Reviewerly/exHarmony huggingface dataset:
Beyond technical measures: a value-focused thinking appraisal of strategic drivers in improving information security policy compliance

The evolving sophistication of threats and the impact of security breaches have caused managers to continually grapple with strategies to reduce these risks. One common security control is the adoption of information security policies (ISPs) geared at improving employees’ compliance behaviour. However, there is mounting empirical evidence that shows that ISP compliance is a challenging undertaking with less than satisfactory outcomes. Further, little attention is placed on developing economies in the study of this phenomenon. This research adopts a values-based methodology to determine fundamental and means objectives in maximising employees’ compliance with ISPs in a developing economy context. The research identifies 30 objectives and demonstrates that risk mitigation, people, technical and organisational factors are essential to improving compliance. The results contribute objectives, contextualised to the people for whom the results are relevant, thus promoting deeper understanding. The research offers utility to managers in the design and implementation of InfoSec strategies and policies. The findings can also inform investment decisions regarding compliance tools, methods and technologies. Recognising that security (information and cyber) threats are a global dilemma, we contend that investigating forms of security risks and potential solutions can mitigate the social and economic costs of security incidents.
Proposal for a continuous wave laser with linewidth well below the standard quantum limit

Due to their high coherence, a laser is a ubiquitous tool in science. We show that by engineering the coupling between the gain medium and the laser cavity as well as the laser cavity and the output port, it is possible to eliminate most of the noise due to photons entering as well as leaving the laser cavity. Hence, it is possible to reduce the laser linewidth by a factor equal to the number of photons in the laser cavity below the standard quantum limit. We design and theoretically analyze a superconducting circuit that uses Josephson junctions, capacitors and inductors to implement a microwave laser, including the low-noise couplers that allow the design to surpass the standard quantum limit. Our proposal relies on the elements of superconducting quantum information, and thus is an example of how quantum engineering techniques can inspire us to re-imagine the limits of conventional quantum systems.
A Blockchain, 5G and IoT-based transaction management system for Smart Logistics: an Hyperledger framework

The recent technological progress has started a revolution in the logistic and supply chain environment, known as Logistics 4.0. Such a revolution is strongly based on information sharing and digitalization. For this reason, distributed ledger technologies (and blockchain in particular) are attracting the interest of countries and companies. However, the proper verification of data coming from oracles is a huge issue. This paper proposes a solution integrating Blockchain and IoT, by exploiting the enhanced data security and integrity provided by Narrowband-IoT. A preliminary performance evaluation of Hyperledger Fabric and Hyperladger Sawtooth is also presented.
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning

The right to erasure requires removal of a user's information from data held by organizations, with rigorous interpretations extending to downstream products such as learned models. Retraining from scratch with the particular user's data omitted fully removes its influence on the resulting model, but comes with a high computational cost. Machine "unlearning" mitigates the cost incurred by full retraining: instead, models are updated incrementally, possibly only requiring retraining when approximation errors accumulate. Rapid progress has been made towards privacy guarantees on the indistinguishability of unlearned and retrained models, but current formalisms do not place practical bounds on computation. In this paper we demonstrate how an attacker can exploit this oversight, highlighting a novel attack surface introduced by machine unlearning. We consider an attacker aiming to increase the computational cost of data removal. We derive and empirically investigate a poisoning attack on certified machine unlearning where strategically designed training data triggers complete retraining when removed.
The generalized extreme learning machines: Tuning hyperparameters and limiting approach for the Moore–Penrose generalized inverse

In this paper, we propose the generalized extreme learning machine (GELM). GELM is an ELM that incorporates the analyzed hyperparameters of ELM, such as sizes and ranks of weight matrices, and a limiting approach for the Moore-Penrose generalized inverse (M-P GI) into the learning process. ELM overcomes shortcomings of traditional deep learning, such as time-consuming due to iterative executions, as it learns quickly by removing the adjustment time of hyperparameters. There are desirable numbers of hidden nodes in ELM for single hidden layer feedforward neural networks, minimizing prediction error. However, it is difficult to use the desired number because it is related to the number of data used and datasets tend to be large. We consider ELM for multiple hidden layer feedforward neural networks. We analyze matrices derived in the network and figure out the characteristics of weight matrices and biases considering accurate prediction and learning speed, based on mathematical theories and a limiting approach for the M-P GI. The final output matrix of GELM is formulated explicitly. Experiments are conducted to verify the analysis using network traffic data, including DDoS attacks. The performances of GLEM, such as accuracies and learning speed, are compared for the networks with single and multiple hidden layers. Numerical results show the advantages of GELM in the performance measures, and the use of multiple hidden layers in GELM does not significantly affect performance. The theory-based prediction performances obtained from GELM will be the criterion for the margin of deep learning performance.
Page Layout Analysis System for Unconstrained Historic Documents

