Reviewerly/exHarmony huggingface dataset:
Cybersecurity in Accounting Research

This chapter aims to update the cybersecurity-related accounting literature by synthesising 39 recent theoretical and empirical studies on the topic. Furthermore, the chapter provides a set of categories into which the studies fit. It is a synthesis chapter that summarises the research literature on cybersecurity, introducing knowledge from the extant research and revealing areas requiring further examination. This synthesis identifies a research framework that consists of the following research themes: cybersecurity and information sharing; cybersecurity investments; internal auditing and controls related to cybersecurity; disclosure of cybersecurity activities; and security threats and security breaches. Academics, practitioners, and the public would benefit from a research framework that categorises the research topics related to cybersecurity in the accounting field. This type of analysis is vital to enhance the understanding of the academic research on cybersecurity and can be used to support the identification of new lines for future research. This analysis has significant implications for research and practice by detailing, for example, the benefits of and obstacles to information sharing. This synthesis also highlights the importance of the model for cybersecurity investments. Further, the review emphasises the role of internal auditing and controls to improve cybersecurity.
Evolution in Software Product Lines: Defining and Modelling for Management

Evolution in Software Product Lines: Defining and Modelling for Management by Amougou Ngoumou * , Marcel Fouda Ndjodo Department of Computer Science, Higher Teacher Training College, University of Yaounde I, Cameroon * Author to whom correspondence should be addressed. Journal of Engineering Research and Sciences, Volume 1, Issue 5, Page # 179-185, 2022; DOI: 10.55708/js0105019
Special Issue on “Operational Research Models for Blockchain”

International Transactions in Operational ResearchVolume 29, Issue 6 p. 3800-3801 Call for Papers Special Issue on “Operational Research Models for Blockchain” Tsan-Ming CHOI (Jason), Tsan-Ming CHOI (Jason) Yushan Fellow and Professor, Department and Graduate Institute of Business Administration, College of Management, National Taiwan University, TaiwanSearch for more papers by this author Tsan-Ming CHOI (Jason), Tsan-Ming CHOI (Jason) Yushan Fellow and Professor, Department and Graduate Institute of Business Administration, College of Management, National Taiwan University, TaiwanSearch for more papers by this author First published: 08 June 2022 https://doi.org/10.1111/itor.12832Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume29, Issue6November 2022Pages 3800-3801 RelatedInformation
Effects of Data Enrichment with Image Transformations on the Performance of Deep Networks

Images cannot always be expected to come in a certain standard format and orientation. Deep networks need to be trained to take into account unexpected variations in orientation or format. For this purpose, training data should be enriched to include different conditions. In this study, the effects of data enrichment on the performance of deep networks in the super resolution problem were investigated experimentally. A total of six basic image transformations were used for the enrichment procedures. In the experiments, two deep network models were trained with variants of the ILSVRC2012 dataset enriched by these six image transformation processes. Considering a single image transformation, it has been observed that the data enriched with 180 degree rotation provides the best results. The most unsuccessful result was obtained when the models were trained on the enriched data generated by the flip upside down process. Models scored highest when trained with a mix of all transformations.
Causality Learning with Wasserstein Generative Adversarial Networks

Conventional methods for causal structure learning from data face significant challenges due to combinatorial search space. Recently, the problem has been formulated into a continuous optimization framework with an acyclicity constraint to learn Directed Acyclic Graphs (DAGs). Such a framework allows the utilization of deep generative models for causal structure learning to better capture the relations between data sample distributions and DAGs. However, so far no study has experimented with the use of Wasserstein distance in the context of causal structure learning. Our model named DAG-WGAN combines the Wasserstein-based adversarial loss with an acyclicity constraint in an auto-encoder architecture. It simultaneously learns causal structures while improving its data generation capability. We compare the performance of DAG-WGAN with other models that do not involve the Wasserstein metric in order to identify its contribution to causal structure learning. Our model performs better with high cardinality data according to our experiments.
Winning the 3rd Japan Automotive AI Challenge - Autonomous Racing with the Autoware.Auto Open Source Software Stack

The 3rd Japan Automotive AI Challenge was an international online autonomous racing challenge where 164 teams competed in December 2021. This paper outlines the winning strategy to this competition, and the advantages and challenges of using the Autoware.Auto open source autonomous driving platform for multi-agent racing. Our winning approach includes a lane-switching opponent overtaking strategy, a global raceline optimization, and the integration of various tools from Autoware.Auto including a Model-Predictive Controller. We describe the use of perception, planning and control modules for high-speed racing applications and provide experience-based insights on working with Autoware.Auto. While our approach is a rule-based strategy that is suitable for non-interactive opponents, it provides a good reference and benchmark for learning-enabled approaches.
Remote Sensing Studies for Mapping of Iron Oxide Regions in Keonjhor district, Odisha, India

