Reviewerly/exHarmony huggingface dataset:
Multimodal Approaches for Alzheimer’s Detection Using Patients’ Speech and Transcript

Alzheimer’s disease (AD) is a common form of dementia that severely impacts patient health. As AD impairs the patient’s language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease. This study investigates various methods for detecting AD using patients’ speech and transcripts data from the DementiaBank Pitt database. The proposed approach involves pre-trained language models and Graph Neural Network (GNN) that constructs a graph from the speech transcript, and extracts features using GNN for AD detection. Data augmentation techniques, including synonym replacement and GPT-based augmenter, were used to address the limited sample size issue. Audio data from the patient’s speech was also included in the proposed model, where the WavLM model was used to extract audio features. These features were then fused with text features using various fusion strategies. We also investigated a novel fusion approach, where transcripts data were converted back to audio data and analyzed through a contrastive learning scheme along with the original audio data, with the premise that a single-modal (audio) detection model could be easier to train with better generalizability. We conducted intensive experiments and analysis on the above methods. Our findings shed light on the challenges and potential solutions in AD detection using multi-modal speech data.
Efisiensi Pembelajaran Kelas Model Daring

The purpose of this article is to describe the effectiveness of online learning. Qualitative research is research that is detailed, in-depth, descriptive and produces data from interviews with people observed. These qualitative research methods, such as in-depth interviews, are used to understand the behavior of subjects or subjects. Due to the online nature of this research, interviews were conducted in the form of online interviews using the WhatsApp application. The primary data source is grade VI B students of SDN 1 Lemahtamba, Panguragan, Cirebon Regency totaling 31 people, while the secondary data source is library materials, books, and journals related to this study. The results of a survey of a large number of students showed that as many as 21 students thought they supported online learning. The rest are not supportive because their home environment is not yet supportive. From the discussion above, it can be said that online Indonesian learning is very effective. This can be seen from the achievement of the goal of language learning, namely improving Indonesian language skills.
Multi-scale feature fusion kernel estimation with masked interpolation loss for real-world remote sensing images super-resolution

In recent years, the application of deep learning to remote sensing image super-resolution has achieved promising results. However, most deep learning-based methods are often trained on remote sensing datasets constructed by bicubic downsampling, and their recovery effects on real-world remote sensing images are often unsatisfactory. This is because the process of generating low-resolution (LR) images from high-resolution (HR) images to construct training data pairs (LR-HR) using simple bicubic downsampling cannot reflect the degradation process of real-world remote sensing dataset images. In this paper, we propose a multi-scale feature fusion kernel estimation with masked interpolation loss for real-world remote sensing images super-resolution (MFFILSR) to address this problem. MFFILSR is divided into two stages: degenerate kernel estimation and SR network training. In the first stage, we propose a multi-scale feature fusion kernel estimation network that can effectively fuse multi-scale information, making the estimated downsampling kernel closer to the degradation patterns of real-world remote sensing images. In the second stage, we introduce a masked interpolation loss during generator training, by masking the interpolation loss, the artefacts of generated images can be effectively reduced. Extensive experiments show that MFFILSR has satisfactory super-resolution reconstruction performance for real-world remote sensing images.
Trust in word of voice communication: why consumers adhere to purchase recommendations made by smart voice assistants

Purpose This study explores the role of smart voice assistants (SVAs) as purchase recommenders, a phenomenon the authors term “word of voice” (WOV) communication. By integrating human–computer interaction (HCI) literature and electronic word of mouth (eWOM) research, the authors examine what makes consumers trust in SVA-transmitted WOV communication following their initial interactions with their SVAs during a purchase process (i.e. post-trust); and the authors propose that consumers' perceptions of their SVAs' smart capabilities (i.e. cognitive, emotional and social) are critically important for building this trust. Moreover, the study explores the influence of post-trust on consumers' adherence to WOV communication, measured by three types of behavioural intentions. Design/methodology/approach Data from a survey of 202 United States (US)-based SVA users who employ them to obtain purchase recommendations were collected and analysed. They confirmed the validity of the measurement scales and provided input for the partial least squares modelling (PLS-SEM). Findings The results demonstrated that post-trust in WOV communication partially or totally mediates the effect of smart capabilities on consumer adherence to WOV communication; identified the key role of cognitive, emotional and social smart capabilities for building consumers' post-trust in WOV and demonstrated the influence of this trust on behavioural intentions. Originality/value The present study contributes by examining the employment of SVAs as recommenders during the purchase process; the authors term this type of communication WOV. It analyses consumers with experience of using SVAs in their purchase processes, revealing that post-trust in WOV communication is the psychological mechanism that explains how the smart capabilities of SVAs determine consumer adherence to the recommendations they receive.
Efficient Network Representation Learning via Cluster Similarity

