Reviewerly/exHarmony huggingface dataset:
Artificial Intelligence in Service Delivery Systems: A Systematic Literature Review

Artificial intelligence (AI) is transforming the 21st century service industries. With increased availability of virtual channels, new approaches to resource management are required for effective service delivery. A notable example is Amazon, which is reshaping itself with AI-based technologies, relying on robot service delivery systems, either through faster inventory checks or product delivery that reached unprecedented speed. This study provides an overview of the existing theory concerning the next generation of AI technologies that are revolutionizing the service delivery systems (SDS). To this end, we have systematically reviewed the literature to identify and synthesize the existing body of knowledge and update academics and practitioners regarding the latest AI developments on the SDS’s. This article argues that AI technologies are driving the service industry and have had promising results in reducing the service lead time while is being more cost-effective and error-free. Future studies should contribute to strengthen the theoretical production, while AI is being continuously reinforced with new empirical evidence.
Active Learning Stopping Strategies for Technology-Assisted Sensitivity Review

Active learning strategies are often deployed in technology-assisted review tasks, such as e-discovery and sensitivity review, to learn a classifier that can assist the reviewers with their task. In particular, an active learning strategy selects the documents that are expected to be the most useful for learning an effective classifier, so that these documents can be reviewed before the less useful ones. However, when reviewing for sensitivity, the order in which the documents are reviewed can impact on the reviewers' ability to perform the review. Therefore, when deploying active learning in technology-assisted sensitivity review, we want to know when a sufficiently effective classifier has been learned, such that the active learning can stop and the reviewing order of the documents can be selected by the reviewer instead of the classifier. In this work, we propose two active learning stopping strategies for technology-assisted sensitivity review. We evaluate the effectiveness of our proposed approaches in comparison with three state-of-the-art stopping strategies from the literature. We show that our best performing approach results in a significantly more effective sensitivity classifier (+6.6% F2) than the best performing stopping strategy from the literature (McNemar's test, p<0.05).
A Guide for Private Outlier Analysis

The increasing societal demand for data privacy has led researchers to develop methods to preserve privacy in data analysis. However, outlier analysis, a fundamental data analytics task with critical applications in medicine, finance, and national security, has only been analyzed for a few specialized cases of data privacy. This work is the first to provide a general framework for private outlier analysis, which is a two-step process. First, we show how to identify the relevant problem-specifications and then provide a practical solution that formally meets these specifications.
Online Technology in Knowledge Transfer

Abstract The transfer of knowledge entails a challenge for any research activity. It drives the promise and results towards implemented and replicable facts. The transfer is frequently crystallised in contracts and patents, but not solely: scientific communication, general publication, property rights, or public R&amp;D + innovation projects generated in the heat of research are also transference products and tools. This article lays out the benefits and weaknesses of these devices, underlining the participation of technology, especially online tech, when appropriate. We found that there are a variety of resources for transference, and that technology is only valid in some of them.
Resistance of the Montgomery kP Algorithm against Simple SCA: Theory and Practice

The Montgomery kP algorithm is reported in literature as resistant against simple SCA due to the fact that the processing of each key bit value of the scalar k is done using the same sequence of operations. We implemented the Montgomery kP algorithm using Lopez-Dahab projective coordinates for the NIST elliptic curve B-233. We instantiated the same VHDL code for wide range of clock frequencies for the same target FPGA and using the same compiler options. We measured electromagnetic traces of the kP executions using the same input data, i.e. scalar k and elliptic curve point P, and measurement setup. Our experiments clearly show that the success of simple electromagnetic analysis depends on the target frequency for which the design was implemented and at which it is executed significantly. In our experiments the used scalar k was successfully revealed via simple visual inspection of the trace for frequencies from 40 MHz to 100 MHz for standard compile options as well as from 50 MHz up to 240 MHz for performance optimizing compile options.
Risk Assessment on Software Development using Fishbone Analysis

Risks are intrinsic part of any software project. Most software projects face severe risk during the development process, but some finds minor risks. Vulnerabilities of these risks are threat to a quality of a software product. One of the main causes that may lead to failure of the project is negligence to perform risk assessment due to lack of knowledge about risk factors. The objective of this paper is to identify, classify and assess the risks that may cause negative impact to software project. We first analyze the root cause which may lead to terrible risk. Then we classified and prioritized the risks according to their nature. For this purpose, Fishbone Analysis is used. To find out risk factors probability and severity we have done questionnaire-based survey and interviews from various organizations. We collect data from different organization and perform Quantitative and Qualitative Risk Assessment. Through this assessment we find likelihood, severity and Risk Exposure (r). Finally, some future direction is discussed to control overall risk factors.
TAKING NOTE: Annotated Portfolio as a Method to Analyse the Experience of Design Research Practice

