Reviewerly/exHarmony huggingface dataset:
OneShotDA: Online Multi-Object Tracker With One-Shot-Learning-Based Data Association

Tracking multiple objects in a video sequence can be accomplished by identifying the objects appearing in the sequence and distinguishing between them. Therefore, many recent multi-object tracking (MOT) methods have utilized re-identification and distance metric learning to distinguish between objects by computing the similarity/dissimilarity scores. However, it is difficult to generalize such approaches for arbitrary video sequences, because some important information, such as the number of objects (classes) in a video, is not known in advance. Therefore, in this study, we applied a one-shot learning framework to the MOT problem. Our algorithm tracks objects by classifying newly observed objects into existing tracks, irrespective of the number of objects appearing in a video frame. The proposed method, called OneShotDA, exploits the one-shot learning framework based on an attention mechanism. Our neural network learns to classify unseen data samples using labels from a support set. Once the network has been trained, it predicts correct labels for newly received detection results based on the set of existing tracks. To analyze the effectiveness of our method, it was tested on the MOTchallenge benchmark datasets (MOT16 and MOT17 datasets). The results reveal that the performance of the proposed method was comparable with those of current state-of-the-art methods. In particular, it is noteworthy that the proposed method ranked first among the online trackers on the MOT17 benchmark.
A Distributed Multi-Sensor Machine Learning Approach to Earthquake Early Warning

Our research aims to improve the accuracy of Earthquake Early Warning (EEW) systems by means of machine learning. EEW systems are designed to detect and characterize medium and large earthquakes before their damaging effects reach a certain location. Traditional EEW methods based on seismometers fail to accurately identify large earthquakes due to their sensitivity to the ground motion velocity. The recently introduced high-precision GPS stations, on the other hand, are ineffective to identify medium earthquakes due to its propensity to produce noisy data. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a significant volume of data consequently, affecting the response time and the robustness of EEW systems.In practice, EEW can be seen as a typical classification problem in the machine learning field: multi-sensor data are given in input, and earthquake severity is the classification result. In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes. DMSEEW is based on a new stacking ensemble method which has been evaluated on a real-world dataset validated with geoscientists. The system builds on a geographically distributed infrastructure, ensuring an efficient computation in terms of response time and robustness to partial infrastructure failures. Our experiments show that DMSEEW is more accurate than the traditional seismometer-only approach and the combined-sensors (GPS and seismometers) approach that adopts the rule of relative strength.
Accelerated Adaptive Fuzzy Optimal Control of Three Coupled Fractional-Order Chaotic Electromechanical Transducers

In this article, we investigate the issue of the accelerated adaptive fuzzy optimal control of three coupled fractional-order chaotic electromechanical transducers. A small network where every transducer has the nearest-neighbor coupling configuration is used to form the coupled fractional-order chaotic electromechanical transducers. The mathematical model of the coupled electromechanical transducers with nearest-neighbors is established and the dynamical analysis reveals that its behaviors are very sensitive to external excitation and fractional order. In the controller design, the recurrent nonsingleton type-2 sequential fuzzy neural network (RNT2SFNN) with the transformation is designed to estimate unknown functions of dynamics system in the feedforward fuzzy controller, and it is constructed to approximate the critic value and actor control functions by using policy iteration (PI) in the optimal feedback controller. Meanwhile, the speed functions are employed to achieve accelerated convergence within a pregiven finite time and a tracking differentiator is used to solve the explosion of terms associated with traditional backstepping. The whole control strategy consists of a feedforward controller integrating with the RNT2SFNN, tracking differentiator, and speed function in the framework of the backstepping control and a feedback controller fusing with the RNT2SFNN and PI under an actor/critic structure to solve the Hamilton-Jacobi-Bellman equation. The proposed scheme not only guarantees the boundness of all signals and realizes the chaos suppression, synchronization, and accelerated convergence, but also minimizes the cost function. Simulations demonstrate and validate the effectiveness of the proposed scheme.
Bandwidth-Efficient Threshold EC-DSA

