Reviewerly/exHarmony huggingface dataset:
Reconfigurable nonlinear optical element using tunable couplers and inverse-designed structure

Abstract In recent years, wave-based analog computing has been at the center of attention for providing ultra-fast and power-efficient signal processing enabled by wave propagation through artificially engineered structures. Building on these structures, various proposals have been put forward for performing computations with waves. Most of these proposals have been aimed at linear operations, such as vector-matrix multiplications. The weak and hardly controllable nonlinear response of electromagnetic materials imposes challenges in the design of wave-based structures for performing nonlinear operations. In the present work, first, by using the method of inverse design we propose a three-port device, which consists of a combination of linear and Kerr nonlinear materials, exhibiting the desired power-dependent transmission properties. Then, combining a proper arrangement of such devices with a collection of Mach–Zehnder interferometers (MZIs), we propose a reconfigurable nonlinear optical architecture capable of implementing a variety of nonlinear functions of the input signal. The proposed device may pave the way for wave-based reconfigurable nonlinear signal processing that can be combined with linear networks for full-fledged wave-based analog computing.
Fraud detection in capital markets: A novel machine learning approach

Traditional auditing methods require collating massive amounts of financial indicators and related transaction data, which can be labor-intensive. Typical machine learning models are relatively weak for imbalanced data, and this work aims to focus on a novel approach to fraud detection. This paper presents a fraud detection framework via adopting a machine learning method integrated with a recently proposed meta-heuristics algorithm Egret Swarm Optimization Algorithm (ESOA). A cost-sensitive objective function and loss function were then constructed, and a non-linear model was used to map the predicted values into the labels of 0 (non-fraud) and 1 (fraud). In the experiment section, an AAER benchmark dataset collected by the UCB's Center for Financial Reporting and Management is utilized to verify the performance of the proposed approach. A detailed comparison with recently proposed state-of-the-art algorithms such as Logit (67.20%), SVM-FK (62.60%), RUSBoost (72.60%), as well as BAS (84.90%) indicates that ESOA (96.27%) outperforms the other algorithms in terms of Accuracy (ACC), Sensitivity (SEN), Precision (PREC), and Area Under the Curve (AUC) metrics. To our knowledge, this is the highest fraud detection accuracy reported in the existing literature.
Area-Efficient Realization of Binary Elliptic Curve Point Multiplication Processor for Cryptographic Applications

This paper proposes a novel hardware design for a compact crypto processor devoted to elliptic-curve point multiplication over GF(2233). We focus on minimizing hardware usage, which we obtain using an iterative bit–serial finite field modular multiplier for polynomial coefficient multiplication. The same multiplier is also used for modular squares and inversion computations, further optimizing the hardware footprint. Our design offers flexibility by permitting users to load different curve parameters and secret keys while keeping a low-area hardware design. To efficiently generate the control signals, we utilize a finite-state-machine-based controller. We have implemented the proposed crypto processor on Virtex-6 and Virtex-7 FPGA devices, and we have evaluated its performance at clock frequencies of 100, 50, and 10 MHz. Specifically, for one point multiplication computation on Virtex-7 FPGA, our crypto processor uses 391 slices, attains a maximum frequency of 161 MHz, has a latency of 4.45 ms, and consumes 77 mW of power. These results, along with a comparison to state-of-the-art designs, clearly demonstrate the practicality of our crypto processor for applications requiring efficient and compact cryptographic computations.
The effect of immersion on sense of presence and affect when experiencing an educational scenario in virtual reality: A randomized controlled study

Virtual reality (VR) technology has been used to learn skills for decades. While no standardized measure exists for learning outcomes in VR training, commonly explored outcomes are immersion, sense of presence and emotions.In this paper, the objective was to investigate these outcomes in two VR conditions, immersive and desktop in a randomized controlled trial with a parallel design. The sample consisted of 134 university students (70 women, mean age 23 years, SD = 2.99). These were randomized using a covariate-adaptive randomization procedure based on stratification by gender into two interventions; play out a VR scenario in either desktop (control group) or immersive VR (intervention group). The setting was a university lab.There was a significant within subject effect for positive affect and a significant between-group effect for the immersive compared to desktop VR groups. Positive affect was reduced after interacting with the VR scenario in both the immersive and desktop versions, however, positive affect was overall higher in the immersive, compared to the desktop version. The results show higher scores for sense of presence (d = 0.90, p < 0.001) and positive affect pre- and post-scenario in the immersive VR condition (d = 0.42, p = 0.017 and d = 0.54, p = 0.002) compared to the desktop condition.Immersive VR may be beneficial in higher education as it promotes high levels of sense of presence as well as positive emotions. When it comes to changing the immediate emotions of the students, type of VR does not seem to matter. The project was funded by the Norwegian Directorate for Higher Education and Skills.
Circinus: Fast Redundancy-Reduced Subgraph Matching

