Reviewerly/exHarmony huggingface dataset:
Enhancing Neural Models with Vulnerability via Adversarial Attack

Natural Language Sentence Matching (NLSM) serves as the core of many natural language processing tasks.1) Most previous work develops a single specific neural model for NLSM tasks.2) There is no previous work considering adversarial attack to improve the performance of NLSM tasks.3) Adversarial attack is usually used to generate adversarial samples that can fool neural models.In this paper, we first find a phenomenon that different categories of samples have different vulnerabilities.Vulnerability is the difficulty degree in changing the label of a sample.Considering the phenomenon, we propose a general two-stage training framework to enhance neural models with Vulnerability via Adversarial Attack (VAA).We design criteria to measure the vulnerability which is obtained by adversarial attack.VAA framework can be adapted to various neural models by incorporating the vulnerability.In addition, we prove a theorem and four corollaries to explain the factors influencing vulnerability effectiveness.Experimental results show that VAA significantly improves the performance of neural models on NLSM datasets.The results are also consistent with the theorem and corollaries.The code is released on https://github.com/rzhangpku/VAA.
Autonomous Parking by Successive Convexification and Compound State Triggers

In this paper, we propose an algorithm for optimal generation of nonholonomic paths for planning parking maneuvers with a kinematic car model. We demonstrate the use of Successive Convexification algorithms (SCvx), which guarantee path feasibility and constraint satisfaction, for parking scenarios. In addition, we formulate obstacle avoidance with state-triggered constraints which enables the use of logical constraints in a continuous formulation of optimization problems. This paper contributes to the optimal nonholonomic path planning literature by demonstrating the use of SCvx and state-triggered constraints which allows the formulation of the parking problem as a single optimisation problem. The resulting algorithm can be used to plan constrained paths with cusp points in narrow parking environments.
The invariant eigen-operator method in matrix form and the eigenfrequency of several mesoscopic circuits

The Invariant Eigen-operator (IEO) method is widely used in solving the eigenfrequencies of the coulped quantum mesoscopic circuits. The previous IEO method is complicated but stylized, we always wasted much time in this boring processes. Here we extended the IEO method to the matrix form based on Lagrangian of the complex mesoscopic circuits, and express the ideas and processes of the previous calculations of the IEO method in a very simple matrix form. The mathematical methods we used is the indicator representation of the matrix, and we got a very simple and convenient matrix form of the IEO method. This form has important significance for the calculation of large-scale complex multi-loop mesoscopic circuits. Moreover, the matrix form of the IEO method is very friendly to the programming implementation of the complex quantum mesoscopic &lt;i&gt;L&lt;/i&gt;−&lt;i&gt;C&lt;/i&gt; circuits, it is probably a most optimal algorithm for calculating the eigenfrequencies of the quantum mesoscopic &lt;i&gt;L&lt;/i&gt;−&lt;i&gt;C&lt;/i&gt; circuits. In addition, with some help of computer programs, we used this method to calculate the eigenfrequencies of three &lt;i&gt;L&lt;/i&gt;−&lt;i&gt;C&lt;/i&gt; mesoscopic circuits, including two cases with and without mutual inductance. We revealed some relevant properties of these circuits by calculating results, indicating that the eigenfrequency is only related to the element properties of the mesoscopic circuit itself. Finally, we found that this method can also be used in other areas like atom-light coupling systems and solid state physics.
Deep Learning at the Speed of Light: Lightmatter bets that optical computing can solve AI's efficiency problem

In 2011, Marc Andreessen, general partner of venture capital firm Andreessen Horowitz, wrote an influential article in The Wall Street Journal titled, “Why Software Is Eating the World.” A decade later now, it's deep learning that's eating the world.
Robust Cell Image Segmentation via Improved Markov Random Field Based on a Chinese Restaurant Process Model

In this paper, a segmentation method for cell images using Markov random field (MRF) based on a Chinese restaurant process model (CRPM) is proposed. Firstly, we carry out the preprocessing on the cell images, and then we focus on cell image segmentation using MRF based on a CRPM under a maximum a posteriori (MAP) criterion. The CRPM can be used to estimate the number of clusters in advance, adjusting the number of clusters automatically according to the size of the data. Finally, the conditional iteration mode (CIM) method is used to implement the MRF based cell image segmentation process. To validate our proposed method, segmentation experiments are performed on oral mucosal cell images. The segmentation results were compared with other methods, using precision, Dice, and mean square error (MSE) as the objective evaluation criteria. The experimental results show that our method produces accurate cell image segmentation results, and our method can effectively improve segmentation for the nucleus, binuclear cell, and micronucleus cell. This work will play an important role in cell image recognition and analysis.
Stronger Baselines for Grammatical Error Correction Using a Pretrained Encoder-Decoder Model

