OpenRaiser/Intern-Atlas huggingface dataset:
Perception of visual numerosity in humans and machines
Venue: arXiv cs.CV 2019
Year: 2019

Numerosity perception is foundational to mathematical learning, but its computational bases are strongly debated. Some investigators argue that humans are endowed with a specialized system supporting numerical representation; others argue that visual numerosity is estimated using continuous magnitudes, such as density or area, which usually co-vary with number. Here we reconcile these contrasting perspectives by testing deep networks on the same numerosity comparison task that was administered to humans, using a stimulus space that allows to measure the contribution of non-numerical features. Our model accurately simulated the psychophysics of numerosity perception and the associated developmental changes: discrimination was driven by numerosity information, but non-numerical features had a significant impact, especially early during development. Representational similarity analysis further highlighted that both numerosity and continuous magnitudes were spontaneously encoded even when no task had to be carried out, demonstrating that numerosity is a major, salient property of our visual environment.
Gender Balance in Computer Science and Engineering in Italian Universities
Venue: arXiv cs.CY 2019
Year: 2019

Multiple studies have shown that gender balance in the fields of Science, Technology, Engineering and Maths -- and in particular in ICT -- is still far to be achieved. Several initiatives have been recently taken to increase the women participation, but it is difficult, at present, to evaluate their impact and their potential of changing the situation. This paper contributes to the discussion by presenting a descriptive analysis of the gender balance in Computer Science and Computer Engineering in Italian Universities.
Location Privacy in Conservation
Venue: arXiv cs.CR 2019
Year: 2019

The growing public nature of academic journals along with current best practices of sharing primary data for scientific research are profoundly valuable for the understanding of a species and their conservation efforts. On the other hand, public spatial data on endangered species may be easily abused by wildlife criminals. In this paper, we discuss how geo-indistinguishability, a formal notion of privacy for location-based systems, can be used to add noise to published spatial data whilst allowing quantification of such tradeoff.
Prediction of neural network performance by phenotypic modeling
Venue: arXiv cs.NE 2019
Year: 2019

Surrogate models are used to reduce the burden of expensive-to-evaluate objective functions in optimization. By creating models which map genomes to objective values, these models can estimate the performance of unknown inputs, and so be used in place of expensive objective functions. Evolutionary techniques such as genetic programming or neuroevolution commonly alter the structure of the genome itself. A lack of consistency in the genotype is a fatal blow to data-driven modeling techniques: interpolation between points is impossible without a common input space. However, while the dimensionality of genotypes may differ across individuals, in many domains, such as controllers or classifiers, the dimensionality of the input and output remains constant. In this work we leverage this insight to embed differing neural networks into the same input space. To judge the difference between the behavior of two neural networks, we give them both the same input sequence, and examine the difference in output. This difference, the phenotypic distance, can then be used to situate these networks into a common input space, allowing us to produce surrogate models which can predict the performance of neural networks regardless of topology. In a robotic navigation task, we show that models trained using this phenotypic embedding perform as well or better as those trained on the weight values of a fixed topology neural network. We establish such phenotypic surrogate models as a promising and flexible approach which enables surrogate modeling even for representations that undergo structural changes.
Construction and enumeration for self-dual cyclic codes of even length over $\mathbb{F}_{2^m} + u\mathbb{F}_{2^m}$
Venue: arXiv cs.IT 2019
Year: 2019

Let $\mathbb{F}_{2^m}$ be a finite field of cardinality $2^m$, $R=\mathbb{F}_{2^m}+u\mathbb{F}_{2^m}$ $(u^2=0)$ and $s,n$ be positive integers such that $n$ is odd. In this paper, we give an explicit representation for every self-dual cyclic code over the finite chain ring $R$ of length $2^sn$ and provide a calculation method to obtain all distinct codes. Moreover, we obtain a clear formula to count the number of all these self-dual cyclic codes. As an application, self-dual and $2$-quasi-cyclic codes over $\mathbb{F}_{2^m}$ of length $2^{s+1}n$ can be obtained from self-dual cyclic code over $R$ of length $2^sn$ and by a Gray map preserving orthogonality and distances from $R$ onto $\mathbb{F}_{2^m}^2$.
EnforceNet: Monocular Camera Localization in Large Scale Indoor Sparse LiDAR Point Cloud
Venue: arXiv cs.CV 2019
Year: 2019

