OpenRaiser/Intern-Atlas huggingface dataset:
Assessing the Helpfulness of Review Content for Explaining Recommendations
Venue: arXiv cs.IR 2020
Year: 2020

Despite the maturity already achieved by recommender systems algorithms, little is known about how to obtain and provide users with a proper rationale for a recommendation. Transparency and effectiveness of recommender systems may be increased when explanations are provided. In particular, identifying of helpful argumentative content from reviews can be leveraged to generate textual explanations. In this paper, we investigate the reasons why a review might be considered helpful, and show that the perception of credibility and convincingness mediates the relationship between helpfulness and the perception of objectivity and relevant aspects addressed. Our findings led us to suggest an argumentbased approach to automatically extracting helpful content from hotel reviews, a domain that differs from those that best fit classical argumentation theories.
FPSelect: Low-Cost Browser Fingerprints for Mitigating Dictionary Attacks against Web Authentication Mechanisms
Venue: arXiv cs.CR 2020
Year: 2020

Browser fingerprinting consists into collecting attributes from a web browser. Hundreds of attributes have been discovered through the years. Each one of them provides a way to distinguish browsers, but also comes with a usability cost (e.g., additional collection time). In this work, we propose FPSelect, an attribute selection framework allowing verifiers to tune their browser fingerprinting probes for web authentication. We formalize the problem as searching for the attribute set that satisfies a security requirement and minimizes the usability cost. The security is measured as the proportion of impersonated users given a fingerprinting probe, a user population, and an attacker that knows the exact fingerprint distribution among the user population. The usability is quantified by the collection time of browser fingerprints, their size, and their instability. We compare our framework with common baselines, based on a real-life fingerprint dataset, and find out that in our experimental settings, our framework selects attribute sets of lower usability cost. Compared to the baselines, the attribute sets found by FPSelect generate fingerprints that are up to 97 times smaller, are collected up to 3,361 times faster, and with up to 7.2 times less changing attributes between two observations, on average.
Detecting Anomalies from Video-Sequences: a Novel Descriptor
Venue: arXiv cs.CV 2020
Year: 2020

We present a novel descriptor for crowd behavior analysis and anomaly detection. The goal is to measure by appropriate patterns the speed of formation and disintegration of groups in the crowd. This descriptor is inspired by the concept of one-dimensional local binary patterns: in our case, such patterns depend on the number of group observed in a time window. An appropriate measurement unit, named "trit" (trinary digit), represents three possible dynamic states of groups on a certain frame. Our hypothesis is that abrupt variations of the groups' number may be due to an anomalous event that can be accordingly detected, by translating these variations on temporal trit-based sequence of strings which are significantly different from the one describing the "no-anomaly" one. Due to the peculiarity of the rationale behind this work, relying on the number of groups, three different methods of people group's extraction are compared. Experiments are carried out on the Motion-Emotion benchmark data set. Reported results point out in which cases the trit-based measurement of group dynamics allows us to detect the anomaly. Besides the promising performance of our approach, we show how it is correlated with the anomaly typology and the camera's perspective to the crowd's flow (frontal, lateral).
Pagsusuri ng RNN-based Transfer Learning Technique sa Low-Resource Language
Venue: arXiv cs.CL 2020
Year: 2020

Low-resource languages such as Filipino suffer from data scarcity which makes it challenging to develop NLP applications for Filipino language. The use of Transfer Learning (TL) techniques alleviates this problem in low-resource setting. In recent years, transformer-based models are proven to be effective in low-resource tasks but faces challenges in accessibility due to its high compute and memory requirements. For this reason, there's a need for a cheaper but effective alternative. This paper has three contributions. First, release a pre-trained AWD-LSTM language model for Filipino language. Second, benchmark AWD-LSTM in the Hate Speech classification task and show that it performs on par with transformer-based models. Third, analyze the the performance of AWD-LSTM in low-resource setting using degradation test and compare it with transformer-based models. ----- Ang mga low-resource languages tulad ng Filipino ay gipit sa accessible na datos kaya't mahirap gumawa ng mga applications sa wikang ito. Ang mga Transfer Learning (TL) techniques ay malaking tulong para sa low-resource setting o mga pagkakataong gipit sa datos. Sa mga nagdaang taon, nanaig ang mga transformer-based TL techniques pagdating sa low-resource tasks ngunit ito ay mataas na compute and memory requirements kaya nangangailangan ng mas mura pero epektibong alternatibo. Ang papel na ito ay may tatlong kontribusyon. Una, maglabas ng pre-trained AWD-LSTM language model sa wikang Filipino upang maging tuntungan sa pagbuo ng mga NLP applications sa wikang Filipino. Pangalawa, mag benchmark ng AWD-LSTM sa Hate Speech classification task at ipakita na kayang nitong makipagsabayan sa mga transformer-based models. Pangatlo, suriin ang performance ng AWD-LSTM sa low-resource setting gamit ang degradation test at ikumpara ito sa mga transformer-based models.
The DongNiao International Birds 10000 Dataset
Venue: arXiv cs.CV 2020
Year: 2020