Threshold Signatures allow n parties to share the power of issuing digital signatures so that any coalition of size at least $$t+1$$ can sign, whereas groups of t or less players cannot. Over the last few years many schemes addressed the question of realizing efficient threshold variants for the specific case of EC-DSA signatures. In this paper we present new solutions to the problem that aim at reducing the overall bandwidth consumption. Our main contribution is a new variant of the Gennaro and Goldfeder protocol from ACM CCS 2018 that avoids all the required range proofs, while retaining provable security against malicious adversaries in the dishonest majority setting. Our experiments show that – for all levels of security – our signing protocol reduces the bandwidth consumption of best previously known secure protocols for factors varying between 4.4 and 9, while key generation is consistently two times less expensive. Furthermore compared to these same protocols, our signature generation is faster for 192-bits of security and beyond.
Emoji can facilitate recognition of conveyed indirect meaning

In face-to-face communication there are multiple paralinguistic and gestural features that facilitate recognition of a speaker's intended meaning, features that are lacking when people communicate digitally (e.g., texting). As a result, substitutes have emerged (expressive punctuation, capitalization, etc.) to facilitate communication in these situations. However, little is known about the comprehension processes involved in digital communication. In this research we examined the role of emoji in the comprehension of face-threatening, indirect replies. Participants in two experiments read question–reply sequences and then judged the accuracy of interpretations of the replies. On critical trials the reply violated the relation maxim and conveyed a negative, face-threatening response. On one-third of the trials the reply contained only text, on one-third of the trials the reply contained text and an emoji, and on one-third of the trials the reply contained only an emoji. When the question requested potentially negative information about one of the interactants (disclosures and opinions), participants were more likely to endorse the indirect meaning of the reply, and did so faster, when the reply contained an emoji than when it did not. This effect did not occur when the question was a request for action, a more conventional type of indirect reply. Overall, then, this research demonstrates that emoji can sometimes facilitate the comprehension of meaning. Future research is needed to examine the boundary conditions for this effect.
A Reinforced Generation of Adversarial Examples for Neural Machine Translation

Neural machine translation systems tend to fail on less decent inputs despite its significant efficacy, which may significantly harm the credibility of these systems—fathoming how and when neural-based systems fail in such cases is critical for industrial maintenance. Instead of collecting and analyzing bad cases using limited handcrafted error features, here we investigate this issue by generating adversarial examples via a new paradigm based on reinforcement learning. Our paradigm could expose pitfalls for a given performance metric, e.g., BLEU, and could target any given neural machine translation architecture. We conduct experiments of adversarial attacks on two mainstream neural machine translation architectures, RNN-search, and Transformer. The results show that our method efficiently produces stable attacks with meaning-preserving adversarial examples. We also present a qualitative and quantitative analysis for the preference pattern of the attack, demonstrating its capability of pitfall exposure.
AutoGroup

Modelling feature interactions is key in Click-Through Rate (CTR) predictions. State-of-the-art models usually include explicit feature interactions to better model non-linearity in a deep network, but enumerating all feature combinations of high orders is not efficient and brings challenges to network optimization. In this work, we use AutoML to seek useful high-order feature interactions to train on without manual feature selection. For this purpose, an end-to-end model, AutoGroup, is proposed, which casts the selection of feature interactions as a structural optimization problem. In a nutshell, AutoGroup first automatically groups useful features into a number of feature sets. Then, it generates interactions of any order from these feature sets using a novel interaction function. The main contribution of AutoGroup is that it performs both dimensionality reduction and feature selection which are not seen in previous models. Offline experiments on three public large-scale benchmark datasets demonstrate the superior performance and efficiency of AutoGroup over state-of-the-art models. Furthermore, a ten-day online A/B test verifies that AutoGroup can be reliably deployed in production and outperform the commercial baseline by 10% on average in terms of CTR and CVR.
Facial recognition system using LBPH face recognizer for anti-theft and surveillance application based on drone technology

Providing security to the citizens is one of the most important and complex task for the governments around the world which they have to deal with. Street crimes and theft are the biggest threats for the citizens and their belonging. In order to provide security, there is an urgent need of a system that is capable of identifying the criminal in the crowded area. This paper proposes a facial recognition system using Local Binary Patterns Histogram Face recognizer mounted on drone technology. The facial recognition capability is a key feature for a drone to have in order to find or identify the person within the crowd. With the inception of drone technology in the proposed system, we can use it as a surveillance drone as well through which it can cover more area as compared to the stationary system. As soon as the system identifies the desired person, it tags him and transmits the image along with the co-ordinates of the location to the concerned authorities using mounted global positioning system. Proposed system is capable of identifying the person with the accuracy of approximately 89.1%.
A linear multivariate binary decision tree classifier based on K-means splitting