Pose estimation is a fundamental building block for robotic applications such as autonomous vehicles, UAV, and large scale augmented reality. It is also a prohibitive factor for those applications to be in mass production, since the state-of-the-art, centimeter-level pose estimation often requires long mapping procedures and expensive localization sensors, e.g. LiDAR and high precision GPS/IMU, etc. To overcome the cost barrier, we propose a neural network based solution to localize a consumer degree RGB camera within a prior sparse LiDAR map with comparable centimeter-level precision. We achieved it by introducing a novel network module, which we call resistor module, to enforce the network generalize better, predicts more accurately, and converge faster. Such results are benchmarked by several datasets we collected in the large scale indoor parking garage scenes. We plan to open both the data and the code for the community to join the effort to advance this field.
Online Local Boosting: improving performance in online decision trees
Venue: arXiv cs.LG 2019
Year: 2019

As more data are produced each day, and faster, data stream mining is growing in importance, making clear the need for algorithms able to fast process these data. Data stream mining algorithms are meant to be solutions to extract knowledge online, specially tailored from continuous data problem. Many of the current algorithms for data stream mining have high processing and memory costs. Often, the higher the predictive performance, the higher these costs. To increase predictive performance without largely increasing memory and time costs, this paper introduces a novel algorithm, named Online Local Boosting (OLBoost), which can be combined into online decision tree algorithms to improve their predictive performance without modifying the structure of the induced decision trees. For such, OLBoost applies a boosting to small separate regions of the instances space. Experimental results presented in this paper show that by using OLBoost the online learning decision tree algorithms can significantly improve their predictive performance. Additionally, it can make smaller trees perform as good or better than larger trees.
Real-time Vision-based Depth Reconstruction with NVidia Jetson
Venue: arXiv cs.CV 2019
Year: 2019

Vision-based depth reconstruction is a challenging problem extensively studied in computer vision but still lacking universal solution. Reconstructing depth from single image is particularly valuable to mobile robotics as it can be embedded to the modern vision-based simultaneous localization and mapping (vSLAM) methods providing them with the metric information needed to construct accurate maps in real scale. Typically, depth reconstruction is done nowadays via fully-convolutional neural networks (FCNNs). In this work we experiment with several FCNN architectures and introduce a few enhancements aimed at increasing both the effectiveness and the efficiency of the inference. We experimentally determine the solution that provides the best performance/accuracy tradeoff and is able to run on NVidia Jetson with the framerates exceeding 16FPS for 320 x 240 input. We also evaluate the suggested models by conducting monocular vSLAM of unknown indoor environment on NVidia Jetson TX2 in real-time. Open-source implementation of the models and the inference node for Robot Operating System (ROS) are available at https://github.com/CnnDepth/tx2_fcnn_node.
Learning Multimodal Fixed-Point Weights using Gradient Descent
Venue: arXiv cs.LG 2019
Year: 2019

Due to their high computational complexity, deep neural networks are still limited to powerful processing units. To promote a reduced model complexity by dint of low-bit fixed-point quantization, we propose a gradient-based optimization strategy to generate a symmetric mixture of Gaussian modes (SGM) where each mode belongs to a particular quantization stage. We achieve 2-bit state-of-the-art performance and illustrate the model's ability for self-dependent weight adaptation during training.
Fairness-enhancing interventions in stream classification
Venue: arXiv cs.LG 2019
Year: 2019

The wide spread usage of automated data-driven decision support systems has raised a lot of concerns regarding accountability and fairness of the employed models in the absence of human supervision. Existing fairness-aware approaches tackle fairness as a batch learning problem and aim at learning a fair model which can then be applied to future instances of the problem. In many applications, however, the data comes sequentially and its characteristics might evolve with time. In such a setting, it is counter-intuitive to "fix" a (fair) model over the data stream as changes in the data might incur changes in the underlying model therefore, affecting its fairness. In this work, we propose fairness-enhancing interventions that modify the input data so that the outcome of any stream classifier applied to that data will be fair. Experiments on real and synthetic data show that our approach achieves good predictive performance and low discrimination scores over the course of the stream.
Relevancy Classification of Multimodal Social Media Streams for Emergency Services
Venue: arXiv cs.SI 2019
Year: 2019

Social media has become an integral part of our daily lives. During time-critical events, the public shares a variety of posts on social media including reports for resource needs, damages, and help offerings for the affected community. Such posts can be relevant and may contain valuable situational awareness information. However, the information overload of social media challenges the timely processing and extraction of relevant information by the emergency services. Furthermore, the growing usage of multimedia content in the social media posts in recent years further adds to the challenge in timely mining relevant information from social media. In this paper, we present a novel method for multimodal relevancy classification of social media posts, where relevancy is defined with respect to the information needs of emergency management agencies. Specifically, we experiment with the combination of semantic textual features with the image features to efficiently classify a relevant multimodal social media post. We validate our method using an evaluation of classifying the data from three real-world crisis events. Our experiments demonstrate that features based on the proposed hybrid framework of exploiting both textual and image content improve the performance of identifying relevant posts. In the light of these experiments, the application of the proposed classification method could reduce cognitive load on emergency services, in filtering multimodal public posts at large scale.
A Survey of Automatic Methods for Nutritional Assessment
Venue: arXiv cs.CY 2019
Year: 2019

