Reviewerly/exHarmony huggingface dataset:
Chatbots Assisting German Business Management Applications

In most companies Business-Management software has become omnipresent in recent years. These systems have been introduced to streamline productivity and handle data in a more centralized fashion. Yet, these systems are often modelled by complex processes which makes navigating through them a challenging task. In this work, we introduce a text-based chatbot to a mid-sized Austrian company to facilitate the interaction with these software solutions and thus support them in managing their customer relationships. To learn more about positive as well as negative effects the chatbot has on the employees, we conduct a technical as well as an empirical evaluation. We, for instance, hypothesize that younger staff, who grew up with computers and smart phones, are more open to the conversational system than older employees. We implement a customized solution which integrates seamlessly into the company's system. The implementation process is informed by related work on Customer-Relationship-Management software, structures of a conversation as well as the typical architecture of a chatbot. In the technical evaluation, objective metrics such as average response time were measured. For the empirical evaluation, a questionnaire was sent out to 15 participating employees asking about subjective metrics such as task ease or user experience. In sum, employees were satisfied with Usage, Interaction Pace and System Response.
Hybrid Splitting Criteria

In the previous chapters, various types of splitting criteria were proposed. Each of the presented criteria is constructed using one specific impurity measure (or, more precisely, the corresponding split measure function). Therefore we will refer to such criteria as ‘single’ splitting criteria. The experiments conducted in Chap. 5 demonstrate that various single splitting criteria have their own advantages and drawbacks. Based on this observation a new kind of splitting criteria can be proposed, which combine together two different single criteria in a heuristic manner. We refer to them as hybrid splitting criteria. In this chapter, we discuss premises which demonstrate that such an approach may lead to satisfactory results.
Experiences in Context Aware-Services

Context-aware services are one of the most typical services in ambient intelligent environments. Although there have been many academic projects on context-aware services, most of them were evaluated in laboratory-level or small-scale experiments. Context-aware services in real spaces suffer from a variety of problems. This paper addresses such problems. It describes experiences learned from our experiments on context-aware services in real public spaces, e.g., museums, and proposes solutions to them. Most of the problems presented in this paper are still common to other context-aware services.
A Retrospective on Path ORAM

Path oblivious RAM (ORAM) is an ORAM protocol that simultaneously enjoys simplicity and efficiency. As a result, it holds promise to provide cryptographic-grade and practical access pattern protection in multiple application domains, including but not limited to secure hardware. In this paper, we review Path ORAM's key ideas and contribution, summarize its impact and subsequent works, and discuss future directions.
Implementation of virtual digital forensic class and laboratory for training, education and investigation

In this paper, we present theoretical development and practical implementation of digital forensic laboratory computing infrastructure in a private cloud environment. The idea is to develop a scalable environment of forensic workstations and management facilities to support both the learning process and practical work in digital forensic education. Experience gathered in this process will be later used for setting up a production digital forensic laboratory in a cloud environment. To gauge student perception and experience in online learning and using virtual digital forensic classes and laboratory students filled the Student Perception Survey. The results are analyzed, and suggestions and conclusion used to improve future use.
Extending sparse text with induced domain-specific lexicons and embeddings: A case study on predicting donations

This paper addresses the problem of expanding sparse textual content to increase the accuracy of data-driven prediction tasks. We evaluate the use of word embeddings and lexicons within the context of a donation prediction task, where we classify potential donors as either likely or unlikely to donate. We perform several comparative experiments and analyses, and show that our methods to automatically enhance sparse textual data significantly improve the predictive performance on this task.
Experimental Verification of Visual Feedback Control for a Pendubot with Varying Moment of Inertia

This paper considers a visual feedback control problem incorporating the gain-scheduling concept. Specifically, we focus on the stabilization of a pendubot with varying moment of inertia under image distortion. In our previous work, we proposed the controller design method for the above situation. In this paper, the effectiveness of our design method is verified through experiments.
Coordinated Control of Multiple Aerial Manipulators

This chapter is focused on the control of systems composed by multiple Aerial Robotic Manipulators (i.e., aerial vehicles equipped with grippers and/or robotic arms). In detail, a hierarchical framework for the control of coordinated motion is presented, based on a three-layer architecture. The developed approach is experimentally tested on a system composed by two multi-rotor aerial platforms, each equipped with a 6-Degrees of Freedom manipulator.
Learning Similar Tasks Based On PPO By Transferring Trajectory