DongNiao International Birds 10000 (DIB-10K) is a challenging image dataset which has more than 10 thousand different types of birds. It was created to enable the study of machine learning and also ornithology research. DIB-10K does not own the copyright of these images. It only provides thumbnails of images, in a way similar to ImageNet.
Deep Reinforcement Learning for Real-Time Optimization of Pumps in Water Distribution Systems
Venue: arXiv cs.AI 2020
Year: 2020

Real-time control of pumps can be an infeasible task in water distribution systems (WDSs) because the calculation to find the optimal pump speeds is resource-intensive. The computational need cannot be lowered even with the capabilities of smart water networks when conventional optimization techniques are used. Deep reinforcement learning (DRL) is presented here as a controller of pumps in two WDSs. An agent based on a dueling deep q-network is trained to maintain the pump speeds based on instantaneous nodal pressure data. General optimization techniques (e.g., Nelder-Mead method, differential evolution) serve as baselines. The total efficiency achieved by the DRL agent compared to the best performing baseline is above 0.98, whereas the speedup is around 2x compared to that. The main contribution of the presented approach is that the agent can run the pumps in real-time because it depends only on measurement data. If the WDS is replaced with a hydraulic simulation, the agent still outperforms conventional techniques in search speed.
mdspan in C++: A Case Study in the Integration of Performance Portable Features into International Language Standards
Venue: arXiv cs.DC 2020
Year: 2020

Multi-dimensional arrays are ubiquitous in high-performance computing (HPC), but their absence from the C++ language standard is a long-standing and well-known limitation of their use for HPC. This paper describes the design and implementation of mdspan, a proposed C++ standard multidimensional array view (planned for inclusion in C++23). The proposal is largely inspired by work done in the Kokkos project---a C++ performance-portable programming model deployed by numerous HPC institutions to prepare their code base for exascale-class supercomputing systems. This paper describes the final design of mdspan after a five-year process to achieve consensus in the C++ community. In particular, we will lay out how the design addresses some of the core challenges of performance-portable programming, and how its customization points allow a seamless extension into areas not currently addressed by the C++ Standard but which are of critical importance in the heterogeneous computing world of today's systems. Finally, we have provided a production-quality implementation of the proposal in its current form. This work includes several benchmarks of this implementation aimed at demonstrating the zero-overhead nature of the modern design.
Autotuning Search Space for Loop Transformations
Venue: arXiv cs.DC 2020
Year: 2020

One of the challenges for optimizing compilers is to predict whether applying an optimization will improve its execution speed. Programmers may override the compiler's profitability heuristic using optimization directives such as pragmas in the source code. Machine learning in the form of autotuning can assist users in finding the best optimizations for each platform. In this paper we propose a loop transformation search space that takes the form of a tree, in contrast to previous approaches that usually use vector spaces to represent loop optimization configurations. We implemented a simple autotuner exploring the search space and applied it to a selected set of PolyBench kernels. While the autotuner is capable of representing every possible sequence of loop transformations and their relations, the results motivate the use of better search strategies such as Monte Carlo tree search to find sophisticated loop transformations such as multilevel tiling.
Controlling the Interaction Between Generation and Inference in Semi-Supervised Variational Autoencoders Using Importance Weighting
Venue: arXiv cs.LG 2020
Year: 2020

Even though Variational Autoencoders (VAEs) are widely used for semi-supervised learning, the reason why they work remains unclear. In fact, the addition of the unsupervised objective is most often vaguely described as a regularization. The strength of this regularization is controlled by down-weighting the objective on the unlabeled part of the training set. Through an analysis of the objective of semi-supervised VAEs, we observe that they use the posterior of the learned generative model to guide the inference model in learning the partially observed latent variable. We show that given this observation, it is possible to gain finer control on the effect of the unsupervised objective on the training procedure. Using importance weighting, we derive two novel objectives that prioritize either one of the partially observed latent variable, or the unobserved latent variable. Experiments on the IMDB english sentiment analysis dataset and on the AG News topic classification dataset show the improvements brought by our prioritization mechanism and exhibit a behavior that is inline with our description of the inner working of Semi-Supervised VAEs.
IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads
Venue: arXiv cs.DC 2020
Year: 2020

