver. For the radar estimation part, we derive an efficient approximated Maximum Likelihood algorithm and the corresponding Cramér- Rao lower bound for range and velocity estimation. Numerical examples demonstrate that multi-carrier digital formats such as OTFS can achieve as accurate radar estimation as state-of-the-art radar waveforms such as frequency-modulated continuous wave (FMCW). For the data detection part, we focus on separate detection and decoding and consider a soft-output detector that exploits efficiently the channel sparsity in the Doppler-delay domain. We quantify the detector performance in terms of its pragmatic capacity, i.e. the achievable rate of the channel induced by the signal constellation and the detector soft output. Simulations show that the proposed scheme outperforms concurrent state-of-the-art solutions. Overall, our work shows that a suitable digitally modulated waveform enables to efficiently operate joint radar and communication by achieving full information rate of the modulation and near-optimal radar estimation performance. Furthermore, OTFS appears to be particularly suited to the scope.
Attacking Vision-based Perception in End-to-End Autonomous Driving Models
Venue: arXiv cs.LG 2019
Year: 2019

Recent advances in machine learning, especially techniques such as deep neural networks, are enabling a range of emerging applications. One such example is autonomous driving, which often relies on deep learning for perception. However, deep learning-based perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images. Nevertheless, the vast majority of such demonstrations focus on perception that is disembodied from end-to-end control. We present novel end-to-end attacks on autonomous driving in simulation, using simple physically realizable attacks: the painting of black lines on the road. These attacks target deep neural network models for end-to-end autonomous driving control. A systematic investigation shows that such attacks are easy to engineer, and we describe scenarios (e.g., right turns) in which they are highly effective. We define several objective functions that quantify the success of an attack and develop techniques based on Bayesian Optimization to efficiently traverse the search space of higher dimensional attacks. Additionally, we define a novel class of hijacking attacks, where painted lines on the road cause the driver-less car to follow a target path. Through the use of network deconvolution, we provide insights into the successful attacks, which appear to work by mimicking activations of entirely different scenarios. Our code is available at https://github.com/xz-group/AdverseDrive
Dynamic Joint Variational Graph Autoencoders
Venue: arXiv cs.LG 2019
Year: 2019

Learning network representations is a fundamental task for many graph applications such as link prediction, node classification, graph clustering, and graph visualization. Many real-world networks are interpreted as dynamic networks and evolve over time. Most existing graph embedding algorithms were developed for static graphs mainly and cannot capture the evolution of a large dynamic network. In this paper, we propose Dynamic joint Variational Graph Autoencoders (Dyn-VGAE) that can learn both local structures and temporal evolutionary patterns in a dynamic network. Dyn-VGAE provides a joint learning framework for computing temporal representations of all graph snapshots simultaneously. Each auto-encoder embeds a graph snapshot based on its local structure and can also learn temporal dependencies by collaborating with other autoencoders. We conduct experimental studies on dynamic real-world graph datasets and the results demonstrate the effectiveness of the proposed method.
Order Acceptance and Scheduling with Sequence-dependent Setup Times: a New Memetic Algorithm and Benchmark of the State of the Art
Venue: arXiv cs.NE 2019
Year: 2019

The Order Acceptance and Scheduling (OAS) problem describes a class of real-world problems such as in smart manufacturing and satellite scheduling. This problem consists of simultaneously selecting a subset of orders to be processed as well as determining the associated schedule. A common generalization includes sequence-dependent setup times and time windows. A novel memetic algorithm for this problem, called Sparrow, comprises a hybridization of biased random key genetic algorithm (BRKGA) and adaptive large neighbourhood search (ALNS). Sparrow integrates the exploration ability of BRKGA and the exploitation ability of ALNS. On a set of standard benchmark instances, this algorithm obtains better-quality solutions with runtimes comparable to state-of-the-art algorithms. To further understand the strengths and weaknesses of these algorithms, their performance is also compared on a set of new benchmark instances with more realistic properties. We conclude that Sparrow is distinguished by its ability to solve difficult instances from the OAS literature, and that the hybrid steady-state genetic algorithm (HSSGA) performs well on large instances in terms of optimality gap, although taking more time than Sparrow.
Succinct Determinisation of Counting Automata via Sphere Construction (Technical Report)
Venue: arXiv cs.FL 2019
Year: 2019

