Reviewerly/exHarmony huggingface dataset:
“It may take ages”: Understanding Human-Centred Lateral Phishing Attack Detection in Organisations

Lateral phishing attacks can be devastating for users and organisational IT teams as these originate from legitimate, but compromised, email accounts that benefit from the implicit trust between sender and recipients. In this paper, we begin to explore the human-centred space of lateral phishing attacks through interviews with 5 security practitioners and 17 employees from the UK and India. We report how security practitioners predominantly rely on employees to alert them to compromised accounts, and how this can create a delay during which the attack can continue. Our interviews with employees, on the other hand, found that individuals may not be reliable; they struggled to detect slight changes to messages, and over-relied on markers that cannot identify lateral attacks. We discuss the symbiotic relationship between security practitioners and employees for combatting lateral phishing attacks within organisations, and present recommendations for improving resistance to these attacks.
Encouraging Organisational Information Security Incident Reporting

21st-century organisations can only learn how to respond effectively to, and recover from, adverse information security incidents if their employees report any incidents they notice. This should happen irrespective of whether or not they themselves triggered the incident. Organisations have started to inform their employees about their incident reporting obligations. However, there is little research that organisations can benefit from to make their reporting provisions maximally effective. For this work, we follow a multi-step approach.(1) We review the related research on reporting, including reporting reluctance, and the legalities of incident reporting in the European Union. (2) We explain how we developed variations of information texts that raise awareness of incident reporting obligations and aim to ameliorate reporting reluctance. (3) We conducted an online user study (n=257) to identify the most effective information text. (4) The most effective text was deployed by the CISO of a German energy company and we collected feedback from 24 employees to support a qualitative analysis. We discuss our experiences and the implications of such information text design. We make recommendations for encouraging information security incident reporting and suggest future work.
Overcoming Theory: Designing Brainwave Authentication for the Real World

Recent advancements in consumer-grade Brain-Computer Interfaces (BCIs) have opened up new possibilities for the exploration and deployment of brainwave-based user authentication. However, research on real-world usability and security is still lacking. Additionally, existing prototype implementations are not widely accessible. To address these limitations and facilitate future prototyping, we developed NeuroPack, a Python library tailored toward the prototyping of brainwave-based authentication systems. Contained within the library is KeyWave, an easy-to-use and customizable example implementation allowing for continuous authentication using Electroencephalography (EEG) signals. We integrated KeyWave into a password manager and conducted a preliminary user study with nine participants to assess its usability and security, while also investigating the general acceptance of users toward this new technology. The results of the study reveal high usability scores on the System Usability Scale (85.28) but also highlight possible concerns regarding user willingness to use a separate recording device for authentication. Our results further indicate, that existing machine learning models perform well with real-world data, showing promising results with an Equal Error Rate of 7.95%.
PinchKey: A Natural and User-Friendly Approach to VR User Authentication

This study introduces PinchKey, a highly accurate and user-friendly behavioral biometric authentication method for VR users. Authentication is performed using the natural and intuitive ‘pinch’ gesture used when operating VR devices. Since this two-finger action is a natural and intuitive gesture used when operating VR devices, we expect that the use of the pinch gesture will provide enhanced usability compared to traditional VR device authentication methods such as pattern lock or PIN entry. The system analyzes the three-dimensional trajectory of the user’s finger movements during the pinch gesture and applies a supervised machine learning model to perform authentication. We implemented PinchKey on the Meta Quest 2 platform and conducted a comprehensive evaluation with 29 users, focusing on key parameters such as authentication accuracy, time, and overall usability. We conducted an offline evaluation using the pre-collected user pinch data. The results revealed high authentication accuracy, with an average area under the curve (AUC) of 99.7% and an average equal error rate (EER) of 1.7% in the initial phase. Based on the above results, we conducted real-time authentication trials on a Meta Quest 2 equipped with PinchKey. The tests emphasized the system’s ability to provide efficient and fast authentication, with an average authentication accuracy of 93.8% and a fast average authentication time of 1.65 s. PinchKey achieved a commendable median system usability scale (SUS) score of 86.25, which is the highest A rating on the SUS. In addition, we evaluated PinchKey’s practicality in real-world applications, including system resilience in the presence of potential attackers.
Improving human–object interaction with auxiliary semantic information and enhanced instance representation

