Reviewerly/exHarmony huggingface dataset:
The Drivers of Customer Satisfaction in Interactions with Virtual Agents: Evidence from South Africa

The advent of the fourth industrial revolution has increased the application of artificial intelligence in various marketing related activities. Organisations are increasingly using artificial intelligence through virtual agents to aid and facilitate interactions with consumers. Clearly, virtual agents need to be capable of fulfilling customer needs. However, it raises the question of whether they should be cold and unfeeling or should imitate the most innate human qualities. This research therefore set out to examine what impact humanness, perceived agency, trust, and emotionality have on customer satisfaction in interactions with virtual agents, or, more specifically, AI chatbots. A quantitative research design was employed in the study. Data were collected by using a survey questionnaire and a total of 207 respondents were obtained. Data were analysed using IBM SPSS 28, where a linear regression was performed. The results indicate that humanness and perceived agency were significant predictors of customer satisfaction. On the other hand, emotionality and trust were not significant predictors. The results of this research have theoretical and practical implications for both practitioners and researchers.
Performance Analysis of Public and Private Blockchains and Future Research Directions

Blockchain is growing rapidly and becoming popular to handle business needs over the globe. Smart contracts are one of the unique features of blockchain 2.0 which are disrupting the industry and creating new value for businesses that are looking for opportunities to grow in an untrusted environment. There are many blockchain platforms evolved for serving different purposes, but each has its own limitations and advantages. Due to the lack of performance analysis tools and a complete study comparing public and private blockchain platforms, the researchers are focusing more on analyzing performance and comparing results. Considering these aspects, there is a necessity to conduct a performance study and comparison of public and private blockchain platforms. Here in this work, the performance and scalability issues of blockchain platforms are presented and provided in detail study, future scope. This work compares the existing blockchain platforms’ performance and discusses their pros and cons, in terms of performance and scalability.
Face Mask Detection Exploiting CNN and MobileNetV2

The whole world was suffering from the COVID-19 pandemic since 2019, which originated in the city of Wuhan, China and has quickly spread various countries, with many cases having been reported worldwide. Around 31.3crores cases had been recorded worldwide, among which India has been recorded 3.59crore cases according to recent data. Each time, this virus comes into existence with a new variant or strain. Recent studies have shown that through proper use of masks this kind of viruses can be prevented from spreading. Every nation is attempting to stop the disease's spread. Each individual must put on a mask in a public area in order to remedy the issue. Therefore, using a convolutional neural network, we propose a model that can distinguish between masked and non-masked faces. This investigation approaches to check whether a person is wearing a proper mask or not, and the mask is secure or not towards these viruses. This system mainly can be used in different transport system such as public bus, trains, aeroplane, etc., where bus conductors, ticket checker in trains and air hostage, respectively, did not had to go to near the passengers to ask them for using a proper mask. Based on CNN and MobileNetV2, a deep learning (DL) framework, this model is capable of identifying individuals without masks. It is accurate to a maximum of 99.76% using MobileNetV2 and 99.83% through CNN approach. A model has been compared with state-of-the-art methods which will be used to track the use of masks in locations including schools, offices, and other public spaces.
Watermark-Based Image Authentication with Coefficient Value Differencing and Histogram Shifting

Image-based watermarking has become essential in the digital authentication system. This work uses coefficient value differencing and histogram shifting techniques for watermark embedding on the coefficients of integer wavelet transform to take advantages of both spatial and transform techniques. After some necessary preprocessing, the cover image is applied 3/1 integer wavelet transform (IWT) to get four sub-bands. Thereafter, SHA256 algorithm is executed on the watermark image to generate hash code for authentication. The watermark and the hash code are embedded in the HH and HH of the LH sub-band, respectively, using coefficient value differencing and histogram shifting techniques. In the authentication phase, the stego-image is processed to extract the watermark and SHA256 code, followed by computation of SHA256 code from extracted watermark. These two SHA256 codes are compared to establish authentication. The experimental results on standard dataset show average “mean square error” (MSE), “peak signal to noise ratio” (PSNR), and “structural similarity index measure” (SSIM) between the cover and stego-image to be around 0.10, 60 dB, and 0.999, respectively. The robustness of the system is verified with well know active attacks by computing normalized cross-correlation (NCC).
Pendampingan Pembuatan Alat Permainan Edukatif Berbasis STEAM dan Loose Parts Pada Guru di TK Mancasan Sukoharjo

