OpenRaiser/Intern-Atlas huggingface dataset:
Similarity Based Stratified Splitting: an approach to train better classifiers
Venue: arXiv cs.LG 2020
Year: 2020

We propose a Similarity-Based Stratified Splitting (SBSS) technique, which uses both the output and input space information to split the data. The splits are generated using similarity functions among samples to place similar samples in different splits. This approach allows for a better representation of the data in the training phase. This strategy leads to a more realistic performance estimation when used in real-world applications. We evaluate our proposal in twenty-two benchmark datasets with classifiers such as Multi-Layer Perceptron, Support Vector Machine, Random Forest and K-Nearest Neighbors, and five similarity functions Cityblock, Chebyshev, Cosine, Correlation, and Euclidean. According to the Wilcoxon Sign-Rank test, our approach consistently outperformed ordinary stratified 10-fold cross-validation in 75\% of the assessed scenarios.
Session-layer Attack Traffic Classification by Program Synthesis
Venue: arXiv cs.PL 2020
Year: 2020

Writing classification rules to identify malicious network traffic is a time-consuming and error-prone task. Learning-based classification systems automatically extract such rules from positive and negative traffic examples. However, due to limitations in the representation of network traffic and the learning strategy, these systems lack both expressiveness to cover a range of attacks and interpretability in fully describing the attack traffic's structure at the session layer. This paper presents Sharingan system, which uses program synthesis techniques to generate network classification programs at the session layer. Sharingan accepts raw network traces as inputs, and reports potential patterns of the attack traffic in NetQRE, a domain specific language designed for specifying session-layer quantitative properties. Using Sharingan, network operators can better analyze the attack pattern due to the following advantages of Sharingan's learning process: (1) it requires minimal feature engineering, (2) it is amenable to efficient implementation of the learnt classifier, and (3) the synthesized program is easy to decipher and edit. We develop a range of novel optimizations that reduce the synthesis time for large and complex tasks to a matter of minutes. Our experiments show that Sharingan is able to correctly identify attacks from a diverse set of network attack traces and generates explainable outputs, while achieving accuracy comparable to state-of-the-art learning-based intrusion detection systems.
Performance Evaluation and Modeling of Cryptographic Libraries for MPI Communications
Venue: arXiv cs.DC 2020
Year: 2020

In order for High-Performance Computing (HPC) applications with data security requirements to execute in the public cloud, the cloud infrastructure must ensure the privacy and integrity of data. To meet this goal, we consider incorporating encryption in the Message Passing Interface (MPI) library. We empirically evaluate four contemporary cryptographic libraries, OpenSSL, BoringSSL, Libsodium, and CryptoPP using micro-benchmarks and NAS parallel benchmarks on two different networking technologies, 10Gbps Ethernet and 40Gbps InfiniBand. We also develop accurate models that allow us to reason about the performance of encrypted MPI communication in different situations and give guidance on how to improve encrypted MPI performance.
Re-Transmission Diversity Multiple Access based on SIC and HARQ-IR
Venue: arXiv cs.IT 2020
Year: 2020

We consider multiple access with re-transmission diversity (RTxD) based on a hybrid automatic request (HARQ) protocol with incremental redundancy (IR) in this paper. In order to mitigate multiple access interference (MAI), we employ successive interference cancellation (SIC) at a receiver and derive conditions that all the signals from active users can be successfully decoded within a certain number of re-transmissions of IR blocks. We consider two special cases, and based on them we derive two multichannel random access schemes that have subchannels in the power and rate domains. Through the analysis and simulations, we can show that the proposed RTxD random access schemes can outperform other existing similar RTxD random access schemes.
Improving Text Generation Evaluation with Batch Centering and Tempered Word Mover Distance
Venue: arXiv cs.CL 2020
Year: 2020

Recent advances in automatic evaluation metrics for text have shown that deep contextualized word representations, such as those generated by BERT encoders, are helpful for designing metrics that correlate well with human judgements. At the same time, it has been argued that contextualized word representations exhibit sub-optimal statistical properties for encoding the true similarity between words or sentences. In this paper, we present two techniques for improving encoding representations for similarity metrics: a batch-mean centering strategy that improves statistical properties; and a computationally efficient tempered Word Mover Distance, for better fusion of the information in the contextualized word representations. We conduct numerical experiments that demonstrate the robustness of our techniques, reporting results over various BERT-backbone learned metrics and achieving state of the art correlation with human ratings on several benchmarks.
Differential radial basis function network for sequence modelling
Venue: arXiv cs.LG 2020
Year: 2020

