Reviewerly/exHarmony huggingface dataset:
Respecting Partial Privacy of Unstructured Data via Spectrum-Based Encoder

Since the popularity of Machine Learning as a Service (MLaaS) has been increasing significantly, users are facing the risk of exposing sensitive information that is not task-related. The reason is that the data uploaded by users may include some information that is not useful for inference but can lead to privacy leakage. One straightforward approach to mitigate this issue is to filter out task-independent information to protect user privacy. However, this method is feasible for structured data with naturally independent entries, but it is challenging for unstructured data. Therefore, we propose a novel framework, which employs a spectrum-based encoder to transform unstructured data into the latent space and a task-specific model to identify the essential information for the target task. Our system has been comprehensively evaluated on three benchmark visual datasets and compared to previous works. The results demonstrate that our framework offers superior protection for task-independent information and maintains the usefulness of task-related information.
Model-Agnostic Binary Patch Grouping for Bone Marrow WSI Representation

Histopathology is the reference standard for pathology diagnosis, and has evolved with the digitization of glass slides, i.e. whole slide images (WSIs). Trained histopathologists can help to diagnose disease by examining WSIs visually, but this process is time-consuming and prone to variability. To address these issues, AI models are being developed to create slide-level representations of WSIs, summarizing the entire slide as a single vector. This enables various computational pathology applications, including inter-slide search, multi-modal training, and slide-level classification. Achieving expressive and robust slide-level representations hinges on patch feature extraction and aggregation steps. We propose integrating an additional Binary Patch Grouping (BPG) step, a plugin that can be integrated into various slide-level representation pipelines to enhance the quality of slide-level representation in bone marrow histopathology. BPG excludes patches with less clinical relevance through minimal interaction with the pathologist: a one-time human intervention for the entire process. We further investigated domain-general versus domain-specific feature extraction models based on convolution and attention and examined two different feature aggregation methods, with and without BPG, showing BPG’s generalizability. We show that BPG boosts the performance of WSI retrieval (mAP@10) by 4% and improves WSI classification (weighted-F1) by 5% relative to not using BPG. Additionally, we found that the pipeline with BPG, domain-general large models and parameterized pooling produced the best-quality slide-level representations.
Touch or click friendly: Towards adaptive user interfaces for complex applications

This study evaluated the usability of a direct manipulation device (touchscreen) vs. indirect manipulation devices (mouse and touchpad) on the selected Microsoft (MS) Word tasks as per ISO-9241-11 standard. MS Word was taken as an example of a complex application. The tasks were evaluated in terms of touch-friendly or click-friendly using efficiency, effectiveness, and satisfaction parameters to propose a customized task menu. The experiment was conducted with fifty-four participants, divided into three MS Word usage-based expertise groups. Each participant performed fifty-six tasks using a mouse, a touchpad, and a touchscreen. To assess task-level usability, individual one-way ANOVAs were performed for each task to gauge both efficiency and effectiveness. It’s worth noting that the touchscreen significantly outperformed other input methods in just one specific task regarding effectiveness. Consequently, an ANCOVA was employed, with task completion time as the independent variable and the number of errors as a covariate, to further investigate effectiveness. A total of 19 (34%) of the total tasks were found to be significantly efficient with a mouse, while 21 (37.5%) were significantly efficient with a touchscreen. Based on the results, a customized menu is recommended for MS Word-like applications that combine actions in touch-friendly tasks and mouse-friendly tasks separately.
Demystifying Compiler Unstable Feature Usage and Impacts in the Rust Ecosystem