Odisha is known for having a bounty of mineral resources, which are required for the economic and industrialdevelopment of the state. Ore minerals such as hematite, magnetite, pyrolusite, psilomelane, chromite etcare richly available in Odisha. A lot work has been done on the mineral property, texture, behaviour ofindividual ions in different chemical conditions within the domain of the aforesaid minerals and a very fewworks has reportedly been done on the spectral behaviour of these ore minerals. For the development ofhuman societies, economic and industrial demand of iron has been raised which resulted in search of newcost-effective techniques for the exploration of ore minerals containing Fe metal. Remote sensing methodsare now considered as modern and cost effective highly dynamic techniques for exploration of economic ore minerals containing iron oxides. In this study, various digitalimage processing techniques of remote sensingsuch as False Colour Composites (FCCs), Band Ratios (BRs), and Principal Components Analysis (PCA) were used on satellite derived images for mapping iron oxide mineralization zone. The results were compared with the already published maps and also with the field inspection. From the results, it was found that various band combinations of BRs and PCA may be used for mapping of iron oxide ore regions.
Convex-roof entanglement measures of density matrices block diagonal in disjoint subspaces for the study of thermal states

We provide a proof that entanglement of any density matrix which is block diagonal in subspaces which are disjoint in terms of the Hilbert space of one of the two potentially entangled subsystems can simply be calculated as the weighted average of entanglement present within each block. This is especially useful for thermal-equilibrium states which always inherit the symmetries present in the Hamiltonian, since block-diagonal Hamiltonians are common, as are interactions which involve only a single degree of freedom of a greater system. We exemplify our method on a simple Hamiltonian, showing the diversity in possible temperature dependences of Gibbs state entanglement which can emerge in different parameter ranges.
A Cautionary Note about Having the Right Mixture Model but Classifying the Wrong People

Mixture models can be used for explanation or individual prediction and classification. In practice, researchers are often tempted to make the class membership manifest by classifying cases according to their class of maximum posterior probability and using the “observed” class membership directly or as a variable in follow-up analyses to predict distal outcomes. This study revisits the issue of correct class assignment in latent profile analysis by providing an example where the number of classes is known (3-classes), sampling variability is eliminated, and precise estimates of classification indices are provided. This pseudo-population study design assumes the data-generating mechanism is known and provides a “best-case” scenario for evaluating correct class assignment. We use a variety of classification indices and graphical displays to show that correct classification may be poor despite relatively high entropy and overall correct class assignment metrics (e.g., percent correct). Our study serves as a reminder of the risks associated with trying to make latent class memberships manifest.
Latent Drummer: A New Abstraction for Modular Sequencers

Automated processes in musical instruments can serve to free a performer from the physical and mental constraints of music performance, allowing them to expressively control more aspects of music simultaneously.Modular synthesis has been a prominent platform for exploring automation through the use of sequencers and has therefore fostered a tradition of user interface design utilizing increasingly complex abstraction methods.We investigate the history of sequencer design from this perspective and introduce machine learning as a potential source for a new type of intelligent abstraction.We then offer a case study based on this approach and present Latent Drummer, which is a prototype system dedicated to integrating machine learningbased interface abstractions into the tradition of sequencers for modular synthesis.
Analisis Penerimaan Pengguna Sistem Informasi Akademik STIQ Al-Lathifiyyah Menggunakan Task Technology Fit

SIAKAD is applied to obtain all information related to computerized academic activities. One of the initial keys to the success or failure of an application/software implementation is the willingness to accept the technology among users. This study aims to measure the level of user acceptance and user ease of academic information systems and provide efficient and relevant service facilities. To obtain efficient and relevant services, it is necessary to implement a system with the best quality so that it can be accepted by users. One way that can be done is by analyzing user acceptance of academic information systems. In this study the method used is Task Technology fit (TTF). In this study, 4 variables were used, namely Task Characteristics, Technology Characteristics, Task Technology Fit, and Performance Impact. The data in this study were collected through questionnaires distributed to 135 respondents. Analysis of the data used by using the PLS-SEM tool used is SmartPLS version 3.0. The results showed that the task characteristics had no significant effect on the suitability of the task technology fit with a T-Statistics value of 0.847, the technology characteristics had a significant effect on the suitability of the task technological fit with a T-Statistics value of 13,861, and the suitability of the task technology fit had a significant effect on the performance impact with a T value. -Statistics of 5,570. From the test results, it is stated that there is 1 accepted hypothesis and 2 rejected hypotheses. The overall results of this study indicate that the acceptance of the use of SIAKAD STIQ Al-Lathifiyyah Palembang using the Task Technology Fit (TTF) Model has an effect of 97.3%. From this percentage, it can be concluded that SIAKAD STIQ Al-Lathifiyyah Palembang has been accepted and has been used by users, namely students, lecturers, and admins.
(Retracted) Algorithm of generating music melody based on single-exposure high dynamic range digital image using convolutional neural network