Subgraph matching is one of the most important problems in graph analytics. Many algorithms and systems have been proposed for subgraph matching. Most of these works follow Ullmann's backtracking approach as it is memory-efficient in handling an explosive number of intermediate matching results. However, they have largely overlooked an intrinsic problem of backtracking, namely repeated computation, which contributes to a large portion of the heavy computation in subgraph matching. This paper proposes a subgraph matching system, Circinus, which enables effective computation sharing by a new compression-based backtracking method. Our extensive experiments show that Circinus significantly reduces repeated computation, which transfers to up to several orders of magnitude performance improvement.
How To Optimize My Blockchain? A Multi-Level Recommendation Approach

Aside from the conception of new blockchain architectures, existing blockchain optimizations in the literature primarily focus on system or data-oriented optimizations within prevailing blockchains. However, since blockchains handle multiple aspects ranging from organizational governance to smart contract design, a holistic approach that encompasses all the different layers of a given blockchain system is required to ensure that all optimization opportunities are taken into consideration. In this vein, we define a multi-level optimization recommendation approach that identifies optimization opportunities within a blockchain at the system, data, and user level. Multiple metrics and attributes are derived from a blockchain log and nine optimization recommendations are formalized. We implement an automated optimization recommendation tool, BlockOptR, based on these concepts. The system is extensively evaluated with a wide range of workloads covering multiple real-world scenarios. After implementing the recommended optimizations, we observe an average of 20% improvement in the success rate of transactions and an average of 40% improvement in latency.
Enhancing feature selection with GMSMFO: A global optimization algorithm for machine learning with application to intrusion detection

Abstract The paper addresses the limitations of the Moth-Flame Optimization (MFO) algorithm, a meta-heuristic used to solve optimization problems. The MFO algorithm, which employs moths' transverse orientation navigation technique, has been used to generate solutions for such problems. However, the performance of MFO is dependent on the flame production and spiral search components, and the search mechanism could still be improved concerning the diversity of flames and the moths' ability to find solutions. The authors propose a revised version called GMSMFO, which uses a Novel Gaussian mutation mechanism and shrink MFO to enhance population diversity and balance exploration and exploitation capabilities. The study evaluates the performance of GMSMFO using the CEC 2017 benchmark and 20 datasets, including a high-dimensional intrusion detection system dataset. The proposed algorithm is compared to other advanced metaheuristics, and its performance is evaluated using statistical tests such as Friedman and Wilcoxon rank-sum. The study shows that GMSMFO is highly competitive and frequently superior to other algorithms. It can identify the ideal feature subset, improving classification accuracy and reducing the number of features used. The main contribution of this research paper includes the improvement of the exploration/exploitation balance and the expansion of the local search. The ranging controller and Gaussian mutation enhance navigation and diversity. The research paper compares GMSMFO with traditional and advanced metaheuristic algorithms on 29 benchmarks and its application to binary feature selection on 20 benchmarks, including intrusion detection systems. The statistical tests (Wilcoxon rank-sum and Friedman) evaluate the performance of GMSMFO compared to other algorithms. The algorithm source code is available at https://github.com/MohammedQaraad/GMSMFO-algorithm.
Context-Aware and Click Session-Based Graph Pattern Mining With Recommendations for Smart EMS Through AI

In the field of Artificial Intelligence (AI), Smart Enterprise Management Systems (Smart EMS) and big data analytics are the most prominent computing technologies. A key component of the Smart EMS system is E-commerce, especially Session-based Recommender systems (SRS), which are typically utilized to enhance the user experience by providing recommendations analyzing user behavior encoded in browser sessions. Also the work of the recommender is to predict users’ next actions (click on an item) using the sequence of actions in the current session. Current developments in session-based recommendation have primarily focused on mining more information accessible within the current session. On the other hand, those approaches ignored sessions with identical context for the current session that includes a wealth of collaborative data. Therefore this paper proposed Context-aware and Click session-based graph pattern mining with recommendations for Smart EMS through AI. It employs a novel Triple Attentive Neural Network (TANN) for SRS. Specifically, TANN contains three main components, i.e., Enhanced Sqrt-Cosine Similarity based Neighborhood Sessions Discovery (NSD), Frequent Subgraph Mining (FSM) using Neighborhood Click session-based graph pattern mining and Top-K possible Next-clicked Items Discovery (TNID). The NSD module uses a session-level attention mechanism to find m most similar sessions of the query session, and the FSM module also extracts the frequent subgraphs from the already discovered m most similar sessions of the query session via item-level attention. Then, TNID module is used to discover the top-K possible next-clicked items using the NSD and FSM module via a target-level attention. Finally, we perform comprehensive experiments on one big dataset, DIGINETICA, to verify the effectiveness of the TANN model, and the results of this experiment clearly illustrate the performance of TANN.
Scientific School Image Development of a University Based on the System of Public Relations

