OpenRaiser/Intern-Atlas huggingface dataset:
Temperate Fish Detection and Classification: a Deep Learning based Approach
Venue: arXiv cs.CV 2020
Year: 2020

A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) object detection technique. In the second step, we adopt a Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE) architecture for classifying each fish in the image without pre-filtering. We apply transfer learning to overcome the limited training samples of temperate fishes and to improve the accuracy of the classification. This is done by training the object detection model with ImageNet and the fish classifier via a public dataset (Fish4Knowledge), whereupon both the object detection and classifier are updated with temperate fishes of interest. The weights obtained from pre-training are applied to post-training as a priori. Our solution achieves the state-of-the-art accuracy of 99.27\% on the pre-training. The percentage values for accuracy on the post-training are good; 83.68\% and 87.74\% with and without image augmentation, respectively, indicating that the solution is viable with a more extensive dataset.
Two-way Nanoscale automata
Venue: arXiv cs.FL 2020
Year: 2020

In this paper, we show the all final subclass of two-way Watson-Crick automata have the same computational power as the classical two-way Watson-Crick automata. Here we compare the computational power of two-way Watson-Crick automata and two-way Quantum finite automata and we observe that two-way Watson-Crick automata can accept a language which two-way quantum finite automata cannot accept.
HeCSON: Heuristic for Configuration Selectionin Optical Network Planning
Venue: arXiv cs.NI 2020
Year: 2020

We present a transceiver configuration selection heuristic combining Enhanced Gaussian Noise (EGN) models, which shows a 40\% increase in throughput and 87\% decrease in execution time, compared to only approximate EGN and Full-Form EGN respectively.
SysScale: Exploiting Multi-domain Dynamic Voltage and Frequency Scaling for Energy Efficient Mobile Processors
Venue: arXiv cs.AR 2020
Year: 2020

There are three domains in a modern thermally-constrained mobile system-on-chip (SoC): compute, IO, and memory. We observe that a modern SoC typically allocates a fixed power budget, corresponding to worst-case performance demands, to the IO and memory domains even if they are underutilized. The resulting unfair allocation of the power budget across domains can cause two major issues: 1) the IO and memory domains can operate at a higher frequency and voltage than necessary, increasing power consumption and 2) the unused power budget of the IO and memory domains cannot be used to increase the throughput of the compute domain, hampering performance. To avoid these issues, it is crucial to dynamically orchestrate the distribution of the SoC power budget across the three domains based on their actual performance demands. We propose SysScale, a new multi-domain power management technique to improve the energy efficiency of mobile SoCs. SysScale is based on three key ideas. First, SysScale introduces an accurate algorithm to predict the performance (e.g., bandwidth and latency) demands of the three SoC domains. Second, SysScale uses a new DVFS (dynamic voltage and frequency scaling) mechanism to distribute the SoC power to each domain according to the predicted performance demands. Third, in addition to using a global DVFS mechanism, SysScale uses domain-specialized techniques to optimize the energy efficiency of each domain at different operating points. We implement SysScale on an Intel Skylake microprocessor for mobile devices and evaluate it using a wide variety of SPEC CPU2006, graphics (3DMark), and battery life workloads (e.g., video playback). On a 2-core Skylake, SysScale improves the performance of SPEC CPU2006 and 3DMark workloads by up to 16% and 8.9% (9.2% and 7.9% on average), respectively.
Embedding Information in Radiation Pattern Fluctuations
Venue: arXiv cs.IT 2020
Year: 2020

The radiation pattern of transmit antennas varies and fluctuates as receivers change their location, other objects move around, and due to the antenna design itself. In this paper, we demonstrate how this observation can be exploited to align most of the interference signal power and significantly increase the average achievable communication rates. More precisely, in the context of $K$-user interference channels, we propose a blind interference alignment scheme that combines multi-layer coding at the transmitters and a post-processing methodology at the receivers to align a significant portion of the interference signal power. Our scheme does not rely on any channel state information (CSI), hence the term blind, and only relies on the statistics of the radiation pattern fluctuations. Our proposed communication methodology overcomes some of the barriers in practical implementation of the interference alignment concept. Due to the complexity of the expressions, in this work, we numerically evaluate the achievable rates in different scenarios, demonstrate the gains of our proposed strategy, and compare our results to the prior works with perfect CSI.
Analyzing Temporal Relationships between Trending Terms on Twitter and Urban Dictionary Activity
Venue: arXiv cs.CL 2020
Year: 2020