A novel linear multivariate decision tree classifier, Binary Decision Tree based on K-means Splitting (BDTKS), is presented in this paper. The unsupervised K-means clustering is recursively integrated into the binary tree, building a hierarchical classifier. The introduction of the unsupervised K-means clustering provides the powerful generalization ability for the resulting BDTKS model. Then, the good generalization ability of BDTKS ensures the classification performance. A novel non-split condition with an easy-setting hyperparameter which focuses more on minority classes of the current node is proposed and applied in the BDTKS model, avoiding ignoring the minority classes in the class imbalance cases. Furthermore, the K-means centroid based BDTKS model is converted into the hyperplane based decision tree, speeding up the process of classification. Extensive experiments on the publicly available data sets have demonstrated that the proposed BDTKS matches or outperforms the previous decision trees.
Multiple Data Augmentation Strategies for Improving Performance on Automatic Short Answer Scoring

Automatic short answer scoring (ASAS) is a research subject of intelligent education, which is a hot field of natural language understanding. Many experiments have confirmed that the ASAS system is not good enough, because its performance is limited by the training data. Focusing on the problem, we propose MDA-ASAS, multiple data augmentation strategies for improving performance on automatic short answer scoring. MDA-ASAS is designed to learn language representation enhanced by data augmentation strategies, which includes back-translation, correct answer as reference answer, and swap content. We argue that external knowledge has a profound impact on the ASAS process. Meanwhile, the Bidirectional Encoder Representations from Transformers (BERT) model has been shown to be effective for improving many natural language processing tasks, which acquires more semantic, grammatical and other features in large amounts of unsupervised data, and actually adds external knowledge. Combining with the latest BERT model, our experimental results on the ASAS dataset show that MDA-ASAS brings a significant gain over state-of-art. We also perform extensive ablation studies and suggest parameters for practical use.
GaaS-X: Graph Analytics Accelerator Supporting Sparse Data Representation using Crossbar Architectures

Graph analytics applications are ubiquitous in this era of a connected world. These applications have very low compute to byte-transferred ratios and exhibit poor locality, which limits their computational efficiency on general purpose computing systems. Conventional hardware accelerators employ custom dataflow and memory hierarchy organization to overcome these challenges. Processing-in-memory (PIM) accelerators leverage massively parallel compute capable memory arrays to perform the in-situ operations on graph data or employ custom compute elements near the memory to leverage larger internal bandwidths. In this work, we present GaaS-X, a graph analytics accelerator that inherently supports the sparse graph data representations using an in-situ compute-enabled crossbar memory architectures. We alleviate the overheads of redundant writes, sparse to dense conversions, and redundant computations on the invalid edges that are present in the state of the art crossbar-based PIM accelerators. GaaS-X achieves 7.7× and 2.4× performance and 22× and 5.7×, energy savings, respectively, over two state-of-the-art crossbar accelerators and offers orders of magnitude improvements over GPU and CPU solutions.
Learning from lines: Critical COVID data visualizations and the quarantine quotidian

In response to the ubiquitous graphs and maps of COVID-19, artists, designers, data scientists, and public health officials are teaming up to create counter-plots and subaltern maps of the pandemic. In this intervention, we describe the various functions served by these projects. First, they offer tutorials and tools for both dataviz practitioners and their publics to encourage critical thinking about how COVID-19 data is sourced and modeled—and to consider which subjects are not interpellated in those data sets, and why not. Second, they demonstrate how the pandemic’s spatial logics inscribe themselves in our immediate material landscapes. And third, they remind us of our capacity to personalize and participate in the creation of meaningful COVID visualizations—many of which represent other scales and dimensions of the pandemic, especially the quarantine quotidian. Together, the official maps and counter-plots acknowledge that the pandemic plays out differently across different scales: COVID-19 is about global supply chains and infection counts and TV ratings for presidential press conferences, but it is also about local dynamics and neighborhood mutual aid networks and personal geographies of mitigation and care.
FinEst BERT and CroSloEngual BERT

