Reviewerly/exHarmony huggingface dataset:
Design of adaptive weld quality monitoring for multiple‐conditioned robotic welding tasks

Abstract Multiple‐conditioned welding monitoring is a challenging issue in complex robotic welding tasks. In practice, the monitoring system has to be sensitive to different welding conditions (WCs) and weld quality changes. In this paper, a swing high temperature sensor system is used in order to obtain the temperature distribution curve under different WCs. A sigmoid feature extraction (SFE) method is proposed to obtain the geometric features of the temperature distribution curve, and a weld monitoring algorithm is proposed for multiple‐conditioned welding tasks using multi‐layer perceptron (MLP) classifier and Kalman filter‐based Gaussian probability density function (PDF) prediction for the probabilistic weld quality estimation. When there are unknown WCs, the proposed method uses an efficient incremental learning for the MLP and an online maximum likelihood estimation for the Gaussian models of the unknown WCs. The experimental results show that the proposed framework can accurately reflect the weld quality changing in both single‐WC and multiple‐WC tasks. In addition, the proposed adaptive updating methodology can achieve comparable performance with unknown WCs, as compared to the results with knowing all the WCs.
Hybrid node‐based tensor graph convolutional network for aspect‐category sentiment classification of microblog comments

Summary Aspect‐category sentiment classification of microblog comments aims to identify the sentiment polarity of different opinion aspects in microblog comments, which is meaningful for the analysis of public opinion. At present, most of aspect‐category sentiment classification methods need much annotation data, and regard comments as independent samples, without using of the relationship between comments. This article proposes an aspect‐category sentiment classification method based on tensor graph convolutional networks. First, the combination of a comment and its aspect category is regarded as a hybrid node, and the original representation of a hybrid node is encoded by the Bert model. Second, sentiment graph and semantic graph are constructed according to the semantic similarity and sentimental relevance between hybrid nodes, and they are stacked into a tensor. Then two convolution operations, including intra‐graph convolution and inter‐graph convolution, are performed for each layer of graph tensor. In this way, hybrid nodes can learn and merge the heterogeneous information of different graphs. Finally, under the supervision of few labeled comments, the sentiment classification can be completed based on the features of the hybrid nodes. Experimental results on two microblog datasets show that the proposed model can significantly improve the performance of sentiment classification compared with other baseline models.
“Life After Coal”: Renewable Energy Impacts on SME Conduct

In recent years, increasing resource consumption worldwide has alarmed experts. This problem contributes to the need for more ecological solutions and processes including the use of renewable energies, which reduces organizations’ environmental impact. To find ways to improve new green solutions and process integration, researchers need to study and measure these innovation effects, especially on small- and medium-sized enterprises (SMEs). However, evaluating renewable energy impacts on SMEs is not as easy as would first appear. This challenge can be addressed by developing an analysis model to mitigate the limitations of recent studies and, concurrently, deal with this complex subjective issue. In this article, the combined use of cognitive mapping and the decision-making trial and evaluation laboratory (DEMATEL) technique facilitated the generation of a complete, transparent, and informed model that can help managers make better decisions in this context. Specifically, grounded in intensive group work sessions with renewable energy specialists, the results address some of the limitations of more generic models and include a multicriteria analysis system that provides guidelines for renewable energy management practices, with the potential to improve SME performance. To enable a fuller interpretation of the decision problem in question, the most important indicators were identified and the cause-and-effect relationships between these criteria were analyzed. The findings were validated by the panel members and a member of the Portuguese Renewable Energy Association. The advantages and limitations of the proposed model are also discussed.
Fast adaptive phase unwrapping algorithm based on improved bucket sorting

As a key step in active structured light reconstruction technology, the phase unwrapping algorithm has a direct impact on improving measurement accuracy and efficiency. A fast adaptive phase unwrapping algorithm based on improved bucket sorting (PUIBS) is proposed, which effectively improves the relative performance of the measurement system. First, the unreliability function of a pixel is defined according to the second-order difference, and adjacent pixels are combined into a pixel group. Then adaptively construct bucket arrays with different unreliability value ranges to meet the error transfer requirements, and put the pixel groups into bucket arrays corresponding to the unreliability value ranges in turn. Finally, according to the unreliability value of the bucket array, the phase unwrapping is performed in order from small to large. A verification experiment was carried out in a single-shot structured light system. The results show that the proposed algorithm is efficient, and successfully compared with the existing phase unwrapping algorithm. At the same time, it has a good effect on phase images with phase jump and phase occlusion.
Trust but Verify: Crowdsourced Mobile Network Measurements and Statistical Validity Measures