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In silicomethodologies need to be improved to better select lead compounds that can proceed to later stages of the drug discovery protocol accelerating the entire process. No single methodological approach can achieve the necessary accuracy with required efficiency. Here we describe multiple algorithmic innovations to overcome this fundamental limitation, development and deployment of computational infrastructure at scale integrates multiple artificial intelligence and simulation-based approaches. Three measures of performance are:(i) throughput, the number of ligands per unit time; (ii) scientific performance, the number of effective ligands sampled per unit time and (iii) peak performance, in flop/s. The capabilities outlined here have been used in production for several months as the workhorse of the computational infrastructure to support the capabilities of the US-DOE National Virtual Biotechnology Laboratory in combination with resources from the EU Centre of Excellence in Computational Biomedicine.
Enhancing the Identification of Cyberbullying through Participant Roles
Venue: arXiv cs.CL 2020
Year: 2020

Cyberbullying is a prevalent social problem that inflicts detrimental consequences to the health and safety of victims such as psychological distress, anti-social behaviour, and suicide. The automation of cyberbullying detection is a recent but widely researched problem, with current research having a strong focus on a binary classification of bullying versus non-bullying. This paper proposes a novel approach to enhancing cyberbullying detection through role modeling. We utilise a dataset from ASKfm to perform multi-class classification to detect participant roles (e.g. victim, harasser). Our preliminary results demonstrate promising performance including 0.83 and 0.76 of F1-score for cyberbullying and role classification respectively, outperforming baselines.
Sensitivity of BLANC to human-scored qualities of text summaries
Venue: arXiv cs.CL 2020
Year: 2020

We explore the sensitivity of a document summary quality estimator, BLANC, to human assessment of qualities for the same summaries. In our human evaluations, we distinguish five summary qualities, defined by how fluent, understandable, informative, compact, and factually correct the summary is. We make the case for optimal BLANC parameters, at which the BLANC sensitivity to almost all of summary qualities is about as good as the sensitivity of a human annotator.
Evaluating Tree Explanation Methods for Anomaly Reasoning: A Case Study of SHAP TreeExplainer and TreeInterpreter
Venue: arXiv cs.AI 2020
Year: 2020

Understanding predictions made by Machine Learning models is critical in many applications. In this work, we investigate the performance of two methods for explaining tree-based models- Tree Interpreter (TI) and SHapley Additive exPlanations TreeExplainer (SHAP-TE). Using a case study on detecting anomalies in job runtimes of applications that utilize cloud-computing platforms, we compare these approaches using a variety of metrics, including computation time, significance of attribution value, and explanation accuracy. We find that, although the SHAP-TE offers consistency guarantees over TI, at the cost of increased computation, consistency does not necessarily improve the explanation performance in our case study.
Contracts under Moral Hazard and Adverse Selection
Venue: arXiv cs.GT 2020
Year: 2020

In the classical principal-agent problem, a principal must design a contract to incentivize an agent to perform an action on behalf of the principal. We study the classical principal-agent problem in a setting where the agent can be of one of several types (affecting the outcome of actions they might take). This combines the contract theory phenomena of "moral hazard" (incomplete information about actions) with that of "adverse selection" (incomplete information about types). We examine this problem through the computational lens. We show that in this setting it is APX-hard to compute either the profit-maximizing single contract or the profit-maximizing menu of contracts (as opposed to in the absence of types, where one can efficiently compute the optimal contract). We then show that the performance of the best linear contract scales especially well in the number of types: if agent has $n$ available actions and $T$ possible types, the best linear contract achieves an $O(n\log T)$ approximation of the best possible profit. Finally, we apply our framework to prove tight worst-case approximation bounds between a variety of benchmarks of mechanisms for the principal.
TriNE: Network Representation Learning for Tripartite Heterogeneous Networks
Venue: arXiv cs.LG 2020
Year: 2020

In this paper, we study network representation learning for tripartite heterogeneous networks which learns node representation features for networks with three types of node entities. We argue that tripartite networks are common in real world applications, and the essential challenge of the representation learning is the heterogeneous relations between various node types and links in the network. To tackle the challenge, we develop a tripartite heterogeneous network embedding called TriNE. The method considers unique user-item-tag tripartite relationships, to build an objective function to model explicit relationships between nodes (observed links), and also capture implicit relationships between tripartite nodes (unobserved links across tripartite node sets). The method organizes metapath guided random walks to create heterogeneous neighborhood for all node types in the network. This information is then utilized to train a heterogeneous skip-gram model based on a joint optimization. Experiments on real-world tripartite networks validate the performance of TriNE for the online user response prediction using embedding node features.
Towards Optimal Filter Pruning with Balanced Performance and Pruning Speed
Venue: arXiv cs.CV 2020
Year: 2020

