OpenRaiser/Intern-Atlas huggingface dataset:
Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization
Venue: arXiv cs.CV 2021
Year: 2021

Recently, the low-rank property of different components extracted from the image has been considered in man hyperspectral image denoising methods. However, these methods usually unfold the 3D tensor to 2D matrix or 1D vector to exploit the prior information, such as nonlocal spatial self-similarity (NSS) and global spectral correlation (GSC), which break the intrinsic structure correlation of hyperspectral image (HSI) and thus lead to poor restoration quality. In addition, most of them suffer from heavy computational burden issues due to the involvement of singular value decomposition operation on matrix and tensor in the original high-dimensionality space of HSI. We employ subspace representation and the weighted low-rank tensor regularization (SWLRTR) into the model to remove the mixed noise in the hyperspectral image. Specifically, to employ the GSC among spectral bands, the noisy HSI is projected into a low-dimensional subspace which simplified calculation. After that, a weighted low-rank tensor regularization term is introduced to characterize the priors in the reduced image subspace. Moreover, we design an algorithm based on alternating minimization to solve the nonconvex problem. Experiments on simulated and real datasets demonstrate that the SWLRTR method performs better than other hyperspectral denoising methods quantitatively and visually.
UNTANGLE: Unlocking Routing and Logic Obfuscation Using Graph Neural Networks-based Link Prediction
Venue: arXiv cs.CR 2021
Year: 2021

Logic locking aims to prevent intellectual property (IP) piracy and unauthorized overproduction of integrated circuits (ICs). However, initial logic locking techniques were vulnerable to the Boolean satisfiability (SAT)-based attacks. In response, researchers proposed various SAT-resistant locking techniques such as point function-based locking and symmetric interconnection (SAT-hard) obfuscation. We focus on the latter since point function-based locking suffers from various structural vulnerabilities. The SAT-hard logic locking technique, InterLock [1], achieves a unified logic and routing obfuscation that thwarts state-of-the-art attacks on logic locking. In this work, we propose a novel link prediction-based attack, UNTANGLE, that successfully breaks InterLock in an oracle-less setting without having access to an activated IC (oracle). Since InterLock hides selected timing paths in key-controlled routing blocks, UNTANGLE reveals the gates and interconnections hidden in the routing blocks upon formulating this task as a link prediction problem. The intuition behind our approach is that ICs contain a large amount of repetition and reuse cores. Hence, UNTANGLE can infer the hidden timing paths by learning the composition of gates in the observed locked netlist or a circuit library leveraging graph neural networks. We show that circuits withstanding SAT-based and other attacks can be unlocked in seconds with 100% precision using UNTANGLE in an oracle-less setting. UNTANGLE is a generic attack platform (which we also open source [2]) that applies to multiplexer (MUX)-based obfuscation, as demonstrated through our experiments on ISCAS-85 and ITC-99 benchmarks locked using InterLock and random MUX-based locking.
Study of keyword extraction techniques for Electric Double Layer Capacitor domain using text similarity indexes: An experimental analysis
Venue: arXiv cs.IR 2021
Year: 2021

Keywords perform a significant role in selecting various topic-related documents quite easily. Topics or keywords assigned by humans or experts provide accurate information. However, this practice is quite expensive in terms of resources and time management. Hence, it is more satisfying to utilize automated keyword extraction techniques. Nevertheless, before beginning the automated process, it is necessary to check and confirm how similar expert-provided and algorithm-generated keywords are. This paper presents an experimental analysis of similarity scores of keywords generated by different supervised and unsupervised automated keyword extraction algorithms with expert provided keywords from the Electric Double Layer Capacitor (EDLC) domain. The paper also analyses which texts provide better keywords like positive sentences or all sentences of the document. From the unsupervised algorithms, YAKE, TopicRank, MultipartiteRank, and KPMiner are employed for keyword extraction. From the supervised algorithms, KEA and WINGNUS are employed for keyword extraction. To assess the similarity of the extracted keywords with expert-provided keywords, Jaccard, Cosine, and Cosine with word vector similarity indexes are employed in this study. The experiment shows that the MultipartiteRank keyword extraction technique measured with cosine with word vector similarity index produces the best result with 92% similarity with expert provided keywords. This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques.
Learning Data Teaching Strategies Via Knowledge Tracing
Venue: arXiv cs.LG 2021
Year: 2021

