OpenRaiser/Intern-Atlas huggingface dataset:
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
Venue: arXiv cs.CV 2020
Year: 2020

Detecting vehicles and representing their position and orientation in the three dimensional space is a key technology for autonomous driving. Recently, methods for 3D vehicle detection solely based on monocular RGB images gained popularity. In order to facilitate this task as well as to compare and drive state-of-the-art methods, several new datasets and benchmarks have been published. Ground truth annotations of vehicles are usually obtained using lidar point clouds, which often induces errors due to imperfect calibration or synchronization between both sensors. To this end, we propose Cityscapes 3D, extending the original Cityscapes dataset with 3D bounding box annotations for all types of vehicles. In contrast to existing datasets, our 3D annotations were labeled using stereo RGB images only and capture all nine degrees of freedom. This leads to a pixel-accurate reprojection in the RGB image and a higher range of annotations compared to lidar-based approaches. In order to ease multitask learning, we provide a pairing of 2D instance segments with 3D bounding boxes. In addition, we complement the Cityscapes benchmark suite with 3D vehicle detection based on the new annotations as well as metrics presented in this work. Dataset and benchmark are available online.
Global Convergence of Sobolev Training for Overparameterized Neural Networks
Venue: arXiv cs.LG 2020
Year: 2020

Sobolev loss is used when training a network to approximate the values and derivatives of a target function at a prescribed set of input points. Recent works have demonstrated its successful applications in various tasks such as distillation or synthetic gradient prediction. In this work we prove that an overparameterized two-layer relu neural network trained on the Sobolev loss with gradient flow from random initialization can fit any given function values and any given directional derivatives, under a separation condition on the input data.
Identification of main factors affecting trust and determination of their importance in electronic businesses in Iran
Venue: arXiv cs.CY 2020
Year: 2020

Today, trust has become one of the main concerns of the electronic business in Iran. The role of trust especially in electronic businesses those directly deal with selling physical goods through internet is a lot more evident. Reviewing literature shows that several factors affect establishing of trust in potential customers. Since trust establishment needs to be noticed in each triple stages of an electronic purchase (before, during and finally after purchase). In this study by using field research, the importance of influential factors affecting the potential customers in three stages of an electronic purchase is determined. Based on the results from conducting the research, the certainty of traceability of the purchase with importance factor of 85.97% in pre-purchase stage, safety of transactions and the time of delivery of goods with the importance factor of 85.67% in the middle stage of the purchase and receiving a fault-free and undamaged good with the importance factor of 89.55% in the post purchase stage make up the top three most important factors.
Equilibrium of Blockchain Miners with Dynamic Asset Allocation
Venue: arXiv cs.CR 2020
Year: 2020

We model and analyze blockchain miners who seek to maximize the compound return of their mining businesses. The analysis of the optimal strategies finds a new equilibrium point among the miners and the mining pools, which predicts the market share of each miner or mining pool. The cost of mining determines the share of each miner or mining pool at equilibrium. We conclude that neither miners nor mining pools who seek to maximize their compound return will have a financial incentive to occupy more than 50% of the hash rate if the cost of mining is at the same level for all. However, if there is an outstandingly cost-efficient miner, then the market share of this miner may exceed 50% in the equilibrium, which can threaten the viability of the entire ecosystem.
Architecture Support for FPGA Multi-tenancy in the Cloud
Venue: arXiv cs.AR 2020
Year: 2020

Cloud deployments now increasingly provision FPGA accelerators as part of virtual instances. While FPGAs are still essentially single-tenant, the growing demand for hardware acceleration will inevitably lead to the need for methods and architectures supporting FPGA multi-tenancy. In this paper, we propose an architecture supporting space-sharing of FPGA devices among multiple tenants in the cloud. The proposed architecture implements a network-on-chip (NoC) designed for fast data movement and low hardware footprint. Prototyping the proposed architecture on a Xilinx Virtex Ultrascale+ demonstrated near specification maximum frequency for on-chip data movement and high throughput in virtual instance access to hardware accelerators. We demonstrate similar performance compared to single-tenant deployment while increasing FPGA utilization ( we achieved 6x higher FPGA utilization with our case study), which is one of the major goals of virtualization. Overall, our NoC interconnect achieved about 2x higher maximum frequency than the state-of-the-art and a bandwidth of 25.6 Gbps.
Survey on Physical Layer Security for 5G Wireless Networks
Venue: arXiv cs.IT 2020
Year: 2020

