Reviewerly/exHarmony huggingface dataset:
Designing Data Visualisations for Self-Compassion in Personal Informatics

Wearable personal trackers offer exciting opportunities to contribute to one's well-being, but they also can foster negative experiences. It remains a challenge to understand how we can design personal informatics experiences that help users frame their data in a positive manner and foster self-compassion. To explore this, we conducted a study where we compared different visualisations for user-generated screen time data. We examined positive, neutral and negative framings of the data and whether or not a point of reference was provided in a visualisation. The results show that framing techniques have a significant effect on reflection, rumination and self-compassion. We contribute insights into what design features of data representations can support positive experiences in personal informatics.
English Major Students' Text Messaging Practices in Discourse Production: Language Proficiency Progression vs. Regression

This research investigates the impact of text messaging on language proficiency among English major students, particularly in discourse production. It aims to identify factors contributing to language progression or regression in students using text messaging for communication. Objectives include examining the relationship between text messaging frequency and language errors and investigating the effect of text messaging on language development. Adopting an epistemological approach, the study utilizes qualitative data analysis. Limitations involve a small sample size of fifteen English major students and one group chat. Findings reveal that text messages often mimic spoken language, facilitating friendly connections. The language used is understandable and suitable for communication purposes. Text messaging introduces abbreviations, acronyms, and new lexical forms, shaping contemporary communication practices. While some research participants argue that text messaging may hinder language proficiency, others see it as language creativity and adaptation. The influence of text messaging on language evolution remains an ongoing process that needs further research.
A novel quantum ring signature based on secret sharing

Quantum signature has become a hot topic in the current research of digital signature schemes due to its unconditional security. As a special kind of digital signature, ring signatures are frequently employed in practical situations because of their superior anonymity. In this work, we propose a quantum ring signature scheme based on secret sharing that does not use entanglement states. To guarantee the complete security of the participants’ secret keys, the idea of secret sharing is incorporated into the quantum key distribution process during the key distribution stage. Each ring member contributes his private key share to the final verification formula through a recursive relationship throughout the signing and verification phases, and the verifier can utilize his private key share along with the user’s public key to confirm the ring members’ authenticity; The [Formula: see text]-AS2U hash function is used to encrypt the ring signer’s identity during the entire signature process, which assures the signer’s identity’s complete anonymity, effectively protects the signer’s privacy, and is more in line with practical requirements.
Design Mechanism of Intelligent English Teaching Platform Based on Multi-modal Knowledge Graph

Technologies using knowledge graphs have been developed considerably. The application of knowledge graph and its associated technologies improve production efficiency and reduce labor costs. In education, it is used to improve the level of teaching in diverse modes and complex formats. Huge data in the education industry is efficiently processed and applied with knowledge graph. Thus, it is important to design a mechanism for an intelligent English teaching platform based on a multi-modal knowledge graph. In this study, the mechanism of video processing and linking for college English courses was developed to design a college English multi-modal teaching platform and its organizational structure. The platform was used for various applications. The knowledge graph was used as the carrier to combine artificial intelligence (AI) technology and support teachers to make teaching plans and theoretical guidance for students to review the knowledge.
KETERAMPILAN MEMBACA DAN MENULIS ANAK DINI DI DESA CIPELANG

This service aims to determine the role of the early literacy movement in the family and to determine the level of basic literacy skills, especially reading and writing in early childhood in Cipelang Village, Cijeruk District, Bogor Regency. The service method uses survey methods, mentoring, and reading and writing ability tests carried out on children aged 4-6 years with pre-test and post-test analysis. The results of the service show that before the early literacy movement education was carried out, it could be seen that the condition of early childhood literacy was 27.5% of the total sample of 10 children and after the early childhood literacy education was carried out it increased to 92.5% of the total sample of 10 children. . In the sense that children and parents feel enthusiastic about providing learning facilities, providing children's reading materials, parents are enthusiastic about reading stories to children so that young children have a high interest in reading. Meanwhile, the reading and writing skills of young children before assistance was provided, only 31.9% of the total of 10 young children could read and write. After assisting, the reading and writing ability of young children increased to 85.4%. In other words, this service has an impact on increasing the early literacy movement and improving the reading and writing abilities of young children. So the recommendation given is the need for active involvement of the community and various related parties to encourage the implementation and improvement of the quality of the early literacy movement for young children in Cipelang Village.
Image Coding based Orthogonal Polynomials Multiresolution Analysis with Joint Probability Context Modeling and Modified Golomb-Rice Entropy Coding