STEAM and Loose Parts-based educational game tools are learning media that can provide stimulation for children to have 21st century mindsets and skills. However, reality shows that teachers at Mancasan Kindergarten still need activities to improve their skills in implementing STEAM and Loose Parts media. Activities are carried out in the form of provision and assistance. The provision takes the form of delivering material that introduces STEAM-based APE and Loose Parts with five stages, namely 1) preparation 2) pre-implementation 3) implementation of PkM 4) media design and 5) discussion. Next, participants practiced making APE and then presented it. The work results were in the form of STEAM-Based APE and Loose Parts and were given appreciation. Through this assistance, Mancasan Kindergarten teachers have the insight and skills to create an APE.
Digital facelift

The rapid development and proliferation of synthetic media and AI-generated avatars present both challenges and opportunities for higher education. Increasingly, educational institutions must navigate complex ethical and pedagogical considerations, as well as adapt to the latest AI technologies (Siemens et al., 2022). Ethical and pedagogical principles to guide AI use are urgently needed in education (Bozkurt et al., 2021). In this presentation, we share a practical perspective, framed by a specific case-study: the development and implementation of a large-scale, interactive business module featuring AI-generated ‘Guest Lecturers’ The AI-generated video content, along with other online activities, were designed to stimulate critical reflection and discussion about the ethical implications of AI in business. Now in its second iteration, the project represents a significant trial and evaluation of synthetic media to present educational concepts in a cohort of over 700 students. Fundamental ethical and pedagogical questions surfaced during the development and implementation of this learning media. How transparent should educators be about the processes and tools they adopt in producing content, for example (Pataranutaporn et al., 2021)? When does human presence add value in recorded video, be it in a pedagogical or parasocial sense (Beege et al., 2019)? And what forms of learning might benefit (or not) from automating video content (Li et al., 2016)? Based on evaluation data from focus groups and video analytics, we explore these questions and the potential affordances and limitations of synthetic media. We also reflect on the tools and techniques that were used to create the project, and implications for educational institutions as they seek to build capacity for AI content creation within traditional production ecosystems. An emergent typology for AI in educational media is explored, to help educators design for teaching and learning based on learning purposes and modes of delivery. Finally, we discuss how student feedback might inform the design, production and delivery of learning media, and future directions for student co-creation and agency.
AI-powered peer review process

Code review is a common type of peer review in Computer Science (CS) education. It’s a peer review process that involves CS students other than the original author examining source code and is widely acknowledged as an effective method for reducing software errors and enhancing the overall quality of software projects. While code review is an essential skill for CS students, they often feel uncomfortable to share their work or to provide feedback to peers due to concerns related to coding experience, validity, reliability, bias, and fairness. An automated code review process could offer students the potential to access timely, consistent, and independent feedback about their coding artifacts. We investigated the use of generative Artificial Intelligence (genAI) to automate a peer review process to enhance CS students’ engagement with code review in an industry-based subject in the School of Computing and Information System, University of Melbourne. Moreover, we evaluated the effectiveness of genAI at performing checklist-based assessments of code. A total of 80 CS students performed over 36 reviews in two different weeks. We found our genAI-powered reviewing process significantly increased students’ engagement in code review and, could also identify a larger number of code issues in short times, leading to more fixes. These results suggest that our approach could be successfully used in code reviews, potentially helping to address issues related to peer review in higher education settings.
Dual Image-Based Watermarking Scheme Using Interpolation

Digital watermarking is commonly used to hide information for confidentiality and copyright protection and to provide data authentication of medical images. This paper represents an algorithm that uses double images, interpolation, and the SHA-512 method to protect digital documents from illegal access. This increases the ability of the watermarked image to hide and limits the alteration of the cover image. The algorithm depends on some significant steps: the image expansion, i.e., a procedure for converting to an image of high-resolution from a image of low-resolution, hiding secret data within the cover image, selection of one image from a dual image by applying a secret key, and selection of watermarked image using the secret key and finally extract the original cover image. This proposed technique is more authentic. The results of this paper define the quality of image of the used techniques outperform the earlier works.
A Publicly Verifiable E-Voting System Based on Biometrics