Studies on grammatical error correction (GEC) have reported on the effectiveness of pretraining a Seq2Seq model with a large amount of pseudodata. However, this approach requires time-consuming pretraining of GEC because of the size of the pseudodata. In this study, we explored the utility of bidirectional and auto-regressive transformers (BART) as a generic pretrained encoder-decoder model for GEC. With the use of this generic pretrained model for GEC, the time-consuming pretraining can be eliminated. We find that monolingual and multilingual BART models achieve high performance in GEC, with one of the results being comparable to the current strong results in English GEC.
Automatic UAV-Based Inspection of Overhead Lines and Substations: beyond visible spectrum

Recent technological development, decreasing costs coupled with an increasing availability of unmanned aerial vehicles (UAVs) have made UAV-based condition monitoring and diagnosis of power equipment attractive. Traditional labor-intensive manual interventions have been steadily replaced by solutions where the level of automation is gradually increasing. At the same time, a tendency to scale up inspection missions to cover entire overhead line segments, as opposed to spot investigations, has been emerging. Along this line of trends, a system addressing power line monitoring with a focus on revealing faults invisible to the naked eye is presented in this work. In order to meet safety constraints and at the same time cover a certain distance to be financially appealing (typically around 100 km per day), flight missions are recommended to keep at least 15-20 meters from the infrastructure and at the same time be executed at a velocity of at least 5 m/s. Under these challenging dynamic conditions, research project VOLTAIR relies on commercial ultraviolet and infrared imaging devices while aiming at a drastic reduction of time-consuming on-site visits through the use of UAVs and automatic data analysis.
Understanding the one-pixel attack: Propagation maps and locality analysis

Deep neural networks were shown to be vulnerable to single pixel modifications. However, the reason behind such phenomena has never been elucidated. Here, we propose Propagation Maps which show the influence of the perturbation in each layer of the network. Propagation Maps reveal that even in extremely deep networks such as Resnet, modification in one pixel easily propagates until the last layer. In fact, this initial local perturbation is also shown to spread becoming a global one and reaching absolute difference values that are close to the maximum value of the original feature maps in a given layer. Moreover, we do a locality analysis in which we demonstrate that nearby pixels of the perturbed one in the one-pixel attack tend to share the same vulnerability, revealing that the main vulnerability lies in neither neurons nor pixels but receptive fields. Hopefully, the analysis conducted in this work together with a new technique called propagation maps shall shed light into the inner workings of other adversarial samples and be the basis of new defense systems to come.
Apakah Kinerja Lingkungan dan Biaya Lingkungan Berpengaruh Terhadap Pengungkapan Informasi Lingkungan?

Environmental information disclosure is one type of disclosure that is still voluntary. Only a few manufacturing companies in Indonesia have disclosed environmental information. This study aims to determine the effect of environmental performance and environmental costs on environmental information disclosure. The population of this research is all manufacturing companies in Indonesia from 2014-2018. This study uses secondary data with sample selection using purposive sampling method. The data obtained were 20 companies and processed using SPSS software with multiple linear tests. The results showed that environmental performance had a positive and significant effect on environmental information disclosure. Meanwhile, environmental costs have no effect on environmental information disclosure. The implication of this research is that companies that already have good environmental performance and already have an efficient environmental cost are willing to disclose environmental information continuously even though they are still voluntary.
An Efficient Computational Cost Reduction Strategy for the Population-Based Intelligent Optimization of Nonlinear Dynamical Systems

Population-based intelligent optimization algorithms are popular due to their global optimality. However, it will be time-consuming if they are directly applied to nonlinear dynamical systems. In this article, an efficient computational cost reduction strategy is presented for the population-based intelligent optimization algorithms when they are employed to search for the global optimum of nonlinear systems in dynamic equilibrium. Specifically, a novel constrained optimization problem is formulated according to the demand of dynamic equilibrium in industry, and the reason why population-based methods take much more computing time is analyzed from the perspective of solving nonlinear equations. Since the computational complexity of solving nonlinear equations is sensitive to their initial values, a sensitivity transfer condition is provided to explain the significance of candidate testing order. Finally, an optimal evaluation sequence, which minimizes the Euclidean distance of adjacent candidates, is designed for the population-based intelligent optimization algorithms. Simulations show the effectiveness.
Mapping Vicon Motion Tracking to 6-Axis IMU Data for Wearable Activity Recognition