The work proposes, a JPEG2000 like compression technique which is based on multiresolution analysis of orthogonal polynomials transformation (OPT) coefficients has been presented with bit modeling for Golomb-Rice entropy coding. Initially, the image under analysis is divided into blocks and OPT is applied to each divided blocks. Then, transformed coefficients are represented as sub bands like structure (multiresolution) and scalar quantization is carried out to the transformed coefficients to reduce the precision. The quantized coefficients are then bit modelled in the bit plane using a joint probability statistical model, and significant bits in the bit plane are chosen. On the selected relevant bits, a geometrically distributed set of context is modelled for further encoding with modified Golomb-Rice encoding to provide compressed data. The decompression procedure is just the reverse of compression procedure. Experiments and analysis are carried out to demonstrate the efficiency of the proposed compression scheme in terms of compression ratio and Peak-Signal-to Noise Ratio (PSNR), and the results are encouraging
Parental Support and Juvenile Involvement in Internet Fraud

The researcher investigated parental support and juvenile involvement in internet fraud in the Delta North Senatorial District of Delta State. Two null hypotheses were tested at 0.05 levels of significance. The Relative Deprivation Theory was used to explain why parents might support internet fraud. Hence, the theory is very cogent, as it explains the fact that a parent who feels relatively deprived might vent their frustration by encouraging their offspring to seek an alternative route (internet fraud) to achieve material wealth. The study used a descriptive survey research design. All SS 1–2 students who registered in public secondary schools in the Delta North Senatorial District of Delta State make up the target population. The sample consisted of 480 students, 160 from each of the three constituencies (Aniocha/Oshimili, Ika, and Ndokwa/Ukuwani), who were specifically chosen from the three constituencies using stratified random selection techniques. A questionnaire on parental support and Juvenile involvement in internet fraud was used for data collection. At a 0.05 level of significance, the T-test statistic was utilized to evaluate the stated hypotheses. Findings from the study reveal that there is a significant difference in parental materialism support and juvenile involvement in internet fraud. Recommendations were made that parents should not place much emphasis on materialism but rather encourage and foster positive and socially acceptable behavior, such as honesty, contentment, discipline, and so on, rather than sending their juveniles to HK (the hustle kingdom) to make money through internet fraud.
Explainability and trust in AI: Bridging the gap between users and complex models

As artificial intelligence (AI) systems continue to permeate various aspects of our daily lives, understanding and fostering trust in these complex models have become paramount. This review paper delves into the critical intersection of explainability and trust in AI, aiming to bridge the gap between users and intricate machine learning models. The evolving landscape of AI applications, ranging from predictive analytics to autonomous decision-making systems, necessitates a nuanced examination of the factors contributing to user comprehension and trust. The paper begins by elucidating the significance of explainability, delineating how transparent, interpretable models serve as the foundation for establishing trust among users. It investigates the challenges associated with increasingly intricate AI architectures, emphasizing the potential pitfalls of
Prediction of Cross-Platform and Native Apps Technology Opportunities for Beginner Developers Using C 4.5 and Naive Bayes Algorithms

The competition between native and cross-platform app development makes application development simpler, safer, and more scalable. However, developers must have sufficient fundamentals, and the industry must conduct good research to shorten development time and minimize expenses. In order to solve these problems, this study made a prediction that discusses the technology that has a chance to survive in the industry so as not to be left behind in technology. Using Naïve Bayes and C 4.5 algorithms into a dataset with nine programming languages related to mobile app development. Results obtained in This research show Dart as a programming language that supports cross-platform frameworks and Kotlin as a programming language that supports native app frameworks is a technology that would have the opportunity in the future with an accuracy level above 90% with Naïve Bayes and C 4.5 algorithms. These results are obtained by testing an algorithm model using MAPE, consistent dataset sharing, and careful data processing. This research Can help entry-level developers learn and deepen the fundamentals of technology and can add knowledge to the industry in choosing a technology.
Coordination Algorithm of Autonomous Mobile Robot Swarms