Music is a very important art. However, professional music production has a relatively high investment cost and a single model, which cannot meet people’s individual needs. Single-exposure high dynamic range (HDR) digital images based on convolutional neural networks (CNNs) can solve this problem. A comparative experiment was carried out. The final experimental results show that in terms of the quality of melody generation, the algorithm is almost the same as manual creation. In terms of generation speed, the CNN single-exposure HDR digital image model is 1.1 times faster than the traditional algorithm model, which proves its advantage of higher creative ability. Popularizing it in the music creation market at the current stage can effectively improve the efficiency of music creation and promote the intelligent development of the music market.
STUDENT'S MATHEMATICS LITERACY ABILITY THROUGH THE ASSISTED LEARNING CYCLE MODEL WITH GOOGLE CLASSROOM

The purpose of the study was to determine students' mathematical literacy skills in working on problems through the learning cycle model. The type of research used is descriptive research using a qualitative approach. The subjects in this study were 6 students of class XI MIPA MA Al-Ma'arif for the 2020/2021 academic year. Data collection techniques used are tests and questionnaires. The instruments used are students' mathematical literacy ability tests, google classroom questionnaires. The data analysis technique used the Miles and Huberman model, namely, data reduction, data presentation, and drawing conclusions. The results showed that 1) the mathematical literacy ability in the very good category was 33%; 2) mathematical literacy ability in good category by 33%; and 3) mathematical literacy ability in the less category of 33%. In conclusion, students' abilities related to working on items with indicators of mathematical literacy skills combined with the learning cycle model are on average good. The implication, interest and motivation to learn as well as the ability to use high technology
A Conceptual Hybrid Model for Privacy Preservation of Consumers in Cloud

Privacy is one of the most popular technologies in the IT industry. A lot of data is collected from users from various sources. The same IoT (Internet of Things) application raised concerns about privacy in IoT systems. Sensor data collects information about users' daily activities and makes life easier, but compromises privacy and security. Implementing privacy and security into applications is a huge challenge. This article presents common methods for maintaining privacy and protecting statistics in IoT applications. To this end, many encryption methods are analysed, and finally, a comparative analysis of personal information protection methods and their applications is presented.
A Study on the factors Affecting the Acceptance of Digital Logistics Platform Using the Technology Acceptance Model

With the growth of the global economy, along with the rapid spread of mobile devices, e-commerce and COVID-19, transactions between shippers and logistics service companies and domestic import and export trades have shown a drastic increase. Platforms merging from the 4th industrial revolution have allowed Small and Medium Enterprises (SME) logistics service companies to participate as equal bidding service providers in Business-to-Business (B2B) logistics field. However, SME logistics services have experienced difficulties in accessing and utilizing the B2B logistics platforms because of the existing trading practices, such as information asymmetry and closures led by existing large corporations in the logistics industry. Therefore, it is necessary for the SME to study the factors that influence users’ decision to use the platform when securing its competitiveness in the logistics field. Since it is difficult to reflect a company’s relationship characteristics of B2B transactions with existing Technology Acceptance Models(TAMs), it is extended by adding fair trade and risk factors to study the technology acceptance behavior of users. The studies show that social influence, price efficiency and fair trade are the crucial variables for the users’ decision to use the platform. It also demonstrates that fair trade, in correlation to risk, has the significant impact on users’ acceptance of the platform. Based on the results of this study, it is beneficial if a company designs a platform structure from the perspective of fair trade and risks when developing a B2B platform in the future.
Signalling conditional relations