Human–Object Interaction (HOI) detection has garnered considerable attention among computer vision researchers as it involves identifying and describing actions between humans and objects. Numerous approaches, such as sequential and end-to-end methods, have been proposed to tackle this problem, with a recent focus on exploring end-to-end systems. This study presents an enhanced end-to-end transformer-based human–object detector based on HOTR, which introduces three improvements. The proposed model improves instance representation through a simple yet effective mechanism, utilizes semantic information to provide contextual understanding and additional knowledge, and incorporates a cross-attention mechanism for fusing multi-level high-level feature maps within the Transformer architecture. Experimental results demonstrate significant performance gains over the baseline HOTR model, making it competitive with other state-of-the-art models on two widely-used datasets: V-COCO and HICO-DET.
Agent-based sensor location strategy for smart irrigation of large crop fields

Efficient monitoring of large crop fields is important to ensure the optimal use of resources such as water in irrigation policies, but at the same time represents a challenge to determine the structure of the sensor network. A balance must be accomplished between the acquisition, operation, and maintenance costs of this sensor network with the amount of information that can be collected in real-time to support the optimal use of the resource, e.g., an optimal irrigation policy. In this study, a sensor location strategy is proposed based on an agent-based model (ABM) of the crop–soil system, a state estimation algorithm reconstructing non-measured variables, and an objective function balancing the convergence of the estimation technique and the costs of the sensor network. The ABM model describes the crop–soil dynamics and allows conveniently representing uneven landscapes where water exchanges take place between different portions of the land. Various state estimation techniques can be considered and an extended Kalman filter is implemented in the present study, whose error covariance matrix can be exploited to assess practical observability and observer convergence. Finally, an economic cost function combines the observability measure with the sensor costs in order to select an optimal or suboptimal sensor array. For validation purposes, a numerical simulation case study, corresponding to a rugged land located in Colombia, is used to test various scenarios including the variability of climatic inputs.
Design and implementation of Model-Based Predictive Control for two-axis Solar Tracker

This paper presents the design and implementation of a model-based predictive controller (MPC) with the aim of reducing electrical energy consumption during the development of solar trajectory tracking tasks for a two-axis Solar Tracker. For the design of the MPC, it is necessary to know the specific parameters of each actuator of the Solar Tracker. Therefore, the design and implementation of an experimental methodology based on an algebraic approach to offline parameter identification is also presented. On the other hand, to validate the MPC proposal, an experimental methodology was carried out that allows evaluating and comparing its performance with that of two classical controllers. The above, in terms of tracking error and energy consumption. The results show that the MPC reduces on average 90% of the energy consumption, but it has a higher tracking error of approximately 0.5°. Therefore, based on this, analysis and discussion are provided.
Automation in public sector jobs and services: a framework to analyze public digital transformation’s impact in a data-constrained environment

Purpose This study aims to estimate the value of the impact from digital transformation (DX) focusing on its automation effect, looking at the time and cost savings coming from the substitution effect with an adoption of digital technologies. For example, cloud and artificial intelligence technologies such as ChatGPT have the potential to change ways of working, substituting and replacing several of the tasks that are currently carried out by public administration (PA) employees and labor processes underpinning PA services. Design/methodology/approach The paper outlines a new framework to estimate the potential impact of DX on the public sector. The authors apply this framework to estimate the value of the impact of DX on the Italian PA, defining the latter by the collection of the value of its labor (i.e. PA workforce salaries) and by the collection of the value of its outputs (i.e. public services’ costs). Findings This study ultimately maps out the magnitude and trends of how likely the PA occupations and services could be substituted in a wider process of DX. To do this, the authors apply their framework to the Italian PA, and they triangulate secondary data collection, from official accounts of the Italian Ministry of Economics and the National Statistical Institute, with methodological antecedents from the UK Office for National Statistics and experts’ insights. Results provide a snapshot on the type and magnitude of PA jobs and services projected to be affected by automation over the next 10 years. Originality/value To the best of the authors’ knowledge, this paper provides for the first time an approach to estimate the value of the impact of DX on the public sector in a data-constrained environment – or in the lack of the required primary data. Once applied to the Italian PA, this approach provides a granular map of the automatability of each of the PA occupations and of the PA services. Finally, this paper mentions preliminary insights on potential challenges related to equity in public sector jobs and implications on recruitment processes.
Traffic Violations Management System Using Blockchain Technology