An algorithm is presented that coordinates the movements of multiple robots, ensuring that their positions remain relative to each other as they move. Also, this algorithm allows it to adapt to changing conditions, such as the presence of obstacles. To test the effectiveness of the proposed algorithm, a swarm of nine robots was simulated. The results showed that the algorithm successfully allowed the robots to maintain the desired formation while moving along a given route. Based on these results, it can be concluded that the proposed algorithm is a promising approach for coordinating the movement of a swarm of robots. The practical applications of this algorithm are numerous, including military missions, search and rescue operations, and vehicle control systems. The algorithm can also be extended to larger robot swarms or other types of autonomous systems. In addition, it is possible to further develop this algorithm to solve more complex problems related to robot swarm motion in a dynamic and unpredictable environment.
Public data security using Ethereum Smart Contract

Smart contracts, a unique form of blockchain technology, enable financial transactions on the Ethereum blockchain. However, the blockchain paradigm's decentralized structure raises security concerns and has been linked to significant financial losses. Contrary to typical financial entities, Ethereum lacks centralized controls to solve these challenges. These problems have been addressed and Ethereum's security has been enhanced by symbolic execution, which has grown to be a well-known technique for guaranteeing programme integrity. The security of the blockchain can be improved more efficiently by using this method to assess Ethereum's security and identify areas that require the attention of security experts.
Cross-Platform Mobile Development Approaches and Frameworks

The use of mobile applications is rapidly increasing, due to rapid development of smartphones. Mobile apps, on the other hand, are platform-specific, making development more difficult and expensive. Cross platform or multiplatform application development is a fairly new idea in which developers apply a single code to create apps for several platforms like Android, BlackBerry, Windows Mobile, iOS etc. With the rising usage of these frameworks, it is important to comprehend both contributions and limitations in this emerging field. This paper presents a systematic literature review (SLR) of the research studies in the field of cross platform mobile app Development including approaches and tools. The SLR has been compiled by reviewing the research studies published between 2012-2022 in reputed venues. A Total of 22 studies has been selected and classified by using systematic process. The review has been presented on cross platform approaches, tools and challenges. Further an approach has been proposed for cross platform mobile application development. Finally, open issues and challenges in the field of cross platform have been presented to provide future direction to the researchers.
SEMANTIC AND NATURAL LANGUAGE PROCESSING DEVELOPMENT APPLICATIONS FOR CHATBOTS TO ENHANCE ONLINE STORE CUSTOMER SERVICE

Since customer support is time-limited, chatbot programs can assist potential online store visitors before they make a purchase. The general public cannot always answer inquiries or respond to customer requests. Virtual customer support allows potential customers to contact vendors regarding products they wish to purchase. This technology is very helpful in providing quick and accurate answers to various customer concerns and issues. The study focuses on the online retail environment where customer support is crucial for potential buyers before making a purchase. The Artificial Intelligence Markup Language (AIML) and Semantic Ontology were used by A.L.I.C.E. (Artificial Internet Linguistic Computer Agency) to develop an AI chatbot application. There are no online stores that use virtual customer service (chatbots) for customer support, so Batik Cloth, an application that offers batik textiles for sale in Malang, was chosen as the online store chatbot application for this study. Creating a chatbot with semantic capabilities involves using ontologies to process queries with more precise meaning. It achieves 92% accuracy for 15 types of relevant queries and responses, followed by 10 frequently asked questions as answers. Created by a potential buyer. Virtual customer support systems (chatbots) can respond to queries with similar terms or meanings by employing ontologies and semantics to deliver answers that fit the queries.
Fast and Accurate Transferability Measurement by Evaluating Intra-class Feature Variance

Given a set of pre-trained models, how can we quickly and accurately find the most useful pre-trained model for a downstream task? Transferability measurement is to quantify how transferable is a pre-trained model learned on a source task to a target task. It is used for quickly ranking pre-trained models for a given task and thus becomes a crucial step for transfer learning. Existing methods measure transferability as the discrimination ability of a source model for a target data before transfer learning, which cannot accurately estimate the fine-tuning performance. Some of them restrict the application of transferability measurement in selecting the best supervised pre-trained models that have classifiers. It is important to have a general method for measuring transferability that can be applied in a variety of situations, such as selecting the best self-supervised pre-trained models that do not have classifiers, and selecting the best transferring layer for a target task.In this work, we propose TMI (Transferability Measurement with Intra-class feature variance), a fast and accurate algorithm to measure transferability. We view transferability as the generalization of a pre-trained model on a target task by measuring intra-class feature variance. Intra-class variance evaluates the adaptability of the model to a new task, which measures how transferable the model is. Compared to previous studies that estimate how discriminative the models are, intra-class variance is more accurate than those as it does not require an optimal feature extractor and classifier. Extensive experiments on real-world datasets show that TMI outperforms competitors for selecting the top-5 best models, and exhibits consistently better correlation in 13 out of 17 cases.
Explaining Adversarial Robustness of Neural Networks from Clustering Effect Perspective