Abstract We investigate how discourse relations and their subtypes are signalled, extending the set of discourse signals from connectives and lexical cue phrases to the wide range of semantic, syntactic, and orthographic signals of the RST Signalling Corpus (Das, Debopam &amp; Maite Taboada. 2018. RST signalling corpus. Language Resources and Evaluation 52. 149–184). This extension requires re-evaluating previous predictions on discourse signalling, in particular, those of Sanders, Ted. 2005. Coherence, causality and cognitive complexity in discourse. In M. Aurnague, M. Bras, A. Le Draoulec &amp; L. Vieu (eds.), Proceedings/Actes SEM-05, first international symposium on the exploration and modelling of meaning , 105–114. Biarritz causality-by-default hypothesis, the hypothesis of uniform information density (Frank, Austin &amp; Florian Jaeger. 2008. Speaking rationally: Uniform information density as an optimal strategy for language production. In Proceedings of the 30th annual meeting of the Cognitive Science Society , 933–938. https://escholarship.org/uc/item/7d08h6j4 (accessed 18 May 2022)), and the hypothesis that discourse is continuous by preference (Segal, Erwin, Judith Duchan &amp; Paula Scott. 1991. The role of interclausal connectives in narrative structuring. Discourse Processes 14. 27–54; Murray, John. 1997. Connectives and narrative text. Memory and Cognition 25. 227–236). We evaluate the predictions of these theories on the conditional relations in the RST Discourse Treebank (Carlson, Lynn, Daniel Marcu &amp; Mary Ellen Okurowski. 2002. RST Discourse Treebank. LDC2002T07 . Philadelphia: Linguistic Data Consortium), using causal relations as a control group. Informativity and continuity are operationalized in terms of semantic complexity and Givón, Talmy. 1993. English grammar: A function-based introduction , vol. 2. Amsterdam: John Benjamins dimensions of deictic shift. Our results show that the hypotheses make accurate predictions only for the relation groups in their entirety but not for the observed in-group variation, in particular, the low amount of marking for the hypothetical subtype of conditional relations. We attribute this difference to the distribution of intra- and inter-sentential occurrences across the conditional subtypes: intra-sentential relations are consistently more marked than inter-sentential ones, and hypothetical relations are special in that they appear predominantly inter-sententially.
Adaptive, Continuous Entanglement Generation for Quantum Networks

Quantum networks, which enable the transfer of quantum information across long distances, promise to provide exciting benefits and new possibilities in many areas including communication, computation, security, and metrology. These networks rely on entanglement between qubits at distant nodes to transmit information; however, creation of these quantum links is not dependent on the information to be transmitted. Researchers have explored schemes for continuous generation of entanglement, where network nodes may generate entanglement links before receiving user requests. In this paper we present an adaptive scheme that uses information from previous requests to better guide the choice of randomly generated quantum links before future requests are received. We analyze parameter spaces where such a scheme may provide benefit and observe an increase in performance of up to 75% over other continuous schemes on single-bottleneck and autonomous systems networks. We also test the scheme for other parameter choices and observe continued benefits of up to 95%. The power of our adaptive scheme on a randomized request queue is demonstrated on a single-bottleneck topology. We also explore quantum memory allocation scenarios, where a difference in latency performance implies the necessity of optimal allocation of resources for quantum networks.
Nonlinear quantum error correction

We introduce a theory of quantum error correction (QEC) for a subclass of states within a larger Hilbert space. In the standard theory of QEC, the set of all encoded states is formed by an arbitrary linear combination of the codewords. However, this can be more general than required for a given quantum protocol which may only traverse a subclass of states within the Hilbert space. Here we propose the concept of nonlinear QEC (NLQEC), where the encoded states are not necessarily a linear combination of codewords. We introduce a sufficiency criterion for NLQEC with respect to the subclass of states. The new criterion gives a more relaxed condition for the formation of a QEC code, such that under the assumption that the states are within the subclass of states, the errors are correctable. This allows us, for instance, to effectively circumvent the no-go theorems regarding optical QEC for Gaussian states and channels, for which we present explicit examples.
Preparing many copies of a quantum state in the black-box model

We describe a simple quantum algorithm for preparing $K$ copies of an $N$-dimensional quantum state whose amplitudes are given by a quantum oracle. Our result extends a previous work of Grover, who showed how to prepare one copy in time $O(\sqrt{N})$. In comparison with the naive $O(K\sqrt{N})$ solution obtained by repeating this procedure $K$ times, our algorithm achieves the optimal running time of $\mathrm{\ensuremath{\Theta}}(\sqrt{KN})$. Our technique uses a refinement of the quantum rejection sampling method employed by Grover. As a direct application, we obtain a similar speedup for obtaining $K$ independent samples from a distribution whose probability vector is given by a quantum oracle.