This paper presents the implementation of a traffic violation management system. A vehicle tracking system uses the GPS, GSM and a microcontroller to detect speed violations on roads. Any violation is sent to a central database.Payments can be performed off-line; without involving a third party during payment, or on-line; contacting an intermediate server for each payment. In this paper, we focus on using online payment without third party by using a new technology called blockchain for the authentication and authorization purposes in case of traffic violations payment. The contribution of this paper is providing a model of electronic payment for traffic violations to enhance two importantfactors; trust and security of the e- payment system. Blockchain as a shared ledger holds a many set of entities thatfaithfully records a series of transactions combined together by using a smart contract platform for embedding scripts that run across the network and enables adding new entries to that ledger. Blockchain technology is used in this paper for securing traffic violations management. Using the Blockchain guarantees highly secured traffic violations payment as no hacker can change the balance in driver's wallet or a vehicle property papers in a network around the world without the requirement for a central management, as all transactions are recorded in all copies distributed among all parties in the network, where each party has the same copy of the ledger. The proposed model should reduce the response times of violation requests and payment at any time and from any place.
A Broad TSK Fuzzy Classifier with a Simplified Set of Fuzzy Rules for Class-Imbalanced Learning

With the expansion of data scale and diversity, the issue of class imbalance has become increasingly salient. The current methods, including oversampling and under-sampling, exhibit limitations in handling complex data, leading to overfitting, loss of critical information, and insufficient interpretability. In response to these challenges, we propose a broad TSK fuzzy classifier with a simplified set of fuzzy rules (B-TSK-FC) that deals with classification tasks with class-imbalanced data. Firstly, we select and optimize fuzzy rules based on their adaptability to different complex data to simplify the fuzzy rules and therefore improve the interpretability of the TSK fuzzy sub-classifiers. Secondly, the fuzzy rules are weighted to protect the information demonstrated by minority classes, thereby improving the classification performance on class-imbalanced datasets. Finally, a novel loss function is designed to derive the weights for each TSK fuzzy sub-classifier. The experimental results on fifteen benchmark datasets demonstrate that B-TSK-FC is superior to the comparative methods from the aspects of classification performance and interpretability in the scenario of class imbalance.
A Fractional-Order Fidelity-Based Total Generalized Variation Model for Image Deblurring

Image deblurring is a fundamental image processing task, and research for efficient image deblurring methods is still a great challenge. Most of the currently existing methods are focused on TV-based models and regularization term construction; little efforts are paid to model proposal and correlated algorithms for the fidelity term in fractional-order derivative space. In this paper, we propose a novel fractional-order variational model for image deblurring, which can efficiently address three different blur kernels. The objective functional contains a fractional-order gradient fidelity term and a total generalized variation (TGV) regularization term, and it highlights the ability to preserve details and eliminate the staircase effect. To solve the problem efficiently, we provide two numerical algorithms based on the Chambolle-Pock primal-dual method (PD) and the alternating direction method of multipliers (ADMM). A series of experiments show that the proposed method achieves a good balance between detail preservation and deblurring compared with several existing advanced models.
Bidirectional Representations for Low-Resource Spoken Language Understanding

Speech representation models lack the ability to efficiently store semantic information and require fine tuning to deliver decent performance. In this research, we introduce a transformer encoder–decoder framework with a multiobjective training strategy, incorporating connectionist temporal classification (CTC) and masked language modeling (MLM) objectives. This approach enables the model to learn contextual bidirectional representations. We evaluate the representations in a challenging low-resource scenario, where training data is limited, necessitating expressive speech embeddings to compensate for the scarcity of examples. Notably, we demonstrate that our model’s initial embeddings outperform comparable models on multiple datasets before fine tuning. Fine tuning the top layers of the representation model further enhances performance, particularly on the Fluent Speech Command dataset, even under low-resource conditions. Additionally, we introduce the concept of class attention as an efficient module for spoken language understanding, characterized by its speed and minimal parameter requirements. Class attention not only aids in explaining model predictions but also enhances our understanding of the underlying decision-making processes. Our experiments cover both English and Dutch languages, offering a comprehensive evaluation of our proposed approach.
Calibrating the Ångström–Prescott Model with Solar Radiation Data Collected over Long and Short Periods of Time over the Tibetan Plateau

As the coefficients of the Ångström–Prescott model is site-dependent, the sparsity of radiation stations in regions like the Tibetan Plateau (TP) presents challenges for model calibration. Due to the unique climate and the clean air conditions over the TP, it might be feasible to calibrate the Ångström–Prescott model with short-term observations from scientific expeditions. To test this hypothesis, we used various datasets with different lengths at four stations, together with 435 daily radiations measured during a scientific expedition at Banga in the central TP from 2014 to 2015, to calibrate the Ångström–Prescott model. We found that calibration with a 1-year data length resulted in model performances comparable to those with a 20-year data length. Analysis of the expedition observations showed that the monthly average daily radiation ranged from 15.2 MJ/m2d in December 2014 to 26.5 MJ/m2d in July 2015, with an average value of 20.6 MJ/m2d. When this set of expedition data was used for calibration, the Ångström–Prescott model performed well with an NSE (Nash–Sutcliffe efficiency) of 0.820. If no data were available for calibration, the coefficients of the Ångström–Prescott model could also be directly calculated by parameterization methods established with calibrations at the other radiation stations. In this situation, the LiuJD method performed the best with the highest NSE of 0.792, followed by the LiuXY method with an NSE of 0.764. The FAO method performed poorly with an NSE of 0.578, while the Gopinathan method performed the worst with the lowest NSE of 0.218. Thus, the best strategy to calibrate the Ångström–Prescott model in the Tibetan Plateau is to use data from local observations, even if collected over short periods. When these are not available, the coefficients of the Ångström–Prescott model should be calculated using the parameterization method established with calibrations over the Tibetan Plateau.
TinyFallNet: A Lightweight Pre-Impact Fall Detection Model