Network operators, regulators, and big data companies use crowdsourced measurements to study the performance of mobile networks on a large scale. Such a type of measurement is defined as the collection and processing of data measured by the crowd, here the crowd of mobile subscribers. Crowdsourced network measurements make it relatively easy and inexpensive to obtain large amounts of network data that also reflect the quality actually received by the end user. However, this measurement method also involves some uncertainties, since, for example, it is not possible to precisely control when, where and with which devices measurements are taken. Thus, there is a tradeoff between the reliability of the individual measurement and the scope of the measurements. Therefore, how data of this type is analyzed is particularly important in order to obtain valid results. To address this issue, our paper defines concepts and guidelines for analyzing the validity of crowdsourced mobile network measurements. In particular, we address precision, for example the number of measurements needed to make valid statements, and also representativeness, for example the spatial and temporal distribution of the data. In addition to the formal definition of these two aspects, we illustrate the issue and possible evaluation approaches with the help of an extensive example data set. This data set consists of more than 11.7M crowdsourced mobile measurements from all over France, measured by a commercial mobile data provider. In the end, we provide an evaluation guideline and two possible use cases.
Usability Testing on Tractor’s HMI: A Study Protocol

The success of a human-machine interface (HMI) heavily depends on its usability. An highly usable interface allows the user to more easily achieve his/her goals and in general have a better User eXperience (UX). In work environments, a structured and ready-to-use usability testing protocol can encourage companies to carry out this type of study and focus on UX from the early design phases. Even though numerous methods to test usability exist, industrial companies still have great difficulties to apply them and choose the best ones for the specific purposes. They should be guided into the analysis by a universal step-by-step approach, which helps also not experienced designers selecting the most reliable and useful methods among the available ones. In this direction, the paper proposes a structured protocol to focus on UX and guide companies in testing setup, execution and debriefing in an easy and quick way. Checklists are defined to help during user testing and assure its success. As a consequence, end users can be easily involved to give an added value in design problems identification. The novelty of this paper is the definition of a ready-to-use study protocol that can also be used by non-usability experts, in order to make them familiar with UX analysis and extend this practice also in industrial HMI design. As validation, the proposed protocol was applied to the design of interfaces for agricultural tractors during two different stages of the HMI redesign process.
Designing the Security Enhancement Features in the Future Headphone Experience

In recent years, headphones are becoming a more and more essential personal electronic device to carry cutting-edge consumer technology into everyday life. Hence, it’s an urgent need to enhance security features on headphones. This study analyzed different techniques, technologies and applications to enhance security features in mainstream personal electronic devices, introducing a new taxonomy (Loss-Ownership-Data, LOD) of security features which is based on three categories: Loss Protection (LP), Ownership Protection (OP), Data Protection (DP). And it proposed ten concepts to enhance security features on headphones using this framework. An online user survey was conducted and the results and findings were discussed. This paper unified the general security design techniques into a framework which could be used to analyze and enhance security features in future personal electronic devices and it also proposed some possible solutions to enhance security features in headphone experience. Thirdly, it reported the user attitude, their acceptance, and expectation which can be used as a reference to related design practice.
Performance Analysis: AI-based VIST Audio Player by Microsoft Speech API

Speech recognition has gained much attention from researchers for almost last two decades. Isolated words, connected words, and continuous speech are the main focused areas of speech recognition. Researchers have adopted many techniques to solve speech recognition challenges under the umbrella of Artificial Intelligence (AI), Pattern Recognition and Acoustic Phonetic approaches. Variation in pronunciation of words, individual accents, unwanted ambient noise, speech context, and quality of input devices are some of these challenges in speech recognition. Many Application Programming Interface (API)s are developed to overcome the issue of accuracy in a speech-to-text conversion such as Microsoft Speech API and Google Speech API. In this paper, the performance of Microsoft Speech API is analyzed against other Speech APIs mentioned in the literature on the special dataset (without background noise) prepared. A Voice Interactive Speech to Text (VIST) audio player was developed for the analysis of Microsoft Speech API. VIST audio player creates runtime subtitles of the audio files running on it; the player is responsible for speech to text conversion in real-time. Microsoft Speech API was incorporated in the application to validate and make the performance of API measurable. The experiments proved the Microsoft Speech API more accurate with respect to other APIs in the context of the prepared dataset for the VIST audio player. The accuracy rate according to the precision-recall is 96% for Microsoft Speech API, which is better than previous ones as mentioned in the literature.
A Scalable Cloud-based Architecture to Deploy JupyterHub for Computational Social Science Research