Physical layer security is a promising approach that can benefit traditional encryption methods. The idea of physical layer security is to take advantage of the features of the propagation medium and its impairments to ensure secure communication in the physical layer. This work introduces a comprehensive review of the main information-theoretic metrics used to measure the secrecy performance in physical layer security. Furthermore, a theoretical framework related to the most commonly used physical layer security techniques to improve the secrecy performance is provided. Finally, our work surveys physical layer security research over several enabling 5G technologies, such as massive multiple-input multiple-output, millimeter-wave communications, heterogeneous networks, non-orthogonal multiple access, and full-duplex. Also, we include the key concepts of each of the aforementioned technologies. Future fields of research and technical challenges of physical layer security are also identified.
Timely Detection and Mitigation of Stealthy DDoS Attacks via IoT Networks
Venue: arXiv cs.CR 2020
Year: 2020

Internet of Things (IoT) networks consist of sensors, actuators, mobile and wearable devices that can connect to the Internet. With billions of such devices already in the market which have significant vulnerabilities, there is a dangerous threat to the Internet services and also some cyber-physical systems that are also connected to the Internet. Specifically, due to their existing vulnerabilities IoT devices are susceptible to being compromised and being part of a new type of stealthy Distributed Denial of Service (DDoS) attack, called Mongolian DDoS, which is characterized by its widely distributed nature and small attack size from each source. This study proposes a novel anomaly-based Intrusion Detection System (IDS) that is capable of timely detecting and mitigating this emerging type of DDoS attacks. The proposed IDS's capability of detecting and mitigating stealthy DDoS attacks with even very low attack size per source is demonstrated through numerical and testbed experiments.
Extreme Gradient Boosted Multi-label Trees for Dynamic Classifier Chains
Venue: arXiv cs.LG 2020
Year: 2020

Classifier chains is a key technique in multi-label classification, since it allows to consider label dependencies effectively. However, the classifiers are aligned according to a static order of the labels. In the concept of dynamic classifier chains (DCC) the label ordering is chosen for each prediction dynamically depending on the respective instance at hand. We combine this concept with the boosting of extreme gradient boosted trees (XGBoost), an effective and scalable state-of-the-art technique, and incorporate DCC in a fast multi-label extension of XGBoost which we make publicly available. As only positive labels have to be predicted and these are usually only few, the training costs can be further substantially reduced. Moreover, as experiments on eleven datasets show, the length of the chain allows for a more control over the usage of previous predictions and hence over the measure one want to optimize.
Classification and Recognition of Encrypted EEG Data Neural Network
Venue: arXiv cs.LG 2020
Year: 2020

With the rapid development of Machine Learning technology applied in electroencephalography (EEG) signals, Brain-Computer Interface (BCI) has emerged as a novel and convenient human-computer interaction for smart home, intelligent medical and other Internet of Things (IoT) scenarios. However, security issues such as sensitive information disclosure and unauthorized operations have not received sufficient concerns. There are still some defects with the existing solutions to encrypted EEG data such as low accuracy, high time complexity or slow processing speed. For this reason, a classification and recognition method of encrypted EEG data based on neural network is proposed, which adopts Paillier encryption algorithm to encrypt EEG data and meanwhile resolves the problem of floating point operations. In addition, it improves traditional feed-forward neural network (FNN) by using the approximate function instead of activation function and realizes multi-classification of encrypted EEG data. Extensive experiments are conducted to explore the effect of several metrics (such as the hidden neuron size and the learning rate updated by improved simulated annealing algorithm) on the recognition results. Followed by security and time cost analysis, the proposed model and approach are validated and evaluated on public EEG datasets provided by PhysioNet, BCI Competition IV and EPILEPSIAE. The experimental results show that our proposal has the satisfactory accuracy, efficiency and feasibility compared with other solutions.
Selection of an Integrated Security Area for locating a State Military Organization (SMO) based on group decision system: a multicriteria approach
Venue: arXiv cs.AI 2020
Year: 2020

Over the past few years there has been growing concern among authorities over crimes committed worldwide. In Brazil it is no different. High crime rates have encouraged government authorities involved in public safety to identify solutions to minimize crimes. In this context, one way to plan and manage security is in the division of neighborhoods in ISA (Integrated Security Areas). Each ISA has a neighborhood conglomerates taking into account their geolocation. From this it becomes possible to maximize security management and combat crime. Based on that, one of the main points that generate great discussion at the governmental level is the choice of a certain integrated security area for the installation of a certain police battalion. This choice involves multiple decision makers since several hierarchies are involved. Thus, this paper aims to identify the best ISA to deploy a police battalion using group decision techniques and tools. For this work the Group Decision Support System (GDSS) called GRoUp Support (GRUS) was used from two main Vote techniques: Condorcet and Borda. With this it was possible to identify the best ISA taking into account the pre-established criteria.
Neural gradients are near-lognormal: improved quantized and sparse training
Venue: arXiv cs.CV 2020
Year: 2020

