Reviewerly/exHarmony huggingface dataset:
What Makes It Findable? An Exploration on User Search Behavior and the Findability of Technical Documentation

This paper was presented at the Invited Panel session “Technical Communication in China”. Findability is one of the most important qualitative factors of websites. With the rapid growth in navigation complexity and in number of technical documentations in help centers, whether users can easily locate the target document could directly determine the information retrieval task outcome. Providing users with a fine guide to target documents and then helping them find solutions to their problems is the most important function of a help center. Investigation on user search behavior data and perceived findability of documentation has to be done in order to further apply website log data to predicting user subjective assessment. In this paper we analyze the correlation between subjective document findability, subjective task complexity, and user search behavior. We found several search behavior metrics which significantly correlate with the two subjective measures above.
Evaluating Calibration and Robustness of Pedestrian Detectors

In this work the robustness and calibration of modern pedestrian detectors are evaluated. Pedestrian detection is a crucial perception component in autonomous driving and in this article its performance under different image distortions is studied. Furthermore, we provide an analysis of classification calibration of pedestrian detectors and a positive effect of using the style-transfer augmentation technique is presented. Our analysis is aimed as a step towards understanding and improving current safety-critical detection systems.
An Exploratory Field Study of Programmer Assistance-Seeking during Software Development

Developers often face a dilemma: to seek assistance from a colleague or to expend effort to answer a question herself. On one hand, seeking help is fast and reliable. But on the other, seeking assistance can distract colleagues and reduce their productivity. In this paper, we report our preliminary findings of assistance-seeking from an observational study at a medium-sized software company. We found that developers have varying levels of spoken communication when seeking help. We believe this is correlated with their different years of experience working as developers, among other factors. We also found that many employees would avoid asking for help several times due to various work-related and reported personal reasons. This has driven us to explore a new, exciting research area discovering the complexities of developers seeking help. This paper is our first analysis of this kind, and we hope to receive the community's feedback before continued work.
Boot Log Anomaly Detection with K-Seen-Before

Software development for embedded systems, in particular code which interacts with boot-up procedures, can pose considerable challenges. In this work we propose the K-Seen-Before (KSB) approach to detect and highlight anomalous boot log messages, thus relieving developers from repeatedly having to manually examine boot log files of 1000+ lines. We describe the KSB instance based anomaly detection system and its relation to KNN. An industrial data set related to development of high-speed networking equipment is utilized to examine the effects of the KSB parameters on the amount of detected anomalies. The obtained results highlight the utility of KSB and provide indications of suitable KSB parameter settings for obtaining an appropriate trade-off for the cognitive workload of the developer with regards to log file analysis.
Matching Neural Network for Extreme Multi-Label Learning

Abstract Multi-label learning involving hundreds of thousands or even millions of labels is referred to as extreme multi-label learning, in which the labels often follow a power-law distribution with the majority occurring in very few data points as tail labels. As a promising solution of multi-label learning, however, the embedding-based methods face a problem that most of them have the basic low-rank assumption but the widespread of tail labels in data violates it. Recently, research efforts have been put on building tail label tolerant embedding-based models, however, for real-life datasets containing substantial data points with only tail labels, simply treating them as label matrix outliers will incur severe information loss, meanwhile accurately computing the pairwise distances between label vectors turns infeasible. In light of this, we present the Matching Neural Network (MNN), which learns two neural mapping functions that encode feature vectors and label vectors into their distributed representations, respectively. A noise contrastive loss is also proposed to guide the training of the functions so as to ensure matched features and labels have similar distributed representation measured by cosine similarity. Extensive experiments on various benchmark datasets with state-of-the-art baselines demonstrate the more accurate predictions of MNN.
Advantages of Imputation vs. Data Swapping for Statistical Disclosure Control

Data swapping is an approach long-used by public agencies to protect respondent confidentiality in which values of some variables are swapped with similar records for a small portion of respondents. Synthetic data is a newer method in which many if not all values are replaced with multiple imputations. Synthetic data can be difficult to implement for complex data; however, when the portion of data replaced is similar to data swapping, it becomes simple to implement using publicly available software. This paper describes how this simplification of synthetic data can be used to provide a better balance of data quality and disclosure protection compared to data swapping. This is illustrated via an empirical comparison using data from the Survey of Earned Doctorates.
Setting Expectations and Deviations

