Reviewerly/exHarmony huggingface dataset:
Towards Burmese (Myanmar) Morphological Analysis

This article presents a comprehensive study on two primary tasks in Burmese (Myanmar) morphological analysis: tokenization and part-of-speech (POS) tagging. Twenty thousand Burmese sentences of newswire are annotated with two-layer tokenization and POS-tagging information, as one component of the Asian Language Treebank Project. The annotated corpus has been released under a CC BY-NC-SA license, and it is the largest open-access database of annotated Burmese when this manuscript was prepared in 2017. Detailed descriptions of the preparation, refinement, and features of the annotated corpus are provided in the first half of the article. Facilitated by the annotated corpus, experiment-based investigations are presented in the second half of the article, wherein the standard sequence-labeling approach of conditional random fields and a long short-term memory (LSTM)-based recurrent neural network (RNN) are applied and discussed. We obtained several general conclusions, covering the effect of joint tokenization and POS-tagging and importance of ensemble from the viewpoint of stabilizing the performance of LSTM-based RNN. This study provides a solid basis for further studies on Burmese processing.
Increasing Image Memorability with Neural Style Transfer

Recent works in computer vision and multimedia have shown that image memorability can be automatically inferred exploiting powerful deep-learning models. This article advances the state of the art in this area by addressing a novel and more challenging issue: “ Given an arbitrary input image, can we make it more memorable? ” To tackle this problem, we introduce an approach based on an editing-by-applying-filters paradigm: given an input image, we propose to automatically retrieve a set of “style seeds,” i.e., a set of style images that, applied to the input image through a neural style transfer algorithm, provide the highest increase in memorability. We show the effectiveness of the proposed approach with experiments on the publicly available LaMem dataset, performing both a quantitative evaluation and a user study. To demonstrate the flexibility of the proposed framework, we also analyze the impact of different implementation choices, such as using different state-of-the-art neural style transfer methods. Finally, we show several qualitative results to provide additional insights on the link between image style and memorability.
Privacy Risks of Explaining Machine Learning Models.

Can an adversary exploit model explanations to infer sensitive informationabout the models' training set? To investigate this question, we first focus onmembership inference attacks: given a data point and a model explanation, theattacker's goal is to decide whether or not the point belongs to the trainingdata. We study this problem for two popular transparency methods:gradient-based attribution methods and record-based influence measures. Wedevelop membership inference attacks based on these model explanations, andextensively test them on a variety of datasets. For gradient-based methods, weshow that the explanations can leak a significant amount of information aboutthe individual data points in the training set, much beyond what is leakedthrough the predicted labels. We also show that record-based measures can beeffectively, and even more significantly, exploited for membership inferenceattacks. More importantly, we design reconstruction attacks against this classof model explanations. We demonstrate that they can be exploited to recoversignificant parts of the training set. Finally, our results indicate thatminorities and outliers are more vulnerable to these type of attacks than therest of the population. Thus, there is a significant disparity for the privacyrisks of model explanations across different groups.
On The Unforkability of Monero

Monero, ranked as one of the top privacy-preserving cryptocurrencies by market cap, introduced semi-annual hard fork in 2018. Although hard fork is not an uncommon event in the cryptocurrency industry, the two hard forks in 2018 caused an anonymity risk to Monero where transactions became traceable due to the problem of key reuse. Thisproblem was triggered by the existence of multiple copies of the same coin on different Monero blockchain branches such that the users spent the coins multiple times without preemptive action. We investigate the Monero hard fork events by analysing the transaction data on three different branches of the Monero blockchain. Although we have discovered an insignificant portion of traceable inputs compared to the total available inputs in our dataset, our analyses show that the scalability of the event depends on external factors such as market price and market availability. We propose a cheap, easy to implement strategy to prevent the problem of key reuse, should in the future stronger Monero forks emerge in the market.
Continuously Constructive Deep Neural Networks

Traditionally, deep learning algorithms update the network weights, whereas the network architecture is chosen manually using a process of trial and error. In this paper, we propose two novel approaches that automatically update the network structure while also learning its weights. The novelty of our approach lies in our parameterization, where the depth, or additional complexity, is encapsulated continuously in the parameter space through control parameters that add additional complexity. We propose two methods. In tunnel networks, this selection is done at the level of a hidden unit, and in budding perceptrons, this is done at the level of a network layer; updating this control parameter introduces either another hidden unit or layer. We show the effectiveness of our methods on the synthetic two-spiral data and on three real data sets of MNIST, MIRFLICKR, and CIFAR, where we see that our proposed methods, with the same set of hyperparameters, can correctly adjust the network complexity to the task complexity.
Backbone Cannot Be Trained at Once: Rolling Back to Pre-Trained Network for Person Re-Identification

