Reviewerly/exHarmony huggingface dataset:
A critical review of cost-benefit analysis for business software investments

Purpose Cost-benefit (C/B) analysis helps to determine the economic feasibility of business software investments. Research literature and published practices do not recognize substantial software maintenance costs in C/B analysis. Current analyses emphasize the benefits of an initial investment but do not consider the recurring benefits of each enhancement during the software lifecycle. Such analyses could lead to incorrect investment decisions and lost business opportunities. This article aims to review current research on software lifecycle costs and develop a theoretically sound C/B analysis. Design/methodology/approach This article reviews current C/B analyses and discusses their shortcomings in treating the significant recurring maintenance costs. It analyzes the findings of various studies on software maintenance and synthesizes these findings to identify the nature of various maintenance costs and their benefits. Based on the synthesis, it theorizes various cost and benefit elements for inclusion in a revised C/B analysis. Findings This article identifies each recurring maintenance cost relevant to C/B analysis. It also identifies recurring benefits from each enhancement that hitherto have been omitted. Finally, this article discusses how these costs and benefits should be treated in the revised C/Bs analysis. Research limitations/implications This is a conceptual paper proposing a new C/B analysis and requires an empirical validation. Practical implications This article provides a revision of the C/B analysis that is long overdue. It will help to justify a software investment correctly, rank software projects that compete for limited funds and help create a sound software project portfolio. Since 20% of software products may incur 80% of software investment, this analysis will help to make correct software investments and avoid lost business opportunities. This article also describes a practical method to use the revised C/B analysis. Originality/value This article provides a revision of the C/B analysis that is long overdue. It will help to justify a software investment correctly, rank software projects that compete for limited funds and help create a sound software project portfolio. Since 20% of software products may incur 80% of software investment, this analysis will help to make correct software investments and avoid lost business opportunities. This article also describes a practical method to use the revised C/B analysis.
Research on the construction of event corpus with document-level causal relations for social security

Event corpora are imperative to train event extraction models. Currently, most existing event corpora suffer from being available only in English, and their construction is limited by high annotation costs. This paper aims to construct a corpus that concerns social security causality events in Chinese and proposes a faster and less expensive construction method. The contributions are as follows: (i) An event corpus SSECau for the social security field in Chinese is constructed. They are from 2,235 web texts and microblogs, with event causality annotated at the document level. (ii) A corpus construction method with manual annotation and machine pre-tagging is proposed to improve accuracy and speed. (iii) A pre-tagging method based on BiLSTM-CRF (bidirectional long short-term memory and conditional random field) is deployed to extract events automatically. The experimental results show the best consistency between automatic pre-tagging and manual annotation can reach up to 82 %; while the dynamic tagging process improves both the labeling speed and accuracy. The SSECau corpus can aid the development and evaluation of event extraction models for the social security field; annotated cause-effect relationships at the document level can potentially enhance the training of complex extraction models; the proposed dynamic process with pre-tagging can serve as a reference for future corpus construction.
Effectiveness of the Inquiry Based Learning Model Based on Mobile Learning on Students' Creative Thinking Skills: A Meta-Analysis

This study aims to determine the effect of the inquiry-based learning model based on Mobile Learning on Students' Creative Thinking Skills. This type of research is quantitative research with meta-analysis methods. The data source comes from analysis of national and international journals. The process of searching for data sources through the Google Scholar, ERIC, DOAJ, Wiley, Taylor of Francis and Plos ONE databases. Inclusion criteria are data sources from national and international journals indexed by SINTA; Scopus, WOS, DOAJ and EBSCO, Research must be related to inquiry models, mobile learning and critical thinking skills, Journals must be published in 2017-2023 and have a value (r), (t), or F N ≥ 25. Research results of 18 studies explains that the inquiry-based learning model based on mobile learning has a significant effect on students' creative thinking skills (z = 6.713; p &lt; 0.001; CI 95% (0.784; 1.206). The effect size in this study is high criteria (rRE = 0.885). These findings show the application of mobile learning-based inquiry learning models has a major effect on students' creative thinking skills.
Non-Local SAR Image Despeckling Based on Sparse Representation

Speckle noise is an inherent problem of synthetic aperture radar (SAR) images, which not only seriously affects the acquisition of SAR image information, but also greatly reduces the efficiency of image segmentation and feature classification. Therefore, research on how to effectively suppress speckle noise while preserving SAR image content information as much as possible has received increasing attention. Based on the non-local idea of SAR image block-matching three-dimensional (SAR-BM3D) algorithm and the concept of sparse representation, a novel SAR image despeckling algorithm is proposed. The new algorithm uses K-means singular value decomposition (K-SVD) to learn the dictionary to distinguish valid information and speckle noise and constructs a block filter based on K-SVD for despeckling, so as to avoid strong point diffusion problem in SAR-BM3D and achieve better speckle noise suppression with stronger adaptability. The experimental results on real SAR images show that the proposed algorithm achieves better comprehensive effect of speckle noise suppression in terms of evaluation indicators and information preservation of SAR images compared with several existing algorithms.
Learning Human Postures Using Lab-Depth HOG Descriptors

