Reviewerly/exHarmony huggingface dataset:
Multi-Regulation Computing

This work examines privacy laws and regulations that limit disclosure of personal data, and explores whether and how these restrictions apply when participants use cryptographically secure multi-party computation (MPC). By protecting data during use, MPC offers the promise of conducting data science in a way that (in some use cases) meets or even exceeds most people's conceptions of data privacy. With MPC, it is possible to correlate individual records across multiple datasets without revealing the underlying records, to conduct aggregate analysis across datasets which parties are otherwise unwilling to share for competitive reasons, and to analyze aggregate statistics across datasets which no individual party may lawfully hold. However, most adoptions of MPC to date involve data that is not subject to privacy protection under the law. We posit that a major impediment to the adoption of MPC - on the data that society has deemed most worthy of protection - is the difficulty of mapping this new technology onto the design principles of data privacy laws. While a computer scientist might reasonably believe that transforming any data analysis into its privacy-protective variant using MPC is a clear win, we show in this work that the technological guarantees of MPC do not directly imply compliance with privacy laws. Specifically, a lawyer will likely want to ask several important questions about the pre-conditions that are necessary for MPC to succeed, the risk that data might inadvertently or maliciously be disclosed to someone other than the output party, and what recourse to take if this bad event occurs. We have two goals for this work: explaining why the privacy law questions are nuanced and that the lawyer is correct to proceed cautiously, and providing a framework that lawyers can use to reason systematically about whether and how MPC implicates data privacy laws in the context of a specific use case. Our framework revolves around three questions: a definitional question on whether the encodings still constitute 'personal data,' a process question about whether the act of executing MPC constitutes a data disclosure event, and a liability question about what happens if something goes wrong. We conclude by providing advice to regulators and suggestions to early adopters to spur uptake of MPC. It is our hope that this work provides the first step toward a methodology that organizations can use when contemplating the use of MPC.
Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation

With the development of unmanned vehicles and other technologies, the technical demand for scene semantic segmentation is more and more intense. Semantic segmentation requires not only rich high-level semantic information, but also rich detail information to ensure the accuracy of the segmentation task. Using a multipath structure to process underlying and semantic information can improve efficiency while ensuring segmentation accuracy. In order to improve the segmentation accuracy and efficiency of some small and thin objects, a detail guided multilateral segmentation network is proposed. Firstly, in order to improve the segmentation accuracy and model efficiency, a trilateral parallel network structure is designed, including the context fusion path (CF-path), the detail information guidance path (DIG-path), and the semantic information supplement path (SIS-path). Secondly, in order to effectively fuse semantic information and detail information, a feature fusion module based on an attention mechanism is designed. Finally, experimental results on CamVid and Cityscapes datasets show that the proposed algorithm can effectively balance segmentation accuracy and inference speed.
Multi-scale contextual semantic enhancement network for 3D medical image segmentation

Objective. Accurate and automatic segmentation of medical images is crucial for improving the efficiency of disease diagnosis and making treatment plans. Although methods based on convolutional neural networks have achieved excellent results in numerous segmentation tasks of medical images, they still suffer from challenges including drastic scale variations of lesions, blurred boundaries of lesions and class imbalance. Our objective is to design a segmentation framework named multi-scale contextual semantic enhancement network (3D MCSE-Net) to address the above problems.Approach. The 3D MCSE-Net mainly consists of a multi-scale context pyramid fusion module (MCPFM), a triple feature adaptive enhancement module (TFAEM), and an asymmetric class correction loss (ACCL) function. Specifically, the MCPFM resolves the problem of unreliable predictions due to variable morphology and drastic scale variations of lesions by capturing the multi-scale global context of feature maps. Subsequently, the TFAEM overcomes the problem of blurred boundaries of lesions caused by the infiltrating growth and complex context of lesions by adaptively recalibrating and enhancing the multi-dimensional feature representation of suspicious regions. Moreover, the ACCL alleviates class imbalances by adjusting asy mmetric correction coefficient and weighting factor.Main results. Our method is evaluated on the nasopharyngeal cancer tumor segmentation (NPCTS) dataset, the public dataset of the MICCAI 2017 liver tumor segmentation (LiTS) challenge and the 3D image reconstruction for comparison of algorithm and DataBase (3Dircadb) dataset to verify its effectiveness and generalizability. The experimental results show the proposed components all have unique strengths and exhibit mutually reinforcing properties. More importantly, the proposed 3D MCSE-Net outperforms previous state-of-the-art methods for tumor segmentation on the NPCTS, LiTS and 3Dircadb dataset.Significance. Our method addresses the effects of drastic scale variations of lesions, blurred boundaries of lesions and class imbalance, and improves tumors segmentation accuracy, which facilitates clinical medical diagnosis and treatment planning.
VStore: Feasibility and acceptability of a novel virtual reality functional cognition task