The article considers the problem of educational institution promotion in the external socio-cultural environment via innovative management, in particular, the Public relations system. The solution to this problem is important in terms of fierce competition in the educational services market. The authors draw attention to the fact that scientific schools as unique associations of university science representatives can be considered as one of the effective elements of the university image policy development. They outlined the primary tasks of the university scientific school positive image development in the public relations system, PR tools are highlighted in the internal university environment and in the external sphere of the university, where the following segments are considered as the target audience: the scientific community outside the university, potential employers of university graduates, and the population represented by potential applicants and their parents.
Adaptive Impact Factor Research Concerning Effectiveness of the Introduction and use of Digital Twins for Oil and Gas Deposits

In recent years, there have been significant changes in the conditions of oil production leading to an increase in its cost and wasteful use of resources. This necessitates the search for a system of adaptive factors that can adapt to environmental changes, affect the cost reduction and increase in the efficiency of oil and gas fields. Studies show that this problem needs to be solved on the basis of the creation of digital oil or gas fields being digital counterparts of existing enterprises, which allow preserving nature and use resources economically due to the existing field development at a new qualitative level. However, the transformation of existing fields through their transformation into digital oil or gas fields requires serious justification, and above all, from a financial and economic points of view. At the same time, one should in no case ignore the natural factor contributing to saving, restoring the used oil and gas resources and preserving the external space being the human environment. The purpose of this study is to develop recommendations for assessing the economic efficiency of the implementation of the project concerning a digital oil or gas field being a digital twin of oil or gas enterprise, and their use in practice. Such an assessment will be carried out based on the analysis of the ratio between capital investments and operating costs necessary to create a digital oil or gas field, as well as by comparing the expected costs and benefits derived from its use.
Identifying ESG Trends of International Container Shipping Companies Using Semantic Network Analysis and Multiple Case Theory

Strengthened International Maritime Organization environmental regulations, the Poseidon principles, and other environmental changes require shipping companies to expand their Environmental, Social, and Governance (ESG) investments. ESG management is evolving as an essential requirement to enhance the competitiveness of global liner shipping companies. Under these circumstances, this study aims to provide policy guidelines and future directions for the development of ESG management in Korean shipping companies. Accordingly, we selected four companies under THE Alliance, which is an ESG standard leader in shipping. Based on each shipping company’s sustainability reports from 2019 to 2021, we analyzed the major ESG performance factors required for a company to achieve its sustainability goals, developing four research questions. Based on multiple case study theory, we compared each company’s ESG management status and main characteristics using the quadruple helix model, proposing three measures to improve the ESG management of Korean shipping companies: 1. Cultivate ESG experts specialized in the maritime field; 2. Strengthen support for small- and medium-sized companies; 3. Construct a maritime-specialized ESG evaluation system and introduce voluntary management culture. Our findings contribute to the maritime industry’s development by identifying necessary factors and proposing improvement directions that will help Korean shipping companies to secure global competitiveness.
The Impact of Blockchain on Enterprises Sharing Real Data Based on Dynamic Evolutionary Game Analysis