Teaching plays a fundamental role in human learning. Typically, a human teaching strategy would involve assessing a student's knowledge progress for tailoring the teaching materials in a way that enhances the learning progress. A human teacher would achieve this by tracing a student's knowledge over important learning concepts in a task. Albeit, such teaching strategy is not well exploited yet in machine learning as current machine teaching methods tend to directly assess the progress on individual training samples without paying attention to the underlying learning concepts in a learning task. In this paper, we propose a novel method, called Knowledge Augmented Data Teaching (KADT), which can optimize a data teaching strategy for a student model by tracing its knowledge progress over multiple learning concepts in a learning task. Specifically, the KADT method incorporates a knowledge tracing model to dynamically capture the knowledge progress of a student model in terms of latent learning concepts. Then we develop an attention pooling mechanism to distill knowledge representations of a student model with respect to class labels, which enables to develop a data teaching strategy on critical training samples. We have evaluated the performance of the KADT method on four different machine learning tasks including knowledge tracing, sentiment analysis, movie recommendation, and image classification. The results comparing to the state-of-the-art methods empirically validate that KADT consistently outperforms others on all tasks.
Prediction of Listener Perception of Argumentative Speech in a Crowdsourced Dataset Using (Psycho-)Linguistic and Fluency Features
Venue: arXiv cs.CL 2021
Year: 2021

One of the key communicative competencies is the ability to maintain fluency in monologic speech and the ability to produce sophisticated language to argue a position convincingly. In this paper we aim to predict TED talk-style affective ratings in a crowdsourced dataset of argumentative speech consisting of 7 hours of speech from 110 individuals. The speech samples were elicited through task prompts relating to three debating topics. The samples received a total of 2211 ratings from 737 human raters pertaining to 14 affective categories. We present an effective approach to the classification task of predicting these categories through fine-tuning a model pre-trained on a large dataset of TED talks public speeches. We use a combination of fluency features derived from a state-of-the-art automatic speech recognition system and a large set of human-interpretable linguistic features obtained from an automatic text analysis system. Classification accuracy was greater than 60% for all 14 rating categories, with a peak performance of 72% for the rating category 'informative'. In a secondary experiment, we determined the relative importance of features from different groups using SP-LIME.
Towards One Shot Search Space Poisoning in Neural Architecture Search
Venue: arXiv cs.LG 2021
Year: 2021

We evaluate the robustness of a Neural Architecture Search (NAS) algorithm known as Efficient NAS (ENAS) against data agnostic poisoning attacks on the original search space with carefully designed ineffective operations. We empirically demonstrate how our one shot search space poisoning approach exploits design flaws in the ENAS controller to degrade predictive performance on classification tasks. With just two poisoning operations injected into the search space, we inflate prediction error rates for child networks upto 90% on the CIFAR-10 dataset.
New Performance Measures for Object Tracking under Complex Environments
Venue: arXiv cs.CV 2021
Year: 2021

Various performance measures based on the ground truth and without ground truth exist to evaluate the quality of a developed tracking algorithm. The existing popular measures - average center location error (ACLE) and average tracking accuracy (ATA) based on ground truth, may sometimes create confusion to quantify the quality of a developed algorithm for tracking an object under some complex environments (e.g., scaled or oriented or both scaled and oriented object). In this article, we propose three new auxiliary performance measures based on ground truth information to evaluate the quality of a developed tracking algorithm under such complex environments. Moreover, one performance measure is developed by combining both two existing measures ACLE and ATA and three new proposed measures for better quantifying the developed tracking algorithm under such complex conditions. Some examples and experimental results conclude that the proposed measure is better than existing measures to quantify one developed algorithm for tracking objects under such complex environments.
PhysXNet: A Customizable Approach for LearningCloth Dynamics on Dressed People
Venue: arXiv cs.CV 2021
Year: 2021

We introduce PhysXNet, a learning-based approach to predict the dynamics of deformable clothes given 3D skeleton motion sequences of humans wearing these clothes. The proposed model is adaptable to a large variety of garments and changing topologies, without need of being retrained. Such simulations are typically carried out by physics engines that require manual human expertise and are subjectto computationally intensive computations. PhysXNet, by contrast, is a fully differentiable deep network that at inference is able to estimate the geometry of dense cloth meshes in a matter of milliseconds, and thus, can be readily deployed as a layer of a larger deep learning architecture. This efficiency is achieved thanks to the specific parameterization of the clothes we consider, based on 3D UV maps encoding spatial garment displacements. The problem is then formulated as a mapping between the human kinematics space (represented also by 3D UV maps of the undressed body mesh) into the clothes displacement UV maps, which we learn using a conditional GAN with a discriminator that enforces feasible deformations. We train simultaneously our model for three garment templates, tops, bottoms and dresses for which we simulate deformations under 50 different human actions. Nevertheless, the UV map representation we consider allows encapsulating many different cloth topologies, and at test we can simulate garments even if we did not specifically train for them. A thorough evaluation demonstrates that PhysXNet delivers cloth deformations very close to those computed with the physical engine, opening the door to be effectively integrated within deeplearning pipelines.
Speech Emotion Recognition System by Quaternion Nonlinear Echo State Network
Venue: arXiv cs.SD 2021
Year: 2021