This brief chapter provides an overview and outline of the book, with a particular emphasis on the specific topics covered and the recipes described within each individual chapter.
Recommendation on Heterogeneous Information Network with Type-Sensitive Sampling

Most entities and relations for recommendation tasks in the real world are of multiple types, large-scale, and power-law. The heterogeneous information network (HIN) based approaches are widely used in recommendations to model the heterogeneous data. However, most HIN based approaches learn the latent representation of entities through meta-path, which is predefined by prior knowledge and thus limits the combinatorial generalization of HIN. Graph neural networks (GNNs) collect and generalize the information of nodes on the receptive field, but most works focus on homogeneous graphs and fail to scale up with regard to power-law graphs. In this paper, we propose a HIN based framework for recommendation tasks, where we utilize GNNs with a type-sensitive sampling to handle the heterogeneous and power-law graphs. For each layer, we adopt schema-based attention to output the distribution of sampling over types, and then we use the importance sampling inside each type to output the sampled neighbors. We conduct extensive experiments on four public datasets and one private dataset, and all datasets are selected carefully for covering the different scales of the graph. In particular, on the largest heterogeneous graph with 0.4 billion edges, we improve the square error by 2.5% while yielding a 26% improvement of convergence time during training, which verifies the effectiveness and scalability of our method regarding the industrial recommendation tasks .
Identifying Important Citations by Incorporating Generative Model into Discriminative Classifiers

Since Budapest open access initiative was launched, a large number of full-text articles in the format of XML are available, which further promotes the technology management on the basis of citation context analysis, such as emerging technology forecasting, technology opportunity detection and innovation measurement. Inspired by the success of kernel functions utilized to promote the performance of SVM (Support Vector Machine) model, we explore the potential of combining generative and discriminative models for the task of citation function and importance classification. In more details, generative features are generated from a topic model, Citation Influence Model (CIM), and then fed to two state-of-the-art discriminative models, SVM and RF (Random Forest), with other 13 features derived from citation contexts directly to identify important citations from a brand new perspective. Extensive experimental results on a dataset from the Association for Computational Linguistics anthology indicate that our approach outperforms the counterparts.
Pre-Service Teachers’ Motivation, Practices and Challenges in Using Mobile Learning to Enhance Teaching Activities in Tanzania

The study explored the motivation, practices and challenges of pre-service teachers in using mobile learning to enhance teaching activities. The ground of the study was the digital revolution of mobile electronic devices that penetrated to the population and therefore facilitated access to vast online resources as an alternative to traditional resources. A case study design with a mixed approach was employed, involving 110 pre-service teachers who pursued Bachelor of Education programs. Data collection methods involved in-depth interviews and questionnaires. The findings show that pre-service teachers were motivated to use mobile learning by individual factors and contextual situations. The use of mobile learning involved searching for teaching materials as well as answers to critical and challenging questions. Further, pre-service teachers learned new methods of managing students and lesson presentations through online lectures. Mobile learning nevertheless faced challenges of poor Internet connectivity in some areas and pre-service teachers’ ability to determine whether the materials accessed came from valid and reliable sources. The study recommends that pre-services teachers should be trained in the skills of identifying reliable and valid sources of materials for effective teaching. There is also a need to educate pre-service teachers on how to access valid and reliable materials that are aligned to the curriculum and national education goals. Therefore, different sites should be made accessible to teachers to help them improve not only classroom teaching practices but also for learners’ better performance.
Design of Teaching System for Complex Skills Training Based on Desktop Virtual Reality System

This paper starts with the main components and influencing factors of complex skills. Combined with the characteristics of desktop virtual reality system, this research proposes the particularity of complex skills training in the virtual reality environment. In terms of its particularity, it designs a teaching system for complex skills training based on desktop virtual reality system. Finally, according to different learning conditions and combined with the study of learning theory, this paper puts forward the teaching strategies and methods of the teaching system of complex skills training in the virtual reality environment.
Application of Mobile App in Japanese Language Learning System