Human Posture Recognition is gaining increasing attention in the field of computer vision due to its promising applications in the areas of health care, human-computer interaction, and surveillance systems. This paper presents a novel method for human posture recognition by combining both color and depth images and feeding the resulting information into the vision transformer (ViT) model. We want to take advantage of integrating the Lab-D HOG descriptor [18] into the ViT architecture [8]. First, we compute the multispectral Lab-D edge detector by opting for the maximum eigenvalue of the multiplication of the jacobian matrix by its transpose. Second, we select the multispectral corner points by picking the minimum of the eigenvalues of the multispectral Harris matrix. Third, for each selected corner point, we compute a Lab-D HOG descriptor. Last, we feed the extracted Lab-D HOG descriptors into the transformer encoder/decoder by implementing two different strategies. Results show that we outperform state-of-the-art methods.
Sparse Backdoor Attack Against Neural Networks

Abstract Recent studies show that neural networks are vulnerable to backdoor attacks, in which compromised networks behave normally for clean inputs but make mistakes when a pre-defined trigger appears. Although prior studies have designed various invisible triggers to avoid causing visual anomalies, they cannot evade some trigger detectors. In this paper, we consider the stealthiness of backdoor attacks from input space and feature representation space. We propose a novel backdoor attack named sparse backdoor attack, and investigate the minimum required trigger to induce the well-trained networks to make incorrect results. A U-net-based generator is employed to create triggers for each clean image. Considering the stealthiness of the trigger, we restrict the elements of the trigger between −1 and 1. In the aspect of the feature representation domain, we adopt an entanglement cost function to minimize the gap between feature representations of benign and malicious inputs. The inseparability of benign and malicious feature representations contributes to the stealthiness of our attack against various model diagnosis-based defences. We validate the effectiveness and generalization of our method by conducting extensive experiments on multiple datasets and networks.
Corpus Linguistics and Translation Tools for Digital Humanities: Research Methods and Applications. Stefania M. Maci &amp; Michele Sala

Journal Article Corpus Linguistics and Translation Tools for Digital Humanities: Research Methods and Applications. Stefania M. Maci & Michele Sala Get access Corpus Linguistics and Translation Tools for Digital Humanities: Research Methods and Applications. Stefania M. Maci Michele Sala. London: Bloomsbury Academic, 2022, 248 pp. ISBN: 9781350275225. £95.00 (H/B) Mehrdad Vasheghani Farahani Mehrdad Vasheghani Farahani Leipzig University, Germany E-mail: submittingpapers@yahoo.com Search for other works by this author on: Oxford Academic Google Scholar Digital Scholarship in the Humanities, fqad060, https://doi.org/10.1093/llc/fqad060 Published: 06 October 2023
Image super resolution based channel estimation for future wireless communication

In this paper, a novel image-based Deep Learning (DL) approach for channel estimation for future wireless communications is proposed. The time-frequency response of the fast-fading wireless channel is represented as a 2D image to estimate the unknown values of the channel response using known values at the pilot locations. With given images, both image Super-Resolution (SR) and image Denoising Network (DnN), termed as Super-Resolution and Denoising Network (SRDnN), are combined to estimate the wireless channel. To show the effectiveness, the proposed SRDnN is applied to Massive Multiple-Input Multiple-Output (mMIMO) with Non-Orthogonal Multiple Access (NOMA). The enhanced performances of SRDnN are quantified in terms of Mean Square Error (MSE) and Symbol Error Rate (SER). In addition, the influence of pilot numbers on SRDnN performance for next-generation mMIMO-NOMA networks is presented. The simulation results demonstrate that SRDnN is comparable to the level of Maximum Likelihood (ML) detection for both with and without complete Channel State Information (CSI) at the receiver with less number of pilots.
Technical performance metrics of a security operations center

This research introduces a novel framework for creating metrics intended for security operations centers (SOCs). The framework is developed using the design science research methodology and has been validated by generating four novel metrics to assess the technical performance of a SOC. Additionally, the study examines the existing landscape of metrics for SOCs and concludes that a majority of the metrics discussed in the literature primarily focus on operational aspects rather than technical performance. The absence of adequate technical performance metrics makes it challenging to accurately evaluate the tangible impact of a SOC on overall cyber defense capabilities. The research also highlights the insufficiency of current methods in constructing metrics and frameworks tailored for measuring SOCs' technical performance. The resulting framework offers SOCs means to create high-quality metrics for performance evaluation. Furthermore, the metrics the framework was validated with offer SOCs an opportunity to enhance their ability to quantify their threat detection capabilities.
Graph- and transformer-guided boundary aware network for medical image segmentation

