Reviewerly/exHarmony huggingface dataset:
Finding and Interpreting Arguments: An Important Challenge for Humanities Computing and Scholarly Practice.

Skillful identification and interpretation of arguments is a cornerstone of learning, scholarly activity and thoughtful civic engagement. These are difficult skills for people to learn, and they are beyond the reach of current computational methods from artificial intelligence and machine learning, despite hype suggesting the contrary. In previous work, we have attempted to build systems that scaffold these skills in people. In this paper we reflect on the difficulties posed by this work, and we argue that it is a serious challenge which ought to be taken up within the digital humanities and related efforts to computationally support scholarly practice. Network analysis, bibliometrics, and stylometrics, essentially leave out the fundamental humanistic skill of charitable argument interpretation because they touch very little on the meanings embedded in texts. We present a problematisation of the design space for potential tool development, as a result of insights about the nature and form of arguments in historical texts gained from our attempt to locate and map the arguments in one corner of the Hathi Trust digital library.
No Regret Sample Selection with Noisy Labels

Deep neural networks (DNNs) suffer from noisy-labeled data because of the risk of overfitting. To avoid the risk, in this paper, we propose a novel DNN training method with sample selection based on adaptive k-set selection, which selects k (< n) clean sample candidates from the whole n noisy training samples at each epoch. It has a strong advantage of guaranteeing the performance of the selection theoretically. Roughly speaking, a regret, which is defined by the difference between the actual selection and the best selection, of the proposed method is theoretically bounded, even though the best selection is unknown until the end of all epochs. The experimental results on multiple noisy-labeled datasets demonstrate that our sample selection strategy works effectively in the DNN training; in fact, the proposed method achieved the best or the second-best performance among state-of-the-art methods, while requiring a significantly lower computational cost. The code is available at https://github.com/songheony/TAkS.
Optimization analysis of autonomous obstacle avoidance path for self-driving vehicles based on improved ant colony algorithm

Abstract In order to further improve the accurate response and fast response ability of autonomous obstacle avoidance in real dynamic environment, an improved AC (ant colony algorithm) model based on PSO (Particle Swarm Optimization) is proposed to realize the global and fast optimization analysis of autonomous obstacle avoidance path planning for self-driving vehicles. Firstly, an autonomous obstacle avoidance path planning model for self-driving vehicles is established; secondly, the global pheromone is searched by using the cooperation and information sharing mode between particles in PSO; thirdly, the global pheromone updating strategy is used to optimize the path searching ability of AC; finally, an innovative way of integrating PSO and AC is used to obtain the optimal path of autonomous obstacle avoidance for self-driving vehicles. The simulation results show that the AC model is improved by PSO to realize the fast and accurate planning of autonomous obstacle avoidance path for self-driving vehicles.
Optimization of Network Services Embedding Costs over Public and Private Clouds

Public cloud providers offer individuals and businesses the ability to rent IT resources to meet their needs without investing in their own hardware. At the same time, the Network Functions Virtualisation (NFV) concept promotes the migration of network operators from expensive and poorly scalable hardware network devices to virtual softwarized network functions. In order to embed those functions, network operators may decide to subscribe to public cloud offers. However, their diversity, both in terms of resource capacity and price, makes it difficult to find the optimal combination of offers that meets all needs at the lowest cost. In this paper we propose to solve this issue with an algorithm designed to help a network operator to select the best combination of offers (in terms of price) to reserve the virtual machines needed to support a set of network services. We analyze the computation time of our solution against various metrics, and estimate the cost savings compared to a traditional resource provision scheme or an unplanned resource rental strategy. Finally we evaluate the opportunity for a network operator to build its own datacenter, considering the existence of offers from public clouds.
Describing and Simulating Concurrent Quantum Systems

Abstract We present a programming language for describing and analysing concurrent quantum systems. We have an interpreter for programs in the language, using a symbolic rather than a numeric calculator, and we give its performance on examples from quantum communication and cryptography.
A power sum formula by Carlitz and its applications to permutation rational functions of finite fields

A formula discovered by L. Carlitz in 1935 finds an interesting application in permutation rational functions of finite fields. It allows us to determine all rational functions of degree three that permute the projective line $\Bbb P^1(\Bbb F_q)$ over $\Bbb F_q$, a result previously obtained by Ferraguti and Micheli through a different method. It also allows us to determine all rational functions of degree four that permute $\Bbb P^1(\Bbb F_q)$ under a certain condition. (A complete determination of all rational functions of degree four that permute $\Bbb P^1(\Bbb F_q)$ without any condition will appear in a separate forthcoming paper.)
Application of Computer Information Technology in Modern Distance Education System