With the continuous development of modern information technology, smart phones have reached a high degree of popularity, which means that all fields, including school teaching, should actively explore a new mode of operation based on modern information technology, especially in the context of smart phones As an important learning content of language majors in Colleges and secondary schools in China, Japanese has a relatively unique teaching value dimension. In the current context of information technology, making full use of the advantages of mobile app to improve the quality of Japanese teaching is an important proposition in the current context of teaching reform. Based on the actual use of mobile app, what kind of learning effect it has, whether it has repetition with the classroom learning content, how much help it can have and so on. This paper aims to investigate the use of Japanese learning software from the perspective of students, understand the changes brought by the popularization of information technology to students’ learning concepts and learning methods, and provide some reference for traditional Japanese teaching.
Methodological Features of Preparation for Solving Economic Problems of the Unified State Exam in Mathematics

The article focuses on the substantive features of the economic tasks of the unified state examination in mathematics and the methodological features of preparing schoolchildren to solve them. The necessity and methodological expediency of these tasks in the structure of the profile exam in mathematics is justified. Special attention is paid to the formalization process - the first and mandatory stage of solving mathematical problems of economic content, as well as solving problems in various ways with the possibility of the student choosing a rational method of solving. Typical errors of students when working with economic tasks of the unified state examination in mathematics arising at different stages of educational and cognitive activity are highlighted. Recommendations for mathematics teachers are presented, the observance of which helps to overcome these mistakes and difficulties of students. Disaggregated and methodically characterized methods of solving one of the economic problems of the unified state examination in mathematics can be extended to tasks of different types and various socio-economic content. An important place in the system of presented methods is the vector-coordinate method and the method of using the properties of functions - traditionally not sufficiently applied in the school course of mathematics, but fully accessible to high school pupils. The selected methodological features of preparation for solving economic problems can be useful for improving the system of preparation of schoolchildren for passing the unified state examination in mathematics, as well as for updating the content of vocational guidance work with high school pupils.
Multi-Streams Network for Action Recognition

In this paper, combined with depth information and rich texture information in RGB video sequences, a multi-stream behavior recognition algorithm is proposed. The algorithm uses DenseNet as the main network to obtain color texture information, optical flow information and depth information, and uses them as input to the information flow network. Then use LSTMs for feature fusion and behavior classification. Through experiments, the recognition rate on the UTD-MHAD data set of the public action recognition library is 93.88%, which is significantly better than similar algorithms.
Comparison between integer splitting cipher and traditional substitution ciphers, based on modular arithmetic

Abstract The integer splitting cipher is a special mathematical method that is proposed by the author and it can be considered as a generalization of modular arithmetic operation. In this cipher, each plaintext character is replaced on the base of another integer number with a sequence of k integers ( k -splitting level) by the usage of modular arithmetic, so this cipher can be classified under substitution ciphers that are used the modular arithmetic during the encryption process. In this article the main differences among splitting cipher and four traditional substitution ciphers, which are Caesar, Vigenère, Affine and Hill, will be listed and as a conclusion we can notice that the splitting method complicate the statistical and semantic restoration of the plaintext from the point view of an unauthorized user. Furthermore, the decryption process of the splitting cipher meets the goal of Chinese reminder theorem, which states that it can recover an integer from a unique set of its residues modulo, so a comparison is conducted to show the main differences between the integer splitting theories and the Chinese remainder theorem.
Personalization of User Interaction with Corporate Information Providing System Based on Analysis of User Preferences

The implementation of a cyber-physical approach to information provision control in a corporate information system brings a wide range of opportunities to improve the quality of the user experience both for employees and for visitors of an organization. A dynamic analysis of user preferences based on data collected via various sensors and history of user activity allows personalization of the provision of information services. In this paper, an approach to customize the user interaction with information services is considered through an example of a Corporate In-formation Providing System to a scientific organization. The architecture of the system, which is developed to manage intelligent display notifications for various groups of users, is discussed. Several types of users and their access rights are discussed, taking into account the specifics of information consumption in a scientific organization. An algorithm for analyzing user preferences in providing a corporate notification service is presented. Deploying the proposed approach allows to cover the content delivered according to user preferences, which will further improve the user experience and the service personalization.
Desarrollo de recursos didácticos con el uso de las Tecnologías de la información y comunicación disponibles en una plataforma virtual