There are several large datasets available that are captured from motion tracking systems which could be useful to train wearable human activity recognition (HAR) systems, if only their spatial data could be mapped into the equivalent inertial measurement unit (IMU) data that would be sensed on the body. In this paper, we describe a mapping from 3D Vicon motion tracking data to data collected from a BlueSense on-body IMU. We characterise the error incurred in order to discern the extent to which it is possible to generate useful training data for a wearable activity recognition system from data collected with a motion capture system. We analyse this by mapping Vicon motion tracking data to rotational velocity and linear acceleration at the head, and compare this to actual gyroscope and accelerometer data collected by an IMU mounted on the head. In a 15 min dataset comprising three static activities—sitting, standing and lying down—we find that 95% of the reconstructed gyroscope data is within an error of [−7.25;+7.46] $$deg \cdot s^{-1}$$ , while 95% of the reconstructed accelerometer data was contained within [−96.1;+72.9] $$m \cdot G$$ . However, when we introduce more movement by including data collected while walking this increases to [−19.0;+18.2] $$deg \cdot s^{-1}$$ for the gyroscope and [−208;+186] $$m \cdot G$$ for the accelerometer. We conclude that generating accurate IMU data from motion capture datasets is possible and could be useful in providing larger volumes of data for activity recognition tasks and in helping enable advanced, data-hungry techniques such as deep learning to be employed on a larger scale within the domain of human activity recognition.
Discussion of “Prediction, Estimation, and Attribution” by Bradley Efron

Summary Professor Efron has presented us with a thought‐provoking paper on the relationship between prediction, estimation, and attribution in the modern era of data science. While we appreciate many of his arguments, we see more of a continuum between the old and new methodology, and the opportunity for both to improve through their synergy.
Combinets v2: Improving Conceptual Expansion using SGD

Modern deep neural networks perform well on a variety of tasks where a large amount of training data is available, but in low data settings, humans still outperform neural networks significantly. An important reason for this is the ability to reuse knowledge from other problems. Guzdial and Riedl proposed the approach of conceptual expansion[1], a general and efficient technique for reusing the knowledge of trained models for new tasks. In this paper we propose modifications to the original method of conceptual expansion to greatly increase the accuracy of the approach.
3 Exploring Learner Corpus Data for Language Testing and Assessment Purposes: The Case of Verb + Noun Collocations

Exploring Learner Corpus Data for Language Testing and Assessment Purposes : The Case of Verb + Noun Collocations
Age Detection with Face Mask Using Deep Learning and FaceMaskNet-9

Age detection can be a challenging problem due to the variations in the aging of every individual depending on one’s health, lifestyle, etc. Analyzing the face of humans using computer vision can help in estimating the age of humans as the face holds most of the important attributes. However, the corona virus disease 2019 (COVID-19) pandemic has forced people all over the world to wear face masks to prevent human-to-human transmission of the virus thereby making it difficult to detect the age of the person wearing a mask. The analysis of a human face without a facial mask in the images provided by cameras or webcams is a tedious process as the human face in an image can have variations due to changes in position, orientation, and a lot of other factors such as lighting conditions, image resolution, etc. Thus, analyzing a human face with a facial mask becomes even more difficult as most of the prominent facial attributes such as the nose, wrinkles on cheeks, etc. are not visible due to the face mask. To overcome this problem, we have proposed a system to perform age detection using FaceMaskNet-9, a deep learning network that will detect the age of the person with a face mask. We have used a deep learning-based age detector model for age prediction. The FaceMaskNet-9 used for the process of predicting the age of a person increases the accuracy of the task and precisely classifies people with masked faces into different age groups. Our age detector can be used in CCTV footage, for medical diagnosis, for recommending videos and advertisements according to the target audience, to give extra privileges to people depending on their age and even on social media where there are age restrictions to view or use certain contents on the social platforms.
Mechanized Formal Model of Bitcoin’s Blockchain Validation Procedures

We present the first mechanized formal model of Bitcoin’s transaction and blockchain data structures including the formalization of the blockchain validation procedures. Our formal model, though still a simplified representation of an actual Bitcoin blockchain, includes regular and coinbase transactions, segregated witnesses, relative and absolute locktime, the Bitcoin Script language expressions together with a denotational semantics, transaction fees and block rewards. We formally specify the details of validity checks performed when adding new blocks to the blockchain. We assume perfect cryptography and use the symbolic approach for modeling hash functions and digital signatures.To demonstrate the utility of the model, we formally state and prove several essential properties of a valid blockchain - transactions are unique, each coin can be spent at most once and the new value is only created through block rewards. The model and the proofs are largely independent of Bitcoin specific details and easily generalize to any cryptocurrency blockchain based on the Unspent Transaction Output (UTXO) paradigm.We mechanize all the results using the Coq proof assistant.
Autonomous Modulation Classification using Single Inception Module based Convolutional Neural Network

