OpenRaiser/Intern-Atlas huggingface dataset:
DeepInsight: Interpretability Assisting Detection of Adversarial Samples on Graphs
Venue: arXiv cs.SI 2021
Year: 2021

With the rapid development of artificial intelligence, a number of machine learning algorithms, such as graph neural networks have been proposed to facilitate network analysis or graph data mining. Although effective, recent studies show that these advanced methods may suffer from adversarial attacks, i.e., they may lose effectiveness when only a small fraction of links are unexpectedly changed. This paper investigates three well-known adversarial attack methods, i.e., Nettack, Meta Attack, and GradArgmax. It is found that different attack methods have their specific attack preferences on changing the target network structures. Such attack pattern are further verified by experimental results on some real-world networks, revealing that generally the top four most important network attributes on detecting adversarial samples suffice to explain the preference of an attack method. Based on these findings, the network attributes are utilized to design machine learning models for adversarial sample detection and attack method recognition with outstanding performance.
Multi-Modal Prototype Learning for Interpretable Multivariable Time Series Classification
Venue: arXiv cs.LG 2021
Year: 2021

Multivariable time series classification problems are increasing in prevalence and complexity in a variety of domains, such as biology and finance. While deep learning methods are an effective tool for these problems, they often lack interpretability. In this work, we propose a novel modular prototype learning framework for multivariable time series classification. In the first stage of our framework, encoders extract features from each variable independently. Prototype layers identify single-variable prototypes in the resulting feature spaces. The next stage of our framework represents the multivariable time series sample points in terms of their similarity to these single-variable prototypes. This results in an inherently interpretable representation of multivariable patterns, on which prototype learning is applied to extract representative examples i.e. multivariable prototypes. Our framework is thus able to explicitly identify both informative patterns in the individual variables, as well as the relationships between the variables. We validate our framework on a simulated dataset with embedded patterns, as well as a real human activity recognition problem. Our framework attains comparable or superior classification performance to existing time series classification methods on these tasks. On the simulated dataset, we find that our model returns interpretations consistent with the embedded patterns. Moreover, the interpretations learned on the activity recognition dataset align with domain knowledge.
Non-intrusive Nonlinear Model Reduction via Machine Learning Approximations to Low-dimensional Operators
Venue: arXiv cs.LG 2021
Year: 2021

Although projection-based reduced-order models (ROMs) for parameterized nonlinear dynamical systems have demonstrated exciting results across a range of applications, their broad adoption has been limited by their intrusivity: implementing such a reduced-order model typically requires significant modifications to the underlying simulation code. To address this, we propose a method that enables traditionally intrusive reduced-order models to be accurately approximated in a non-intrusive manner. Specifically, the approach approximates the low-dimensional operators associated with projection-based reduced-order models (ROMs) using modern machine-learning regression techniques. The only requirement of the simulation code is the ability to export the velocity given the state and parameters as this functionality is used to train the approximated low-dimensional operators. In addition to enabling nonintrusivity, we demonstrate that the approach also leads to very low computational complexity, achieving up to $1000\times$ reduction in run time. We demonstrate the effectiveness of the proposed technique on two types of PDEs.
Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study
Venue: arXiv cs.CL 2021
Year: 2021

Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes. Predicting missing links in these graphs can boost many important applications, such as drug design and repurposing. Recent work has shown that general-domain language models (LMs) can serve as "soft" KGs, and that they can be fine-tuned for the task of KG completion. In this work, we study scientific LMs for KG completion, exploring whether we can tap into their latent knowledge to enhance biomedical link prediction. We evaluate several domain-specific LMs, fine-tuning them on datasets centered on drugs and diseases that we represent as KGs and enrich with textual entity descriptions. We integrate the LM-based models with KG embedding models, using a router method that learns to assign each input example to either type of model and provides a substantial boost in performance. Finally, we demonstrate the advantage of LM models in the inductive setting with novel scientific entities. Our datasets and code are made publicly available.
Machining Cycle Time Prediction: Data-driven Modelling of Machine Tool Feedrate Behavior with Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Accurate prediction of machining cycle times is important in the manufacturing industry. Usually, Computer Aided Manufacturing (CAM) software estimates the machining times using the commanded feedrate from the toolpath file using basic kinematic settings. Typically, the methods do not account for toolpath geometry or toolpath tolerance and therefore under estimate the machining cycle times considerably. Removing the need for machine specific knowledge, this paper presents a data-driven feedrate and machining cycle time prediction method by building a neural network model for each machine tool axis. In this study, datasets composed of the commanded feedrate, nominal acceleration, toolpath geometry and the measured feedrate were used to train a neural network model. Validation trials using a representative industrial thin wall structure component on a commercial machining centre showed that this method estimated the machining time with more than 90% accuracy. This method showed that neural network models have the capability to learn the behavior of a complex machine tool system and predict cycle times. Further integration of the methods will be critical in the implantation of digital twins in Industry 4.0.
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction
Venue: arXiv cs.CL 2021
Year: 2021