While training can mostly be accelerated by reducing the time needed to propagate neural gradients back throughout the model, most previous works focus on the quantization/pruning of weights and activations. These methods are often not applicable to neural gradients, which have very different statistical properties. Distinguished from weights and activations, we find that the distribution of neural gradients is approximately lognormal. Considering this, we suggest two closed-form analytical methods to reduce the computational and memory burdens of neural gradients. The first method optimizes the floating-point format and scale of the gradients. The second method accurately sets sparsity thresholds for gradient pruning. Each method achieves state-of-the-art results on ImageNet. To the best of our knowledge, this paper is the first to (1) quantize the gradients to 6-bit floating-point formats, or (2) achieve up to 85% gradient sparsity -- in each case without accuracy degradation. Reference implementation accompanies the paper.
The PSPACE-hardness of understanding neural circuits
Venue: arXiv cs.CC 2020
Year: 2020

In neuroscience, an important aspect of understanding the function of a neural circuit is to determine which, if any, of the neurons in the circuit are vital for the biological behavior governed by the neural circuit. A similar problem is to determine whether a given small set of neurons may be enough for the behavior to be displayed, even if all other neurons in the circuit are deactivated. Such a subset of neurons forms what is called a degenerate circuit for the behavior being studied. Recent advances in experimental techniques have provided researchers with tools to activate and deactivate subsets of neurons with a very high resolution, even in living animals. The data collected from such experiments may be of the following form: when a given subset of neurons is deactivated, is the behavior under study observed? This setting leads to the algorithmic question of determining the minimal vital or degenerate sets of neurons when one is given as input a description of the neural circuit. The algorithmic problem entails both figuring out which subsets of neurons should be perturbed (activated/deactivated), and then using the data from those perturbations to determine the minimal vital or degenerate sets. Given the large number of possible perturbations, and the recurrent nature of neural circuits, the possibility of a combinatorial explosion in such an approach has been recognized in the biology and the neuroscience literature. In this paper, we prove that the problems of finding minimal or minimum-size degenerate sets, and of finding the set of vital neurons, of a neural circuit given as input, are in fact PSPACE-hard. More importantly, we prove our hardness results by showing that a simpler problem, that of simulating such neural circuits, is itself PSPACE-hard.
Inner Ensemble Networks: Average Ensemble as an Effective Regularizer
Venue: arXiv cs.LG 2020
Year: 2020

We introduce Inner Ensemble Networks (IENs) which reduce the variance within the neural network itself without an increase in the model complexity. IENs utilize ensemble parameters during the training phase to reduce the network variance. While in the testing phase, these parameters are removed without a change in the enhanced performance. IENs reduce the variance of an ordinary deep model by a factor of $1/m^{L-1}$, where $m$ is the number of inner ensembles and $L$ is the depth of the model. Also, we show empirically and theoretically that IENs lead to a greater variance reduction in comparison with other similar approaches such as dropout and maxout. Our results show a decrease of error rates between 1.7\% and 17.3\% in comparison with an ordinary deep model. We also show that IEN was preferred by Neural Architecture Search (NAS) methods over prior approaches. Code is available at https://github.com/abduallahmohamed/inner_ensemble_nets.
Exact and heuristic methods for the discrete parallel machine scheduling location problem
Venue: arXiv cs.AI 2020
Year: 2020

The discrete parallel machine makespan scheduling location (ScheLoc) problem is an integrated combinatorial optimization problem that combines facility location and job scheduling. The problem consists in choosing the locations of $p$ machines among a finite set of candidates and scheduling a set of jobs on these machines, aiming to minimize the makespan. Depending on the machine location, the jobs may have different release dates, and thus the location decisions have a direct impact on the scheduling decisions. To solve the problem, it is proposed a new arc-flow formulation, a column generation and three heuristic procedures that are evaluated through extensive computational experiments. By embedding the proposed procedures into a framework algorithm, we are able to find proven optimal solutions for all benchmark instances from the related literature and to obtain small percentage gaps for a new set of challenging instances.
Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews
Venue: arXiv cs.CL 2020
Year: 2020

In this work we propose a machine learning model for depression detection from transcribed clinical interviews. Depression is a mental disorder that impacts not only the subject's mood but also the use of language. To this end we use a Hierarchical Attention Network to classify interviews of depressed subjects. We augment the attention layer of our model with a conditioning mechanism on linguistic features, extracted from affective lexica. Our analysis shows that individuals diagnosed with depression use affective language to a greater extent than not-depressed. Our experiments show that external affective information improves the performance of the proposed architecture in the General Psychotherapy Corpus and the DAIC-WoZ 2017 depression datasets, achieving state-of-the-art 71.6 and 68.6 F1 scores respectively.
Automated Diagram Generation to Build Understanding and Usability
Venue: arXiv cs.AI 2020
Year: 2020