In person re-identification (ReID) task, because of its shortage of trainable dataset, it is common to utilize fine-tuning method using a classification network pre-trained on a large dataset. However, it is relatively difficult to sufficiently finetune the low-level layers of the network due to the gradient vanishing problem. In this work, we propose a novel fine-tuning strategy that allows low-level layers to be sufficiently trained by rolling back the weights of high-level layers to their initial pre-trained weights. Our strategy alleviates the problem of gradient vanishing in low-level layers and robustly trains the low-level layers to fit the ReID dataset, thereby increasing the performance of ReID tasks. The improved performance of the proposed strategy is validated via several experiments. Furthermore, without any addons such as pose estimation or segmentation, our strategy exhibits state-of-the-art performance using only vanilla deep convolutional neural network architecture.
BIM: Towards Quantitative Evaluation of Interpretability Methods with Ground Truth.

Interpretability is an important area of research for safe deployment of machine learning systems. One particular type of interpretability method attributes model decisions to input features. Despite active development, quantitative evaluation of feature attribution methods remains difficult due to the lack of ground truth: we do not know which input features are in fact important to a model. In this work, we propose a framework for Benchmarking Attribution Methods (BAM) with a priori knowledge of relative feature importance. BAM includes 1) a carefully crafted dataset and models trained with known relative feature importance and 2) three complementary metrics to quantitatively evaluate attribution methods by comparing feature attributions between pairs of models and pairs of inputs. Our evaluation on several widely-used attribution methods suggests that certain methods are more likely to produce false positive explanations---features that are incorrectly attributed as more important to model prediction. We open source our dataset, models, and metrics.
Embodied Flight with a Drone

Most human-robot interfaces, such as joysticks and keyboards, require training and constant cognitive effort and provide a limited degree of awareness of the robots' state and its environment.Embodied interactions, that is the bidirectional link between the physical bodies and control systems of the robot and of the human, could not only enable a more intuitive control of robots, even for novices, but also provide users with more immersive sensations.But providing an embodied interaction by mapping human movements into a non-anthropomorphic robot is particularly challenging.In this paper, we describe a natural and immersive embodied interaction that allows users to control and experience drone flight with their own bodies.The setup uses a commercial flight simulator that tracks hand movements and provides haptic and visual feedback.The paper discusses how to map body movement with drone motion, and how the resulting embodied interaction provides a more natural and immersive flight experience to unskilled users with respect to a conventional RC remote controller.
Egocentric Meets Top-View

Thanks to the availability and increasing popularity of wearable devices such as GoPro cameras, smart phones, and glasses, we have access to a plethora of videos captured from first person perspective. Surveillance cameras and Unmanned Aerial Vehicles (UAVs) also offer tremendous amounts of video data recorded from top and oblique view points. Egocentric and surveillance vision have been studied extensively but separately in the computer vision community. The relationship between these two domains, however, remains unexplored. In this study, we make the first attempt in this direction by addressing two basic yet challenging questions. First, having a set of egocentric videos and a top-view surveillance video, does the top-view video contain all or some of the egocentric viewers? In other words, have these videos been shot in the same environment at the same time? Second, if so, can we identify the egocentric viewers in the top-view video? These problems can become extremely challenging when videos are not temporally aligned. Each view, egocentric or top, is modeled by a graph and the assignment and time-delays are computed iteratively using the spectral graph matching framework. We evaluate our method in terms of ranking and assigning egocentric viewers to identities present in the top-view video over a dataset of 50 top-view and 188 egocentric videos captured under different conditions. We also evaluate the capability of our proposed approaches in terms of temporal alignment. The experiments and results demonstrate the capability of the proposed approaches in terms of jointly addressing the temporal alignment and assignment tasks.
ChaLearn Looking at People: IsoGD and ConGD Large-Scale RGB-D Gesture Recognition

The ChaLearn large-scale gesture recognition challenge has run twice in two workshops in conjunction with the International Conference on Pattern Recognition (ICPR) 2016 and International Conference on Computer Vision (ICCV) 2017, attracting more than 200 teams around the world. This challenge has two tracks, focusing on isolated and continuous gesture recognition, respectively. It describes the creation of both benchmark datasets and analyzes the advances in large-scale gesture recognition based on these two datasets. In this article, we discuss the challenges of collecting large-scale ground-truth annotations of gesture recognition and provide a detailed analysis of the current methods for large-scale isolated and continuous gesture recognition. In addition to the recognition rate and mean Jaccard index (MJI) as evaluation metrics used in previous challenges, we introduce the corrected segmentation rate (CSR) metric to evaluate the performance of temporal segmentation for continuous gesture recognition. Furthermore, we propose a bidirectional long short-term memory (Bi-LSTM) method, determining video division points based on skeleton points. Experiments show that the proposed Bi-LSTM outperforms state-of-the-art methods with an absolute improvement of 8.1% (from 0.8917 to 0.9639) of CSR.
DM-MCDA: A web-based platform for data mining and multiple criteria decision analysis: A case study on road accident