Deep reinforcement learning (DRL) has many achievements in playing games and robotic control. However, transfer learning, a normal way of training a deep neural network for computer vision tasks, cannot be directly applied to DRL. This is an issue in the field of DRL. Many methods are proposed to address this problem. In this paper, a new method based on the proximal policy optimization (PPO) algorithm for learning similar tasks is proposed. The method uses a function to transform the trajectory from the previous task to the current task, both of those are similar. As shown in the result, our method can bring a more stable performance to our model at the early stage of training with compare to the model trained by using PPO without our method.
Delivering the Next-Generation Research Repository

Academic libraries are looking for ways to grow their involvement in and scale-up their support for research activities. The successful transition depends to a large extent on the library's ability to systematically manage data, break down information silos and unify workflows across the library, research office and researchers. Data repositories are at the heart of this challenge, yet often institutional repositories are not built to address the needs of modern research data management due to inability to store all research assets, lack of consistent data models, and insufficient workflows. This chapter will present a new approach to research data management that ensures visibility of research output and data, data coherency, and compliance with open access standards. The authors will discuss a ‘Next-Generation Research Repository' that spans multiple data management activities, including automated data capture, metadata enrichment, dissemination, compliance-related workflows, automated publication to scholarly profiles, as well as open integration with the research ecosystem.
The “Tokenization” of the eParticipation in Public Governance: An Opportunity to Hack Democracy

Currently Distributed Ledger Technologies-DLTs, and especially the Blockchain technology, are an excellent opportunity for public institutions to transform the channels of citizen participation and reinvigorate democratic processes. These technologies permit the simplification of processes and make it possible to safely and securely manage the data stored in its records. This guarantees the transmission and public transparency of information, and thus leads to the development of a new citizen governance model by using technology such as a BaaS (Blockchain as a Service) platform. G-Cloud solutions would facilitate a faster deployment in the cities and provide scalability to foster the creation of Smart Citizens within the philosophy of Open Government. The development of an eParticipation model that can configure a tokenizable system of the actions and processes that citizens currently exercise in democratic environments is an opportunity to guarantee greater participation and thus manage more effective local democratic spaces. Therefore, a Blockchain solution in eDemocracy platforms is an exciting new opportunity to claim a new pattern of management amongst the agents that participate in the public sphere.
Self-weighted Multi-view Subspace Clustering With Low Rank Tensor Constraint

Manufacturing process is making a difference in the Product-Service Systems (PSS). In the manufacturing process, the real-time high-dimensional data will be generated by the production line. In the field of high-dimensional data analysis, a very important analysis method is high-dimensional data clustering, and subspace clustering is one of the effective methods of high-dimensional data clustering. Clustering is an important topic in data mining. In real world, the data represented with multi view information makes the clustering algorithms more difficult. In this paper, we explore an improved multi-view clustering algorithm. Unlike the conventional multi-view subspace clustering methods that usually average the weight of views, the proposed algorithm simultaneously takes into account the expression and weight of each view. Specifically, every view is re-represented with the complementary information from multiple views by low rank tensor, and the views are fused by exploring a Laplacian rank constrained graph. With the complementary information from multiple views and the different weights of the views, the proposed algorithm is improved. Experimental results on various real-world datasets demonstrate the effectiveness of our research.
Correctness, Explanation and Intention

There appear to be two fundamentally different notions of program correctness that emanate from two different notions of program: the mathematical correctness of abstract programs and the empirical correctness of their implemented physical manifestations [2, 16, 17]. In the abstract case, a program is taken to be correct when it meets its specification. This is a mathematical affair with all the precision and clarity that follows. But physical correctness raises some concerns and puzzles that have their origins in Putnam's notion of physical computation [15]. Moreover, these concerns would appear to effect the mathematical case. Comparing the two cases will draw out some underling philosophical issues in the traditional approaches to correctness. In particular, we examine the different concepts of explanation that accompany the different notions of correctness, and expose the underlying role of agency in both.
Function-Dependent Commitments from Homomorphic Authenticators