This paper builds on traditional portfolio annotation to aid the design researcher to capture and interpret practice-based research through design. Going beyond an object to understand the researcher's observing, designing and making experiences the method spans systems, processes and materials. The approach is discussed through a short literature review followed by a case study which uses traditional field research methods such as conversation and note-taking, as well as placing a particular emphasis on photography. Using a zoomed in photographic technique, this generates the 'portfolio of images' ready for annotation. Drawing on Kolb's experiential learning theory, accentuating reflection and abstraction in the research process, this adapted annotated portfolio method crosses three progressive stages; Reflective, Thematic and Holistic. This involves the capturing and organising of complex data in a visual way that broadens and deepens insights and facilitates peer communication. This provides an approach to enable researchers to bridge the gap between academic research and design practice. It is hoped that the method proposed in this paper not only aids the abstraction through to creation of new knowledge for the design researcher but could also have relevance to other disciplines.
Special issue on autonomous agents modelling other agents: Guest editorial

Much research in artificial intelligence is concerned with enabling autonomous agents to reason about various aspects of other agents (such as their beliefs, goals, plans, or decisions) and to utilise such reasoning for effective interaction. This special issue contains new technical contributions addressing open problems in autonomous agents modelling other agents, as well as research perspectives about current developments, challenges, and future directions.
Augmented reality for visualizing security data for cybernetic and cyberphysical systems

The paper discusses the use of virtual (VR) and augmented (AR) reality for visual analytics in information security. Paper answers two questions: “In which areas of information security visualization VR/AR can be useful?” and “What is the difference of the VR/AR from similar methods of visualization at the level of perception of information?”. The first answer is based on the investigation of information security areas and visualization models that can be used in VR/AR security visualization. The second answer is based on experiments that evaluate perception of visual components in VR.
Lighting Estimation via Differentiable Screen-Space Rendering

In this paper, we present a method that estimates the real-world lighting condition from a single RGB image of an indoor scene, with information of support plane provided by commercial Augmented Reality (AR) frameworks (e.g., ARCore, ARKit, etc.). First, a Deep Neural Network (DNN) is used to segment the foreground. We only focus on the foreground objects to reduce computation complexity. Then we introduce Differentiable Screen-Space Rendering (DSSR), a novel approach for estimating the normal and lighting condition jointly. We recover the most plausible lighting condition using spherical harmonics. Our approach provides plausible results and considerably enhances the visual realism in AR applications.
Single and Multibranch CNN-Bidirectional LSTM for IMDb Sentiment Analysis

Online users now frequently use the internet to voice opinions, ask for advice, or choose products and services based on the feedback of others. This provides a window into the way users feel about specific topics. The study of natural language processing, a sub-category of sentiment analysis, takes on this task by extracting meaning out of user text through observing the way in which words are grouped and used. Machine learning techniques have made significant advances which allow us to further explore mechanisms for interpreting such data. This research aims to use the internet movie database (IMDb) dataset and the Keras API to compare single and multibranch CNN-Bidirectional LSTMs of various kernel sizes (Maas et al. Learning word vectors for sentiment analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 142–150, 2011; Chollet et al. Keras. 2015. https://keras.io). The results show that while only time to train varies between single and multibranch models, their maximum accuracies are close in range. The highest accuracy model was the single branch with kernel size 9 with an accuracy of 89.54%. While slightly more accurate than the multibranch model with 88.94%, the time savings for the single branch is approximately of 64% (2 h and 20 min).
Centering Projection Methods for Wavelet Feasibility Problems

We revisit the feasibility approach to the construction of compactly supported smooth orthogonal wavelets on the line. We highlight its flexibility and illustrate how symmetry and cardinality properties are easily embedded in the design criteria. We solve the resulting wavelet feasibility problems using recently introduced centering methods, and we compare performance. Solutions admit real-valued compactly supported smooth orthogonal scaling functions and wavelets with near symmetry and near cardinality properties.
Hasse diagrams as a visual aid for linear models and analysis of variance