Abstract The key to the application of computer information technology in distance education system is how to effectively apply the advantages of information technology to better achieve the goal of curriculum learning, cultivate students’ information literacy, innovative spirit and time ability. Computer information technology does play a huge role in distance education. And good teaching resources have laid a solid foundation for teachers to implement distance education, attaching importance to the construction of teaching resources. This paper introduces the necessity of applying computer information technology in modern distance education system and its application in modern distance education.
Towards Rapid and Robust Adversarial Training with One-Step Attacks

Adversarial training is the most successful empirical method for increasing the robustness of neural networks against adversarial attacks. However, the most effective approaches, like training with Projected Gradient Descent (PGD) are accompanied by high computational complexity. In this paper, we present two ideas that, in combination, enable adversarial training with the computationally less expensive Fast Gradient Sign Method (FGSM). First, we add uniform noise to the initial data point of the FGSM attack, which creates a wider variety of adversaries, thus prohibiting overfitting to one particular perturbation bound. Further, we add a learnable regularization step prior to the neural network, which we call Pixelwise Noise Injection Layer (PNIL). Inputs propagated trough the PNIL are resampled from a learned Gaussian distribution. The regularization induced by the PNIL prevents the model form learning to obfuscate its gradients, a factor that hindered prior approaches from successfully applying one-step methods for adversarial training. We show that noise injection in conjunction with FGSM-based adversarial training achieves comparable results to adversarial training with PGD while being considerably faster. Moreover, we outperform PGD-based adversarial training by combining noise injection and PNIL.
Aspect Based Sentiment Classification and Contradiction Analysis of Product Reviews

Due to the rapid growth of Internet infrastructure and E-Commerce, people can easily buy different products from different E-commerce websites. The reviews posted by the customers in the E-commerce websites can help us to get an idea about the products. It also helps us to identify the behavior of the individuals. This is because users’ reviews on items are inevitably dependent on many social effects such as peer influence, user profile information, user preference etc. Considering this fact, in this paper, we present a framework to analyze users’ reviews about the products to identify various aspects of the products listed online. In our approach, we use a large volume of publicly available product review data and perform different experiments. Experimental results show that our system provides better performance in case of negative aspect.
Operation mode of electric business logistics based on the application of two-dimensional code technology

In recent years, with the irresistible pace of e-commerce development, e-commerce logistics orders show blowout growth. For the logistics industry, it is both an opportunity and a difficult problem as there are many problems such as low technology content and poor distribution efficiency. Therefore, it is a challenge that needs to be tackled to integrate technology accurately with the operation mode of e-commerce logistics. In order to improve the effect of logistics operation, bring convenience to the management of business logistics, and ultimately realize the maximum profit of logistics enterprises, based on two-dimensional code technology, this study took the specific mode of logistics operation as the research subject, applied the corresponding two-dimensional code generated to three different logistics processes, put forward specific optimization schemes, and carried out the analysis of the application results. The results showed that the intervention of two-dimensional code could protect users' privacy, prevent channel conflict, and realize logistics tracing of product information, thus effectively improve the logistics operation mode.
Swachh Robo Ones Cleanliness Identity

The development of robotics technology has taken over manual work, robotics cleaners have taken major attention in robotics research due to their effectiveness of assisting human in cleaning process. The robot technology and many of the related robot appliances are being used smarter and more automated. Here represents the technology that proposed the working of robot for cleaning. Vacuum cleaner is integrated with rocker bogie model. Rocker Bogie is six wheeled model that helps to move in uneven surface. The robot basically works in both automatic and manual mode as per the need. Microcontroller places a major role in the development of the entire system with hardware and soft ware involved in it. The major functionality of the robot is to perform the picking and storing operation. It is programmed to accept inputs to sense obstacles around it and control the robot to avoid any collision. The objects are detected by the system and with the help of vacuum the cleaning is carried out. In the automatic mode robot control all the operations itself and change the lane in case of hurdle detection and moves back. In the manual mode, the keypad is used to perform the expected task and to operate the robot. The RF module has been used to transmit and receive the information between remote and robot. The whole circuitry is connected with solar powered battery. This in turn benefits the society to keep the environment clean and eradicate the diseases.
Multi-Dimensional effect of Social Media (with Special Reference to Health)