In cloud computing, delegated computing raises the security issue of guaranteeing data authenticity during a remote computation. In this context, the recently introduced function-dependent commitments (FDCs) are the only approach providing both fast correctness verification, information-theoretic input-output privacy, and strong unforgeability. Homomorphic authenticators—the established approach to this problem—do not provide information-theoretic privacy and always reveal the computation's result upon verification, thus violating output privacy. Since many homomorphic authenticator schemes already exist, we investigate the relation between them and FDCs to clarify how existing schemes can be supplemented with information-theoretic output privacy. Specifically, we present a generic transformation turning any structure-preserving homomorphic authenticator scheme into an FDC scheme. This facilitates the design of multi-party computation schemes with full information-theoretic privacy. We also introduce a new structure-preserving, linearly homomorphic authenticator scheme suitable for our transformation. It is the first both context hiding and structure-preserving homomorphic authenticator scheme. Our scheme is also the first structure-preserving homomorphic authenticator scheme to achieve efficient verification.
Randomized Reference Classifier with Gaussian Distribution and Soft Confusion Matrix Applied to the Improving Weak Classifiers

In this paper, an issue of building the RRC model using probability distributions other than beta distribution is addressed. More precisely, in this paper, we propose to build the RRR model using the truncated normal distribution. Heuristic procedures for expected value and the variance of the truncated-normal distribution are also proposed. The proposed approach is tested using SCM-based model for testing the consequences of applying the truncated normal distribution in the RRC model. The experimental evaluation is performed using four different base classifiers and seven quality measures. The results showed that the proposed approach is comparable to the RRC model built using beta distribution. What is more, for some base classifiers, the truncated-normal-based SCM algorithm turned out to be better at discovering objects coming from minority classes.
Initial Point Prediction Based Parametric Active Contour Model for Left Ventricle Segmentation of CMRI Images

This research work proposes an automatic segmentation approach of the left ventricle (LV) from cardiac magnetic resonance image (CMRI). Although the parametric active contour model (PACM) can be used to segment the LV of CMRI image, it requires the initial contour. This is a manual and time-consuming approach and the accuracy is subjective. The main contribution of this proposal is to introduce an artificial neural network based regression model to predict the initial contour to detect the LV area of a CMRI image. With the automatic predicted contour points by the proposed method, a number of CMRI images are segmented by PACM to get the LV area. The results demonstrate that the proposed automatic segmentation procedure can segment the LV area with negligible deviation with less required time. In addition, this research work also customizes the finely fitted parametric values of the PACM for the CMRI images.
Efficient Parallel Computing of Graph Edit Distance

With the prevalence of graph data, graph edit distance (GED), a well-known measure of similarity between two graphs, has been widely used in many real applications, such as graph classification and clustering, similar object detection, and biology network analysis. Despite its usefulness and popularity, GED is computationally costly, because it is NP-hard. Currently, most existing solutions focus on computing GED in a serial manner and little attention has been paid for parallel computing. In this paper, we propose a novel efficient parallel algorithm for computing GED. Our algorithm is based on the state-of-the-art GED algorithm AStar <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -LSa, and is called PGED. The main idea of PGED is to allocate the heavy workload of searching the optimal vertex mapping between two graphs, which is the most time consuming step, to multiple threads based on an effective allocation strategy, resulting in high efficiency of GED computation. We have evaluated PGED on two real datasets, and the experimental results show that by using multiple threads, PGED is more efficient than AStar <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> -LSa. In addition, by carefully tuning the parameters, the performance of PGED can be further improved.
RAG: Facial Attribute Editing by Learning Residual Attributes

Facial attribute editing aims to modify face images in the desired manner, such as changing hair color, gender, and age, adding or removing eyeglasses, and so on.Recent researches on this topic largely leverage the adversarial loss so that the generated faces are not only realistic but also well correspond to the target attributes.In this paper, we propose Residual Attribute Generative Adversarial Network (RAG), a novel model to achieve unpaired editing for multiple facial attributes.Instead of directly learning the target attributes, we propose to learn the residual attributes, a more intuitive and understandable representation to convert the original task as a problem of arithmetic addition or subtraction for different attributes.Furthermore, we propose the identity preservation loss, which proves to facilitate convergence and provide better results.At last, we leverage effective visual attention to localize the related regions and preserve the unrelated content during transformation.The extensive experiments on two facial attribute datasets demonstrate the superiority of our approach to generate realistic and high-quality faces for multiple attributes.Visualization of the residual image, which is defined as the difference between the original image and the generated result, better explains which regions RAG focuses on when editing different attributes.
Graph-based Growing self-organizing map for Single Document Summarization (GGSDS)