The expectation part of a linear model is often presented as a single equation with unknown parameters, and the reader is supposed to know that this is shorthand for a whole family of expectation models (for example, is there interaction or not?). It is helpful to list the whole family of models separately and then represent them on a Hasse diagram. This shows which models are sub-models of others, which helps the user to respect marginality when choosing the most parsimonious model to explain the data. Each row in an analysis-of-variance table corresponds to an edge in the Hasse diagram. In the scaled version of the Hasse diagram, the length of each edge is proportional to the appropriate mean square. This gives a visual display of the analysis of variance (ANOVA). For some people, this is easier to interpret than the standard analysis-of-variance table. Moreover, the scaled Hasse diagram makes clear the difficulties in model choice that can occur under non-orthogonality. The ideas are illustrated using some familiar families of models defined by crossed and nested factors, possibly including polynomial terms for quantitative factors, as well as some more recently introduced families of models for experiments in biodiversity.
The proposed information system design to improve new students

Abstract This study discusses the design of digital monitoring system to monitor prospective new students, the aim of which is to measure the quantity of prospective new students and as a media to help decision making in the future. The method used in this study is the Mobile Development Life Cycle. In building an application on cellular, there is an examination called process, the process in software development is called Software Development Life cycle which functions to check the whole phase in MDLC which includes the Inception, Design, Development, Stabilization, Deployment phases. Apart from the process, there is also a consideration when building a mobile application is checking the influence on the development of the cellphone itself. The results obtained from this study are prospective student data including contact numbers that can be followed up by the admissions committee.
Utilization of deep convolutional and handcrafted features for object tracking

This paper presents anonline learning method to enhance the robustness of the tracker for visual object tracking. Both handcrafted and non-handcrafted features are considered. These features include, histogram of oriented gradients (HOG), color names, saliency, and feature maps from convolutional neural network (CNN) layers. The objective is to model both the object and its surrounding background using a background-aware correlation filter (BACF). In this paper, different handcrafted and non-handcrafted features are considered with the BACF framework to independently compute the new locations of objects. After computing response maps produced using different features, these maps are combined, effectively. We have tested the performance of the proposed method on challenging image sequences, and it showed robustness for all of tested videos.
Mathematics Trails and Learning Barriers

Mathematics trails are a popular way to implement outdoor education outside mathematics classrooms. The MathCityMap project combines the idea of maths trail with the possibilities of modern mobile devices. During a study, in which ninth graders were asked to complete a maths trail with the MathCityMap application, we observed them having trouble with measuring and handling quantities. Analyses of the notes and calculations of the students revealed that over one third had a poor measurement sense. This learning barrier is a big issue, since these competencies are prerequisites to successfully complete a maths trail as well as for modelling and should be addressed prior to carrying out these activities.
Attention‐based end‐to‐end image defogging network

Aiming at the problem that the traditional prior information-based defogging algorithm fails in some special scenarios, an end-to-end convolutional defogging network based on attention mechanism is proposed. The network consists of two modules: parameter estimation and image restoration. First, multi-scale convolution is used to extract image feature information. Residual network and skip connection methods are used to improve the utilisation rate of shallow network feature information. Secondly, the channel domain attention is used to add weight to the feature image input from the previous network and select useful feature information. Finally, the atmospheric visibility model is combined to achieve image visibility restoration. The experimental results show that the proposed algorithm can effectively improve the visibility of the image and the restoration effect is natural. The objective evaluation index of the benchmark datasets also shows the effectiveness of the algorithm.
The Influence of Personality on Software Quality – A Systematic Literature Review

Context: Software quality is an important factor for reducing costs or increasing sales of a software developing company. Humans are involved in the whole software development process. This suggests that human factors have an influence on the result of the development process. Aim: We aimed to understand which distinct human factors or personality traits have an influence on which distinct software quality attributes. Method: For this purpose, we conducted a Systematic Literature Review following the guidelines of Kitchenham and Charters. Results: Existing studies show a link between human factors or personality types and software quality in general. There were only two studies which mentioned an influence on distinct software quality aspects: Extraversion influences decomposition, modularization, testability, functionality, re-usability and programming style. Personality in general influences memory consumption and runtime. Conclusions: We expected to find more such links but research conducted until now is far from being sufficient to provide a strong basis for making clear statements on the link between the personality of a software engineer and distinct software quality attributes.
Exploring Blink-Rate Behaviors for Cybersickness Detection in VR