Rust programming language is gaining popularity rapidly in building reliable and secure systems due to its security guarantees and outstanding performance. To provide extra functionalities, the Rust compiler introduces Rust unstable features (RUF) to extend compiler functionality, syntax, and standard library support. However, these features are unstable and may get removed, introducing compilation failures to dependent packages. Even worse, their impacts propagate through transitive dependencies, causing large-scale failures in the whole ecosystem. Although RUF is widely used in Rust, previous research has primarily concentrated on Rust code safety, with the usage and impacts of RUF from the Rust compiler remaining unexplored. Therefore, we aim to bridge this gap by systematically analyzing the RUF usage and impacts in the Rust ecosystem. We propose novel techniques for extracting RUF precisely, and to assess its impact on the entire ecosystem quantitatively, we accurately resolve package dependencies. We have analyzed the whole Rust ecosystem with 590K package versions and 140M transitive dependencies. Our study shows that the Rust ecosystem uses 1000 different RUF, and at most 44% of package versions are affected by RUF, causing compiling failures for at most 12% of package versions. To mitigate wide RUF impacts, we further design and implement a RUF-compilation-failure recovery tool that can recover up to 90% of the failure. We believe our techniques, findings, and tools can help stabilize the Rust compiler, ultimately enhancing the security and reliability of the Rust ecosystem.
On Using GUI Interaction Data to Improve Text Retrieval-based Bug Localization

One of the most important tasks related to managing bug reports is localizing the fault so that a fix can be applied. As such, prior work has aimed to automate this task of bug localization by formulating it as an information retrieval problem, where potentially buggy files are retrieved and ranked according to their textual similarity with a given bug report. However, there is often a notable semantic gap between the information contained in bug reports and identifiers or natural language contained within source code files. For user-facing software, there is currently a key source of information that could aid in bug localization, but has not been thoroughly investigated - information from the graphical user interface (GUI).
EDEFuzz: A Web API Fuzzer for Excessive Data Exposures

APIs often transmit far more data to client applications than they need, and in the context of web applications, often do so over public channels. This issue, termed Excessive Data Exposure (EDE), was OWASP's third most significant API vulnerability of 2019. However, there are few automated tools---either in research or industry---to effectively find and remediate such issues. This is unsurprising as the problem lacks an explicit test oracle: the vulnerability does not manifest through explicit abnormal behaviours (e.g., program crashes or memory access violations).
Reflecting on Culture in an Immersion Experience: How to Prepare Students for the Unexpected

Her research focus includes self-efficacy, mental toughness, and microaggressions.Darnishia is also
Prescriptive method for fiber polarization compensation in two bases

Single-mode optical fibers exhibit a small but non-negligible birefringence that induces random polarization rotations during light propagation. In classical interferometry these rotations give rise to polarization-induced fading of the interferometric visibility, and in fiber-based polarimetric sensors as well as quantum optics experiments, they scramble the information encoded in the polarization state. Correcting these undesired rotations is consequently an important part of many experiments and applications employing optical fibers. In this Lab Note we review an efficient method for fully compensating fiber polarization rotations for general input states. This method was not originally devised by us, but does, to the best of our knowledge, not appear in the literature, and our interactions with the community have indicated that it is not well known.
An Improved Genetic Algorithm Using Intelligent Symbolic Regression

In this paper, an optimization technique based on intelligent symbolic regression is presented.Intelligent symbolic regression methodology seeks to replace implicit functions of the original design optimization problem with an approximation model less expensive to evaluate.In order to address the issue of the high-computational cost of the implicit function evaluation, genetic programming methodology has been investigated to build approximation models of the best possible quality.The goal of genetic programming is to evolve mathematical expressions with no assumption about the structure, which is given as part of the solution.Once the symbolic regression model is available, the Genetic Algorithm, on subsequent runs, searches the inexpensive regression model for the global optimum, only rarely sampling the high-fidelity model.The efficiency of the proposed method has been tested on a set of standard optimization test problems and it was found to reduce the computational overhead by a significant amount without much loss of accuracy.Test results of optimization problems revealed that real-valued representation technique outperforms the binary representation technique.Such a representation is of particular concern for engineering applications, which motivated the present work.
Creating a Framework for Virtual Reality Learning Environment (VRLE) for Studio-Based Learning