Virtual reality (VR) is becoming an increasingly popular tool in neuroscience and mental health research. In recent years, efforts have been made to virtualise neuropsychological testing with the intent to increase the ecological validity of cognitive assessments. However, there are some limitations in the current literature—feasibility and acceptability data are often not reported or available and sample sizes have generally been small. In this study, we describe the development and establish the feasibility and acceptability of use of a novel functional cognition VR shopping task, VStore, in three separate samples with data from a total of 210 participants. Two samples include healthy volunteers between the ages of 20 and 79 and there is one clinical cohort of patients with psychosis. Main VStore outcomes were: 1) verbal recall of 12 grocery items, 2) time to collect items, 3) time to select items on a self-checkout machine, 4) time to make the payment, 5) time to order hot drink, and 6) total time. Feasibility and acceptability were assessed by the completion rate across the three studies. VR induced adverse effects were assessed pre- and post-VStore administration to establish tolerability. Finally, as an exploratory objective, VStore’s ability to differentiate between younger and older age groups, and between patients and matched healthy controls was examined as preliminary indication of its potential utility. The overall completion rate across the studies was exceptionally high (99.95%), and VStore did not induce any adverse effects. Additionally, there was a clear difference in VStore performance metrics between both the patients and controls and between younger and older age groups, suggesting potential clinical utility of this VR assessment. These findings demonstrate that VStore is a promising neuropsychological tool that is well-tolerated and feasible to administer to both healthy and clinical populations. We discuss the implications for future research involving neuropsychological testing based on our experience and the contemporary literature.
The Effects of Text Type and Text Length on the Experience of Mandarin Voice Perception on Smart Mobile Devices

Voice interaction is a user-friendly interaction method for Mandarin Chinese. Voice perception has been widely implemented on smart devices for Mandarin voice interaction. However, previous studies gave little attention to how language features, such as the text type and text length affect the voice perception experience. Moreover, how the device type influences Mandarin voice perception is unclear, as the device type has been shown to have a significant effect on other interaction methods, such as interactions with keyboard entry and screen displays. Thus, the aim of this study was to investigate the effects of the text type, text length, and device type on the voice perception experience. This study invited seventy-two participants (36 females and 36 males) for an empirical experiment. Three text types, i.e., classical Chinese text, professional text, and daily text, three text lengths, namely, short (i.e., 10 characters), medium (i.e., 20 characters), and long (i.e., 30 characters), and two device types, i.e., mobile phones and smartwatches, were considered. Four measurements were used to evaluate the voice perception experience, namely, performance (i.e., self-reported performance and correct ratio), the satisfaction rating, mental workload, and skin conductance. The results indicate that the text type, text length, and device type had significant effects on the four measurements. Specifically, the voice perception experience was best for daily text and worst for classical Chinese text, shorter text led to a better voice perception experience, and users of smartwatches had a better voice perception experience than mobile phones. Furthermore, the design implications for improving the voice perception experience that consider the text type, text length, and device type were discussed.
Can Gestural Filler Reduce User-Perceived Latency in Conversation with Digital Humans?

The demand for a conversational system with digital humans has increased with the development of artificial intelligence. Latency can occur in such conversational systems because of natural language processing and network issues, which can deteriorate the user’s performance and the availability of the systems. There have been attempts to mitigate user-perceived latency by using conversational fillers in human–agent interaction and human–robot interaction. However, non-verbal cues, such as gestures, have received less attention in such attempts, despite their essential roles in communication. Therefore, we designed gestural fillers for the digital humans. This study examined the effects of whether the conversation type and gesture filler matched or not. We also compared the effects of the gestural fillers with conversational fillers. The results showed that the gestural fillers mitigate user-perceived latency and affect the willingness, impression, competence, and discomfort in conversations with digital humans.
On Dual B-Topological Spaces Determined by Filterbase and Some Sets in a Dual B-algebra

This paper presents dual B-topologies that are determined by filterbase and some sets in a dual B-algebra. Also, some properties of a filterbase in a dual B-topological space are provided. In particular, a commutative dual B-topological space and a symmetric B-topological space are topological dual B-algebras.
A distributed storage MLCS algorithm with time efficient upper bound and precise lower bound