Despite the considerable progress achieved by U-Net-based models, medical image segmentation remains a challenging task due to complex backgrounds, irrelevant noises, and ambiguous boundaries. In this study, we present a novel approach called U-shaped Graph- and Transformer-guided Boundary Aware Network (GTBA-Net) to tackle these challenges.GTBA-Net uses the pre-trained ResNet34 as its basic structure, and involves Global Feature Aggregation (GFA) modules for target localization, Graph-based Dynamic Feature Fusion (GDFF) modules for effective noise suppression, and Uncertainty-based Boundary Refinement (UBR) modules for accurate delineation of ambiguous boundaries. The GFA modules employ an efficient self-attention mechanism to facilitate coarse target localization amidst complex backgrounds, without introducing additional computational complexity. The GDFF modules leverage graph attention mechanism to aggregate information hidden among high- and low-level features, effectively suppressing target-irrelevant noises while preserving valuable spatial details. The UBR modules introduce an uncertainty quantification strategy and auxiliary loss to guide the model's focus towards target regions and uncertain "ridges", gradually mitigating boundary uncertainty and ultimately achieving accurate boundary delineation.Comparative experiments on five datasets encompassing diverse modalities (including X-ray, CT, endoscopic procedures, and ultrasound) demonstrate that the proposed GTBA-Net outperforms existing methods in various challenging scenarios. Subsequent ablation studies further demonstrate the efficacy of the GFA, GDFF, and UBR modules in target localization, noise suppression, and ambiguous boundary delineation, respectively.GTBA-Net exhibits substantial potential for extensive application in the field of medical image segmentation, particularly in scenarios involving complex backgrounds, target-irrelevant noises, or ambiguous boundaries.
Role of locality, fidelity and symmetry regularization in learning explainable representations

Despite their success deep neural networks still lack interpretability and are regarded as black boxes. This hampers a wider adoption in applications with societal, environmental or economical implications, and motivated a variety of techniques for explaining their outputs. Such explanations are however typically produced after model training so there is no guarantee that models learn faithful attributions, a goal they were not trained for. We evaluate the impact of different penalty terms in the loss function that promote explainable feature attributions, and that can be learned during training in an unsupervised way. We show that explainability-constrained models produce better saliency maps based on multiple metrics and tests. Regularizers imposing locality, fidelity and symmetry properties lead to the best performances in terms of MoRF and ROAR scores.
Plane-based clustering with asymmetric distribution loss

Ramp-based twin support vector clustering (RampTWSVC) is a powerful clustering method, which measures the within-cluster and between-cluster scatter by the bounded ramp functions. However, the ramp loss is a symmetric function and it does not consider the data distribution, which makes the RampTWSVC not robust enough to deal with noisy datasets. In this paper, we construct an asymmetric distribution loss function and propose a novel plane-based clustering with asymmetric distribution loss (ADPC). The asymmetric distribution loss function combined the ramp loss with first-order and second-order statistics, it inherits the advantages of ramp loss function and captures the data distribution precisely, which makes ADPC more robust to the samples far from the cluster center plane. Moreover, two adjusting parameters t1 and t2 are introduced to flexibly control the form of the asymmetric distribution loss function and improve the performance of the algorithm. In addition, its nonlinear clustering formation is also provided by using the kernel trick. Some experiments on noise-corrupted benchmark UCI and artificial datasets have been provided, and the detailed comparison results show that the proposed ADPC has better performance on most datasets. Further experiments and analysis on the face clustering have been done to demonstrate the effectiveness and feasibility of the proposed ADPC.
A Lightweight and Secure Three-Factor Access Authentication Scheme in Metaverse

The metaverse is a virtual world that can map, integrate and interact with the real world. People only need to access wearable devices to enter the metaverse with corresponding avatars, interact with others and gain an immersive experience in the virtual world. As the metaverse thrives, it faces numerous security threats which can degrade or even damage the user’s experience when surfing in the virtual world. In this paper, to combat the attacks that exist when users enter the metaverse, such as illegal intrusion into wearable devices, we propose a secure and efficient authentication management and key negotiation scheme based on three-factor security to guarantee the security access between user, wearable device and metaverse server. Specifically, considering that cloud servers hosting the metaverse are prone to Denial-of-Service (DoS) attacks, we rely on edge servers to not only perform the first authentication on access requests, but also render virtual scenes when users enter the metaverse to reduce the latency and bandwidth. In addition, the security analysis and performance evaluation demonstrate that the scheme can provide various security properties with lower communication and computation overhead.
Hiearachical Construction of Pulmonary Vascular Trees from CT Images Based on Combined Deep Learning and Graph Topology Optimization