Detecting what emotions are expressed in text is a well-studied problem in natural language processing. However, research on finer grained emotion analysis such as what causes an emotion is still in its infancy. We present solutions that tackle both emotion recognition and emotion cause detection in a joint fashion. Considering that common-sense knowledge plays an important role in understanding implicitly expressed emotions and the reasons for those emotions, we propose novel methods that combine common-sense knowledge via adapted knowledge models with multi-task learning to perform joint emotion classification and emotion cause tagging. We show performance improvement on both tasks when including common-sense reasoning and a multitask framework. We provide a thorough analysis to gain insights into model performance.
Generalized Learning Vector Quantization for Classification in Randomized Neural Networks and Hyperdimensional Computing
Venue: arXiv cs.LG 2021
Year: 2021

Machine learning algorithms deployed on edge devices must meet certain resource constraints and efficiency requirements. Random Vector Functional Link (RVFL) networks are favored for such applications due to their simple design and training efficiency. We propose a modified RVFL network that avoids computationally expensive matrix operations during training, thus expanding the network's range of potential applications. Our modification replaces the least-squares classifier with the Generalized Learning Vector Quantization (GLVQ) classifier, which only employs simple vector and distance calculations. The GLVQ classifier can also be considered an improvement upon certain classification algorithms popularly used in the area of Hyperdimensional Computing. The proposed approach achieved state-of-the-art accuracy on a collection of datasets from the UCI Machine Learning Repository - higher than previously proposed RVFL networks. We further demonstrate that our approach still achieves high accuracy while severely limited in training iterations (using on average only 21% of the least-squares classifier computational costs).
GEM: A General Evaluation Benchmark for Multimodal Tasks
Venue: arXiv cs.CL 2021
Year: 2021

In this paper, we present GEM as a General Evaluation benchmark for Multimodal tasks. Different from existing datasets such as GLUE, SuperGLUE, XGLUE and XTREME that mainly focus on natural language tasks, GEM is a large-scale vision-language benchmark, which consists of GEM-I for image-language tasks and GEM-V for video-language tasks. Comparing with existing multimodal datasets such as MSCOCO and Flicker30K for image-language tasks, YouCook2 and MSR-VTT for video-language tasks, GEM is not only the largest vision-language dataset covering image-language tasks and video-language tasks at the same time, but also labeled in multiple languages. We also provide two baseline models for this benchmark. We will release the dataset, code and baseline models, aiming to advance the development of multilingual multimodal research.
Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
Venue: arXiv cs.CL 2021
Year: 2021

Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for both humans and machines. This work studies automatic quotation generation in an online conversation and explores how language consistency affects whether a quotation fits the given context. Here, we capture the contextual consistency of a quotation in terms of latent topics, interactions with the dialogue history, and coherence to the query turn's existing content. Further, an encoder-decoder neural framework is employed to continue the context with a quotation via language generation. Experiment results on two large-scale datasets in English and Chinese demonstrate that our quotation generation model outperforms the state-of-the-art models. Further analysis shows that topic, interaction, and query consistency are all helpful to learn how to quote in online conversations.
Sharing in a Trustless World: Privacy-Preserving Data Analytics with Potentially Cheating Participants
Venue: arXiv cs.CR 2021
Year: 2021

Lack of trust between organisations and privacy concerns about their data are impediments to an otherwise potentially symbiotic joint data analysis. We propose DataRing, a data sharing system that allows mutually mistrusting participants to query each others' datasets in a privacy-preserving manner while ensuring the correctness of input datasets and query answers even in the presence of (cheating) participants deviating from their true datasets. By relying on the assumption that if only a small subset of rows of the true dataset are known, participants cannot submit answers to queries deviating significantly from their true datasets. We employ differential privacy and a suite of cryptographic tools to ensure individual privacy for each participant's dataset and data confidentiality from the system. Our results show that the evaluation of 10 queries on a dataset with 10 attributes and 500,000 records is achieved in 90.63 seconds. DataRing could detect cheating participant that deviates from its true dataset in few queries with high accuracy.
A Framework for Real-time Traffic Trajectory Tracking, Speed Estimation, and Driver Behavior Calibration at Urban Intersections Using Virtual Traffic Lanes
Venue: arXiv cs.CV 2021
Year: 2021

