Reviewerly/exHarmony huggingface dataset:
The influence of women’s leadership on corporate sustainability in Indonesia

This article examined the influence of women’s leadership on corporate sustainability by mediating business competence and organizational competence. The research method was quantitative based on positivism and hypothesis testing, combining WarpPLS and the PLS-SEM technique for multivariate analysis. The research was conducted in Indonesian State-Owned Enterprises (SOEs) using a survey, namely an online census of all female leaders at the board of directors and directors’ level minus one (BoD-1) with a response rate of 73%. The characteristics of women’s leadership in Indonesian SOEs tended to be visionary-type leadership compared to organic-type leadership. They were very prominent as crucial players in the organization, built a strong relationship between the leaders and staff, and could make decisions by centralizing decision-making on the leaders. The investigation’s findings using the Structural Equation Model (SEM) revealed that organic leadership was significant for corporate sustainability, while visionary leadership was not significant. Business competence had a full mediation role in the influence of visionary leadership on corporate sustainability. In contrast, organizational competence only partially mediated the influence of organic leadership on corporate sustainability.
Hunger games search optimization with deep learning model for sustainable supply chain management

Abstract The supply chain network is one of the most important areas of focus in the majority of business circumstances. Blockchain technology is a feasible choice for secure information sharing in a supply chain network. Despite the fact that maintaining security at all levels of the blockchain is difficult, cryptographic methods are commonly used in existing works. Effective supply chain management (SCM) offers various benefits to organizations, such as enhanced customer satisfaction, increased operational efficiency, competitive advantage, and cost reduction. Potential SCM for agricultural and food supply chains needs distributors, coordination and collaboration among farmers, retailers, and stakeholders. The use of technology like Block Chain (BC), sensors, and data analytics, can boost traceability and visibility, decrease waste, and ensure safety and quality throughout the supply chain. Therefore, this study develops a Hunger Games Search Optimization with Deep Learning Model for Sustainable agricultural and food Supply Chain Management (HGSODL-ASCM) technique. The fundamental goal of the HGSODL-ASCM technique is to improve decision-making processes for agricultural and food commodity production and storage in order to optimise revenue. In the provided HGSODL-ASCM technique, a bidirectional long short-term memory (Bi-LSTM) model is built to determine the amount of productivity and storage required to maximise profit. In order to boost the performance of the Bi-LSTM classification process, the HGSO algorithm has been utilized in this work. The presented HGSODL-ASCM technique can independently achieve the SCM policies via interaction with complicated and adaptive environments. A brief set of simulations were performed to ensure the improved performance of the HGSODL-ASCM technique. The simulation results demonstrated how superior the HGSODL-ASCM method is to other methods already in use.
AI vs. AI: Can AI Detect AI-Generated Images?

The proliferation of Artificial Intelligence (AI) models such as Generative Adversarial Networks (GANs) has shown impressive success in image synthesis. Artificial GAN-based synthesized images have been widely spread over the Internet with the advancement in generating naturalistic and photo-realistic images. This might have the ability to improve content and media; however, it also constitutes a threat with regard to legitimacy, authenticity, and security. Moreover, implementing an automated system that is able to detect and recognize GAN-generated images is significant for image synthesis models as an evaluation tool, regardless of the input modality. To this end, we propose a framework for reliably detecting AI-generated images from real ones through Convolutional Neural Networks (CNNs). First, GAN-generated images were collected based on different tasks and different architectures to help with the generalization. Then, transfer learning was applied. Finally, several Class Activation Maps (CAM) were integrated to determine the discriminative regions that guided the classification model in its decision. Our approach achieved 100% on our dataset, i.e., Real or Synthetic Images (RSI), and a superior performance on other datasets and configurations in terms of its accuracy. Hence, it can be used as an evaluation tool in image generation. Our best detector was a pre-trained EfficientNetB4 fine-tuned on our dataset with a batch size of 64 and an initial learning rate of 0.001 for 20 epochs. Adam was used as an optimizer, and learning rate reduction along with data augmentation were incorporated.
Trends of cybercrime from 2001 to 2021: cybersecurity action plan for Papua New Guinea