Nutritional assessment is key in order to make decisions about the nature and cause of nutrition related health issues that affect an individual. The systematic process of collecting and interpreting relevant nutrition information, however, is still in its technological infancy. Despite technological advances in storage and analysis of nutritional data, methods for collecting data are largely unchanged over the past two decades. It is well documented that these methods have issues that cause under-reporting. Meanwhile, new developments in wearable biometric logging devices have seen increased traction among individuals. This is sometimes referred to as the Quantified Self movement. One part of this movement is the development of technological means for objectively collecting nutritional data. Nutritional assessment, however, is about to be heavily impacted by emerging computer science technologies, and this survey provides an overview of promising technology approaches supporting nutritional assessment. Both academic and commercial systems are reviewed and categorized.
Iterative temporal differencing with random synaptic feedback weights support error backpropagation for deep learning
Venue: arXiv cs.NE 2019
Year: 2019

This work shows that a differentiable activation function is not necessary any more for error backpropagation. The derivative of the activation function can be replaced by an iterative temporal differencing using fixed random feedback alignment. Using fixed random synaptic feedback alignment with an iterative temporal differencing is transforming the traditional error backpropagation into a more biologically plausible approach for learning deep neural network architectures. This can be a big step toward the integration of STDP-based error backpropagation in deep learning.
Inferring Tracker-Advertiser Relationships in the Online Advertising Ecosystem using Header Bidding
Venue: arXiv cs.CY 2019
Year: 2019

Online advertising relies on trackers and data brokers to show targeted ads to users. To improve targeting, different entities in the intricately interwoven online advertising and tracking ecosystems are incentivized to share information with each other through client-side or server-side mechanisms. Inferring data sharing between entities, especially when it happens at the server-side, is an important and challenging research problem. In this paper, we introduce KASHF: a novel method to infer data sharing relationships between advertisers and trackers by studying how an advertiser's bidding behavior changes as we manipulate the presence of trackers. We operationalize this insight by training an interpretable machine learning model that uses the presence of trackers as features to predict the bidding behavior of an advertiser. By analyzing the machine learning model, we are able to infer relationships between advertisers and trackers irrespective of whether data sharing occurs at the client-side or the server-side. We are also able to identify several server-side data sharing relationships that are validated externally but are not detected by client-side cookie syncing.
End-To-End Prediction of Emotion From Heartbeat Data Collected by a Consumer Fitness Tracker
Venue: arXiv cs.HC 2019
Year: 2019

Automatic detection of emotion has the potential to revolutionize mental health and wellbeing. Recent work has been successful in predicting affect from unimodal electrocardiogram (ECG) data. However, to be immediately relevant for real-world applications, physiology-based emotion detection must make use of ubiquitous photoplethysmogram (PPG) data collected by affordable consumer fitness trackers. Additionally, applications of emotion detection in healthcare settings will require some measure of uncertainty over model predictions. We present here a Bayesian deep learning model for end-to-end classification of emotional valence, using only the unimodal heartbeat time series collected by a consumer fitness tracker (Garmin Vívosmart 3). We collected a new dataset for this task, and report a peak F1 score of 0.7. This demonstrates a practical relevance of physiology-based emotion detection `in the wild' today.
Towards Data-Driven Automatic Video Editing
Venue: arXiv cs.CV 2019
Year: 2019

Automatic video editing involving at least the steps of selecting the most valuable footage from points of view of visual quality and the importance of action filmed; and cutting the footage into a brief and coherent visual story that would be interesting to watch is implemented in a purely data-driven manner. Visual semantic and aesthetic features are extracted by the ImageNet-trained convolutional neural network, and the editing controller is trained by an imitation learning algorithm. As a result, at test time the controller shows the signs of observing basic cinematography editing rules learned from the corpus of motion pictures masterpieces.
$\texttt{DeepSqueeze}$: Decentralization Meets Error-Compensated Compression
Venue: arXiv cs.DC 2019
Year: 2019