Extraction of text regions and individual text lines from historic documents is necessary for automatic transcription. We propose extending a CNN-based text baseline detection system by adding line height and text block boundary predictions to the model output, allowing the system to extract more comprehensive layout information. We also show that pixel-wise text orientation prediction can be used for processing documents with multiple text orientations. We demonstrate that the proposed method performs well on the cBAD baseline detection dataset. Additionally, we benchmark the method on newly introduced PERO layout dataset which we also make public.
Mutual Information-Based Graph Co-Attention Networks for Multimodal Prior-Guided Magnetic Resonance Imaging Segmentation

Multimodal magnetic resonance imaging (MRI) provides complementary information about targets, and the segmentation of multimodal MRI is widely used as an essential preprocessing step for initial diagnosis, stage differentiation, and post-treatment efficacy evaluation in clinical situations. For the main modality or each of the modalities, it is important to enhance the visual information by modeling the connection and effectively fusing the features among them. However, the existing methods for multimodal segmentation have a drawback; they coincidentally drop information of individual modality during the fusion process. Recently, graph learning-based methods have been applied in segmentation, and these methods have achieved considerable improvements by modeling the relationships across feature regions and reasoning using global information. In this paper, we propose a graph learning-based approach to efficiently extract modality-specific features and establish regional correspondence effectively among all modalities. In detail, after projecting features into a graph domain and employing graph convolution to propagate information across all regions for learning global modality-specific features, we propose a mutual information-based graph co-attention module to learn the weight coefficients of one bipartite graph constructed by the fully connected graphs having different modalities in the graph domain and by selectively fusing the node features. Based on the deformation diagram between the spatial-graph space and our proposed graph co-attention module, we present a multimodal prior-guided segmentation framework, which uses two strategies for two clinical situations: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Modality-Specific Learning Strategy</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co-Modality Learning Strategy</i> . Besides, the improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Co-Modality Learning Strategy</i> is used with trainable weights in the multi-task loss for the optimization of the proposed framework. We validated our proposed modules and frameworks on two multimodal MRI datasets: our private liver lesion dataset and a public prostate zone dataset. Our experimental results on both datasets prove the superiority of our proposed approaches.
Several lifted abstract domains for static analysis of numerical program families

Lifted (family-based) static analysis based on abstract interpretation is capable of analyzing all variants of a program family (or any other configurable software system), simultaneously, in a single run without generating any of the variants explicitly. The elements of the underlying lifted domain are tuples, which maintain one property per system variant. Still, explicit property enumeration in tuples, one by one for all variants, immediately yields combinatorial explosion. This is particularly apparent in the case of program families that, apart from Boolean features, contain also numerical features with large domains, thus giving rise to astronomical configuration spaces. The key for an efficient lifted analysis is a proper handling of variability-specific constructs of the language (e.g., feature-based runtime tests and #if directives). In this work, we introduce new symbolic representations of the lifted domain that can efficiently analyze program families with numerical features. This makes sharing between property elements corresponding to different variants explicitly possible. In the first approach, elements of the new lifted domain are decision trees, in which decision nodes are labeled with linear constraints defined over numerical features and the leaf nodes belong to an existing single-program analysis domain. The lifted domain is parametric in the choice of the domains for representing linear constraints and leaf nodes. Furthermore, we propose another alternative approach for efficient lifted analysis. We encode a program family with numerical features as a family with only Boolean features, and then use a BDD lifted domain to analyze the resulting program family. To illustrate the potential of our representations, we have implemented an experimental lifted static analyzer, called SPLNum2Analyzer, for inferring invariants of #if-annotated C programs. The tool implements all three approaches for lifted analysis based on abstract interpretation: tuple-based, decision tree-based, and BDD-based. It uses existing numerical abstract domains (e.g., intervals, octagons, polyhedra) from the APRON library as parameters. An empirical evaluation on benchmarks from SV-COMP and BusyBox yields promising results indicating that our tool can be successfully used for analyzing program families with very large configuration spaces.
Artificial Intelligence, Marketing, and the History of Technology: Kranzberg’s Laws as a Conceptual Lens