Abstract Information and Communication Technologies (ICTs) and by extension the Internet, have brought immense sociological transformations to humanity. Despite the development the Internet has brought to humanity, the Government of Papua New Guinea (GoPNG) has not lost sight of the fact that the use of ICTs and other related supporting devices are fraught with cybercrime and cyber insecurities that constitute challenges to global law enforcement agencies in PNG and other nations of the world. This paper reports the trends on victims of cybercrimes from 2001 to 2021 and presents an action plan for PNG. The data analysed in this study was explicated from the Internet crime reports published by the Federal Bureau of Investigation and National White Collar Crime Center (IC3). The data were analysed using descriptive statistics and XGraph component of the Statistical Package for Social Sciences’ Population Pyramid. The results confirmed that the number of Internet users that were defrauded increased exponentially from 2016 to 2021. Results from the data explicated from the World Bank on Internet use, revealed that the population of Internet users in PNG and Australia will increase at a minimum rate of 1% to 2% for the next few years. These findings suggest that cybercrimes will be on the rise as the number of Internet users increase at 1% to 2% in PNG and Australia in the next few years. The findings present appropriate strategies to address the incidences of cybercrimes in PNG.
Digital communication as part of family language policy: the interplay of multimodality and language status in a Finnish context

Abstract While mobile app-mediated communication between children and members of their family represents a substantial part of contemporary family communication and language input, we still know very little about the role of these technologies in family language policy (FLP). With an explorative questionnaire survey, the current study set out to examine (1) how Finnish state and language-in-education policies intersect with how families make use of their languages in spoken and in app-mediated communication, and (2) to what extent app-mediated FL practices function as a space for spoken and literacy language development. 1002 nine to twelve year-olds in minority-language Swedish-medium schools in Finland responded to the survey. The results showed the dominance of the two national, high-status languages Swedish and Finnish in the families, with texting being the most common app practice. Languages other than Swedish and Finnish (LOTSF) were used in 17% of the families and to a great extent also in the family apps. While app-mediated family communications overall were shown to serve as significant spaces for language and literacy development, in some cases of LOTSF with a lower status and less educational support, and with linguistic and writing systems deviating from Swedish and Finnish, children refrained from texting in the apps. The findings suggest that the relationship between choice of modalities in language(s) of different status and educational support is complex and needs further attention in future FLP studies.
A Novel and Optimized Sine–Cosine Transform Wavelet Threshold Denoising Method Based on the sym4 Basis Function and Adaptive Threshold Related to Noise Intensity

In digital shearography, the speckle noise of the phase fringe pattern has a negative effect on the accuracy and reliability of the phase unwrapping procedure. A novel and optimized sine–cosine transform wavelet threshold denoising method is proposed to suppress speckle noise. Fast phase denoising can be achieved by using the proposed method while preserving the phase reversal information. The details of the selected wavelet basis function, the optimal decomposition level, the threshold function, and the denoising threshold are also provided in this manuscript. In particular, the decomposition level is analyzed and optimized through simulation analysis according to the speckle suppression index and the adaptive denoising method. The experimental results show that the proposed method has more adaptive ability in practical application than the sine–cosine transform average denoising method with the selected mask and iterative procedure, which speeds the denoising process up and takes better-unwrapped phase patterns.
Finding, mapping, and classifying optimal protocols for two-qubit entangling gates

We characterize the set of optimal protocols for two-qubit entangling gates through a mechanism analysis based on quantum pathways, which allows us to compare and rank the different solutions. As an example of a flexible platform with a rich landscape of protocols, we consider trapped neutral atoms excited to Rydberg states by different pulse sequences that extend over several atomic sites, optimizing both the temporal and the spatial features of the pulses. Studying the rate of success of the algorithm under different constraints, we analyze the impact of the proximity of the atoms on the nature and quality of the optimal protocols. We characterize in detail the features of the solutions in parameter space, showing some striking correlations among the set of parameters. Together with the mechanism analysis, the spatiotemporal control allows us to select protocols that operate under mechanisms by design, like finding needles in a haystack.
Cheques or dating scams? Online fraud themes in hip-hop songs across popular music apps