Abstract Network representation learning is a de facto tool for graph analytics. The mainstream of the previous approaches is to factorize the proximity matrix between nodes. However, if n is the number of nodes, since the size of the proximity matrix is $$n \times n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>×</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> , it needs $$O(n^3)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>3</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> time and $$O(n^2)$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>O</mml:mi> <mml:mo>(</mml:mo> <mml:msup> <mml:mi>n</mml:mi> <mml:mn>2</mml:mn> </mml:msup> <mml:mo>)</mml:mo> </mml:mrow> </mml:math> space to perform network representation learning; they are significantly high for large-scale graphs. This paper introduces the novel idea of using similarities between clusters instead of proximities between nodes; the proposed approach computes the representations of the clusters from similarities between clusters and computes the representations of nodes by referring to them. If l is the number of clusters, since $$l \ll n$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>l</mml:mi> <mml:mo>≪</mml:mo> <mml:mi>n</mml:mi> </mml:mrow> </mml:math> , we can efficiently obtain the representations of clusters from a small $$l \times l$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>l</mml:mi> <mml:mo>×</mml:mo> <mml:mi>l</mml:mi> </mml:mrow> </mml:math> similarity matrix. Furthermore, since nodes in each cluster share similar structural properties, we can effectively compute the representation vectors of nodes. Experiments show that our approach can perform network representation learning more efficiently and effectively than existing approaches.
Framing and Group Polarisation in Hate Speeches in Nigeria

Since the Nigerian Civil War in 1970, there has been a mutual suspicion among its three major ethnic groups, Hausa (in the North), Igbo (East) and Yoruba (West). This is not only manifest in their socio-political life but has also generated strife and hate speech, typified by context-sensitive strategies. This paper highlights the key group-motivated strategies of framing and polarisation utilized in the hate speeches raised by different groups in the Nigerian political discourse. The 2017 Kaduna declaration by a northern union (Arewa Youths), threatening to evict the Igbo living in the northern region, and response texts from other unions to the declaration constitute the data. These were subjected to content analysis with insights from van Dijk’s Ideological Representation and Halliday and Matthiessen’s Transitivity Theory. The findings reveal a self-preservationist frame, which is polarised in the Self/Other pattern.
Understanding Walking and Reading with Smart Glasses and Mobile Phones: A Dual-Task Paradigm

Technology advancements such as smart glasses offer novel opportunities to interact with technology on-the-go. This study examined use of two types of smart glasses (Everysight Raptor and Vuzix m100) for reading during walking and compared to a mobile phone. Fifty-two healthy adults performed single-task and dual-task walking and reading in one of two environments. Objective measures included gait speed and variability and reading speed and comprehension, and subjective measures included workload and usability. Results indicated that when using smart glasses, participants walked slower and with larger gait variability, read less text and had lower comprehension scores across both environments. Participants perceived the glasses as less usable and more demanding than the phone, with no differences between the two types of glasses. This work provides a benchmark for the negative impact of using smart glasses during walking indoors and outdoors. Potential implications are higher risk for community ambulation, suggesting a need for consideration of human attentional capacity when designing smart glasses interfaces and future urban environments.
A boosting policy to optimize user forum performance: Model and validation

Abstract Near‐constant Internet access through desktop or mobile devices has turned self‐service support forums into the first port of call for users seeking to troubleshoot product or service issues. The firms providing these products and services also benefit from this trend since it reduces user support costs by diverting service requests away from costlier support channels, such as help desks. For the continued success of such a forum, however, the managing entity must ensure that users receive timely solutions to their inquiries quickly and regularly. We develop a mathematical model of a user forum's operations to obtain a “white box” view of a user forum and reveal the support system's dynamics. Then, using a large and comprehensive dataset of questions and answers from Apple's iPhone user forum, we empirically estimate the forum's performance to validate the predictions of the mathematical model. Our results demonstrate that the predictions closely match the forum's actual performance, with an error of less than 10%. We then propose and analyze an optimal threshold policy that boosts a thread to rekindle user interest and demonstrate the benefit of our intervention policy in managing the iPhone forum.
Dark Web Drug Markets and Cartel Crime