Cybersickness continues to be one of the main barriers for mainstream adoption of Virtual Reality (VR). Despite the wealth of research available on this topic, it is still an unsolved problem. This paper explores the potential influence of cybersickness over the course of a VR experience on player Blink-Rate. This investigation was conducted on the same VR task with two diverging control schemes with a low or high risk of inducing cybersickness. A total of 34 participant data was collected from two separate playing sessions. Although no significant differences were observed, sick individuals showed to have a higher blink-rate frequency over the course of the VR experience while non-sick individuals presented a more stable blink-rate frequency.
Physics-based Concatenative Sound Synthesis of Photogrammetric models for Aural and Haptic Feedback in Virtual Environments

We present a novel physics-based concatenative sound synthesis (CSS) methodology for congruent interactions across physical, graphical, aural and haptic modalities in Virtual Environments. Navigation in aural and haptic corpora of annotated audio units is driven by user interactions with highly realistic photogrammetric based models in a game engine, where automated and interactive positional, physics and graphics data are supported. From a technical perspective, the current contribution expands existing CSS frameworks in avoiding mapping or mining the annotation data to real-time performance attributes, while guaranteeing degrees of novelty and variation for the same gesture.
Research on spoken English analysis model based on transfer learning and machine learning algorithms

From the current situation, it can be seen that there are certain deficiencies in the current models of spoken English analysis. In order to improve the English spoken analysis effect, this study builds an English spoken analysis model based on transfer learning and analyzes the performance of spok en English recognition. In order to make full use of the characteristics of speech feature modes to compensate for the shortcomings of single mode in speech recognition, this paper proposes a multimodal shared speech feature learning method, that is, multimodal shared speech feature learning method based on locality, sparsity, and identifiable typical correlation analysis. The method introduces locality, sparsity and discriminability, and the method effectively improves the English spoken recognition effect to a certain extent. In addition, this paper designs a controlled experiment to analyze the performance of the system model. The research results show that the algorithm has certain effects and can be applied to practice.
On Hamiltonian cycles in the FCC grid

The face centered cubic (FCC) grid is a space-filling grid, one of the alternatives to the traditional cubic one. We show that there are five Hamiltonian cycles (non-equivalent up to rotation and symmetry), connecting the faces of a voxel in the FCC grid. Each of the five cycles can be used to trace the boundary of a class of objects in the grid, constructed by iteratively attaching voxels so that each new voxel shares exactly one face with the set of already attached voxels.
Orthogonality Regularizations for End-to-End Speaker Verification

This paper seeks to explore orthogonal training in end-to-end speaker verification (SV) tasks.In various end-to-end speaker verification systems, cosine similarity has been used as the distance measurement for speaker embeddings.However, the effectiveness of cosine similarity is based on the assumption that the dimensions of the speaker embeddings are orthogonal.In our previous orthogonal training work, we have shown that in SV systems with cosine similarity backend, introducing orthogonality on the weights in speaker-discriminative deep neural networks can significantly improve the system performance.In this paper, we introduce two orthogonality regularizers to endto-end speaker verification systems.The first one is based on the Frobenius norm, and the second one utilizes restricted isometry property.Both regularization methods can be handily incorporated into end-to-end training.We build systems based on the state-of-the-art end-to-end models.Two network architectures, LSTM and TDNN, are used in order to investigate the effects of orthogonality regularization on different types of models.Systems are assessed on the Voxceleb corpus and significant gains are obtained with our new regularized orthogonal training.
Design of Virtual Reality Reach and Grasp Modes Factoring Upper Limb Ergonomics

Successfully performing tasks in virtual environments requires effective interactions between the user and the environment. Virtual reality technology is currently providing consumer-level devices that are being worldwide adopted, thus increasing the user install base. However, the technology presents ‘one-size-fits-all’ solutions that do not account the high variability of users who now have access to it. Virtual reality controllers can be difficult to operate and may fail to map real-world interactions. Efforts on improving human factors have led to custom-made user input devices factoring ergonomics for increasing task completion effectiveness. In this paper, we present a preliminary study on usability, engagement and task completion of two interactive modes that factor the upper limb ergonomics against a normal mode for reaching and grasping objects. Preliminary results from nine participants reaching objects on a table suggest that factoring upper limb ergonomics increase usability, engagement and task success rate.