Modulation recognition plays a crucial role in noncooperative communication in which receivers have no prior information regarding transmitter modulation scheme. This paper presents a novel convolutional neural network with single Stem and Inception module for autonomous modulation classification from raw I/Q received channels. This addresses the computationally intensive problem of conversion of I/Q channels to constellation image processing. The proposed system is capable of classifying 11 standard modulation schemes with both 2D and 3D input array configurations for varying SNR conditions. The performance of proposed system has been evaluated for a realtime communication system simulated with Rician fading channel and AWGN noise model, providing realistic distortion effects. The proposed CNN design achieves an average accuracy of 90% at 10 dB and 99% at 20 dB SNR with reduced network learnables and lower training and testing time, which makes it computationally efficient. These attributes make Inception module based CNN a viable solution for modulation classification in practical low cost and portable yet reliable communication systems.
The Anatomy of User Experience with Conversational Agents: A Taxonomy and Propositions of Service Clues

Conversational agents (CAs) represent a paradigm shift in regards to how humans use information systems. Although CAs have recently attracted considerable research interest, there is still limited shared knowledge about the distinctive characteristics of CAs from a user experience-based perspective. To address this gap, we conducted a systematic literature review to identify CA characteristics from existing research. Building on classifications from service experience theory, we develop a taxonomy that classifies CA characteristics into three major categories (i.e. functional, mechanic, humanic clues). Subsequently, we evaluate the usefulness of the taxonomy by interviewing six domain experts. Based on this categorization and the reviewed literature, we derive three propositions that link these categories to specific user experience dimensions. Our results support researchers and practitioners by providing deeper insights into service design with CAs and support them in systematizing and synthesizing research on the effects of specific CA characteristics from a user experience-based perspective.
Robust and Sparse Support Vector Machines via Mixed Integer Programming

In machine learning problems in general, and in classification in particular, overfitting and inaccuracies can be obtained because of the presence of spurious features and outliers. Unfortunately, this is a frequent situation when dealing with real data. To handle outliers proneness and achieve variable selection, we propose a robust method performing the outright rejection of discordant observations together with the selection of relevant variables. A natural way to define the corresponding optimization problem is to use the $$\ell _0$$ norm and recast it as a mixed integer optimization problem (MIO) having a unique global solution, benefiting from algorithmic advances in integer optimization combined with hardware improvements. We also present an empirical comparison between the $$\ell _0$$ norm approach, the 0–1 loss and the hinge loss classification problems. Results on both synthetic and real data sets showed that, the proposed approach provides high quality solutions.
Correction to: Deep kernel learning in extreme learning machines

In the original publication of the article, the Acknowledgements section was missed unfortunately. The correct Acknowledgements section is given in this correction.
Regional University's Role and Place in Personnel Training System in Assessments of Employers

The article was prepared on the basis of a field study conducted at Udmurt State University within the “Transitive University in the Context of Global, National and Regional Challenges” Project. The article presents the results of a survey of employers on assessment the quality of training of graduates. The study focused on identifying criteria and channels for staff selection, as well as existing forms of cooperation between organizations of Udmurtia and educational institutions. The aim of the study was to clarify the role and place of Udmurt State University in the personnel training in the region. The research method was online questioning, the sample was spontaneous, the number of organizations was 192. The results of the study revealed a rather mixed picture. On the one hand, it is obvious that in general in Udmurtia organizations, rather low assessments of the quality of specialist training at Udmurt State University prevail, since most of the average grades are below 4 points. On the other hand, methods of cooperation with universities are formal and confined to organizing diploma / undergraduate practice and hiring young specialists. The low interest of employers in financing the development of new technologies and training of personnel is also obvious. In the vast majority of cases, enterprises want to get ready-made employees for their current needs without an agreed resource-consuming and long-term cooperation. Channels of communication and coordinated interaction on a long-term basis between the university and employers are most common either absent or are assessed as formal and episodic. Here, several significant reasons can be singled out such as insufficient financial resources of small organizations for working with universities; lack of time or desire on the part of organizations; lack of resources at the university for the implementation of the order; focus on “traditional industries” in the Udmurt Republic while neglecting the needs of the innovative potential development in the region. This results in the persistence of the employers' practice of “self-education in the workplace.”