Finding the longest common subsequence of multiple sequences (i.e., the MLCS problem) is a key issue in data mining, such as pattern recognition of bio-sequences (e.g., DNA and protein sequences), diagnosis of diseases, and deciphering human genetic codes. The length of the longest common subsequence is often used to measure the similarity between sequences. Current state-of-the-art algorithms for the MLCS problem often transform the problem into finding the longest path in a directed acyclic graph (DAG); however, their fatal flaw is that the time cost or constructed DAG is too large. Aiming at solving the problem of the existing algorithms, this paper proposes a time-efficient upper bound estimation method that can estimate the upper bound for the length of any path through a node in much less time, and then proposes an adaptive lower bound estimation strategy that can estimate the more precise lower bound for the length of the longest common subsequences. This study also describes a distributed storage scheme that stores most parts of the DAG in external devices and only a few parts in memory. A comparison with several state-of-the-art algorithms in the experiments indicates that the proposed algorithm is more efficient and effective.
WSFeIn: A Novel, Dynamic Web Service Composition Adapter for Cloud-Based Mobile Application

Service-Oriented Computing (SOC) has been a cornerstone development in today’s fast-paced world, covering the lifecycle of services and contributing to service delivery through distributed applications. SOC is striving to integrate cloud computing with complicated mobile apps. Dynamic and adaptable web service composition is the tip of the iceberg for SOA adoption. Dynamic service binding is vital for mobile computing due to the need for distributed mobile internet consumption at runtime. This study addresses SOC difficulties associated with web service composition, whose growth creates a paradigm change in identifying data type matching solutions. This allows for data type-level matching research to ensure high-quality web service creation. In this work, the composition process is divided into three phases: web service discovery, web service selection, and web service composition, where web service personalization and workflow reliability are emphasized. The final result is a complicated mobile app that runs without depleting device resources and an adaptable, reusable web service composition workflow. This improves matching at the data type level, an SOC pain point.
Filling Control of a Conical Tank Using a Compact Neuro-Fuzzy Adaptive Control System

This document describes the implementation of a conical tank control system using an adaptive neurofuzzy system. For implementation, an indirect approach is used where the controller is optimized using the model obtained during the plant identification carried out using data obtained during the system operation. Furthermore, implementation includes training of neuro fuzzy-systems and application to control a conical tank. Regarding plant identification, preliminary training takes place using data obtained for different input values. The controller configuration is established considering the analogy with a discrete-time linear system. The simulation shows that the control system manages to approach the desired response given by the considered reference model.
Online/Offline Attribute-Based Boolean Keyword Search For Internet Of Things

Abstract With the rapid development of Internet of Things (IoT) and cloud computing, a large amount of IoT data has been stored and shared in cloud servers. However, sensitive IoT data may be leaked by untrusted cloud servers, which is a key problem hindering the development of IoT-cloud systems. To solve this problem, searchable encryption has been presented, which enables IoT devices to encrypt the collected data before uploading them to the cloud and make retrievals over the encrypted data by keyword queries. In this paper, we will study attribute-based keyword search (ABKS), which relieves us from cumbersome key management and provides a fine-grained access control. However, since the existing ABKS schemes are all constructed by bilinear pairings, which incurs high computational costs, they are not suitable for resource-constrained IoT devices. In this article, we propose an online/offline attribute-based boolean keyword search scheme, which can significantly reduce the online computing costs of IoT devices. In addition, our scheme can support boolean keyword search for data users, which has more flexible keyword expressivity than the existing online/offline ABKS schemes. We simulate our scheme and the experimental results show that it is both efficient and practical for the real IoT-cloud systems.
Slow down and frown to improve phishing detection

The move to ‘digital first’ has led to increasing dependence on online services, which increases susceptibility to security incidents. 1 Human behaviours can compromise organisational information security, with myriad perpetrators willing to exploit the human propensity to trust in order to achieve such compromises. 2 Phishing emails – which present recipients with an email containing a fraudulent link or a rogue attachment that can lead to the installation of malware or facilitate a ransomware attack – are a key attack vector. But encouraging users to slow down when processing emails can help combat this threat.
Material fingerprinting as a tool to investigate between and within material type variability with a focus on material hardness

Geochemical and mineralogical datasets from Tropicana Gold Mine, Australia, have been used to define Au-mineralised fingerprints. VNIR-SWIR spectral data were represented by four normalised wavelength regions and were clustered to form spectral classes. Sequentially, these spectral class proportions within a block and co-located pXRF data were clustered to discriminate material types (fingerprints). The hardness of each type was further explored using collocated BWi, Axb, Equotip rebound hardness and penetration rate datasets, but also by considering spatial contextual relationships and the within material type variability. The Tropicana orebody example gave a good illustration of how a phengitic-epidote K-feldspar rich domain (schistosity and softer, ∼15–18 kWh/t) separated from a harder (>20 kWh/t), shorter wavelength phengitic plagioclase-rich feldspar dominated domain. Exploring the within material type differences using the white mica composition (wAlOH) and a new w605 spectral feature demonstrated how the effects of shearing were captured within material types. Such findings will ultimately improve the understanding of the constitutive material hardness and have significance for process optimisation and blending strategy design.
Two-weight or three-weight binary linear codes from cyclotomic mappings