How do hip-hop songs produced from 2017 to 2023 depict and rationalize online fraud? This study examines the depiction of online fraudsters in thirty-three Nigerian hip-hop songs on nine popular streaming platforms such as Spotify, Deezer, iTunes, SoundCloud, Apple Music, and YouTube. Using a directed approach to qualitative content analysis, we coded lyrics based on the moral disengagement mechanism and core themes derived from existing literature. The findings shed light on how songs (a) justify the fraudulent actions of online fraudsters, (b) blame and dehumanize victims of online romance fraud, (c) glamorize online fraud, and (d) provide insights into prevailing attitudes, indigenous linguistics, and worldviews regarding cybercrime victimization. Specifically, analysis reveals the presence of components of moral disengagement mechanisms, including "dehumanization" and "displacement of responsibility," as well as core contextual themes associated with online fraud in a Nigerian context. We highlight the global impact of the musicians whose songs are readily accessible through various music apps in downplaying online fraud victims' plights. We argue that these musicians' persuasive messages downplay or even mock victims' plights contributing to the cultural worldviews and dimensions of online fraud. We recommend that future research explore non-traditional data sources capable of capturing cultural attitudes and dynamics.
Automated Identification of Security and Privacy Requirements from Software Engineering Contracts

The increasing prevalence of information disclosure, data breach and privacy risk has obliged the incorporation of security and privacy measures while designing software systems. With rise in concerns related to security and privacy, different laws and regulations have been enacted to protect vulnerable information. Complying with these laws and regulations is essential for software systems to manage vulnerable information. To comply with these laws, it is important to identify obligatory security and privacy requirements from Software Engineering (SE) contracts while designing software systems. However, manually identifying these requirements from contracts is error-prone and a difficult task given that contracts are written in Legalese and is not comprehensible to software developers who have to implement these security and privacy measures into the software. To mitigate this, we propose an approach to automatically identify obligatory security and privacy requirements from SE contracts. Our approach leverages the power of state-of-the-art Natural Language Generation transformer model T5 (Text-to-Text Transfer Transformer). We achieved a F1-score of 91% with T5 for identification of security and privacy requirements.
Requirements Modeling Aided by ChatGPT: An Experience in Embedded Systems

Requirements modeling is a crucial tool for requirements analysis and has been demonstrated to aid in the comprehension and analysis of requirements. However, constructing requirements models from natural language descriptions in user requirements documents can be a time-consuming task. With the growing attention given to large language models, they have become an integral component of natural language processing. Consequently, utilizing large language models to facilitate the construction of high-quality requirement models is appealing. This paper presents an automated framework for requirement model generation that incorporates ChatGPT-based zero-shot learning to extract requirement models from requirement texts and subsequently compose them using predefined rules. The framework defines the requirement extraction task of ChatGPT by designing appropriate prompt, and it generates requirement models by employing composition rules. Furthermore, a case study on a digital home system is conducted to validate the feasibility of the framework in assisting requirements modeling.
What's Next in my Backlog? Time Series Analysis of User Reviews

User reviews contain valuable information for improving a product e.g., bug reports and feature requests. While large quantities of user reviews for mobile apps are available in app stores, it is very challenging to manually analyze them due to their high volume and existing noise. Therefore, there is a strong need for methods that identify the needle in the haystack, i.e., identify reviews that are worth looking at and that inform the product backlogs with issues to fix or new requirements to consider. Responding to this challenge, we present an approach that identifies such reviews by automatically detecting anomalies (unusual peaks) in time series of user reviews. The approach takes the form of an automatic processing pipeline that ingests user reviews, aggregates them, and produces reports of which aggregates may contain valuable information for software evolution. Both the granularity and sensitivity of the approach can be tuned. With a best-case accuracy and F1-score of 0.88, we show that time-based user review aggregates and automatic anomaly detection serve as a good source of evidence for making estimations as to whether there are events in app store user reviews that warrant the attention of app developers.
OC3: A Reactive Velocity Level Motion Planner with Complementarity Constraint-based Obstacle Avoidance for Mobile Robots

This paper presents a novel real-time motion planning method for differential drive mobile robots. Reactive planners, in the context of mobile robots, are usually vulnerable to challenges like local minima, differential constraint satisfaction, and dynamic obstacles. To this end, we present the Off-Center point Complementarity Constraint (OC3) planner, which respects the differential constraints of robot kinematics, can handle dynamic obstacles, and is more robust to the local minima problem compared to traditional methods like artificial potential fields. Our OC3 planner utilizes a virtual contact mechanism at a distal off-center point of the robot for obstacle avoidance, thereby ensuring smooth maneuvers. We formulate the obstacle avoidance problem as a feasibility problem with complementarity constraints, and derive a closed-form solution. This enables fast online computation of collision-free waypoints for the robot off-center. Once a collision-free state of the off-center point is found, inverse velocity kinematics is used to compute the reference control input velocities (∈ ℝ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for the robot. We further show that OC3 can be used as a local planner in RRT type of sampling-based global planning framework to avoid the local minimum problem. Our extensive analysis through various case studies and rigorous simulation experiments, using the popular ROS based Turtlebot3 simulator, in the presence of static and dynamic obstacles, demonstrate the efficacy of our framework including real-time proficiency.
MMDFD- A Multimodal Custom Dataset for Deepfake Detection