Large pretrained masked language models have become state-of-the-art solutions for many NLP problems. The research has been mostly focused on English language, though. While massively multilingual models exist, studies have shown that monolingual models produce much better results. We train two trilingual BERT-like models, one for Finnish, Estonian, and English, the other for Croatian, Slovenian, and English. We evaluate their performance on several downstream tasks, NER, POS-tagging, and dependency parsing, using the multilingual BERT and XLM-R as baselines. The newly created FinEst BERT and CroSloEngual BERT improve the results on all tasks in most monolingual and cross-lingual situations.
A Systematic Review on Cloud Storage Mechanisms Concerning e-Healthcare Systems

As the expenses of medical care administrations rise and medical services experts are becoming rare, it is up to medical services organizations and institutes to consider the implementation of medical Health Information Technology (HIT) innovation frameworks. HIT permits health associations to smooth out their considerable cycles and offer types of assistance in a more productive and financially savvy way. With the rise of Cloud Storage Computing (CSC), an enormous number of associations and undertakings have moved their healthcare data sources to distributed storage. As the information can be mentioned whenever universally, the accessibility of information becomes an urgent need. Nonetheless, outages in cloud storage essentially influence the accessibility level. Like the other basic variables of cloud storage (e.g., reliability quality, performance, security, and protection), availability also directly impacts the data in cloud storage for e-Healthcare systems. In this paper, we systematically review cloud storage mechanisms concerning the healthcare environment. Additionally, in this paper, the state-of-the-art cloud storage mechanisms are critically reviewed for e-Healthcare systems based on their characteristics. In short, this paper summarizes existing literature based on cloud storage and its impact on healthcare, and it likewise helps researchers, medical specialists, and organizations with a solid foundation for future studies in the healthcare environment.
Memory of Motion for Warm-Starting Trajectory Optimization

Trajectory optimization for motion planning requires good initial guesses to obtain good performance. In our proposed approach, we build a memory of motion based on a database of robot paths to provide good initial guesses. The memory of motion relies on function approximators and dimensionality reduction techniques to learn the mapping between the tasks and the robot paths. Three function approximators are compared: $k$-Nearest Neighbor, Gaussian Process Regression, and Bayesian Gaussian Mixture Regression. In addition, we show that the memory can be used as a metric to choose between several possible goals, and using an ensemble method to combine different function approximators results in a significantly improved warm-starting performance. We demonstrate the proposed approach with motion planning examples on the dual-arm robot PR2 and the humanoid robot Atlas.
Adding linguistic information to parsed corpora

No matter how comprehensively corpus builders design their annotation schemes, users frequently find that information is missing that they need for their research. In this methodological paper I describe and illustrate five methods of adding linguistic information to corpora that have been morphosyntactically annotated (=parsed) in the style of Penn treebanks. Some of these methods involve manual operations; some are executed by CorpusSearch functions; some require a combination of manual and automated procedures. Which method is used depends almost entirely on the type of information to be added and the goals of the user. Of course, the main goal, regardless of method, is to record within the corpus additional information that can be used for analysis and also retained through further searches and data processing.
University Teaching in Times of Confinement: The Light and Shadows of Compulsory Online Learning

A quantitative study was carried out with the aim of analyzing the perspective of the students in relation to the factors that have influenced quality teaching during the confinement period resulting from the SARS-CoV-2 pandemic. To do this, an ad hoc questionnaire was designed and conducted in the months of June–July 2020. The sample consisted of 893 people who were studying at various Spanish universities. The results show little diversity in teaching methodologies; virtual teaching was carried out with the same parameters as face-to-face teaching. The role of the student body was one of passivity, consisting of little interaction with the teachers. Although it is true that there were no difficulties in following the course, there was limited attention paid to the emotional well-being of the students. Amongst the conclusions garnered significant, we point out the need to develop the adaptability of university teaching staff to unforeseen situations, as well as a continuing reflection on the model used in the teaching process mediated by digital technologies and the importance of promoting greater autonomy and self-regulation during learning.
Vision-based manipulation of deformable and rigid objects using subspace projections of 2D contours

This paper proposes a unified vision-based manipulation framework using image contours of deformable/rigid objects. Instead of explicitly defining the features by geometries or functions, the robot automatically learns the visual features from processed vision data. Our method simultaneously generates – from the same data – both visual features and the interaction matrix that relates them to the robot control inputs. Extraction of the feature vector and control commands is done online and adaptively, and requires little data for initialization. Our method allows the robot to manipulate an object without knowing whether it is rigid or deformable. To validate our approach, we conduct numerical simulations and experiments with both deformable and rigid objects.