We propose an efficient algorithm for determinising counting automata (CAs), i.e., finite automata extended with bounded counters. The algorithm avoids unfolding counters into control states, unlike the naïve approach, and thus produces much smaller deterministic automata. We also develop a simplified and faster version of the general algorithm for the sub-class of so-called monadic CAs (MCAs), i.e., CAs with counting loops on character classes, which are common in practice. Our main motivation is (besides applications in verification and decision procedures of logics) the application of deterministic (M)CAs in pattern matching regular expressions with counting, which are very common in e.g. network traffic processing and log analysis. We have evaluated our algorithm against practical benchmarks from these application domains and concluded that compared to the naïve approach, our algorithm is much less prone to explode, produces automata that can be several orders of magnitude smaller, and is overall faster.
Information collection for fraud detection in P2P financial market
Venue: arXiv cs.OH 2019
Year: 2019

Fintech companies have been facing challenges from fraudulent behavior for a long time. Fraud rate in Chinese P2P financial market could go as high as 10%. It is crucial to collect sufficient information of the user as input to the anti-fraud process. Data collection framework for Fintech companies are different fro m conventional internet firms. With individual-based crawling request , we need to deal with new challenges negligible elsewhere . In this paper , we give an outline of how we collect data from the web to facilitate our anti-fraud process. We also overview the challenges and solutions to our problems. Our team at HC Financial Service Group is one of the few companies that are capable of developing full-fledged crawlers on our own.
Online Active Perception for Partially Observable Markov Decision Processes with Limited Budget
Venue: arXiv cs.AI 2019
Year: 2019

Active perception strategies enable an agent to selectively gather information in a way to improve its performance. In applications in which the agent does not have prior knowledge about the available information sources, it is crucial to synthesize active perception strategies at runtime. We consider a setting in which at runtime an agent is capable of gathering information under a limited budget. We pose the problem in the context of partially observable Markov decision processes. We propose a generalized greedy strategy that selects a subset of information sources with near-optimality guarantees on uncertainty reduction. Our theoretical analysis establishes that the proposed active perception strategy achieves near-optimal performance in terms of expected cumulative reward. We demonstrate the resulting strategies in simulations on a robotic navigation problem.
Types for Parallel Complexity in the Pi-calculus
Venue: arXiv cs.LO 2019
Year: 2019

Type systems as a way to control or analyze programs have been largely studied in the context of functional programming languages. Some of those work allow to extract from a typing derivation for a program a complexity bound on this program. We present how to adapt this result for parallel complexity in the pi-calculus, as a model of concurrency and parallel communication. We study two notions of time complexity: the total computation time without parallelism (the work) and the computation time under maximal parallelism (the span). We define reduction relations in the pi-calculus to capture those two notions, and we present two type systems from which one can extract a complexity bound on a process. The type systems are inspired by input/output types and size types, with temporal information about communications.
The Sparse Reverse of Principal Component Analysis for Fast Low-Rank Matrix Completion
Venue: arXiv cs.LG 2019
Year: 2019

Matrix completion constantly receives tremendous attention from many research fields. It is commonly applied for recommender systems such as movie ratings, computer vision such as image reconstruction or completion, multi-task learning such as collaboratively modeling time-series trends of multiple sensors, and many other applications. Matrix completion techniques are usually computationally exhaustive and/or fail to capture the heterogeneity in the data. For example, images usually contain a heterogeneous set of objects, and thus it is a challenging task to reconstruct images with high levels of missing data. In this paper, we propose the sparse reverse of principal component analysis for matrix completion. The proposed approach maintains smoothness across the matrix, produces accurate estimates of the missing data, converges iteratively, and it is computationally tractable with a controllable upper bound on the number of iterations until convergence. The accuracy of the proposed technique is validated on natural images, movie ratings, and multisensor data. It is also compared with common benchmark methods used for matrix completion.
A new algorithm for graph center computation and graph partitioning according to the distance to the center
Venue: arXiv cs.DS 2019
Year: 2019