Develop didactic resources with the use of ICT’S (Information Communication Tecnologies) to strengthen mathematics teaching with students of the Technological University of Huasteca Hidalguense it was the purpose of this study. The results of the diagnostic evaluation applied to a sample of 856 new students indicate that the performance is regular (6.1 points) in Arithmetic and insufficient (4.4) in Algebra (UTHH Academy of Mathematics, 2018); shows the following problems: failing rates, dropping out, mathematical fear calculations and problems solving related to their field of application, so that proposes the development of teaching resources available on an accessible platform. According to (CONOCER, 2016) the learning resources are instruments used in the educational context through their manipulation, observation, or reading, opportunities to learn are offered or with their use they intervene in the development of some teaching function; (Spiegel, 2006), It indicates they are necessary to promote the autonomy of students, future workers in the profile we teach. This work is based on the Methodology for the development of educational applications in multimedia environments (Bianchini, 1992) with the phases: a) Research and Analysis; b) Logical, functional and physical design; c) development; d) Implementation and production. As a result, educational resources were obtained (videos, infographics and digital evaluations) these support the teaching-learning process through organized information that complements, expands and details solution processes for teachers and students available on an institutional virtual platform.
Region‐based scalable self‐recovery for salient‐object images

Self-recovery is a tamper-detection and image recovery methods based on data hiding. It generates two types of data and embeds them into the original image: authentication data for tamper detection and reference data for image recovery. In this paper, a region-based scalable self-recovery (RSS) method is proposed for salient-object images. As the images consist of two main regions, the region of interest (ROI) and the region of non-interest (RONI), the proposed method is aimed at achieving higher reconstruction quality for the ROI. Moreover, tamper tolerability is improved by using scalable recovery. In the RSS method, separate reference data are generated for the ROI and RONI. Initially, two compressed bitstreams at different rates are generated using the embedded zero-block coding source encoder. Subsequently, each bitstream is divided into several parts, which are protected through various redundancy rates, using the Reed-Solomon channel encoder. The proposed method is tested on 10 000 salient-object images from the MSRA database. The results show that the RSS method, compared to related methods, improves reconstruction quality and tamper tolerability by approximately 30% and 15%, respectively.
Efficient Prevention Mechanism Against Spam Attacks for Social Networking Sites

In recent days, the development in the Internet takes a significant part in all types of activities that have been driven specialists to consider research frameworks to help the clients and applications in getting the directions by conveying nature of administration in systems. Some sorts of strategies are appropriate for giving security in correspondence to seized conditions, for example, portable registering, internet business, media transmission, and system administration. In such cases, service providers are focusing more on enriching the service access to end users. Since, there is an intermediate data theft or attack that was occurred during the broadcast process. Hence, an overview on different detection schemes are considered for successful determination and arrangement for attack recognition with various neural systems and some swarm computations has been proposed. The proposed strategies have been valuable for adequately identifying the system interruptions with the objective to give security to the Internet and to upgrade the nature of administration.
An Improved Algorithm of Mixed Cooperative Filter Recommendation Based on Project and User

The traditional collaborative filtering algorithm ignores the impact of the time factor when searching the nearest neighbor set, only from the user or item takes into account the similarity of the user or item unilaterally, and ignores the impact of user characteristics on the recommendation. Aiming to the above problems, the paper introduced the time forgotten function, resources viscosity function and the user feature vector, also improved the process of finding the user’s nearest neighbor set, which reflected the time effect, degree of user preferences and user characteristic. The traditional algorithm consumes too many resources to search the nearest neighbor set, as well as the reliability is poor. In the paper, a novel recommendation algorithm based on user clustering of item attributes is proposed.
Systems Engineering Methods and Tools

The automotive industry faces radical changes induced by new and potentially disruptive technologies emerging from digital transformation. At the same time, the increased demand for energy efficiency, CO2 neutrality, autonomous driving, connectivity, and artificial intelligence is leading to fundamentally new business models. These new business models also open up opportunities for new market entrants. Global competition is also further increasing pressure on the players. This chapter focuses on selected systems engineering methods and tools that support activities throughout the automotive development process. They provide the required functionality to author, modify and manage the results of systems engineering, and adhere to positive user experience fostering user acceptance and tool adoption in daily work. Through systematic use of systems engineering methods and tools, omissions and false assumptions caused by complexity can be identified early, so that their impact can be minimized. The constant change in artifacts is traced and managed through the systems engineering project lifecycle.