The echo state network (ESN) is a powerful and efficient tool for displaying dynamic data. However, many existing ESNs have limitations for properly modeling high-dimensional data. The most important limitation of these networks is the high memory consumption due to their reservoir structure, which has prevented the increase of reservoir units and the maximum use of special capabilities of this type of network. One way to solve this problem is to use quaternion algebra. Because quaternions have four different dimensions, high-dimensional data are easily represented and, using Hamilton multiplication, with fewer parameters than real numbers, make external relations between the multidimensional features easier. In addition to the memory problem in the ESN network, the linear output of the ESN network poses an indescribable limit to its processing capacity, as it cannot effectively utilize higher-order statistics of features provided by the nonlinear dynamics of reservoir neurons. In this research, a new structure based on ESN is presented, in which quaternion algebra is used to compress the network data with the simple split function, and the output linear combiner is replaced by a multidimensional bilinear filter. This filter will be used for nonlinear calculations of the output layer of the ESN. In addition, the two-dimensional principal component analysis technique is used to reduce the number of data transferred to the bilinear filter. In this study, the coefficients and the weights of the quaternion nonlinear ESN (QNESN) are optimized using the genetic algorithm. In order to prove the effectiveness of the proposed model compared to the previous methods, experiments for speech emotion recognition have been performed on EMODB, SAVEE, and IEMOCAP speech emotional datasets. Comparisons show that the proposed QNESN network performs better than the ESN and most currently SER systems.
Code Representation Learning with Prüfer Sequences
Venue: arXiv cs.AI 2021
Year: 2021

An effective and efficient encoding of the source code of a computer program is critical to the success of sequence-to-sequence deep neural network models for tasks in computer program comprehension, such as automated code summarization and documentation. A significant challenge is to find a sequential representation that captures the structural/syntactic information in a computer program and facilitates the training of the learning models. In this paper, we propose to use the Prüfer sequence of the Abstract Syntax Tree (AST) of a computer program to design a sequential representation scheme that preserves the structural information in an AST. Our representation makes it possible to develop deep-learning models in which signals carried by lexical tokens in the training examples can be exploited automatically and selectively based on their syntactic role and importance. Unlike other recently-proposed approaches, our representation is concise and lossless in terms of the structural information of the AST. Empirical studies on real-world benchmark datasets, using a sequence-to-sequence learning model we designed for code summarization, show that our Prüfer-sequence-based representation is indeed highly effective and efficient, outperforming significantly all the recently-proposed deep-learning models we used as the baseline models.
TMS-Crossbars with Tactile Sensing
Venue: arXiv cs.ET 2021
Year: 2021

The first stage of tactile sensing is data acquisition using tactile sensors and the sensed data is transmitted to the central unit for neuromorphic computing. The memristive crossbars were proposed to use as synapses in neuromorphic computing but device intelligence at the sensor level are not investigated in literature. We propose the concept of Transistor Memristor Sensor (TMS)-crossbar by including sensor to memristor crossbar array configuration in the input layer of the neural network architecture. 2 possible cell configurations of TMS crossbar arrays: 1 Transistor 1 Memristor 1 Sensor (1T1M1S) and 2 Transistor 1 Memristor 1 Sensor (2T1M1S) are presented. We verified the proposed TMS-crossbar in the practical design of analog neural networks based Braille character recognition system. The proposed design is verified with SPICE simulations using circuit equivalent of FLX-A501 force sensor, TiO$_2$ memristors and low power 22nm high-k CMOS transistors. The proposed analog neuromorphic computing system presents a scalable solution and is possible to encode 125 symbols with good accuracy in comparison with other Braille character recognition systems in the literature. The benefits of analog implementation of the TMS crossbar arrays is substantiated with results of accuracy, area and power requirements in comparison with the binary counterparts.
A Study on the Efficient Product Search Service for the Damaged Image Information
Venue: arXiv cs.IR 2021
Year: 2021

With the development of Information and Communication Technologies and the dissemination of smartphones, especially now that image search is possible through the internet, e-commerce markets are more activating purchasing services for a wide variety of products. However, it often happens that the image of the desired product is impaired and that the search engine does not recognize it properly. The idea of this study is to help search for products through image restoration using an image pre-processing and image inpainting algorithm for damaged images. It helps users easily purchase the items they want by providing a more accurate image search system. Besides, the system has the advantage of efficiently showing information by category, so that enables efficient sales of registered information.
Learning Enhancement in Higher Education with Wearable Technology
Venue: arXiv cs.HC 2021
Year: 2021