Adversarial training (AT) is the most commonly used mechanism to improve the robustness of deep neural networks. Recently, a novel adversarial attack against intermediate layers exploits the extra fragility of adversarially trained networks to output incorrect predictions. The result implies the insufficiency in the searching space of the adversarial perturbation in adversarial training. To straighten out the reason for the effectiveness of the intermediate-layer attack, we interpret the forward propagation as the Clustering Effect, characterizing that the intermediate-layer representations of neural networks for samples i.i.d. to the training set with the same label are similar, and we theoretically prove the existence of Clustering Effect by corresponding Information Bottleneck Theory. We afterward observe that the intermediate-layer attack disobeys the clustering effect of the AT-trained model. Inspired by these significant observations, we propose a regularization method to extend the perturbation searching space during training, named sufficient adversarial training (SAT). We give a proven robustness bound of neural networks through rigorous mathematical proof. The experimental evaluations manifest the superiority of SAT over other state-of-the-art AT mechanisms in defending against adversarial attacks against both output and intermediate layers. Our code and Appendix can be found at https://github.com/clustering-effect/SAT.
HiFace: High-Fidelity 3D Face Reconstruction by Learning Static and Dynamic Details

3D Morphable Models (3DMMs) demonstrate great potential for reconstructing faithful and animatable 3D facial surfaces from a single image. The facial surface is influenced by the coarse shape, as well as the static detail (e.g., person-specific appearance) and dynamic detail (e.g., expression-driven wrinkles). Previous work struggles to decouple the static and dynamic details through image-level supervision, leading to reconstructions that are not realistic. In this paper, we aim at high-fidelity 3D face reconstruction and propose HiFace to explicitly model the static and dynamic details. Specifically, the static detail is modeled as the linear combination of a displacement basis, while the dynamic detail is modeled as the linear interpolation of two displacement maps with polarized expressions. We exploit several loss functions to jointly learn the coarse shape and fine details with both synthetic and real-world datasets, which enable HiFace to reconstruct high-fidelity 3D shapes with animatable details. Extensive quantitative and qualitative experiments demonstrate that HiFace presents state-of-the-art reconstruction quality and faithfully recovers both the static and dynamic details. Our project page: https://project-hiface.github.io.
Waffling around for Performance: Visual Classification with Random Words and Broad Concepts

The visual classification performance of vision-language models such as CLIP has been shown to benefit from additional semantic knowledge from large language models (LLMs) such as GPT-3. In particular, averaging over LLM-generated class descriptors, e.g. "waffle, which has a round shape", can notably improve generalization performance. In this work, we critically study this behavior and propose WaffleCLIP, a framework for zero-shot visual classification which simply replaces LLM-generated descriptors with random character and word descriptors. Without querying external models, we achieve comparable performance gains on a large number of visual classification tasks. This allows WaffleCLIP to both serve as a low-cost alternative, as well as a sanity check for any future LLM-based vision-language model extensions. We conduct an extensive experimental study on the impact and shortcomings of additional semantics introduced with LLM-generated descriptors, and showcase how - if available - semantic context is better leveraged by querying LLMs for high-level concepts, which we show can be done to jointly resolve potential class name ambiguities. Code is available here: https://github.com/ExplainableML/WaffleCLIP.
Identification of Systematic Errors of Image Classifiers on Rare Subgroups

Despite excellent average-case performance of many image classifiers, their performance can substantially deteriorate on semantically coherent subgroups of the data that were under-represented in the training data. These systematic errors can impact both fairness for demographic minority groups as well as robustness and safety under domain shift. A major challenge is to identify such subgroups with subpar performance when the subgroups are not annotated and their occurrence is very rare. We leverage recent advances in text-to-image models and search in the space of textual descriptions of subgroups ("prompts") for sub-groups where the target model has low performance on the prompt-conditioned synthesized data. To tackle the exponentially growing number of subgroups, we employ combinatorial testing. We denote this procedure as PromptAttack as it can be interpreted as an adversarial attack in a prompt space. We study subgroup coverage and identifiability with PromptAttack in a controlled setting and find that it identifies systematic errors with high accuracy. Thereupon, we apply PromptAttack to ImageNet classifiers and identify novel systematic errors on rare subgroups.