Filter pruning has drawn more attention since resource constrained platform requires more compact model for deployment. However, current pruning methods suffer either from the inferior performance of one-shot methods, or the expensive time cost of iterative training methods. In this paper, we propose a balanced filter pruning method for both performance and pruning speed. Based on the filter importance criteria, our method is able to prune a layer with approximate layer-wise optimal pruning rate at preset loss variation. The network is pruned in the layer-wise way without the time consuming prune-retrain iteration. If a pre-defined pruning rate for the entire network is given, we also introduce a method to find the corresponding loss variation threshold with fast converging speed. Moreover, we propose the layer group pruning and channel selection mechanism for channel alignment in network with short connections. The proposed pruning method is widely applicable to common architectures and does not involve any additional training except the final fine-tuning. Comprehensive experiments show that our method outperforms many state-of-the-art approaches.
Learning Word Representations for Tunisian Sentiment Analysis
Venue: arXiv cs.CL 2020
Year: 2020

Tunisians on social media tend to express themselves in their local dialect using Latin script (TUNIZI). This raises an additional challenge to the process of exploring and recognizing online opinions. To date, very little work has addressed TUNIZI sentiment analysis due to scarce resources for training an automated system. In this paper, we focus on the Tunisian dialect sentiment analysis used on social media. Most of the previous work used machine learning techniques combined with handcrafted features. More recently, Deep Neural Networks were widely used for this task, especially for the English language. In this paper, we explore the importance of various unsupervised word representations (word2vec, BERT) and we investigate the use of Convolutional Neural Networks and Bidirectional Long Short-Term Memory. Without using any kind of handcrafted features, our experimental results on two publicly available datasets showed comparable performances to other languages.
Semantic Flow-guided Motion Removal Method for Robust Mapping
Venue: arXiv cs.CV 2020
Year: 2020

Moving objects in scenes are still a severe challenge for the SLAM system. Many efforts have tried to remove the motion regions in the images by detecting moving objects. In this way, the keypoints belonging to motion regions will be ignored in the later calculations. In this paper, we proposed a novel motion removal method, leveraging semantic information and optical flow to extract motion regions. Different from previous works, we don't predict moving objects or motion regions directly from image sequences. We computed rigid optical flow, synthesized by the depth and pose, and compared it against the estimated optical flow to obtain initial motion regions. Then, we utilized K-means to finetune the motion region masks with instance segmentation masks. The ORB-SLAM2 integrated with the proposed motion removal method achieved the best performance in both indoor and outdoor dynamic environments.
Data compression to choose a proper dynamic network representation
Venue: arXiv cs.SI 2020
Year: 2020

Dynamic network data are now available in a wide range of contexts and domains. Several representation formalisms exist to represent dynamic networks, but there is no well-known method to choose one representation over another for a given dataset. In this article, we propose a method based on data compression to choose between three of the most important representations: snapshots, link streams and interval graphs. We apply the method on synthetic and real datasets to show the relevance of the method and its possible applications, such as choosing an appropriate representation when confronted to a new dataset, and storing dynamic networks in an efficient manner.
Identifying Wrongly Predicted Samples: A Method for Active Learning
Venue: arXiv cs.LG 2020
Year: 2020

State-of-the-art machine learning models require access to significant amount of annotated data in order to achieve the desired level of performance. While unlabelled data can be largely available and even abundant, annotation process can be quite expensive and limiting. Under the assumption that some samples are more important for a given task than others, active learning targets the problem of identifying the most informative samples that one should acquire annotations for. Instead of the conventional reliance on model uncertainty as a proxy to leverage new unknown labels, in this work we propose a simple sample selection criterion that moves beyond uncertainty. By first accepting the model prediction and then judging its effect on the generalization error, we can better identify wrongly predicted samples. We further present an approximation to our criterion that is very efficient and provides a similarity based interpretation. In addition to evaluating our method on the standard benchmarks of active learning, we consider the challenging yet realistic scenario of imbalanced data where categories are not equally represented. We show state-of-the-art results and better rates at identifying wrongly predicted samples. Our method is simple, model agnostic and relies on the current model status without the need for re-training from scratch.