Causal loop and stock and flow diagrams are broadly used in System Dynamics because they help organize relationships and convey meaning. Using the analytical work of Schoenberg (2019) to select what to include in a compressed model, this paper demonstrates how that information can be clearly presented in an automatically generated causal loop diagram. The diagrams are generated using tools developed by people working in graph theory and the generated diagrams are clear and aesthetically pleasing. This approach can also be built upon to generate stock and flow diagrams. Automated stock and flow diagram generation opens the door to representing models developed using only equations, regardless or origin, in a clear and easy to understand way. Because models can be large, the application of grouping techniques, again developed for graph theory, can help structure the resulting diagrams in the most usable form. This paper describes the algorithms developed for automated diagram generation and shows a number of examples of their uses in large models. The application of these techniques to existing, but inaccessible, equation-based models can help broaden the knowledge base for System Dynamics modeling. The techniques can also be used to improve layout in all, or part, of existing models with diagrammatic informtion.
COVID-19 Mobility Data Collection of Seoul, South Korea
Venue: arXiv cs.CY 2020
Year: 2020

The relationship between pandemic and human mobility has received considerable attention from scholars, as it can provide an indication of how mobility patterns change in response to a public health crisis or whether reduced mobility contributes to preventing the spread of an infectious disease. While several studies attempted to unveil such relationship, no studies have focused on changes in human mobility at a finer scale utilizing comprehensive, high-resolution data. To address the complex association between pandemic's spread and human mobility, this paper presents two categories of mobility datasets - trip mode and trip purpose - that concern nearly 10 million citizens' movements during COVID-19 in the capital city of South Korea, Seoul, where no lockdowns has been imposed. We curate hourly data of subway ridership, traffic volume and population present count at selected points of interests. The results to be derived from the presented datasets can be used as an important reference for public health decision making in the post COVID-19 era.
EPIC30M: An Epidemics Corpus Of Over 30 Million Relevant Tweets
Venue: arXiv cs.SI 2020
Year: 2020

Since the start of COVID-19, several relevant corpora from various sources are presented in the literature that contain millions of data points. While these corpora are valuable in supporting many analyses on this specific pandemic, researchers require additional benchmark corpora that contain other epidemics to facilitate cross-epidemic pattern recognition and trend analysis tasks. During our other efforts on COVID-19 related work, we discover very little disease related corpora in the literature that are sizable and rich enough to support such cross-epidemic analysis tasks. In this paper, we present EPIC30M, a large-scale epidemic corpus that contains 30 millions micro-blog posts, i.e., tweets crawled from Twitter, from year 2006 to 2020. EPIC30M contains a subset of 26.2 millions tweets related to three general diseases, namely Ebola, Cholera and Swine Flu, and another subset of 4.7 millions tweets of six global epidemic outbreaks, including 2009 H1N1 Swine Flu, 2010 Haiti Cholera, 2012 Middle-East Respiratory Syndrome (MERS), 2013 West African Ebola, 2016 Yemen Cholera and 2018 Kivu Ebola. Furthermore, we explore and discuss the properties of the corpus with statistics of key terms and hashtags and trends analysis for each subset. Finally, we demonstrate the value and impact that EPIC30M could create through a discussion of multiple use cases of cross-epidemic research topics that attract growing interest in recent years. These use cases span multiple research areas, such as epidemiological modeling, pattern recognition, natural language understanding and economical modeling.
SD-RSIC: Summarization Driven Deep Remote Sensing Image Captioning
Venue: arXiv cs.CV 2020
Year: 2020

Deep neural networks (DNNs) have been recently found popular for image captioning problems in remote sensing (RS). Existing DNN based approaches rely on the availability of a training set made up of a high number of RS images with their captions. However, captions of training images may contain redundant information (they can be repetitive or semantically similar to each other), resulting in information deficiency while learning a mapping from the image domain to the language domain. To overcome this limitation, in this paper, we present a novel Summarization Driven Remote Sensing Image Captioning (SD-RSIC) approach. The proposed approach consists of three main steps. The first step obtains the standard image captions by jointly exploiting convolutional neural networks (CNNs) with long short-term memory (LSTM) networks. The second step, unlike the existing RS image captioning methods, summarizes the ground-truth captions of each training image into a single caption by exploiting sequence to sequence neural networks and eliminates the redundancy present in the training set. The third step automatically defines the adaptive weights associated to each RS image to combine the standard captions with the summarized captions based on the semantic content of the image. This is achieved by a novel adaptive weighting strategy defined in the context of LSTM networks. Experimental results obtained on the RSCID, UCM-Captions and Sydney-Captions datasets show the effectiveness of the proposed approach compared to the state-of-the-art RS image captioning approaches. The code of the proposed approach is publicly available at https://gitlab.tubit.tu-berlin.de/rsim/SD-RSIC.