We propose a new algorithm for finding the center of a graph, as well as the rank of each node in the hierarchy of distances to the center. In other words, our algorithm allows to partition the graph according to nodes distance to the center. Moreover, the algorithm is parallelizable. We compare the performances of our algorithm with the ones of Floyd-Warshall algorithm, which is traditionally used for these purposes. We show that, for a large variety of graphs, our algorithm outperforms the Floyd-Warshall algorithm.
ExpertMatcher: Automating ML Model Selection for Users in Resource Constrained Countries
Venue: arXiv cs.LG 2019
Year: 2019

In this work we introduce ExpertMatcher, a method for automating deep learning model selection using autoencoders. Specifically, we are interested in performing inference on data sources that are distributed across many clients using pretrained expert ML networks on a centralized server. The ExpertMatcher assigns the most relevant model(s) in the central server given the client's data representation. This allows resource-constrained clients in developing countries to utilize the most relevant ML models for their given task without having to evaluate the performance of each ML model. The method is generic and can be beneficial in any setup where there are local clients and numerous centralized expert ML models.
Optimizing Data Freshness in Time-Varying Wireless Networks with Imperfect Channel State
Venue: arXiv cs.IT 2019
Year: 2019

We consider a scenario where a base station (BS) attempts to collect fresh information from power constrained sensors over time-varying band-limited wireless channels. We characterize the data freshness through the recently proposed metric--the Age of Information. We consider a time-varying channel model with power adaptation. Unlike previous work, packet loss may happen due to imperfect channel estimation or decoding error. We propose an asymptotic optimal scheduling algorithm minimizing AoI performance and satisfying both bandwidth and power constraint in such networks. Numerical simulations show that the proposed policy outperforms the greedy one, and we observe that sensors with poor channels are scheduled at higher AoI in order to limit the packet loss.
ChaosNet: A Chaos based Artificial Neural Network Architecture for Classification
Venue: arXiv cs.LG 2019
Year: 2019

Inspired by chaotic firing of neurons in the brain, we propose ChaosNet -- a novel chaos based artificial neural network architecture for classification tasks. ChaosNet is built using layers of neurons, each of which is a 1D chaotic map known as the Generalized Luroth Series (GLS) which has been shown in earlier works to possess very useful properties for compression, cryptography and for computing XOR and other logical operations. In this work, we design a novel learning algorithm on ChaosNet that exploits the topological transitivity property of the chaotic GLS neurons. The proposed learning algorithm gives consistently good performance accuracy in a number of classification tasks on well known publicly available datasets with very limited training samples. Even with as low as 7 (or fewer) training samples/class (which accounts for less than 0.05% of the total available data), ChaosNet yields performance accuracies in the range 73.89 % - 98.33 %. We demonstrate the robustness of ChaosNet to additive parameter noise and also provide an example implementation of a 2-layer ChaosNet for enhancing classification accuracy. We envisage the development of several other novel learning algorithms on ChaosNet in the near future.
Efficacy of Haptic Pedal Feel Compensation on Driving with Regenerative Braking
Venue: arXiv cs.RO 2019
Year: 2019

We study the efficacy of haptic pedal feel compensation on driving safety and performance during regenerative braking. In particular, we evaluate the effectiveness of the preservation of the natural brake pedal feel under two-pedal cooperative braking and one-pedal driving scenarios, through human subject experiments in a simulated vehicle pursuit task. The experimental results indicate that pedal feel compensation can significantly decrease the hard braking instances, improving safety for both two-pedal cooperative braking and one-pedal driving. Volunteers also strongly prefer compensation, while they equally prefer and can effectively utilize both two-pedal and one-pedal driving conditions. Furthermore, the beneficial effects of haptic pedal feel compensation is larger for the two-pedal cooperative braking case.
Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling
Venue: arXiv cs.LG 2019
Year: 2019

User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior analysis and other aspects. this paper advances the HetPathMine model and puts forward TPathMine model. With applying the number of clicks of attributes under each node to express the user's emotional preference information, optimizations of the solution of meta-path weight are also presented. Based on meta-path in heterogeneous information networks, the new model integrates all relationships among objects into isomorphic relationships of classified objects. Matrix is used to realize the knowledge dissemination of category knowledge among isomorphic objects. The experimental results show that: (1) the prediction of user attributes based on heterogeneous information networks can achieve higher accuracy than traditional machine learning classification methods; (2) TPathMine model based on the number of clicks is more accurate in classifying users of different age groups, and the weight of each meta-path is consistent with human intuition or the real world situation.