Abstract Recent evidence suggests that legal marijuana markets in several U.S. states have decreased violence in Mexican-U.S. border regions. As legal markets for production and distribution displace drug cartel distribution, the violence associated with cartel trafficking and distribution decreases. Prior analysis has not considered an important emerging innovation for drug distribution: online anonymous marketplaces. The increasing volume of drug trade that has occurred on this “Dark Web” could result in reduced drug cartel violence as production and distribution use this substitute network and turn away from the cartel distribution networks. This paper investigates the relationship between border violence and the volume of drug trade that occurs on the Dark Web using a difference in differences model. We examine differences in crime rates at the U.S.-Mexico border and away from the border during the emergence of the Dark Web. Data on Dark Web transactions, users, and markets allows us to measure changes in Dark Web activity and the subsequent impact on crime. We find evidence that the rise in Dark Web marketplaces results in crime reductions at the border of the U.S., relative to non-border counties.
Mapping of Design Rainfall at Multiple Return Periods Using Spatial Interpolation in Abab Sub-Watershed, Blitar Regency

The main occupation of residents who live in Abab Sub-Watershed, Blitar Regency, is as farmers in agriculture and plantations. Agriculture and plantations need rain data that have been calculated to maximize the yields to be obtained. Hydrometeorological disasters, specifically floods, often occur at this location. Therefore, research is needed on calculating design rainfall, outlined in easy-to-understand isohyet maps. The research aims to assist the planning of water structures or other hydrological research without having to recalculate or retest data from the beginning. This study's utilized return periods were 2, 5, 10, and 25 years, plotted as isohyet maps. Before calculating the design rainfall, the rainfall data had to be tested first. Calculation of design rainfall used SMADA Distrib 2.13 application and was tested with the distribution suitability test. The results of the distribution suitability test showed that Log Normal is appropriate to be used. The results of plotting the design rainfall on an isohyet map using the IDW, Kriging, and Spline methods showed that the IDW and Spline maps are smoother and not as broken as Kriging. Next, a comparative analysis of RMSE and NSE was carried out. For return periods of 2, 5, 10, and 25 years, IDW has the smallest RMSE mean value of 0.016 and the highest NSE average value of 0.9999.
Collection Development Strategies for Electronic Resources in Two Selected University Libraries in KwaZulu-Natal, South Africa

Purpose: Electronic resources have become critical and instrumental for academic work in the technological era and, as such, useful for students and researchers. This study reports on collection development as a strategy for electronic resources. Design/methodology/approach: An interpretivist research paradigm was adopted, followed by a qualitative research approach. A case study research design was deemed appropriate for the study. The target population comprised librarians from two academic libraries in KwaZulu-Natal. The study employed purposive sampling and used semi-structured interviews to collect data from the participants. Due to Covid-19 regulations, these interviews were conducted online using Google Teams. Thematic analysis was used to analyse the data collected. Findings: The findings revealed that both universities provide different types of electronic resources to support teaching, learning, and research activities. The electronic resources provided in the two selected academic libraries included online databases, e-books, e-journals, e-references, e-magazines, and videos from the JoVE databases and packages. The findings showed that these academic libraries encounter major challenges with limited library budgets and high subscription costs of e-resources. The study recommends that adequate funding to acquire more e-resources should be allocated, and that the consortium must become more involved in the process of purchasing electronic resources so that cost could be shared. Research limitations/implications: This study focused on librarians in two institutions, excluding the management and executive management of these institutions, which are planners and decision-makers influencing finance and some processes. Practical implications: Electronic resources subscribed to by academic libraries play an important part in addressing different user information needs. With the emergence of information and communication technology (ICT), academic libraries strive to remain relevant in their collection development through the provision of electronic resources. The research offered librarians additional collection development strategies through electronic resources that they can adopt in order to provide sound collections to their users, regardless of financial constraints. Originality/value: The study investigated enhancing collection development through electronic resources as a strategy in the selected academic libraries in KwaZulu-Natal, South Africa. It may serve as a valuable resource for academic librarians in carrying out the effective collection development of electronic resources, as currently, world development is driven by technology. It also adds to the body of knowledge in the field of electronic resources, academic librarianship, and information science.
Advanced Mobile Communication Techniques in the Fight against the COVID-19 Pandemic Era and Beyond: An Overview of 5G/B5G/6G