Communication is a key bottleneck in distributed training. Recently, an \emph{error-compensated} compression technology was particularly designed for the \emph{centralized} learning and receives huge successes, by showing significant advantages over state-of-the-art compression based methods in saving the communication cost. Since the \emph{decentralized} training has been witnessed to be superior to the traditional \emph{centralized} training in the communication restricted scenario, therefore a natural question to ask is "how to apply the error-compensated technology to the decentralized learning to further reduce the communication cost." However, a trivial extension of compression based centralized training algorithms does not exist for the decentralized scenario. key difference between centralized and decentralized training makes this extension extremely non-trivial. In this paper, we propose an elegant algorithmic design to employ error-compensated stochastic gradient descent for the decentralized scenario, named $\texttt{DeepSqueeze}$. Both the theoretical analysis and the empirical study are provided to show the proposed $\texttt{DeepSqueeze}$ algorithm outperforms the existing compression based decentralized learning algorithms. To the best of our knowledge, this is the first time to apply the error-compensated compression to the decentralized learning.
On resistance switching and oscillations in tubulin microtubule droplets
Venue: arXiv cs.ET 2019
Year: 2019

We study electrical properties of Taxol-stabilised microtubule (MT) ensembles in a droplet of water. We demonstrate that the MT droplets act as electrical switches. Also, a stimulation of a MT droplet with a positive fast impulse causes oscillation of the droplet's resistance. The findings will pave a way towards future designs of MT-based sensing and computing devices, including data storage and featuring liquid state.
Mutation Testing with Hyperproperties
Venue: arXiv cs.LO 2019
Year: 2019

We present a new method for model-based mutation-driven test case generation. Mutants are generated by making small syntactical modifications to the model or source code of the system under test. A test case kills a mutant if the behavior of the mutant deviates from the original system when running the test. In this work, we use hyperproperties-which allow to express relations between multiple executions-to formalize different notions of killing for both deterministic as well as non-deterministic models. The resulting hyperproperties are universal in the sense that they apply to arbitrary reactive models and mutants. Moreover, an off-the-shelf model checking tool for hyperproperties can be used to generate test cases. We evaluate our approach on a number of models expressed in two different modeling languages by generating tests using a state-of-the-art mutation testing tool.
GIDS: GAN based Intrusion Detection System for In-Vehicle Network
Venue: arXiv cs.CR 2019
Year: 2019

A Controller Area Network (CAN) bus in the vehicles is an efficient standard bus enabling communication between all Electronic Control Units (ECU). However, CAN bus is not enough to protect itself because of lack of security features. To detect suspicious network connections effectively, the intrusion detection system (IDS) is strongly required. Unlike the traditional IDS for Internet, there are small number of known attack signatures for vehicle networks. Also, IDS for vehicle requires high accuracy because any false-positive error can seriously affect the safety of the driver. To solve this problem, we propose a novel IDS model for in-vehicle networks, GIDS (GAN based Intrusion Detection System) using deep-learning model, Generative Adversarial Nets. GIDS can learn to detect unknown attacks using only normal data. As experiment result, GIDS shows high detection accuracy for four unknown attacks.
In-Depth Benchmarking of Graph Database Systems with the Linked Data Benchmark Council (LDBC) Social Network Benchmark (SNB)
Venue: arXiv cs.DB 2019
Year: 2019

In this study, we present the first results of a complete implementation of the LDBC SNB benchmark -- interactive short, interactive complex, and business intelligence -- in two native graph database systems---Neo4j and TigerGraph. In addition to thoroughly evaluating the performance of all of the 46 queries in the benchmark on four scale factors -- SF-1, SF-10, SF-100, and SF-1000 -- and three computing architectures -- on premise and in the cloud -- we also measure the bulk loading time and storage size. Our results show that TigerGraph is consistently outperforming Neo4j on the majority of the queries---by two or more orders of magnitude (100X factor) on certain interactive complex and business intelligence queries. The gap increases with the size of the data since only TigerGraph is able to scale to SF-1000---Neo4j finishes only 12 of the 25 business intelligence queries in reasonable time. Nonetheless, Neo4j is generally faster at bulk loading graph data up to SF-100. A key to our study is the active involvement of the vendors in the tuning of their platforms. In order to encourage reproducibility, we make all the code, scripts, and configuration parameters publicly available online.
The General Data Protection Regulation: Requirements, Architectures, and Constraints
Venue: arXiv cs.SE 2019
Year: 2019

The General Data Protection Regulation (GDPR) in the European Union is the most famous recently enacted privacy regulation. Despite of the regulation's legal, political, and technological ramifications, relatively little research has been carried out for better understanding the GDPR's practical implications for requirements engineering and software architectures. Building on a grounded theory approach with close ties to the Finnish software industry, this paper contributes to the sealing of this gap in previous research. Three questions are asked and answered in the context of software development organizations. First, the paper elaborates nine practical constraints under which many small and medium-sized enterprises (SMEs) often operate when implementing solutions that address the new regulatory demands. Second, the paper elicits nine regulatory requirements from the GDPR for software architectures. Third, the paper presents an implementation for a software architecture that complies both with the requirements elicited and the constraints elaborated.