As an online, crowd-sourced, open English-language slang dictionary, the Urban Dictionary platform contains a wealth of opinions, jokes, and definitions of terms, phrases, acronyms, and more. However, it is unclear exactly how activity on this platform relates to larger conversations happening elsewhere on the web, such as discussions on larger, more popular social media platforms. In this research, we study the temporal activity trends on Urban Dictionary and provide the first analysis of how this activity relates to content being discussed on a major social network: Twitter. By collecting the whole of Urban Dictionary, as well as a large sample of tweets over seven years, we explore the connections between the words and phrases that are defined and searched for on Urban Dictionary and the content that is talked about on Twitter. Through a series of cross-correlation calculations, we identify cases in which Urban Dictionary activity closely reflects the larger conversation happening on Twitter. Then, we analyze the types of terms that have a stronger connection to discussions on Twitter, finding that Urban Dictionary activity that is positively correlated with Twitter is centered around terms related to memes, popular public figures, and offline events. Finally, We explore the relationship between periods of time when terms are trending on Twitter and the corresponding activity on Urban Dictionary, revealing that new definitions are more likely to be added to Urban Dictionary for terms that are currently trending on Twitter.
How to Close Sim-Real Gap? Transfer with Segmentation!
Venue: arXiv cs.RO 2020
Year: 2020

One fundamental difficulty in robotic learning is the sim-real gap problem. In this work, we propose to use segmentation as the interface between perception and control, as a domain-invariant state representation. We identify two sources of sim-real gap, one is dynamics sim-real gap, the other is visual sim-real gap. To close dynamics sim-real gap, we propose to use closed-loop control. For complex task with segmentation mask input, we further propose to learn a closed-loop model-free control policy with deep neural network using imitation learning. To close visual sim-real gap, we propose to learn a perception model in real environment using simulated target plus real background image, without using any real world supervision. We demonstrate this methodology in eye-in-hand grasping task. We train a closed-loop control policy model that taking the segmentation as input using simulation. We show that this control policy is able to transfer from simulation to real environment. The closed-loop control policy is not only robust with respect to discrepancies between the dynamic model of the simulated and real robot, but also is able to generalize to unseen scenarios where the target is moving and even learns to recover from failures. We train the perception segmentation model using training data generated by composing real background images with simulated images of the target. Combining the control policy learned from simulation with the perception model, we achieve an impressive $\bf{88\%}$ success rate in grasping a tiny sphere with a real robot.
A Scientific Information Extraction Dataset for Nature Inspired Engineering
Venue: arXiv cs.CL 2020
Year: 2020

Nature has inspired various ground-breaking technological developments in applications ranging from robotics to aerospace engineering and the manufacturing of medical devices. However, accessing the information captured in scientific biology texts is a time-consuming and hard task that requires domain-specific knowledge. Improving access for outsiders can help interdisciplinary research like Nature Inspired Engineering. This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations. The arguments of these relations can be Multi Word Expressions and have been annotated with modifying phrases to form non-projective graphs. The dataset allows for training and evaluating Relation Extraction algorithms that aim for coarse-grained typing of scientific biological documents, enabling a high-level filter for engineers.
Transformation Based Deep Anomaly Detection in Astronomical Images
Venue: arXiv cs.CV 2020
Year: 2020

In this work, we propose several enhancements to a geometric transformation based model for anomaly detection in images (GeoTranform). The model assumes that the anomaly class is unknown and that only inlier samples are available for training. We introduce new filter based transformations useful for detecting anomalies in astronomical images, that highlight artifact properties to make them more easily distinguishable from real objects. In addition, we propose a transformation selection strategy that allows us to find indistinguishable pairs of transformations. This results in an improvement of the area under the Receiver Operating Characteristic curve (AUROC) and accuracy performance, as well as in a dimensionality reduction. The models were tested on astronomical images from the High Cadence Transient Survey (HiTS) and Zwicky Transient Facility (ZTF) datasets. The best models obtained an average AUROC of 99.20% for HiTS and 91.39% for ZTF. The improvement over the original GeoTransform algorithm and baseline methods such as One-Class Support Vector Machine, and deep learning based methods is significant both statistically and in practice.
FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
Venue: arXiv cs.CV 2020
Year: 2020

Pedestrian intention recognition is very important to develop robust and safe autonomous driving (AD) and advanced driver assistance systems (ADAS) functionalities for urban driving. In this work, we develop an end-to-end pedestrian intention framework that performs well on day- and night- time scenarios. Our framework relies on objection detection bounding boxes combined with skeletal features of human pose. We study early, late, and combined (early and late) fusion mechanisms to exploit the skeletal features and reduce false positives as well to improve the intention prediction performance. The early fusion mechanism results in AP of 0.89 and precision/recall of 0.79/0.89 for pedestrian intention classification. Furthermore, we propose three new metrics to properly evaluate the pedestrian intention systems. Under these new evaluation metrics for the intention prediction, the proposed end-to-end network offers accurate pedestrian intention up to half a second ahead of the actual risky maneuver.
High academic performance is associated with shorter sleep and later bedtimes for young adults
Venue: arXiv cs.SI 2020
Year: 2020