We propose a differential radial basis function (RBF) network termed RBF-DiffNet -- whose hidden layer blocks are partial differential equations (PDEs) linear in terms of the RBF -- to make the baseline RBF network robust to noise in sequential data. Assuming that the sequential data derives from the discretisation of the solution to an underlying PDE, the differential RBF network learns constant linear coefficients of the PDE, consequently regularising the RBF network by following modified backward-Euler updates. We experimentally validate the differential RBF network on the logistic map chaotic timeseries as well as on 30 real-world timeseries provided by Walmart in the M5 forecasting competition. The proposed model is compared with the normalised and unnormalised RBF networks, ARIMA, and ensembles of multilayer perceptrons (MLPs) and recurrent networks with long short-term memory (LSTM) blocks. From the experimental results, RBF-DiffNet consistently shows a marked reduction over the baseline RBF network in terms of the prediction error (e.g., 26% reduction in the root mean squared scaled error on the M5 dataset); RBF-DiffNet also shows a comparable performance to the LSTM ensemble at less than one-sixteenth the LSTM computational time. Our proposed network consequently enables more accurate predictions -- in the presence of observational noise -- in sequence modelling tasks such as timeseries forecasting that leverage the model interpretability, fast training, and function approximation properties of the RBF network.
Deep Reinforcement Learning and Transportation Research: A Comprehensive Review
Venue: arXiv cs.LG 2020
Year: 2020

Deep reinforcement learning (DRL) is an emerging methodology that is transforming the way many complicated transportation decision-making problems are tackled. Researchers have been increasingly turning to this powerful learning-based methodology to solve challenging problems across transportation fields. While many promising applications have been reported in the literature, there remains a lack of comprehensive synthesis of the many DRL algorithms and their uses and adaptations. The objective of this paper is to fill this gap by conducting a comprehensive, synthesized review of DRL applications in transportation. We start by offering an overview of the DRL mathematical background, popular and promising DRL algorithms, and some highly effective DRL extensions. Building on this overview, a systematic investigation of about 150 DRL studies that have appeared in the transportation literature, divided into seven different categories, is performed. Building on this review, we continue to examine the applicability, strengths, shortcomings, and common and application-specific issues of DRL techniques with regard to their applications in transportation. In the end, we recommend directions for future research and present available resources for actually implementing DRL.
Mitigating Gender Bias in Machine Translation with Target Gender Annotations
Venue: arXiv cs.CL 2020
Year: 2020

When translating "The secretary asked for details." to a language with grammatical gender, it might be necessary to determine the gender of the subject "secretary". If the sentence does not contain the necessary information, it is not always possible to disambiguate. In such cases, machine translation systems select the most common translation option, which often corresponds to the stereotypical translations, thus potentially exacerbating prejudice and marginalisation of certain groups and people. We argue that the information necessary for an adequate translation can not always be deduced from the sentence being translated or even might depend on external knowledge. Therefore, in this work, we propose to decouple the task of acquiring the necessary information from the task of learning to translate correctly when such information is available. To that end, we present a method for training machine translation systems to use word-level annotations containing information about subject's gender. To prepare training data, we annotate regular source language words with grammatical gender information of the corresponding target language words. Using such data to train machine translation systems reduces their reliance on gender stereotypes when information about the subject's gender is available. Our experiments on five language pairs show that this allows improving accuracy on the WinoMT test set by up to 25.8 percentage points.
Butson full propelinear codes
Venue: arXiv cs.IT 2020
Year: 2020

In this paper we study Butson Hadamard matrices, and codes over finite rings coming from these matrices in logarithmic form, called BH-codes. We introduce a new morphism of Butson Hadamard matrices through a generalized Gray map on the matrices in logarithmic form, which is comparable to the morphism given in a recent note of Ó Catháin and Swartz. That is, we show how, if given a Butson Hadamard matrix over the $k^{\rm th}$ roots of unity, we can construct a larger Butson matrix over the $\ell^{\rm th}$ roots of unity for any $\ell$ dividing $k$, provided that any prime $p$ dividing $k$ also divides $\ell$. We prove that a $\mathbb{Z}_{p^s}$-additive code with $p$ a prime number is isomorphic as a group to a BH-code over $\mathbb{Z}_{p^s}$ and the image of this BH-code under the Gray map is a BH-code over $\mathbb{Z}_p$ (binary Hadamard code for $p=2$). Further, we investigate the inherent propelinear structure of these codes (and their images) when the Butson matrix is cocyclic. Some structural properties of these codes are studied and examples are provided.
Massive MIMO for Cellular-Connected UAV: Challenges and Promising Solutions
Venue: arXiv cs.IT 2020
Year: 2020