The coronavirus disease 2019 (COVID-19) pandemic has severely affected people's lives worldwide in an unexpected manner. According to the World Health Organization (WHO), several viral epidemics continue to occur and pose a significant public health problem. Until May 2023, there have been 676 million cases of COVID-19 infections and over 6.8 million deaths, globally. This paper surveys the role and effectiveness of advanced fifth-generation (5G)/beyond 5G (B5G)/sixth-generation (6G) technologies, combined with mobile applications (apps) and the Internet of Medical Things (IoMT), in detecting, managing, and mitigating the spread of COVID-19 and designing smart healthcare infrastructures for future pandemics. Analyzing and summarizing the research of relevant scholars based on the impact of 5G/B5G/6G and other technologies on COVID-19. The study tabulates the technical characteristics and effectiveness of different technologies in the context of COVID-19, summarizing the research of previous scholars. Challenges and design issues in the implementation of advanced information and telecommunication systems were demonstrated. These technologies can inspire the design of smart healthcare infrastructures to combat future virus pandemics.
Investigations on the material behaviour of weld seams for the use in finite element analyses

Abstract The design of complex structural steel connections by finite element analysis is common practice nowadays. However, for welded joints there are no normative regulations for the static finite‐element‐analysis. To analyse the specific material behaviour in the weld area and derive a geometry and material model for the finite element analysis, tensile tests on specimens with different weld geometries are made at the Technical University of Darmstadt. During the tests an optical measuring method, called Digital Image Correlation, is used. It records the deformations on the surface of the specimen and assigns the recorded analogue test load to the images. By this way, the load‐deformation‐behaviour in all areas of the weld can be determined. In addition to the tensile tests, microsection examinations are carried out. The applied colour etching makes the microstructure of the weld zones visible so they can be geometrically superimposed with the strain images of the tensile tests. In welded connections the stress state is very complex. Therefore, numerical investigation must be performed to derive the final stress‐strain‐curves of the weld area. These curves can then be implemented as a material model for the finite element analysis of welded connections.
A Survey of Using Blockchain to Build a Decentralized and Trustless Marketplace for Brokered IoT Data Trading

As the data market becomes ubiquitous, it is necessary to study the data streams generated by Internet of Things (IoT) devices. For example, nowadays, security problems are crucial to the process of data transaction. Due to the decentralization of blockchain and difficult data tampering, blockchain can well solve the security problems in data transaction. However, the transaction efficiency of blockchain can hardly meet the current market due to latency and other problems. This article mainly reviews existing research on blockchain and IoT data and put forward possible solutions for dealing with some of the current problems. The present study is mainly divided into three parts, including the classification of research problems, research methods, and the classification of experiments. The main research questions are about insufficient functionality and poor performance on the Internet of Things and data markets. In addition to the discussion on whether to use blockchain, this article also proposes some feasible research directions for future research.
Studies on Current Situation of Private Digital Currency and Central Bank Digital Currency

Digital currency is one of the vital innovative products of Fintech and it has influenced the whole financial industry and supervision field. It first came with personal digital currency, and it changed the forms, circulation, and payment methods of traditional currencies, and different currencies have their own intrinsic value. In the meantime, the central banks of various countries have also tried to control digital currency. Central banks reduce the cost of issuing currency and improve the efficiency of payment. Moreover, the regulatory authorities of various countries have improved the rules when facing the security problems of digital currency. The paper, through methods of qualitative research and literature review, focuses on three parts, which include the difference between private digital currency and central bank digital currency; the difference between each private digital currency; and the difference between central bank digital currencies in various countries. It hopes to provide some insights for further development in this field.
The Influence of Consumer Sentiment onMarketing Effect in Cause Marketing-- Take Hongxing Erke as an Example

With the frequent occurrence of unfortunate events that affect both the public and businesses, such as the COVID-19 pandemic, the importance of cause marketing has become increasingly prominent. However, while the number of studies on cause marketing has increased, there is still a lack of validation and interpretation of the relationship between consumer sentiment and cause marketing. Based on the case of cause marketing of Hongxing Erke and domestic and foreign literature, this study mainly uses the process tracking method to analyze the impact of public sentiment on marketing effect. It is found that after the unfortunate event, enterprises can properly reduce the price of goods in the cause marketing, choose the right point to cause marketing and enhance public sincerity, to better use the consumers' emotional resources, to improve the effect of cause marketing, ultimately stimulate consumption, and set up socially responsible for the enterprise brand image.
The Role of Big Data in Identifying Modern Financial Fraud: A Case Study of Zoneco Group

With the development of market economy and modern science and technology, some listed companies' financial frauds are constantly discovered and exposed. Taking Zoneco as a case, combining with fraud triangle theory, this paper analyzes the background, causes and means of financial fraud in enterprises, which is different from the traditional way of combining financial statement data analysis with on-the-spot inventory. This paper discusses that modern big data technology provides a more innovative and intuitive method for identifying fraud, and broadens the way for maintaining the stability for the securities market.