Falls represent a significant health concern for the elderly. While studies on deep learning-based preimpact fall detection have been conducted to mitigate fall-related injuries, additional efforts are needed for embedding in microcomputer units (MCUs). In this study, ConvLSTM, the state-of-the-art model, was benchmarked, and we attempted to lightweight it by leveraging features from image-classification models VGGNet and ResNet while maintaining performance for wearable airbags. The models were developed and evaluated using data from young subjects in the KFall public dataset based on an inertial measurement unit (IMU), leading to the proposal of TinyFallNet based on ResNet. Despite exhibiting higher accuracy (97.37% &lt; 98.00%) than the benchmarked ConvLSTM, the proposed model requires lower memory (1.58 MB &gt; 0.70 MB). Additionally, data on the elderly from the fall data of the FARSEEING dataset and activities of daily living (ADLs) data of the KFall dataset were analyzed for algorithm validation. This study demonstrated the applicability of image-classification models to preimpact fall detection using IMU and showed that additional tuning for lightweighting is possible due to the different data types. This research is expected to contribute to the lightweighting of deep learning models based on IMU and the development of applications based on IMU data.
Inquiry-based practice in social studies education <b>Inquiry-based practice in social studies education</b> , edited by S.G. Grant, Kathy Swan, and John Lee, New York, Routledge, 2023, pp. 157, $47.28 (pbk), ISBN 978-1-032-20235-8

"Inquiry-based practice in social studies education." Education 3-13, ahead-of-print(ahead-of-print), pp. 1–2 AcknowledgmentThe authors are grateful to Lembaga Pengelola Dana Pendidikan (LPDP/Indonesia Endowment Fund for Education), which is part of the Ministry of Finance of the Republic of Indonesia, for supporting our master’s studies and supporting this publication.
Online lecture strategies for quality training of future primary school teachers

The purpose of the study is to determine the features of online lectures, essential characteristics and possibilities of implementing strategies for their implementation in the training of future primary school teachers. The research was conducted with students of the first (bachelor's) and second (master's) levels of higher education in the speciality 013 Primary education of one of the state universities of Ukraine. For its implementation, the analysis of literature was carried out in the direction of scientific research, which made it possible to determine the conceptual apparatus and theoretical basis of this study. Based on this, strategies for quality training of future primary school teachers were determined. What found practical implementation in the construction of the structure of the online lecture, which will contribute to its effective conduct. Based on the generalization of practical experience for each of the structural parts of the online lecture, means, tools and techniques that promote active interaction, student involvement and reflection have been selected and organized. We see prospects for further research on this topic: in the development and improvement of existing strategies and structures of online lectures for effective training of future primary school teachers; in the study of the impact of online lectures on academic achievement and motivation of students; in the development and implementation of new technologies and tools to improve the quality of online lectures. The results of the study can be used by scientists, teachers of higher education institutions, academic leaders and managers to ensure high-quality and effective training of future primary school teachers in the online environment.
RONSI: a framework for calculating return on network security investment

Abstract This competitive environment is rapidly driving technological modernization. Sophisticated cyber security attacks are expanding exponentially, inflicting reputation damage and financial and economic loss. Since security investments may take time to generate revenues, organizations need more time to convince top management to support them. Even though several ROSI techniques have been put out, they still need to address network-related infrastructure. By addressing gaps in existing techniques, this study delivers a comprehensive framework for calculating Return on Network Security Investment (RONSI). The proposed framework uses a statistical prediction model based on Bayes’ theorem to calculate the RONSI. It is validated by Common Vulnerability Security Systems (CVSS) datasets and compared to existing studies. The results demonstrate that the annual loss is reduced to 75% with the proposed RONSI model after implementing a security strategy, and the proposed model is compared with existing studies. An organization can effectively justify investments in network-related infrastructure while enhancing its credibility and dependability in the cutthroat marketplace.