Massive multiple-input multiple-output (MIMO) is a promising technology for enabling cellular-connected unmanned aerial vehicle (UAV) communications in the future. Equipped with full-dimensional large arrays, ground base stations (GBSs) can apply adaptive fine-grained three-dimensional (3D) beamforming to mitigate the strong interference between high-altitude UAVs and low-altitude terrestrial users, thus significantly enhancing the network spectral efficiency. However, the performance gain of massive MIMO critically depends on the accurate channel state information (CSI) of both UAVs and terrestrial users at the GBSs, which is practically difficult to achieve due to UAV-induced pilot contamination and UAV's high mobility in 3D. Moreover, the increasingly popular applications relying on a large group of coordinated UAVs or UAV swarm as well as the practical hybrid GBS beamforming architecture for massive MIMO further complicate the pilot contamination and channel/beam tracking problems. In this article, we provide an overview of the above challenging issues, propose new solutions to cope with them, and discuss about promising directions for future research. Preliminary simulation results are also provided to validate the effectiveness of proposed solutions.
MeetCues: Supporting Online Meetings Experience
Venue: arXiv cs.HC 2020
Year: 2020

The remote work ecosystem is transforming patterns of communication between teams and individuals located at distance. Particularly, the absence of certain subtle cues in current communication tools may hinder an online's meeting outcome by negatively impacting attendees' overall experience and, often, make them feeling disconnected. The problem here might be due to the fact that current tools fall short in capturing it. To partly address this, we developed an online platform-MeetCues-with the aim of supporting online communication during meetings. MeetCues is a companion platform for a commercial communication tool with interactive and visual UI features that support back-channels of communications. It allows attendees to be more engaged during a meeting, and reflect in real-time or post-meeting. We evaluated our platform in a diverse set of five, real-world corporate meetings, and we found that, not only people were more engaged and aware during their meetings, but they also felt more connected. These findings suggest promise in the design of new communications tools, and reinforce the role of InfoVis in augmenting and enriching online meetings.
BRUMS at SemEval-2020 Task 12 : Transformer based Multilingual Offensive Language Identification in Social Media
Venue: arXiv cs.CL 2020
Year: 2020

In this paper, we describe the team \textit{BRUMS} entry to OffensEval 2: Multilingual Offensive Language Identification in Social Media in SemEval-2020. The OffensEval organizers provided participants with annotated datasets containing posts from social media in Arabic, Danish, English, Greek and Turkish. We present a multilingual deep learning model to identify offensive language in social media. Overall, the approach achieves acceptable evaluation scores, while maintaining flexibility between languages.
Impact of Thermal Throttling on Long-Term Visual Inference in a CPU-based Edge Device
Venue: arXiv cs.CV 2020
Year: 2020

Many application scenarios of edge visual inference, e.g., robotics or environmental monitoring, eventually require long periods of continuous operation. In such periods, the processor temperature plays a critical role to keep a prescribed frame rate. Particularly, the heavy computational load of convolutional neural networks (CNNs) may lead to thermal throttling and hence performance degradation in few seconds. In this paper, we report and analyze the long-term performance of 80 different cases resulting from running 5 CNN models on 4 software frameworks and 2 operating systems without and with active cooling. This comprehensive study was conducted on a low-cost edge platform, namely Raspberry Pi 4B (RPi4B), under stable indoor conditions. The results show that hysteresis-based active cooling prevented thermal throttling in all cases, thereby improving the throughput up to approximately 90% versus no cooling. Interestingly, the range of fan usage during active cooling varied from 33% to 65%. Given the impact of the fan on the power consumption of the system as a whole, these results stress the importance of a suitable selection of CNN model and software components. To assess the performance in outdoor applications, we integrated an external temperature sensor with the RPi4B and conducted a set of experiments with no active cooling in a wide interval of ambient temperature, ranging from 22 °C to 36 °C. Variations up to 27.7% were measured with respect to the maximum throughput achieved in that interval. This demonstrates that ambient temperature is a critical parameter in case active cooling cannot be applied.
An Empirical Study on User Reviews Targeting Mobile Apps' Security & Privacy
Venue: arXiv cs.CR 2020
Year: 2020