Automatic construction of pulmonary vascular trees (PVTs) from computer tomography (CT) images is a critical step for computer-assisted diagnosis and treatment planning of pulmonary diseases. However, robust and accurate construction of the PVTs is a challenging problem due to the difficulties in artery/vein (A/V) classification and touching vessels separation. Current studies use deep learning approaches for A/V classification but lack of the utilization of vessel tree topology knowledge. In this paper, we combine deep learning approach with graph topology optimization for more accurate PVT construction. We first use a 3-D U-Net model to achieve the initial A/V segmentation and obtain the voxel-level probability maps. Based on the initial segmentation, the initial A/V tree-graph is constructed, and the centerlines and radius of each vessel segment are calculated. Using the initial voxel probabilities and the geometric features of the vessel segments (e.g., center line direction, length and radius), a graph optimization step is performed to correct the inaccurate A/V classifications and break the erroneous connections between the touching vessels. Finally, a more accurate hierarchical tree graph is constructed. Experimental results illustrate that the combination of deep learning and graph optimization outperformed the state-of-the-arts (SOTA) methods which only uses deep learning. Our method also simultaneously generates a parametric model of the vascular tree, which is important for vessel morphometry measure, blood flow simulation and etc.
‘Hey Siri, You’re Dumb!’: Investigating Blurting Instances of Voice-Based Assistants

Abstract Voice-based assistants (VBAs) are transforming how we interact with technology. We rely heavily on VBAs (such as Siri, Alexa and Google Assistant) to manage our daily routines and tasks. The current study extends past research on blurting to the human–machine communication (HMC) context. An online survey (n = 185) was conducted to investigate blurting with a VBA. We analysed open-ended accounts of instances in which participants had blurted during conversations with a VBA. In addition, findings show that blurting with a VBA is related to the less sophisticated views of interpersonal arguments and higher argument seeking, verbal aggressiveness, psychological reactance, extraversion and neuroticism. Our findings suggest that although people blurt with VBAs, in line with the general Computers Are Social Actors paradigm, the reasons are unique to HMC.
EFL Students’ Perception of Using AI Paraphrasing Tools in English Language Research Projects

Researchers and educators have different opinions on the use of AI-driven automated paraphrasing tools (APTs). Such tools can improve students’ writing performance but may result in accusations of plagiarism if students do not use them appropriately. This study aimed to examine EFL students’ perceptions of using APTs in their academic writing process. It fills a gap in the literature by highlighting the roles, appropriateness, and effectiveness of APTs use and identifying their advantages and disadvantages. During a research project course, the participants used APTs while writing their research papers. A questionnaire and semi-structured interviews were used to identify students’ perceptions of using APTs and to determine the advantages, disadvantages, and drawbacks of using them. The findings showed that EFL students’ perceptions toward using APTs are favorable; they view them as useful tools that have a significant impact on their academic writing process. The study recommended that APTs are helpful to EFL students but that students must not overly rely on them. Students must learn how to paraphrase to check the appropriateness of the texts produced by APTs.
Accessible Play: Towards Designing a Framework for Customizable Accessibility in Games

Video games are an important form of entertainment and have become an increasingly popular pastime in the 21st Century. However, many people with disabilities are still excluded from gaming due to accessibility barriers. While some progress has been made in recognizing accessibility as a design value, there is still a significant need for further advancements in game accessibility. Our research analyzes accessibility features in games across genres and platforms (PC, Console, Mobile, VR). Using Interactive Process Modelling (IPM), we map customizable accessibility options available in different games. We present our methodology for conducting interviews with game designers and gamers with disabilities to provide insights into existing options. The research project will result in the development of an accessibility-focused framework for game designers that will enable them to effectively design new customizable accessibility options for their players. Through this research, we aim to contribute to the broader discourse on accessibility and inclusivity in gaming for individuals with disabilities. This work-in-progress paper presents the ongoing progress of our research and invites feedback from the community.
Chatbots in Medical Research

Private Academic Consultant, Samraong, Cambodia [email protected] Chandigarh University, Punjab, India. Joseph Ayo Babalola University, Ikeji-Arakeji, Nigeria. Conflicts of interest and sources of funding: none declared.
Foot-Based Game Controller to Improve Interaction between Participants

Trying to improve the international experience of studying abroad, we focused on a solution that would foster social interactions among students. We created a prototype of a game controller employing interactive floor and vertical display, which would allow players to use their natural body movements and interact with each other during the game. We have designed a high-fidelity prototype of a foot controller for a two-person game and have made a preliminary evaluation of the system.