Social media is a platform for people   to discuss their issues and opinions. Before knowing the aspects of social media people must have to know what is social media? Social media are computer tools that allows people to share or exchange information’s, ideas, images, videos and even more with each other through a particular network. In this paper we cover all aspects of social media with its positive and negative effect. Focus is on the particular field like business, education, society,health and youth. During writing  this    paper we describe how   these media will affect society in a broad way. In India and  in advanced other societies, government and courts are beginning their reckoning with the extra ordinary difficulties posed by presently existing centralized social media and the plateform companies that by operating media are changing human civilization.
Designing e-textiles with adults with intellectual disabilities

Electronic textiles, or e-textiles, are an emerging type of wearable and interactive technology that can gather information either actively (i.e. pressing a button) or passively (i.e. pulse rate), or act purely as an output (e.g. LEDs). Developing e-textile technology requires some familiarity with many different concepts, one of which is circuit design. Unfortunately, people with disabilities are rarely included in the design and development of e-textiles; methods to manipulate these wearable interactive technologies---such as ways to learn circuit design---are not accessible to disabled creators. In this research, we explored accessible processes to enable individuals with intellectual disabilities to create their own e-textiles. To make the circuit design process accessible, we introduced circuit design concepts via a "puzzle" metaphor and simplified the sewing process by modifying common components to decrease the level of fine motor control required to sew the circuit. We conducted a pilot study with two participants with intellectual disabilities where both participants successfully created their very own e-textile and provided feedback about the usability of the "circuit puzzle" and the form-factor of puzzle pieces.
Regression via Implicit Models and Optimal Transport Cost Minimization

This paper addresses the classic problem of regression, which involves the inductive learning of a map, $y=f(x,z)$, $z$ denoting noise, $f:\mathbb{R}^n\times \mathbb{R}^k \rightarrow \mathbb{R}^m$. Recently, Conditional GAN (CGAN) has been applied for regression and has shown to be advantageous over the other standard approaches like Gaussian Process Regression, given its ability to implicitly model complex noise forms. However, the current CGAN implementation for regression uses the classical generator-discriminator architecture with the minimax optimization approach, which is notorious for being difficult to train due to issues like training instability or failure to converge. In this paper, we take another step towards regression models that implicitly model the noise, and propose a solution which directly optimizes the optimal transport cost between the true probability distribution $p(y|x)$ and the estimated distribution $\hat{p}(y|x)$ and does not suffer from the issues associated with the minimax approach. On a variety of synthetic and real-world datasets, our proposed solution achieves state-of-the-art results. The code accompanying this paper is available at "https://github.com/gurdaspuriya/ot_regression".
Performance of solar radiation models for obtaining reference evapotranspiration to Santa Maria-RS, Brazil

In the absence of observed data from Solar radiation (Sr), it is possible to estimate it using mathematical models. In general, the models vary in degree of complexity and the adjustment coefficients, and these must be calibrated to the place of interest to obtain the best performance. In this regard, the present work aims to evaluate the performance of simplified models of solar radiation in obtaining reference evapotranspiration. The data were obtained from the automatic meteorological station of the National Meteorological Institute (Inmet), located in Santa Maria – RS, Brazil. In the estimates of global solar radiation and its influence on reference evapotranspiration, ten models were evaluated, five models with the coefficients calibrated and five models, with the coefficients determined by the authors. It was observed that all the models studied presented performance index above 0.82, indicating “optimal” performance. Thus, both calibration and non-calibration models to obtain Sr do not interfere with ETo estimation. Therefore, given the impossibility of obtaining Sr data in meteorological stations, it is possible to use any of the models analyzed.
Fast and Robust Watermarking Method using Walsh Matrix Partition

In the research of watermarking algorithm, the Singular Value Decomposition (SVD) is widely use. It has the capability of maintaining the imperceptibility and robustness at a good level. However, the SVD has a high level of complexity in the process of decomposition and reconstruction. It split a host image into three matrices of U, S, and V. The S matrix contains with the singular value and it is used in the embedding and extraction processes. The other two matrices of U and V are orthogonal and it is used to reconstruct the watermarked image. This scheme can be simplified using a single matrix which is orthogonal and symmetrical to replace the use of matrices of SVD. This paper is proposed a watermarking algorithm using a Walsh matrix which is symmetric, orthogonal and contains signed integer value of 1 and -1. The Walsh matrix is used to transform the host image into Walsh coefficient and reconstruct the watermarked image using a simple equation. The experiment result shows that the proposed method has faster embedding and extraction time with average time of 0.3841 and 0.1854 second compared to the SVD with average time of 0.6235 and 0.2539 second. Meanwhile, the both method has same level of robustness and imperceptibility. The average PSNR value of the proposed method and SVD are 41.4991 and 41.6518 respectively while the average NC values are 0.9416 and 0.9428. The proposed method is able to shorten the processing time without reducing the imperceptibility and robustness.
Corporate Training of Yakutia Teachers to Apply Methods of E-Learning