In a previous study, we presented VT-Lane, a three-step framework for real-time vehicle detection, tracking, and turn movement classification at urban intersections. In this study, we present a case study incorporating the highly accurate trajectories and movement classification obtained via VT-Lane for the purpose of speed estimation and driver behavior calibration for traffic at urban intersections. First, we use a highly instrumented vehicle to verify the estimated speeds obtained from video inference. The results of the speed validation show that our method can estimate the average travel speed of detected vehicles in real-time with an error of 0.19 m/sec, which is equivalent to 2% of the average observed travel speeds in the intersection of the study. Instantaneous speeds (at the resolution of 30 Hz) were found to be estimated with an average error of 0.21 m/sec and 0.86 m/sec respectively for free-flowing and congested traffic conditions. We then use the estimated speeds to calibrate the parameters of a driver behavior model for the vehicles in the area of study. The results show that the calibrated model replicates the driving behavior with an average error of 0.45 m/sec, indicating the high potential for using this framework for automated, large-scale calibration of car-following models from roadside traffic video data, which can lead to substantial improvements in traffic modeling via microscopic simulation.
Variable-Grasping-Mode Gripper With Different Finger Structures For Grasping Small-Sized Items
Venue: arXiv cs.RO 2021
Year: 2021

This letter presents a novel small gripper capable of grasping various types of small-sized items from flat surfaces for the assembly of small devices. Using a single actuator, the proposed gripper realizes two grasping modes: parallel-grip and turn-over modes. The gripper's mode can be switched via contact with a flat surface, such as a table. Handling thin thicknesses and light weights are the key challenges faced in attempts to grasp small-sized items. Although parallel grippers are effective in handling small items, there is a limit to the thinness of objects that can be grasped by parallel grippers. Accordingly, the turn-over mode was adopted to grasp items that exceeded this threshold. In the turn-over mode, one finger lifts the item, while another finger holds the item from above to keep it from flicking out. The proposed gripper is capable of picking up several types of items from a table, including thin (0.05 mm) and lightweight (0.007 g) items.
Extending the GLS endomorphism to speed up GHS Weil descent using Magma
Venue: arXiv cs.CR 2021
Year: 2021

Let $q = 2^n$, and let $E / \mathbb{F}_{q^{\ell}}$ be a generalized Galbraith--Lin--Scott (GLS) binary curve, with $\ell \ge 2$ and $(\ell, n) = 1$.We show that the GLS endomorphism on $E / \mathbb{F}_{q^{\ell}}$ induces an efficient endomorphism on the Jacobian $J_H(\mathbb{F}_q)$ of the genus-$g$ hyperelliptic curve $H$ corresponding to the image of the GHS Weil-descent attack applied to $E/\mathbb{F}_{q^\ell}$, and that this endomorphism yields a factor-$n$ speedup when using standard index-calculus procedures for solving the Discrete Logarithm Problem (DLP) on $J_H(\mathbb{F}_q)$. Our analysis is backed up by the explicit computation of a discrete logarithm defined on a prime-order subgroup of a GLS elliptic curve over the field $\mathbb{F}_{2^{5\cdot 31}}$. A Magma implementation of our algorithm finds the aforementioned discrete logarithm in about $1,035$ CPU-days.
A Survey on Human and Personality Vulnerability Assessment in Cyber-security: Challenges, Approaches, and Open Issues
Venue: arXiv cs.CR 2021
Year: 2021

These days, cyber-criminals target humans rather than machines since they try to accomplish their malicious intentions by exploiting the weaknesses of end users. Thus, human vulnerabilities pose a serious threat to the security and integrity of computer systems and data. The human tendency to trust and help others, as well as personal, social, and cultural characteristics, are indicative of the level of susceptibility that one may exhibit towards certain attack types and deception strategies. This work aims to investigate the factors that affect human susceptibility by studying the existing literature related to this subject. The objective is also to explore and describe state of the art human vulnerability assessment models, current prevention, and mitigation approaches regarding user susceptibility, as well as educational and awareness raising training strategies. Following the review of the literature, several conclusions are reached. Among them, Human Vulnerability Assessment has been included in various frameworks aiming to assess the cyber security capacity of organizations, but it concerns a one time assessment rather than a continuous practice. Moreover, human maliciousness is still neglected from current Human Vulnerability Assessment frameworks; thus, insider threat actors evade identification, which may lead to an increased cyber security risk. Finally, this work proposes a user susceptibility profile according to the factors stemming from our research.
Virtual Temporal Samples for Recurrent Neural Networks: applied to semantic segmentation in agriculture
Venue: arXiv cs.CV 2021
Year: 2021