Wearable technologies have traditionally been used to measure and monitor vital human signs for well-being and healthcare applications. However, there is a growing interest in using and deploying these technologies to facilitate teaching and learning, particularly in a higher education environment. The aim of this paper is therefore to systematically review the range of wearable devices that have been used for enhancing the teaching and delivery of engineering curricula in higher education. Moreover, we compare the advantages and disadvantages of these devices according to the location in which they are worn on the human body. According to our survey, wearable devices for enhanced learning have mainly been worn on the head (e.g. eyeglasses), wrist (e.g. watches) and chest (e.g. electrocardiogram patch). In fact, among those locations, head-worn devices enable better student engagement with the learning materials, improved student attention as well as higher spatial and visual awareness. We identify the research questions and discuss the research inclusion and exclusion criteria to present the challenges faced by researchers in implementing learning technologies for enhanced engineering education. Furthermore, we provide recommendations on using wearable devices to improve the teaching and learning of engineering courses in higher education.
DEEP: DEnoising Entity Pre-training for Neural Machine Translation
Venue: arXiv cs.CL 2021
Year: 2021

It has been shown that machine translation models usually generate poor translations for named entities that are infrequent in the training corpus. Earlier named entity translation methods mainly focus on phonetic transliteration, which ignores the sentence context for translation and is limited in domain and language coverage. To address this limitation, we propose DEEP, a DEnoising Entity Pre-training method that leverages large amounts of monolingual data and a knowledge base to improve named entity translation accuracy within sentences. Besides, we investigate a multi-task learning strategy that finetunes a pre-trained neural machine translation model on both entity-augmented monolingual data and parallel data to further improve entity translation. Experimental results on three language pairs demonstrate that \method results in significant improvements over strong denoising auto-encoding baselines, with a gain of up to 1.3 BLEU and up to 9.2 entity accuracy points for English-Russian translation.
Combinatorial Algorithms for Rooted Prize-Collecting Walks and Applications to Orienteering and Minimum-Latency Problems
Venue: arXiv cs.DS 2021
Year: 2021

We consider the rooted prize-collecting walks (PCW) problem, wherein we seek a collection $C$ of rooted walks having minimum prize-collecting cost, which is the (total cost of walks in $C$) + (total node-reward of nodes not visited by any walk in $C$). This problem arises naturally as the Lagrangian relaxation of both orienteering, where we seek a length-bounded walk of maximum reward, and the $\ell$-stroll problem, where we seek a minimum-length walk covering at least $\ell$ nodes. Our main contribution is to devise a simple, combinatorial algorithm for the PCW problem in directed graphs that returns a rooted tree whose prize-collecting cost is at most the optimum value of the prize-collecting walks problem. We utilize our algorithm to develop combinatorial approximation algorithms for two fundamental vehicle-routing problems (VRPs): (1) orienteering; and (2) $k$-minimum-latency problem ($k$-MLP), wherein we seek to cover all nodes using $k$ paths starting at a prescribed root node, so as to minimize the sum of the node visiting times. Our combinatorial algorithm allows us to sidestep the part where we solve a preflow-based LP in the LP-rounding algorithms of Friggstand and Swamy (2017) for orienteering, and in the state-of-the-art $7.183$-approximation algorithm for $k$-MP in Post and Swamy (2015). Consequently, we obtain combinatorial implementations of these algorithms with substantially improved running times compared with the current-best approximation factors. We report computational results for our resulting (combinatorial implementations of) orienteering algorithms, which show that the algorithms perform quite well in practice, both in terms of the quality of the solution they return, as also the upper bound they yield on the orienteering optimum (which is obtained by leveraging the workings of our PCW algorithm).
Generating Band-Limited Adversarial Surfaces Using Neural Networks
Venue: arXiv cs.CV 2021
Year: 2021

Generating adversarial examples is the art of creating a noise that is added to an input signal of a classifying neural network, and thus changing the network's classification, while keeping the noise as tenuous as possible. While the subject is well-researched in the 2D regime, it is lagging behind in the 3D regime, i.e. attacking a classifying network that works on 3D point-clouds or meshes and, for example, classifies the pose of people's 3D scans. As of now, the vast majority of papers that describe adversarial attacks in this regime work by methods of optimization. In this technical report we suggest a neural network that generates the attacks. This network utilizes PointNet's architecture with some alterations. While the previous articles on which we based our work on have to optimize each shape separately, i.e. tailor an attack from scratch for each individual input without any learning, we attempt to create a unified model that can deduce the needed adversarial example with a single forward run.