Shorter sleep is known to be negatively associated with academic performance. However, this result has mostly been found in homogenous samples (e.g., students from one university) or when using relative measures of academic performance, such as grade point average. Consequently, the relationship between academic performance and sleep patterns at the population level is not well understood. In this paper, we examined the relationship between academic performance as measured by a standardized test and sleep patterns using data from a Russian panel study (N = 4,400) that was nationally representative for one age cohort (20-21 years old). In addition to self-reported sleep patterns, the data set contained information about participants' online activities over a period of up to 10 years, which allowed us to track the evolution of this relationship over time. We found that high academic performance was associated with shorter sleep, later bedtime, and increased online activity at night. The relationship between high academic performance and online activity at night was stable over a period of 5 years. Our findings suggest that the relationship between academic performance and sleep patterns can be more complex than previously believed and that high performance may be achieved at the expense of individual well-being.
Precise XSS detection and mitigation with Client-side Templates
Venue: arXiv cs.CR 2020
Year: 2020

We present XSnare, a fully client-side XSS solution, implemented as a Firefox extension. Our approach takes advantage of available previous knowledge of a web application's HTML template content, as well as the rich context available in the DOM to block XSS attacks. XSnare prevents XSS exploits by using a database of exploit descriptions, which are written with the help of previously recorded CVEs. CVEs for XSS are widely available and are one of the main ways to tackle zero-day exploits. XSnare effectively singles out potential injection points for exploits in the HTML and sanitizes content to prevent malicious payloads from appearing in the DOM. XSnare can protect application users before application developers release patches and before server operators apply them. We evaluated XSnare on 81 recent CVEs related to XSS attacks, and found that it defends against 94.2% of these exploits. To the best of our knowledge, XSnare is the first protection mechanism for XSS that is application-specific, and based on publicly available CVE information. We show that XSnare's specificity protects users against exploits which evade other, more generic, anti-XSS approaches. Our performance evaluation shows that our extension's overhead on web page loading time is less than 10% for 72.6% of the sites in the Moz Top 500 list.
Gentlemen on the Road: Understanding How Pedestrians Interpret Yielding Behavior of Autonomous Vehicles using Machine Learning
Venue: arXiv cs.HC 2020
Year: 2020

Autonomous vehicles (AVs) can prevent collisions by understanding pedestrian intention. We conducted a virtual reality experiment with 39 participants and measured crossing times (seconds) and head orientation (yaw degrees). We manipulated AV yielding behavior (no-yield, slow-yield, and fast-yield) and the AV size (small, medium, and large). Using machine learning approach, we classified head orientation change of pedestrians by time into 6 clusters of patterns. Results indicate that pedestrian head orientation change was influenced by AV yielding behavior as well as the size of the AV. Participants fixated on the front most of the time even when the car approached near. Participants changed head orientation most frequently when a large size AV did not yield (no-yield). In post-experiment interviews, participants reported that yielding behavior and size affected their decision to cross and perceived safety. For autonomous vehicles to be perceived more safe and trustful, vehicle-specific factors such as size and yielding behavior should be considered in the designing process.
Glottal Source Estimation using an Automatic Chirp Decomposition
Venue: arXiv cs.SD 2020
Year: 2020

In a previous work, we showed that the glottal source can be estimated from speech signals by computing the Zeros of the Z-Transform (ZZT). Decomposition was achieved by separating the roots inside (causal contribution) and outside (anticausal contribution) the unit circle. In order to guarantee a correct deconvolution, time alignment on the Glottal Closure Instants (GCIs) was shown to be essential. This paper extends the formalism of ZZT by evaluating the Z-transform on a contour possibly different from the unit circle. A method is proposed for determining automatically this contour by inspecting the root distribution. The derived Zeros of the Chirp Z-Transform (ZCZT)-based technique turns out to be much more robust to GCI location errors.
User Preference Learning-Aided Collaborative Edge Caching for Small Cell Networks
Venue: arXiv cs.NI 2020
Year: 2020