A multi-modal deepfake dataset is relevant in addressing the growing concern of deepfake misuse, which poses a significant security and privacy threat. Deepfakes are becoming increasingly sophisticated, and their potential to deceive individuals and organizations is a significant issue. The ability to generate synthesized human voices using deep learning models and inserting fake subtitles has added to this problem, making it more challenging to detect deepfakes accurately. A superior quality dataset is essential to developing a competent deepfake detector. However, existing datasets are limited and often biased, making it difficult to detect deepfakes accurately. A multi-modal deepfake dataset, such as the proposed multi-modal Audio-Video-Text Deepfake dataset (MMDFD) addresses this gap by providing a more realistic and unbiased dataset. Such a dataset helps develop more accurate and effective deepfake detection methods, which can detect audio, video, and textual deepfakes simultaneously. The proposed dataset is more reflective of situations in the real world since it contains actual YouTube recordings of celebrities from four different racial origins. This helps to avoid the creation of deepfake detectors that are biased toward certain racial or ethnic groups. Overall, a multi-modal deepfake dataset is essential in addressing the growing concerns of deepfake misuse and developing effective detection methods that can detect deepfakes accurately, regardless of the medium.
Superior Automatic Screening for Human Helminthic Ova by Using Self-supervised Learning Approach-Based Object Classification

Human parasitic infections remain one of public health concerns for 1.5 billion people worldwide including Thailand. Conventional microscopic examination is a gold standard method and often used to identify the helminth ova and filariform larvae and also protozoa cyst in stool-dependent simple smear. The benefits of traditional techniques are diminished by time-consuming, complicated procedures, massive labor, and skilled and trained parasitologists. An automatically rapid screening of the most in need of treatment is considered to replace the conventional technique. Here, we aim to develop a deep convolutional residual network based self-supervised learning model to identify mostly common parasite ova in Thailand. Although small amounts of training data was used to train the proposed model, the result shows superior performance over 95% accuracy. As a result, low values of false positive and false negative based confusion matrix table found revealed the robustness of the proposed models. General accuracy of self-supervised learning based the area under a ROC curve proposed with greater than 94% is also support an outstanding model studied. Therefore, rank of 1% to 10% of fine-tuning data labelled used bring us about a comparable model to that of using a 100% labelled training data. These findings emphasize the transformative potential of the BYOL method for screening of parasitic infection, particularly in resource-limited settings where is a lack of supportive lab equipment and skilled parasitologists to manage a large amount of challenging data in the future.
Novel Machine Learning Pipeline for Real-Time Oculometry

In modern medicine, there is a need for widely available and efficient oculometric systems. This is because oculometry provides a simple insight into the general motor status of the patient. A good example are neurodegenerative diseases (ND) such as Parkinson’s disease (PD) with bradykinesia as one of the effects causing a general slowing of movements, also visible in eye movements. PD develops secretly for many years before showing visible effects, but eye-tracking tests can reveal it sooner. That is why we decided to create a modern system for performing this type of tests based on neural network models, modern inference approach and algorithms of various types to help obtain reliable results. Our idea was to disconnect the software from the hardware and base the hardware requirements on consumer grade equipment, such as a web-camera. This step is necessary because modern telemedicine must necessarily be based on basic equipment available to patients in their households. Our results showed that even with 30 Hz frequency and standard reflexive-saccade (RS) test, our system was able to distinguish the results of healthy person from PD patient or young from old person This solution brings the ability to receive quantitative online data, in opposition to standard telemedicine allowing only for verbal and/or visual interactions with the patient. We hope that low hardware requirements will popularize automated oculometric tests in the context of age-related complications and ND diseases, which may contribute to earlier diagnoses and the collection of the appropriate amount of data to develop new means of preventing different diseases.
Applications of Strongly Regular Cayley Graphs to Codebooks

In this paper, we give a construction of strongly regular Cayley graphs on the finite field F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>q</i><sup><i>n</i></sup></sub> . As applications of these strongly regular Cayley graphs, a class of codebooks is presented and proved to be asymptotically optimal with respect to the Welch bound. Further more, these constructed codebooks have new parameters.