Recently, linear codes with few weights have been studied extensively. These linear codes have wide applications in secret sharing schemes and authentication codes. In this paper, we introduce a new construction of defining sets using cyclotomic mappings and construct three new classes of binary linear codes with two or three weights. We also explicitly determine the weight distributions of these codes.
Introduction to Advanced Information Technology

Abstract Over the years Information Systems (IS) have become increasingly complex and are difficult to grapple. The complexity of recent novel technologies like blockchain (BCT), artificial intelligence (AI), and cloud computing constitutes a genuine challenge to IT-auditors tasked with auditing these IS to provide assurance. This chapter discusses the inner-workings, intricacies, and concepts related to these technologies to provide the background necessary to perform an audit.
AI-Oriented Software Engineering (AIOSE): Challenges, Opportunities, and New Directions

Nowadays, the idea of artificial intelligence for software engineering is an emerging research domain and it has been receiving more and more attention recently. Software Engineering (SE) is a well-established technological paradigm, providing clear guidance and direction on every aspect of software development and maintenance. Artificial Intelligence (AI), contrary to the conventional approach, is an emerging and futuristic concept that reshapes a new era, designing intelligent machines that can accomplish tasks that humans do towards a technological wonderland. In parallel with technological advancements, SE is yet to be considered in adopting the state-of-the-art principle for AI-based methodologies. We realize the importance of facilitating the SE towards AI-based intelligent machines, devising novel techniques and approaches for AI-Oriented Software Engineering (AIOSE). In this study, we thoroughly explore the principle of SE within the AI boundary for ensuring effective development activities throughout the AIOSE development process. We believe that such a compendium study will help with linking AI and SE towards overcoming existing limitations for the betterment of both SE and AI communities.
Auditing Complexity

Abstract This chapter includes the primary issues that auditors and managers should address in order to control contemporary complex information systems, such as blockchain and artificial intelligence. These contemporary information systems often also impact an increasing number of stakeholders and regularly make autonomous and influential decisions. We conclude that system quality is primarily influenced by system design; however, independent audits remain one of the best incentives for software engineers and their managers to truly effectuate adequate control measures.
Robust watermarking based on blur‐guided JND model for macrophotography images

Macrophotography Images (MPIs) have recently emerged as an active topic due to the development of mobile phone camera technology. A large number of MPIs have been rapidly increasing in many rich visual services, such as smartphones or high-definition monitors. MPIs are often composed of sharp macroimage and blur background, which exhibit different perceptual properties that often lead to different just noticeable difference (JND) estimation. Inspired by this, we formulate the blur concealment (BC) as another factor to determine the total masking effect: the interaction is relatively straightforward with a limited masking effect in the sharp regions, and is complicated with a strong masking effect in the blur parts. Furthermore, texture and orientation adaption and color information weighting are separately incorporated into the contrast masking and color masking. Finally, considering both BC and masking effects, a novel robust watermarking framework based on the proposed blur-guided JND model for MPIs, targeting at further improving the MPIs copyright protection performance. Extensive experiments on MP2020 and Blur Detection data sets show that the applicability of the proposed JND model in the scenario of perceptually MPIs watermarking, and our proposed scheme can outperform the state-of-the-art watermarking schemes by providing better robustness performance at the uniform visual quality.
Projections onto hyperbolas or bilinear constraint sets in Hilbert spaces

Sets of bilinear constraints are important in various machine learning models. Mathematically, they are hyperbolas in a product space. In this paper, we give a complete formula for projections onto sets of bilinear constraints or hyperbolas in a general Hilbert space.
A Profile of Practices for Reporting Systematic Reviews: A Conference Case

Several criticisms about the quality of reporting systematic reviews (SRs) have been published and new guidelines propose to use standardized instruments to report them. To identify practices for reporting SRs, I reviewed 32 SRs published in the International Conference on Software Process Improvement (CIMPS). Well reported practices are related to the execution of the automatic database search process and the identification of selection criteria. However, issues arise related to the completeness of the search process, procedures for dealing with inconsistencies during selection, extraction, and classification of data. Besides, validity threats only are addressed by a third of SRs. As a conclusion, the identification of reporting practices can help SR authors to identify both strengths and opportunities areas for conducting and reporting SRs.