This paper explores the potential for performing temporal semantic segmentation in the context of agricultural robotics without temporally labelled data. We achieve this by proposing to generate virtual temporal samples from labelled still images. By exploiting the relatively static scene and assuming that the robot (camera) moves we are able to generate virtually labelled temporal sequences with no extra annotation effort. Normally, to train a recurrent neural network (RNN), labelled samples from a video (temporal) sequence are required which is laborious and has stymied work in this direction. By generating virtual temporal samples, we demonstrate that it is possible to train a lightweight RNN to perform semantic segmentation on two challenging agricultural datasets. Our results show that by training a temporal semantic segmenter using virtual samples we can increase the performance by an absolute amount of $4.6$ and $4.9$ on sweet pepper and sugar beet datasets, respectively. This indicates that our virtual data augmentation technique is able to accurately classify agricultural images temporally without the use of complicated synthetic data generation techniques nor with the overhead of labelling large amounts of temporal sequences.
A Survey of Privacy Vulnerabilities of Mobile Device Sensors
Venue: arXiv cs.HC 2021
Year: 2021

The number of mobile devices, such as smartphones and smartwatches, is relentlessly increasing to almost 6.8 billion by 2022, and along with it, the amount of personal and sensitive data captured by them. This survey overviews the state of the art of what personal and sensitive user attributes can be extracted from mobile device sensors, emphasising critical aspects such as demographics, health and body features, activity and behaviour recognition, etc. In addition, we review popular metrics in the literature to quantify the degree of privacy, and discuss powerful privacy methods to protect the sensitive data while preserving data utility for analysis. Finally, open research questions a represented for further advancements in the field.
Predicting Gender by First Name Using Character-level Machine Learning
Venue: arXiv cs.LG 2021
Year: 2021

Predicting gender by the first name is not a simple task. In many applications, especially in the natural language processing (NLP) field, this task may be necessary, mainly when considering foreign names. In this paper, we examined and implemented several machine learning algorithms, such as extra trees, KNN, Naive Bayes, SVM, random forest, gradient boosting, light GBM, logistic regression, ridge classifier, and deep neural network models, such as MLP, RNN, GRU, CNN, and BiLSTM, to classify gender through the first name. A dataset of Brazilian names is used to train and evaluate the models. We analyzed the accuracy, recall, precision, f1 score, and confusion matrix to measure the models' performances. The results indicate that the gender prediction can be performed from the feature extraction strategy looking at the names as a set of strings. Some models accurately predict gender in more than 95% of the cases. The recurrent models overcome the feedforward models in this binary classification problem.
Community Detection in Interval-Weighted Networks
Venue: arXiv cs.SI 2021
Year: 2021

In this paper we introduce and develop the concept of Interval-Weighted Networks (IWN), a novel approach in Social Network Analysis, where the edge weights are represented by closed intervals composed with precise information, comprehending intrinsic variability. We extend IWN for both Newman's modularity and modularity gain and the Louvain algorithm (LA), considering a tabular representation of networks by contingency tables. We apply our methodology in a real-world commuter network in mainland Portugal between the twenty three NUTS 3 regions. The optimal partition of regions is developed and compared using two new different approaches, designated as ``Classic Louvain'' (CL) and ``Hybrid Louvain'' (HL), which allow taking into account the variability observed in the original network, thereby minimizing the loss of information present in the raw data. Our findings suggest the division of the twenty three Portuguese regions in three main communities. However, we find different geographical partitions according to the community detection methodology used. This analysis can be useful in many real-world applications, since it takes into account that the weights may vary within the ranges, rather than being constant.
A Systematic Review of Computational Thinking in Early Ages
Venue: arXiv cs.HC 2021
Year: 2021

Nowadays, technology has become dominant in the daily lives of most people around the world. From children to older people, technology is present, helping in the most diverse daily tasks and allowing accessibility. However, many times these people are just end-users, without any incentive to the development of computational thinking (CT). With advances in technologies, the abstraction of coding, programming languages, and the hardware resources involved will become a reality. However, while we have not progressed to this stage, it is necessary to encourage the development of CT teaching from an early age. This work will present state of the art concerning teaching initiatives and tools on programming (e.g., ScratchJr), robotics (e.g., KIBO), and other playful tools (e.g., Happy Maps) for the development of CT in the early ages, specifically filling the gap of CT at the kindergarten level. This survey presents a systematic review of the literature, emphasizing computational and robotic tools used in preschool classes to develop the CT. The systematic review evaluated more than 60 papers from 2010 to December 2020, electing 31 papers and adding three papers from the qualitative stage. The paper's amount was classified in taxonomy to show CT's principal tools and initiates applied to children early. To conclude this survey, an extensive discussion about the terms and authors related to this research area is present.