Teacher training can assist in initiating digital changes in education. However, to achieve this, the training should use leading examples from the field of digital education solutions and technologies, while university and school educators should be trained to apply them in their work and to create high-tech educational startups in the field of e-learning. The goal of this study was to increase the professional competencies of teachers by conducting a corporate course that would prepare the teachers of the university in Yakutia to apply e-learning methods. The article demonstrates that the introduction of e-learning at the university is an important comprehensive process that implies transformation of all education components under the influence of modern technologies. Its practical implementation requires changing the goals, organizational forms, and technologies of educational activities in line with new technologies as well as the development of productive strategies for integrating the created innovations into the traditional learning process. For this purpose, the authors conducted a sociological study that helped identify the main problems associated with this type of training (for example, developing a course is time-consuming and a lack of required skills to develop courses). Based on the obtained data, the authors proposed a corporate course for university teachers on the creation of educational content for e-learning. It was shown that when applying new types of educational activities, teachers should combine traditional and new technologies, which contributes to the innovative training of teachers to meet the requirements of the digital society. This study focused on practical issues, since the research results were implemented in the work of the North-Eastern Federal University (NEFU), the base of the research. The results can be used when training teachers to apply e-learning methods in other educational institutions.
Preserving the Privacy for University Data Using Blockchain and Attribute-based Encryption

An effective IT solution is required for the administrative processes which is the core of university. The tamper resistance, transparency and auditability for university management data are very important to avoid the corruption. Blockchain occupies the immutability and irreversibility properties thus, it becomes a potential solution. Regrettably, there are some challenges such as privacy concern and limited storage exists in blockchain technology. In this work, a blockchain-based university data storage model is proposed and such barriers of blockchain are handled. The proposed model achieves the tamper resistance, transparency and auditability feature by using blockchain technology. CP-ABE and other cryptographic techniques are used to provide the fine-grained access and privacy preserving facilities. The security analysis is performed and shows that our approach is tamper resistant and provably secure for privacy.
A Study on Acoustic Parameter Selection Strategies to Improve Deep Learning-Based Speech Synthesis

In this paper, we investigate the variation in the performance of a deep learning-based speech synthesis (DLSS) system based on the configuration of output acoustic parameters. Our method is mainly applicable for vocoding-based statistical parametric speech synthesis (SPSS), which has advantages in low-resource scenarios. Given the independence assumption of the source-filter model for the spectral and fundamental frequency F0 parameters, we propose a reliable network architecture for training acoustic parameters. Particularly, the F0 parameter suffers from high fluctuation and an extremely low number of dimensions. To relieve these problems, we introduce a context-window approach. Furthermore, we apply data augmentation to the proposed structure to overcome a lack of training data, which is a frequent issue with multi-speaker TTS systems. Experimental results confirm the superiority of the proposed algorithm over conventional ones in both single-speaker and multi-speaker TTS setups.
Factors Affecting Practical Knowledge Acquisi-tion of Pre-service Computer Science Teachers During the Practicum: A Multiple Regression Analysis

This study aimed to examine and analyze the relationship between five factors in the practicum setting and six components ofÂ practicalÂ knowledge acquisition among pre-service computer science teachers.Â A total of 219 Chinese pre-service computer science teachersÂ were invited to attendÂ this study.Â The method of data analysis was using multiple regression analysis.Â The results can be summed asÂ follows: (a) The factor of knowledge preparation, which has strong practicality in practicum, significantly predicted the five components of practical knowledge acquisition; (b) Teaching reflection factor as a meta-cognitive and a characteristic of higher-order thinking significantly predicted the three components of practical knowledge acquisition; (c) Instructional design factor as a process of systematic reflection significantly predicted the three components of practical knowledge acquisition; (d) Both teaching practice and teamwork factor significantly predicted the two components of practical knowledge acquisition. We also suggested that teacher educators should focus on the integration of theoretical knowledge and practical knowledge by considering the effects of the five factors. https://doi.org/10.26803/ijlter.19.2.13