Killer applications, or killer apps, are technology applications that profoundly change the way any society thinks, works, and functions. This paper explores Artificial Intelligence (AI) as a killer app, with specific application to marketing. Specifically, this paper employs the lens of technology history to explore the relationship between marketing and AI. Using Kranzberg’s six laws of technology, this paper sheds light on all manner of innovations, how technologies have shaped and impacted society, and how marketers can respond to this. This inquiry offers two main contributions: First, it suggests a number of implications for marketing practice and scholars, derived from each of Kranzberg’s laws. These suggestions are intended to guide marketing practice when implementing or using AI. In addition, this article offers a number of research directions that might be fruitful and important areas for investigation in future scholarly work regarding technology’s impact among marketing scholars.
Incorporating Link Prediction into Multi-Relational Item Graph Modeling for Session-based Recommendation

Session-based recommendation aims at predicting the next item that a user is more likely to interact with by a target behavior type. Most of the existing session-based recommendation methods focus on developing powerful representation learning approaches to model items' sequential correlations, whereas they usually encounter the following limitations. Firstly, they only utilize sessions that belong to the target behavior type, neglecting the potential of leveraging other behavior types as auxiliary information for modeling user preference. Secondly, they separately model item-to-item relations for each session, overlooking to globally characterize the relations across different sessions for better item representations. To overcome these limitations, we first build a Multi-Relational Item Graph (MRIG) involving target and auxiliary behavior types over all sessions. Consequently, a novel Graph Neural Network (GNN) based model is devised to encode MRIG's item-to-item relations into target and auxiliary session-based representations, and adaptively fuse them to represent user interests. To facilitate model training, we further incorporate link prediction into multi-relational item graph modeling, acting as a simple but relevant task to session-based recommendation. The extensive experiments on real-world datasets demonstrate the superiority of the model over diverse and competitive baselines, validating its main components' significant contributions.
IMGTR: Image-triangle based multi-view 3D reconstruction for urban scenes

Multi-view depth map reconstruction is a popular approach to generate 3D information with good flexibility and scalability. However, texture-weak regions and repeated textures challenge these methods in urban scenes. To address this need, this paper proposes an image-triangle based multi-view 3D reconstruction (IMGTR) method. Starting from constructing a density-adaptive image triangulation for each image, the main procedure is to determine the corresponding object plane for each image triangle under an objective function consisting of the image similarity measure, the smoothness constraint and the continuity constraint on the edges between adjacent triangles. Qualitative and quantitative experiments show that the proposed method can reconstruct complex urban structures more accurate and achieve higher fidelity in planar urban structures than some recently released multi-view stereo algorithms. Specifically, for the Vaihingen dataset IMGTR achieves an average improvement of 4.27% compared to PMVD, 50.94% to SURE and 54.76% to COLMAP in position accuracy.
Global outliers detection in wireless sensor networks: A novel approach integrating time‐series analysis, entropy, and random forest‐based classification.

Abstract Wireless sensor networks (WSNs) have recently attracted greater attention worldwide due to their practicality in monitoring, communicating, and reporting specific physical phenomena. The data collected by WSNs is often inaccurate as a result of unavoidable environmental factors, which may include noise, signal weakness, or intrusion attacks depending on the specific situation. Sending high‐noise data has negative effects not just on data accuracy and network reliability, but also regarding the decision‐making processes in the base station. Anomaly detection, or outlier detection, is the process of detecting noisy data amidst the contexts thus described. The literature contains relatively few noise detection techniques in the context of WSNs, particularly for outlier‐detection algorithms applying time series analysis, which considers the effective neighbors to ensure a global‐collaborative detection. Hence, the research presented in this article is intended to design and implement a global outlier‐detection approach, which allows us to find and select appropriate neighbors to ensure an adaptive collaborative detection based on time‐series analysis and entropy techniques. The proposed approach applies a random forest algorithm for identifying the best results. To measure the effectiveness and efficiency of the proposed approach, a comprehensive and real scenario provided by the Intel Berkeley Research Laboratory has been simulated. Noisy data have been injected into the collected data randomly. The results obtained from the experiment then conducted experimentation demonstrate that our approach can detect anomalies with up to 99% accuracy.
Early breast cancer diagnostics based on hierarchical machine learning classification for mammography images