Voters use traditional paper ballots, a method limited by the factors of time and space, to ensure their voting rights are exercised; this method requires a lot of manpower and resources. Duplicate voting problems may also occur, meaning the transparency and reliability of the voting results cannot be guaranteed. With the rapid developments in science and technology, E-voting system technology is being adopted more frequently in election activities. However, E-voting systems still cannot address the verifiability of the election process; the results of a given election and the credibility of the host organization will be questioned if the election’s verifiability cannot be ensured. Elections may also pose a series of problems related to privacy, security, and so on. To address these issues, this paper presents a public, and verifiable E-voting system with hidden statistics; this system is based on commitment, zk-SNARKs, and machine learning. The system can deal with a large number of candidates, complex voting methods, and result functions in counting both hidden and public votes and can satisfy the requirements of verifiability, privacy, security, and intelligence. Our security analysis shows that our scheme achieves privacy, hidden vote counting and verifiability. Our performance evaluation demonstrates that our system has reasonable applications in real scenarios.
Trust in the system and human autonomy in customer service chatbots

When Artificial Intelligence systems are not explained clearly to users, it can negatively affect their interactions and compromise their perceptions of a brand. When designing and developing conversational agents that deal with the client, it is crucial to consider that they are a service and follow human-centered Artificial Intelligence (HCAI) approaches. This study discusses two HCAI frameworks, relate them to trust in the system and human autonomy and define how these guidelines could be met in customer service chatbot. A survey was conducted to determine if users' views about their interactions with chatbots aligned with the recommended guidelines and how this affected their senses mentioned above. The analysis of the responses indicates that those human-centered Artificial Intelligence approaches still need to be prioritized or even met in customer service chatbot development. Users have reported unpleasant experiences with such services, leading to a decrease in their trust and autonomy.
CMOS MEMS Resonator for Physical Reservoir Computing

This paper presents a CMOS-MEMS clamped-clamped (CC) beam for physical reservoir computing. The device is designed and fabricated in the CMOS back-end-of-line (BEOL) metal and oxide layers. The resonant beam is electrostatically actuated to operate in the nonlinear states for delay-based physical reservoir computing. The closed-loop reservoir computing is realized by the nonlinear CC beam, a field programmable gate array (FPGA) controller, and a lock-in amplifier. The fabricated and post-processed CMOS MEMS device were characterized. Preliminary parity benchmark tests in different test conditions are presented.
Extending Defeasible Reasoning Beyond Rational Closure

The KLM framework is a well known extension of classical logic for incorporating defeasible reasoning. Central to the KLM framework is rational closure, recognized as the most conservative approach to defeasible reasoning. Rational closure operates through two complementary paradigms: model-theoretic and formula-theoretic. This paper concentrates on the model-theoretic dimension, known as minimal ranked entailment. Unfortunately, the practical implementation of minimal ranked entailment remains largely impractical due to high computational and storage demands. To address this, we present reduced minimal ranked entailment, an optimization that employs reduced ordered binary decision diagrams to eliminate redundant information, thereby enhancing computational efficiency and reducing memory requirements. We further demonstrate that this optimized approach significantly facilitates the practical application and development of model-theoretic extensions of rational closure. This is illustrated through our own Bayesian refinement of minimal ranked entailment, which conceptualizes defeasible entailment as a form of conditional probability.
A Graph-Based Multi-Scale Approach with Knowledge Distillation for WSI Classification

The usage of Multi Instance Learning (MIL) for classifying Whole Slide Images (WSIs) has recently increased. Due to their gigapixel size, the pixel-level annotation of such data is extremely expensive and time-consuming, practically unfeasible. For this reason, multiple automatic approaches have been raised in the last years to support clinical practice and diagnosis. Unfortunately, most state-of-the-art proposals apply attention mechanisms without considering the spatial instance correlation and usually work on a single-scale resolution. To leverage the full potential of pyramidal structured WSI, we propose a graph-based multi-scale MIL approach, DAS-MIL. Our model comprises three modules: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> ) a self-supervised feature extractor, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ii</i> ) a graph-based architecture that precedes the MIL mechanism and aims at creating a more contextualized representation of the WSI structure by considering the mutual (spatial) instance correlation both inter and intra-scale. Finally, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">iii</i> ) a (self) distillation loss between resolutions is introduced to compensate for their informative gap and significantly improve the final prediction. The effectiveness of the proposed framework is demonstrated on two well-known datasets, where we outperform SOTA on WSI classification, gaining a +2.7% AUC and +3.7% accuracy on the popular Camelyon16 benchmark.
Maximum Marginal Relevance and Vector Space Model for Summarizing Students' Final Project Abstracts