While next-generation wireless communication networks intend leveraging edge caching for enhanced spectral efficiency, quality of service, end-to-end latency, content sharing cost, etc., several aspects of it are yet to be addressed to make it a reality. One of the fundamental mysteries in a cache-enabled network is predicting what content to cache and where to cache so that high caching content availability is accomplished. For simplicity, most of the legacy systems utilize a static estimation - based on Zipf distribution, which, in reality, may not be adequate to capture the dynamic behaviors of the contents popularities. Forecasting user's preferences can proactively allocate caching resources and cache the needed contents, which is especially important in a dynamic environment with real-time service needs. Motivated by this, we propose a long short-term memory (LSTM) based sequential model that is capable of capturing the temporal dynamics of the users' preferences for the available contents in the content library. Besides, for a more efficient edge caching solution, different nodes in proximity can collaborate to help each other. Based on the forecast, a non-convex optimization problem is formulated to minimize content sharing costs among these nodes. Moreover, a greedy algorithm is used to achieve a sub-optimal solution. By using mathematical analysis and simulation results, we validate that the proposed algorithm performs better than other existing schemes.
DAMIA: Leveraging Domain Adaptation as a Defense against Membership Inference Attacks
Venue: arXiv cs.CR 2020
Year: 2020

Deep Learning (DL) techniques allow ones to train models from a dataset to solve tasks. DL has attracted much interest given its fancy performance and potential market value, while security issues are amongst the most colossal concerns. However, the DL models may be prone to the membership inference attack, where an attacker determines whether a given sample is from the training dataset. Efforts have been made to hinder the attack but unfortunately, they may lead to a major overhead or impaired usability. In this paper, we propose and implement DAMIA, leveraging Domain Adaptation (DA) as a defense aginist membership inference attacks. Our observation is that during the training process, DA obfuscates the dataset to be protected using another related dataset, and derives a model that underlyingly extracts the features from both datasets. Seeing that the model is obfuscated, membership inference fails, while the extracted features provide supports for usability. Extensive experiments have been conducted to validates our intuition. The model trained by DAMIA has a negligible footprint to the usability. Our experiment also excludes factors that may hinder the performance of DAMIA, providing a potential guideline to vendors and researchers to benefit from our solution in a timely manner.
How Biased is the Population of Facebook Users? Comparing the Demographics of Facebook Users with Census Data to Generate Correction Factors
Venue: arXiv cs.SI 2020
Year: 2020

Censuses around the world are key sources of data to guide government investments and public policies. However, these sources are very expensive to obtain and are collected relatively infrequently. Over the last decade, there has been growing interest in the use of data from social media to complement traditional data sources. However, social media users are not representative of the general population. Thus, analyses based on social media data require statistical adjustments, like post-stratification, in order to remove the bias and make solid statistical claims. These adjustments are possible only when we have information about the frequency of demographic groups using social media. These data, when compared with official statistics, enable researchers to produce appropriate statistical correction factors. In this paper, we leverage the Facebook advertising platform to compile the equivalent of an aggregate-level census of Facebook users. Our compilation includes the population distribution for seven demographic attributes such as gender and age at different geographic levels for the US. By comparing the Facebook counts with official reports provided by the US Census and Gallup, we found very high correlations, especially for political leaning and race. We also identified instances where official statistics may be underestimating population counts as in the case of immigration. We use the information collected to calculate bias correction factors for all computed attributes in order to evaluate the extent to which different demographic groups are more or less represented on Facebook. We provide the first comprehensive analysis for assessing biases in Facebook users across several dimensions. This information can be used to generate bias-adjusted population estimates and demographic counts in a timely way and at fine geographic granularity in between data releases of official statistics
Kaemika app, Integrating protocols and chemical simulation
Venue: arXiv cs.CE 2020
Year: 2020

Kaemika is an app available on the four major app stores. It provides deterministic and stochastic simulation, supporting natural chemical notation enhanced with recursive and conditional generation of chemical reaction networks. It has a liquid-handling protocol sublanguage compiled to a virtual digital microfluidic device. Chemical and microfluidic simulations can be interleaved for full experimental-cycle modeling. A novel and unambiguous representation of directed multigraphs is used to lay out chemical reaction networks in graphical form.
Learning Probabilistic Sentence Representations from Paraphrases
Venue: arXiv cs.CL 2020
Year: 2020

Probabilistic word embeddings have shown effectiveness in capturing notions of generality and entailment, but there is very little work on doing the analogous type of investigation for sentences. In this paper we define probabilistic models that produce distributions for sentences. Our best-performing model treats each word as a linear transformation operator applied to a multivariate Gaussian distribution. We train our models on paraphrases and demonstrate that they naturally capture sentence specificity. While our proposed model achieves the best performance overall, we also show that specificity is represented by simpler architectures via the norm of the sentence vectors. Qualitative analysis shows that our probabilistic model captures sentential entailment and provides ways to analyze the specificity and preciseness of individual words.