The huge collection of text available represents a remarkable challenge to process and exploit it in many fields. Therefore, there is a multitude of articles that are being proposed to summarize text automatically. More accurate and higher performing models are still required for text summarization. It is one of the most common tasks of text mining. In this paper, a novel Graph-based Growing self-organizing map for Single Document Summarization (GGSDS). GGSDS is an unsupervised extractive summarization approach composed mainly of five tasks: text pre-processing, document representation, sub-topics identification, sentence ranking and finally summary generation. The entire text of a document is represented in GGSDS by one accumulative graph. The choice of this representation model supports the extraction of all required features as to achieve the most suitable summary of text, especially the shared phrases between sentences. The impact of the sub-topics on the accuracy and comprehensiveness of the generated summary is taken into account in the design of GGSDS model. For this purpose, G-GSOM is employed to cluster sentences into clusters to represent the sub-topics of text. Next, sentences are scored using TextRank algorithm under the assumption that when a sentence has more relation with others, it is considered as more important and more representative to a sub-topic. Finally, the sentences with the highest score in each cluster are selected for generating the summary. Experimental results showed that GGSDS generated summaries of single documents with more than 80% accuracy of two datasets. Furthermore, these summaries covered most of the sub-topics of the documents.
Leveraging Usage Data of Software Architecture Artefacts

A number of existing fields (such as computational wear, social navigation, collaborative filtering, social filtering, and wear-based filtering) have shown that usage data (comprising interaction and annotation data) can be leveraged to provide assistance for sense-making of materials, for finding and filtering information, and for exploring an information space. We believe providing such assistance in Software Architecture Knowledge Management (SAKM) can enhance two of the five knowledge management activities of SAKM, namely, Architecture Knowledge (AK) sharing and AK application/using. To this end, we propose to include usage data capture and visualization as essential features of SAKM tools. We discuss key requirements and challenges for SAKM tools leveraging such usage data. We describe KaitaroCap, an exemplar tool that captures and provides visualization of usage data, in particular the usage data of software architecture documents.
Structured problem exploration approach for the pre-concept stage of system development

At the pre-concept stage, an architect explores the problem space together with various stakeholders, including engineers, marketing, and future users of the system. This requires competencies and tactics to communicate efficiently while maintaining a system-level scope. We present a practical Structured Problem Exploration approach to assist architects in exploring the problem space. The approach aims to ensure that involved parties are talking on the same level. The approach includes a checklist to elicit information relevant to the system-level scope and activities to warrant convergence of the results. This paper starts with an overview of soft competencies, skills, and methods. After outlining the approach, we report our experiences with applying it to investigate energy improvement opportunities of a connected system. This paper aims to inform discussions on the importance of competencies and methods for the pre-concept exploration stage when creating SoS elements.
Global research studies on “Electronic Resources in Libraries”: A scientometric assessment during 1994-2017

Purpose - This paper seeks to provide a quantitative and qualitative description of global research output on “electronic resources in libraries”, with particular reference to electronic books, electronic journals and electronic thesis published during 1994-17.Design/Methodology/Approach - The data for the study (2086 publications) was sourced from Scopus database covering period 1994-2017. The research output in the subject from Scopus database was analyzed on a series of bibliometric measures like average annual growth, citations per paper, international collaborative papers, relative citation index, activity index, top productive countries, organizations, authors, journals, and highly cited papers.Findings - ‘E-resources in libraries’ is fast emerging as a subject of research within the domain of library and information science. The global research in the subject across top 15 most productive countries in the world is highly skewed. The USA, the top ranking country leads with 46.06% global publications share whereas the rest of 14 top productive countries – including India as the second ranking most productive country (8.10% share) – are distant cousins accounting for their publication share in single digits. The top 25 most productive journals with 15 to 126 papers each accounted for 58.42% (867) of total 1484 papers reported in journals. Only 17 paper (0.81% of 2086 output)) received high citations from 62 to 166 citations per paper during 1994-17. These 17 highly cited papers accounted for 1502 citations, with an average of 88.35 citations per paper. Among the electronic resources, the major emphasis was on electronic books, followed by electronic journals, electronic thesis & dissertations, audio & videos resources and electronic reports during 1994-17. The major areas of research focus as identified from keywords are: overall management of electronic resources, electronic resources & its various types, types of libraries, acquisition, development, management and preservation, information storage & retrieval, information processing, information services, user education & user studies, copyright and licensing.Originality/value – This is the first study of its kind that provides a quantitative and qualitative description of research studies on ‘e-resources in libraries’.