Automatic summarization is reducing a text document with a computer program to create a summary that retains the essential parts of the original document. Automatic summarization is necessary to deal with information overload, and the amount of data is increasing. A summary is needed to get the contents of the article briefly. A summary is an effective way to present extended information in a concise form of the main contents of an article, and the aim is to tell the reader the essence of a central idea. The simple concept of a summary is to take an essential part of the entire contents of the article. Which then presents it back in summary form. The steps in this research will start with the user selecting or searching for text documents that will be summarized with keywords in the abstract as a query. The proposed approach performs text preprocessing for documents: sentence breaking, case folding, word tokenizing, filtering, and stemming. The results of the preprocessed text are weighted by term frequency-inverse document frequency (tf-idf), then weighted for query relevance using the vector space model and sentence similarity using cosine similarity. The next stage is maximum marginal relevance for sentence extraction. The proposed approach provides comprehensive summarization compared with another approach. The test results are compared with manual summaries, which produce an average precision of 88%, recall of 61%, and f-measure of 70%.
Poster: Dynamic Clustered Federated Framework for Multi-domain Network Anomaly Detection

Federated learning as a distributed category yields sub-optimal performance for the presence of Non-iid in multi-domain data. To this end, we proposes a novel clustered federated framework for unsupervised multi-domain network anomaly detection, which implements dynamic clustered federated based on the data distribution of different domain so that each edge user learns the domain-optimized inference model. Compared to baseline, the proposed method enables users to obtain domain-optimal performance.
Apriori Modified for Action Rules Mining

Action rule mining is an extension of classification rule learning, in which an action rule provides information similar to that of a standard classification rule and also suggests a course of action. Action rules are used to obtain counterfactual explanations. Rule-based action rule mining involves two separate steps: mining classification rules, typically using the Apriori algorithm, and a user-set minimum support and confidence threshold. The second step involves forming action rules from the classification rules by utilizing a second set of user-set parameters including the desired and undesired values of the target attribute, a list of stable attributes, and a list of flexible attributes. This two-stage approach is inefficient as the first step tends to generate excessively many classification rules, most of which can never form an action rule meeting the second set of parameters. This paper describes a modified Apriori algorithm for action rule mining (Action-Apriori), which includes an enhanced version of downward closure that further reduces the space of possible candidates by checking whether the candidate itemsets also meet the second set of user-set parameters. The benchmarks show a consistent reduction in the learning time of the new algorithm compared to the state-of-the-art ARAS algorithm. This paper is supplemented by an open-source implementation.
SPaRKLE : Symbolic caPtuRing of knowledge for Knowledge graph enrichment with LEarning

Knowledge graphs (KGs) naturally capture the convergence of data and knowledge, making them expressive frameworks for describing and integrating heterogeneous data in a coherent and interconnected manner. However, based on the Open World Assumption (OWA), the absence of information within KGs does not indicate falsity or non-existence; it merely reflects incompleteness. Inductive learning over KGs involves predicting new relationships based on existing statements in the KG, using either numerical or symbolic learning models. The Partial Completeness Assumption (PCA) heuristic efficiently guides inductive learning methods for Link Prediction (LP) by refining predictions about absent KG relationships. Nevertheless, numeric techniques– like KG embedding models– alone may fall short in accurately predicting missing information, particularly when it comes to capturing implicit knowledge and complex relationships. We propose a hybrid method named SPaRKLE that seamlessly integrates symbolic and numerical techniques, leveraging the PCA heuristic to capture implicit knowledge and enrich KGs. We empirically compare SPaRKLE with state-of-the-art KG embedding and symbolic models, using established benchmarks. Our experimental outcomes underscore the efficacy of this hybrid approach, as it harnesses the strengths of both paradigms. SPaRKLE is publicly available on GitHub1.
Automatic Topic Label Generation using Conversational Models

In probabilistic topic models, a topic is characterised by a set of words, with a probability associated to each of them. Even though it is not necessary to understand the meaning of topics to perform common downstream tasks where topic models are used, such as topic inference or document similarity, there have been attempts to uncover the semantics of topics by providing labels to them, consisting in a couple of concepts. In this paper we propose a methodology, Conversational Probabilistic Topic Labelling (CPTL), to study whether conversational models can be used to generate labels that describe probabilistic topics given their most representative keywords. We evaluate and compare the performance of a selection of conversational models for the topic label generation task with the performance of a task-specific language model trained to generate topic labels.