Application markets provide a communication channel between app developers and their end-users in form of app reviews, which allow users to provide feedback about the apps. Although security and privacy in mobile apps are one of the biggest issues, it is unclear how much people are aware of these or discuss them in reviews. In this study, we explore the privacy and security concerns of users using reviews in the Google Play Store. For this, we conducted a study by analyzing around 2.2M reviews from the top 539 apps of this Android market. We found that 0.5\% of these reviews are related to the security and privacy concerns of the users. We further investigated these apps by performing dynamic analysis which provided us valuable insights into their actual behaviors. Based on the different perspectives, we categorized the apps and evaluated how the different factors influence the users' perception of the apps. It was evident from the results that the number of permissions that the apps request plays a dominant role in this matter. We also found that sending out the location can affect the users' thoughts about the app. The other factors do not directly affect the privacy and security concerns for the users.
Face Mask Assistant: Detection of Face Mask Service Stage Based on Mobile Phone
Venue: arXiv cs.CV 2020
Year: 2020

Coronavirus Disease 2019 (COVID-19) has spread all over the world since it broke out massively in December 2019, which has caused a large loss to the whole world. Both the confirmed cases and death cases have reached a relatively frightening number. Syndrome coronaviruses 2 (SARS-CoV-2), the cause of COVID-19, can be transmitted by small respiratory droplets. To curb its spread at the source, wearing masks is a convenient and effective measure. In most cases, people use face masks in a high-frequent but short-time way. Aimed at solving the problem that we don't know which service stage of the mask belongs to, we propose a detection system based on the mobile phone. We first extract four features from the GLCMs of the face mask's micro-photos. Next, a three-result detection system is accomplished by using KNN algorithm. The results of validation experiments show that our system can reach a precision of 82.87% (standard deviation=8.5%) on the testing dataset. In future work, we plan to expand the detection objects to more mask types. This work demonstrates that the proposed mobile microscope system can be used as an assistant for face mask being used, which may play a positive role in fighting against COVID-19.
RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification
Venue: arXiv cs.CV 2020
Year: 2020

The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how to select informative regions and analyze them are quite challenging during the automatic diagnosis process. The multi-instance learning based on the most discriminative instances can be of great benefit for whole slide gastric image diagnosis. In this paper, we design a recalibrated multi-instance deep learning method (RMDL) to address this challenging problem. We first select the discriminative instances, and then utilize these instances to diagnose diseases based on the proposed RMDL approach. The designed RMDL network is capable of capturing instance-wise dependencies and recalibrating instance features according to the importance coefficient learned from the fused features. Furthermore, we build a large whole-slide gastric histopathology image dataset with detailed pixel-level annotations. Experimental results on the constructed gastric dataset demonstrate the significant improvement on the accuracy of our proposed framework compared with other state-of-the-art multi-instance learning methods. Moreover, our method is general and can be extended to other diagnosis tasks of different cancer types based on WSIs.
Improving Road Signs Detection performance by Combining the Features of Hough Transform and Texture
Venue: arXiv cs.CV 2020
Year: 2020

With the large uses of the intelligent systems in different domains, and in order to increase the drivers and pedestrians safety, the road and traffic sign recognition system has been a challenging issue and an important task for many years. But studies, done in this field of detection and recognition of traffic signs in an image, which are interested in the Arab context, are still insufficient. Detection of the road signs present in the scene is the one of the main stages of the traffic sign detection and recognition. In this paper, an efficient solution to enhance road signs detection, including Arabic context, performance based on color segmentation, Randomized Hough Transform and the combination of Zernike moments and Haralick features has been made. Segmentation stage is useful to determine the Region of Interest (ROI) in the image. The Randomized Hough Transform (RHT) is used to detect the circular and octagonal shapes. This stage is improved by the extraction of the Haralick features and Zernike moments. Furthermore, we use it as input of a classifier based on SVM. Experimental results show that the proposed approach allows us to perform the measurements precision.
Making Every Label Count: Handling Semantic Imprecision by Integrating Domain Knowledge
Venue: arXiv cs.CV 2020
Year: 2020

Noisy data, crawled from the web or supplied by volunteers such as Mechanical Turkers or citizen scientists, is considered an alternative to professionally labeled data. There has been research focused on mitigating the effects of label noise. It is typically modeled as inaccuracy, where the correct label is replaced by an incorrect label from the same set. We consider an additional dimension of label noise: imprecision. For example, a non-breeding snow bunting is labeled as a bird. This label is correct, but not as precise as the task requires. Standard softmax classifiers cannot learn from such a weak label because they consider all classes mutually exclusive, which non-breeding snow bunting and bird are not. We propose CHILLAX (Class Hierarchies for Imprecise Label Learning and Annotation eXtrapolation), a method based on hierarchical classification, to fully utilize labels of any precision. Experiments on noisy variants of NABirds and ILSVRC2012 show that our method outperforms strong baselines by as much as 16.4 percentage points, and the current state of the art by up to 3.9 percentage points.