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<scp>IVQF<sub>IoT</sub></scp>: An intelligent vulnerability quantification framework for scoring internet of things vulnerabilities

Abstract With time smart services have become more domineering than ever before however, the pertinent security considerations fade to correspond with growing heterogeneity in the internet of things (IoT) devices and new technologies coupled with resource constraints, crafting IoT‐based systems more susceptible to cyber‐attacks. To ensure a secure IoT environment, pro‐active security mechanisms, like scanning vulnerabilities and prioritizing to remediate them timely, should be embedded in the system. Motivated by the facts, we in this paper, highlight the state of the art of several works trading with a common vulnerability scoring system (CVSS), its limitations, and the emendations recommended to conclude its maturity. CVSS is an industry standard that has been adopted worldwide to quantify the vulnerabilities in organizations for IT and IoT‐based systems. The vulnerabilities mathematical score coalesces with environmental knowledge for finding attack paths and apt score for prioritization. The specific functionality and exclusive dynamics of IoT and cyber‐physical systems in comparison to traditional computer networks, make the legacy cyber‐security exemplars unfit for these advanced networks. This paper studies the relevance of CVSS for smart systems and present an intelligent vulnerability quantification framework for IoT systems grounded on the CVSS v3.1 framework with threat intelligence and machine learning models. Further by applying blockchain technology in the proposed framework, the issues concerning security, lack of trust, and privacy possibly will resolve by hiring a smart contract.
Public Drone: Attitude Towards Drone Capabilities in Various Contexts

Drone technologies represent a new category of mobile devices that are increasingly present in public spaces. They are becoming increasingly autonomous, featuring a wide range of capabilities from detecting objects to monitoring situations. Yet, little is known about the characteristics that influence their acceptability in public spaces. In this work, we investigate how people's attitude towards drone capabilities is influenced by the context in which they are operating. We present three user studies: first, a participatory design study (N=5) in which we investigated people's expectations towards drone capabilities and contexts of use; second, a pre-study (N=18) performed to select 6 contexts of use for public drones with three different severity levels; and third, a survey-based study (N=26) where we evaluated people's attitude towards 10 drone capabilities in 6 contexts of varying severity levels. Our results demonstrate that people's attitude towards drone capabilities is more positive for severe contexts. In addition, we found positive correlations for all capabilities between attitude and perceived severity of context. This work contributes to the design of context-sensitive human-drone interactions and to the future integration of public drones.
ProtoCF: Prototypical Collaborative Filtering for Few-shot Recommendation

In recent times, deep learning methods have supplanted conventional collaborative filtering approaches as the backbone of modern recommender systems. However, their gains are skewed towards popular items with a drastic performance drop for the vast collection of long-tail items with sparse interactions. Moreover, we empirically show that prior neural recommenders lack the resolution power to accurately rank relevant items within the long-tail.
Real-Time Surveillance Using Deep Learning

It is crucial to ensure proper surveillance for the safety and security of people and their assets. The development of an aerial surveillance system might be very effective in catering to the challenges in surveillance systems. Current systems are expensive and complex. A cost-effective and efficient solution is required, which is easily accessible to anyone with a moderate budget. In aerial surveillance, quadcopters are equipped with state-of-the-art image processing technology that captures detailed photographs of every object underneath. A quadcopter-based solution is proposed to monitor desired premises for any unusual activities, like the movement of persons with weapons and face detection to achieve the desired surveillance. After detection of any unusual activity, the proposed system generates an alert for security personals. The proposed solution is based on quadcopter surveillance and video streaming for anomaly detection in the received video streams through deep learning models. A well-known FasterRCNN algorithm is modified for fast learning with feature reduction in the initial feature extraction step. Five different kinds of CNNs were evaluated for their ability to identify objects of interest in surveillance images. ResNet-50–based FasterRCNN with the highest average precision performed as an excellent solution for threat detection. The average precision of the system is 79% across all categories achieved.