Today’s ultra-connected world is generating a huge amount of data stored in databases and cloud environment especially in the era of transportation. These databases need to be processed and analyzed to extract useful information and present it as a valid element for transportation managers for further use, such as road safety, shipping delays, and shipping optimization. The potential of data mining algorithms is largely untapped, this paper shows large-scale techniques such as associations rule analysis, multiple criteria analysis, and time series to improve road safety by identifying hot-spots in advance and giving chance to drivers to avoid the dangers. Indeed, we proposed a framework DM-MCDA based on association rules mining as a preliminary task to extract relationships between variables related to a road accident, and then integrate multiple criteria analysis to help decision-makers to make their choice of the most relevant rules. The developed system is flexible and allows intuitive creation and execution of different algorithms for an extensive range of road traffic topics. DM-MCDA can be expanded with new topics on demand, rendering knowledge extraction more robust and provide meaningful information that could help in developing suitable policies for decision-makers.
The Effect of Smartphone Addiction, Achievement Motivation, and Textbook Reading Intensity on Students’ Academic Achievement

&lt;h1&gt;This study investigates the effect of smartphone addiction, achievement motivation, and textbook reading intensity on academic achievement. This quantitative study involved 720 students from two public universities. The cluster random sampling technique and three psychological scales namely Smartphone Addiction Scale, Achievement Motivation Scale, and Reading Textbook Intensity Scale were employed for data collection. The data were analyzed using path analysis technique. The study found that (i) smartphone addiction and achievement motivation directly affect the intensity of reading academic textbooks, and (ii) smartphones addiction, achievement motivation, and the intensity of reading academic textbooks directly affect the academic achievement. Although the intensity of reading academic textbook mediated the effect of smartphone addiction on academic achievement, it did not mediate the effect of achievement motivation on academic achievement.&lt;/h1&gt;
A novel data hiding method by using a chaotic system without equilibrium points

In this paper, we investigate how special is the choice of parameter values in the three-dimensional nonlinear system, proposed by Akgul and Pehlivan (2016), in producing a system, which exhibits chaos but has no real equilibrium states. Also, a data hiding method with a three-dimensional chaotic system without equilibrium point, developed by Akgul and Pehlivan, is realized. Numerous encryption studies have recently been made based on chaos. Encryption processes that are used with chaos bring about some security deficiencies in some cases. Steganography, unlike encryption studies, helps communicate the secret data by hiding it in an innocent-looking cover in order to avoid detection by third parties at first glance. In this work, a novel chaos-based data hiding method that hides an image with a different color into color images is proposed. Via the proposed method, data are hidden in bit spaces with the help of the chaotic random number generator (RNG). The generated random numbers are found with a chaotic system without equilibrium point, which is new in the literature. Shilnikov method cannot be applied to find whether the system is chaotic or not because they cannot have homoclinic or heteroclinic orbits. Thus, it can be useful in several engineering applications, especially in chaos-based cryptology and coding information. In the study, bits are hidden in pixels indicated by numbers generated by RNG. As the order of the hiding process is made randomly on a chaotic level, it has made data hiding algorithm stronger. The proposed method hides the data in cover image in such a way that it cannot be easily detected. Furthermore, the proposed method has been evaluated with steganalysis methods and image distortion measurement method PSNR. The chaos-based steganography method realized here has produced more best results in image distortion measurement method PSNR than other studies in the literature.
Geek Talents: Who are the Top Experts on GitHub and Stack Overflow?

In the field of Computer Science, software developers need to use a wide array of social collaborative platforms for learning and cooperating. The most popular ones are GitHub and Stack Overflow. Existing platforms only support search queries to extract relevant repository information from GitHub, or questions and answers from Stack Overflow. This ignores the valuable coder-related part-who are the top experts (geek talents) in a specific area? This information is important to companies, open source projects, and to those who want to learn from an expert role model. Thus, how to find the right developers is quite a crucial yet challenging problem. Most of the current works mainly focus on recommending experts in a particular software engineering task and ignore the relationship between developers within different projects. In this paper, we propose a novel technique that automatically identifies geek talents from GitHub, Stack Overflow, and across both communities. The results show that our work performs well at recommending proper developers in diverse areas.
Fully Unsupervised Crosslingual Semantic Textual Similarity Metric Based on BERT for Identifying Parallel Data