The use of blockchain technology can ensure that data remains untampered with once it is on the chain. However, it doesn’t guarantee the authenticity of data before it enters the chain. In this study, we developed a three-party dynamic evolutionary game model involving core enterprises, small and medium-sized enterprises (SMEs), and financial institutions. Our findings indicate that a blockchain supply chain (BSC) generates more economic benefits than a traditional supply chain (TSC). We then built a dynamic evolutionary game model between core enterprises and SMEs, which revealed that SMEs are influenced by core enterprises and tend to adopt the action strategies of the latter. Additionally, we developed a dynamic evolutionary game model between core enterprises and financial institutions and compared the reward and punishment mechanisms with the synergy payoff mechanism. In the reward and punishment mechanisms, the game is a zero-sum game, where one party’s gains come at the expense of the other party. This mechanism has certain limitations and must meet specific conditions to improve the willingness of enterprises to share data. On the other hand, the synergy payoff mechanism enhances the authenticity of shared data by increasing the payoff for participants. When core enterprises play games with SMEs, the probability of core enterprises uploading real data and the distribution ratio of synergy payoff show an inverted U-shape. Similarly, core enterprises and financial institutions have comparable results in allocating synergy payoff. To leverage the synergy payoff mechanism, the distribution proportion of players participating in the synergy payoff should be considered fair. Finally, we validated our findings by simulating the models. If we can use blockchain technology to enhance the mutual trust between enterprises and banks, both banks and enterprises can achieve sustainable development.
Ecotourism for transformative and youth development in sub-Saharan Africa: the role of corporate social responsibility in Nigeria’s oil host communities

ABSTRACTABSTRACTWe examine the impact of multinational oil companies' (MOCs) corporate social responsibility (CSR) initiatives on enabling youth participation in ecotourism development in the Niger Delta region of Nigeria. Results from the use of average treatment test of a combined propensity score matching and logit model indicate a significant difference between youths in MOCs' CSR global memorandum of understanding (GMoU) households and non-GMoU households in the four parameters measured: availability of finance (3.76), access to adequate training (5.91), direct patronage (18.97), and economic capability of the youths (8.2). It shows that opportunities to supply products and services to the tourism sector can help ensure a sustainable market and increase incomes and other revenues in local communities driven from ecotourism-related activities, while minimizing economic leakages. This suggests that pro-youth GMoU ecotourism projects of MOCs have the potential to play in the formation of linkages to help promote local economic development through job creation and business opportunities. It implies that a younger generation can help to promote economic diversification and contribute to job creation and enterprise development, while helping to address underdevelopment in remoe areas and intractable environmental challenges of sub-Saharan Africa.KEYWORDS: Ecotourism developmentenvironmental justicecorporate social responsibilityNiger Delta youthsub-Saharan Africa AcknowledgementThe author(s) are indebted to the editor and reviewers for constructive comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsJoseph Ikechukwu UdujiJoseph Ikechukwu Uduji holds PhD (marketing), PhD (public administration), MSc (marketing), MSc (public relations), MBA (business administration), and MPA (public administration) and is currently the head, Department of Marketing, University of Nigeria, Nsukka. Whilst he lectures business and development courses at the University of Nigeria, he is also a visiting professor at the Catholic University of Cameroon, Bamenda. His current research interests include social justice, social responsibility, and inequality nexus in developing countries.Elda Nduka Okolo-ObasiElda Nduka Okolo-Obasi holds BSc (agricultural economics) from the Federal University of Technology Owerri and MSc (development studies) from the University of Nigeria, Nsukka and Enugu Campuses. He is a doctoral student and research fellow at the Institute for Development Studies, University of Nigeria. He is also a research fellow at the Development Strategy Centre (A private Think Tank based in) Enugu, Nigeria. His current research interests include economic and social impact evaluation of policies and projects of the public and private sectors in developing countries.
QR code: The global making of an infrastructural gateway

This article traces the history of machine-readable data encoding standards and argues that the QR code has become an infrastructural gateway. Through the analysis of patents, corporate documents and advertising, ethnographic observations, and interviews with professionals, I describe the global making of the QR code and argue that the convergence of data encoding standards, mobile computing, machine vision algorithms, and platform ecosystems has led to the emergence of a new component of computational infrastructures which functions as a gateway between different actors, systems, and practices. The central section of the article covers seven decades of machine-readable data encoding history across different national and regional contexts: from the invention and popularization of the barcode in the United States, through the QR code’s invention in Japan and its success in East Asia, to its platformization in China. By revisiting this history through concepts drawn from the field of infrastructure studies, I argue that QR codes have become infrastructural gateways and conclude that this concept is useful not only to understand the current role of QR codes but also to identify and follow the emergence and change of other gateways in infrastructures to come.
On the Reliability of the Area Under the ROC Curve in Empirical Software Engineering

Binary classifiers are commonly used in software engineering research to estimate several software qualities, e.g., defectiveness or vulnerability. Thus, it is important to adequately evaluate how well binary classifiers perform, before they are used in practice. The Area Under the Curve (AUC) of Receiver Operating Characteristic curves has often been used to this end. However, AUC has been the target of some criticisms, so it is necessary to evaluate under what conditions and to what extent AUC can be a reliable performance metric.