With the increasing popularity of computational approaches to conduct social science research, building a scalable and efficient computing platform has become a topic of interest for academia to empower research labs and institutes to analyze large-scale data. While social science researchers have been very excited about the advancement of emerging technologies in big data, deep learning, computer vision, network analysis, etc., they are also constrained by the available computing resources to analyze data. This paper describes a scalable solution to deploy JupyterHub for computational social science research on the cloud. We use a reference architecture on AWS to walk through the design principles and details. Our architecture has helped facilitate several collaborations between Facebook and academia. The case study (Facebook Open Research and Transparency platform) shows that our architecture, using technologies like containerization and serverless computing, can support thousands of users to analyze web-scale datasets.
Probabilistic RF-Assisted Camera Wake-Up through Self-Supervised Gaussian Process Regression

Research on wireless sensors represents a continuously evolving technological domain thanks to their high flexibility and scalability, fast and economical deployment, pervasiveness in industrial, civil and domestic contexts. However, the maintenance costs and the sensors reliability are strongly affected by the battery lifetime, which may limit their use. In this paper we consider a wireless smart camera, equipped with a low-energy radio receiver, and used to visually detect a moving radio-emitting target. To preserve the camera lifetime without sacrificing the detection capabilities, we design a probabilistic energy-aware controller to switch on/off the camera. The radio signal strength is used to predict the target detectability, via self-supervised Gaussian Process Regression combined with Recursive Bayesian Estimation. The automatic training process minimizes the human intervention, while the controller guarantees high detection accuracy and low energy consumption, as numerical and experimental results show.
Recognition of Isolated English Words of E-Lecture Video Using Convolutional Neural Network

Speech Recognition has been gaining a lot of importance, as there is tremendous growth in its applications such as building subtitles for e-lecture videos, transcription of recorded speech for people with physical disabilities. Speech recognition is a complex task because it involves various strong accents, run-over words, varying rates of speech and background noise. Previously used Speech Recognition systems were speaker dependent and was challenging in constructing acoustic models, which had non-linear boundaries. The paper presents recognition of isolated English words in three steps, pre-processing, segmentation and extraction of Mel Frequency Cepstrum Coefficient (MFCC) of an audio signal from a video. Further, training and classification of audio signals is done using Convolutional Neural Network (CNN). Various types of input features, which play a vital role in the recognition process is described and we infer that, the representation of MFCC feature with varying length in analysis window and window step produces different results. The feature set with higher Winlen and Winstep, yields better result i.e., 97%.
A clustering approach to anonymize locations during dataset de-identification

Companies increasingly rely on massive amounts of data for strategic decision making purposes. In order to optimize business intelligence, companies often try to enrich their models with datasets acquired from third parties. Datasets containing sensitive attributes must be anonymized before release. For large datasets containing microdata, an often applied anonymization technique is data generalization with the goal of achieving privacy metrics such as k-anonymity. Location is an often recurring yet strategic attribute in many use cases. Multiple strategies can be employed to obfuscate precise coordinates. For example, the most significant digits can be dropped or their value can be replaced by a ZIP code. While these methods might be useful in some applications, these approaches often result in too much information loss, undermining strategic decision making. This paper proposes a novel approach to anonymize location by means of clustering. Its feasibility is evaluated and compared to traditional techniques.
Changing Citizens’ Attitude Towards Novel Mobility Measures with a Game: Procedure and Game Concept

Climate change is one of today’s big topics, so climate-friendly mobility habits are needed. The digital transformation, another mega-trend, can support changing these habits. One step in the change process is the establishment of a positive attitude towards novel mobility measures such as the reduction of parking spaces. In this paper, we present an unobtrusive way to change citizens’ attitude towards new mobility measures: We have developed a game, called ‘MiniLautern’, that introduces novel mobility measures to the players and lets them explore their effects on a smart city district. The target group are citizens. We present how we developed this game based on a Design Sprint, provide background information about the design decisions, and show examples from our solution.
A Deep Learning Based Multi-modal Approach for Images and Texts Recommendation