We present a fully unsupervised crosslingual semantic textual similarity (STS) metric, based on contextual embeddings extracted from BERT -Bidirectional Encoder Representations from Transformers (Devlin et al., 2019).The goal of crosslingual STS is to measure to what degree two segments of text in different languages express the same meaning.Not only is it a key task in crosslingual natural language understanding (XLU), it is also particularly useful for identifying parallel resources for training and evaluating downstream multilingual natural language processing (NLP) applications, such as machine translation.Most previous crosslingual STS methods relied heavily on existing parallel resources, thus leading to a circular dependency problem.With the advent of massively multilingual context representation models such as BERT, which are trained on the concatenation of non-parallel data from each language, we show that the deadlock around parallel resources can be broken.We perform intrinsic evaluations on crosslingual STS data sets and extrinsic evaluations on parallel corpus filtering and human translation equivalence assessment tasks.Our results show that the unsupervised crosslingual STS metric using BERT without fine-tuning achieves performance on par with supervised or weakly supervised approaches.
Method for estimating display lag in the Oculus Rift S and CV1

We validated an optical method for measuring the display lag of modern head-mounted displays (HMDs). The method used a high-speed digital camera to track landmarks rendered on a display panel of the Oculus Rift CV1 and S models. We used an Nvidia GeForce RTX 2080 graphics adapter and found that the minimum estimated baseline latency of both the Oculus CV1 and S was extremely short (∼2 ms). Variability in lag was low, even when the lag was systematically inflated. Cybersickness was induced with the small baseline lag and increased as this lag was inflated. These findings indicate the Oculus Rift CV1 and S are capable of extremely low baseline display lag latencies for angular head rotation, which appears to account for their low levels of reported cybersickness.
Continual Learning with Adaptive Weights (CLAW)

Approaches to continual learning aim to successfully learn a set of related tasks that arrive in an online manner. Recently, several frameworks have been developed which enable deep learning to be deployed in this learning scenario. A key modelling decision is to what extent the architecture should be shared across tasks. On the one hand, separately modelling each task avoids catastrophic forgetting but it does not support transfer learning and leads to large models. On the other hand, rigidly specifying a shared component and a task-specific part enables task transfer and limits the model size, but it is vulnerable to catastrophic forgetting and restricts the form of task-transfer that can occur. Ideally, the network should adaptively identify which parts of the network to share in a data driven way. Here we introduce such an approach called Continual Learning with Adaptive Weights (CLAW), which is based on probabilistic modelling and variational inference. Experiments show that CLAW achieves state-of-the-art performance on six benchmarks in terms of overall continual learning performance, as measured by classification accuracy, and in terms of addressing catastrophic forgetting.
Dynamic Sparse Training: Find Efficient Sparse Network From Scratch With  Trainable Masked Layers

We present a novel network pruning algorithm called Dynamic Sparse Training that can jointly find the optimal network parameters and sparse network structure in a unified optimization process with trainable pruning thresholds. These thresholds can have fine-grained layer-wise adjustments dynamically via backpropagation. We demonstrate that our dynamic sparse training algorithm can easily train very sparse neural network models with little performance loss using the same number of training epochs as dense models. Dynamic Sparse Training achieves the state of the art performance compared with other sparse training algorithms on various network architectures. Additionally, we have several surprising observations that provide strong evidence for the effectiveness and efficiency of our algorithm. These observations reveal the underlying problems of traditional three-stage pruning algorithms and present the potential guidance provided by our algorithm to the design of more compact network architectures.
The Local Elasticity of Neural Networks

This paper presents a phenomenon in neural networks that we refer to as local elasticity. Roughly speaking, a classifier is said to be locally elastic if its prediction at a feature vector x' is not significantly perturbed, after the classifier is updated via stochastic gradient descent at a (labeled) feature vector x that is dissimilar to x' in a certain sense. This phenomenon is shown to persist for neural networks with nonlinear activation functions through extensive simulations on synthetic datasets, whereas this is not the case for linear classifiers. In addition, we offer a geometric interpretation of local elasticity using the neural tangent kernel (Jacot et al., 2018). Building on top of local elasticity, we obtain pairwise similarity measures between feature vectors, which can be used for clustering in conjunction with K-means. The effectiveness of the clustering algorithm on the MNIST and CIFAR-10 datasets in turn confirms the hypothesis of local elasticity of neural networks on real-life data. Finally, we discuss implications of local elasticity to shed light on several intriguing aspects of deep neural networks.