Nowadays, the Internet is filled with massive and unstructured image and text resources, which makes users difficulties in obtaining target information. Recommendation system can support user information seeking, providing useful information to better meet their interests. However, different information modalities, such as image and text, have their own characteristics, which can be relevantly used together to provide complementary information. Therefore, we propose a deep learning based multi-modal approach for images and texts recommendation. To conduct the study, first, we develop two intra-modal representations. Second, Multimodal DBN (Deep Belief Network) is used to learn inter-modal representation of image and text. Third, we combine inter-modal representation with the two intra-representations respectively. Then the higher-order representations are obtained. Finally, the model is built in recommendation system to recommend image-text pairs that might be of interest to the user based on his or her history. Wikipedia data set is selected to verify the effectiveness of our proposed approach. This study can contribute to develop a multi-modal recommendation system with higher accuracy and more in line with users' preferences.
Usability and User Experience of a Chat Application with Integrated Educational Chatbot Functionalities

In recent years chatbots have found their use in education, especially in higher education (HE), but are they really serving a purpose and helping both students and faculty staff? Since bad experiences can discourage potential users, chatbots have to be carefully designed to be useful and to offer the best possible experience to its users. To explore this premise, in this paper we will tackle one aspect of chatbot design through evaluation of usability and user experience of a chat application Differ, which has integrated chatbot functionalities. For the duration of one semester at the Faculty of Organization and Informatics' students used Differ on several courses. Differ was introduced as a tool to facilitate communication related to courses and students' obligations, between students and educators by initiating communication through a Differ's chatbot called Buddy Bot. Differ was also seen as a tool to enhance students' connections and social life through creation of private student communities. After the end of semester, students' opinions about Differ were evaluated through means of UEQ and SUS questionnaires which are frequently used for user experience and usability evaluation. Results of the study showed that Differ's perceived usability and user experience are slightly above average, but with a lot of room for improvements, especially in the context of novelty, stimulation and chatbot functionalities.
Development of a Holistic Web-Based Interface Assistance System to Support the Intralingual Translation Process

At the political and social level, easy-to-read texts are regarded as an important tool for creating barrier-free communication. In some countries, the right to comprehensible information is enshrined in law, which means that more and more standard-language texts have to be translated into easy-to-read texts. To support this intralingual translation process, previous approaches address the automation of specific sub-processes and thus focus on specific actors in the translation process. Interaction between those involved in the translation process is mostly decentralized and takes place using various applications. There is a lack of a holistic system that connects all actors involved in the process and allows interactions between the participants. This paper focuses on the implementation of a web-based assistance system and suitable interfaces that allow the representation of sub-processes of the intralingual translation process, taking into account the actors involved, and enable interactions between the participants. In order to store the data generated during the translation process in a structured and clear manner, various data management approaches could be used in this context and evaluated by means of functional tests. The results indicate that the interfaces between the actors have been optimized by the interaction components integrated in the system. Based on this, user-oriented further developments of web-based assistance systems can be carried out in the context of the intralingual translation process, whereby the system is to be used in real application contexts in the future in order to elicit the need for further assistance functions.
Privacy and Security Issues in Cloud Computing: A Survey Paper

Cloud computing is a new paradigm. In recent years, cloud computing has been a popular model for hosting and providing services, as it has gained great popularity in providing multiple services such as storage and cloud hosting. Moreover, the cloud provides a powerful computing platform that enables many individuals and organizations to perform many tasks such as: use of online storage space, approval on business applications, developing custom computer software, and creating a virtual network environment. In previous years, the number of people using cloud services increased dramatically and a lot of data was stored in cloud computing environments. Meanwhile, data breaches of cloud services are also increasing every year due to hackers who are always trying to exploit security holes in the cloud architecture, so the security problems in these systems are horrific because they store a lot of private and confidential data. In this paper, the authors review security and privacy issues, bottlenecks associated with cloud computing, and the specific security issues in cloud computing layers and vulnerabilities, and how to avoid and control them. This paper helps the reader understand the problems related to cloud computing.
Retrieval from software libraries for bug localization

This retrospective on our 2011 MSR publication starts with the research milieu that led to the work reported in our paper. We brie y review the competing ideas of a decade ago that could be applied to solving the problem of identifying the les in a software library related to a query. We were especially interested in nding out if the more complex text retrieval methods of that time would be e ective in the software context. A surprising conclusion of our paper was that the reality was exactly the opposite: the more traditional simpler methods outperformed the complex methods. In addition to this surprising result, our paper was also the rst to report what was considered at that time a large-scale quantitative evaluation of the IR-based approaches to automatic bug localization. Over the years, such quantitative evaluations have become the norm. We believe that these contributions were largely responsible for the popularity of this paper in the research literature.