Higher education academics are lacking the knowledge on how to implement Design Thinking (DT) for transdisciplinary design studies that would produce Fourth Industrial Revolution Workforce Skills (4IRSkills), due to the sparsity in literature and implementation guidelines. This article discusses on the creation process of a Teaching and Learning Framework (TLF) for transdisciplinary design studies with a desktop-based Virtual Reality Learning Environment (VRLE) simulation of studio-based learning. The framework was created using a Selander’s Learning Design Sequence (LDS) with DT approach. The LDS helped the researcher to understand the processes of teaching and learning, especially on the affordances of various modes. The DT approach was used to facilitate the process of solving problems through the practice of empathy, reframing, prototyping, experimentation, testing, and redesign. A sequential explanatory mixed method was used, and the data were analyzed through inferential statistical analysis, thematic analysis and convergent coding matrix triangulation protocol. The findings indicated that the TLF with VRLE showed a positive and significant relationship with students' achievement of 4IRSkills. The TLF Design Process and Collaborative Design variables were positively correlated with students' 4IRSkills, while there was no correlation for the VRLE Representational Fidelity and Learner Interaction variables. In conclusion, the TLF with VRLE proves to be an effective and suitable method for guiding the implementation of pedagogies that teaches 4IRSkills at the tertiary level in fully online learning environments.
The Impact of Transfer Learning on AI Performance Across Domains

This study investigates the profound impact of transfer learning on the performance of artificial intelligence (AI) models when applied across diverse domains. Transfer learning, a machine learning technique that leverages knowledge gained from one task to improve performance on a related task, has demonstrated remarkable success in various applications. The article explores the underlying principles of transfer learning, its mechanisms, and the ways in which it enhances AI performance. The findings highlight the potential of transfer learning to facilitate knowledge transfer between domains, reduce training data requirements, and accelerate model convergence, ultimately contributing to the broader adaptability and efficiency of AI systems
Towards a more practical analysis of Newton’s rings using deep learning

As a typical form of optical fringes with a quadratic phase, Newton's ring patterns play an important role in spherical measurements and optical interferometry. A variety of methods have been used to analyze Newton's ring patterns. However, it is still rather challenging to fulfill the analysis. We present a deep-learning-based method to estimate the parameters of Newton's ring patterns and fulfill the analysis accordingly. The experimental results indicate the excellent accuracy, noise robustness, and demodulation efficiency of our method. It provides another applicable approach to analyzing Newton's ring patterns and brings insights into fringe analysis and interferometry-based measurements.
Analysis of Fitness Based on Smart Watch Data

Smart watch, a new trend to many young as well as aged persons now a days, can serve as a fitness tracker and be far more accurate than a phone. Besides many technological advantages, a smartwatch can easily collect the data related to fitness and other movements also. Smart watch provides such an organized human health and activities dataset so that by this dataset a human activities analysis can be performed and by this analysis a conclusion can be drawn about their health. Accordingly, our Project aim is to analyze the data and examine whether the data collected through smart watches can describe the physical fitness of individuals on the basis of some of the factors related to daily activities. Keywords— Data Visualization, Correlation plot, Scatter plot, MANOVA, Fisher Exact Test, P-value
Self-Confidence of Undergraduate Students in Designing Software Architecture

Software architecture students, often, lack self-confidence in their ability to use their knowledge to design software architectures.This paper investigates the relations between undergraduate software architecture students' self-confidence and their course expectations, cognitive levels, preferred learning methods, and critical thinking.We developed a questionnaire with open-ended questions to assess the self-confidence levels and related factors, which was taken by one-hundred ten students in two semesters.The students answers were coded and analyzed afterward.We found that self-confidence is weakly associated with the students' critical thinking and independent from their cognitive levels, preferred learning methods, and expectations from the course.The results suggest that to improve the self-confidence of the students, the instructors should work on improving the students' critical thinking capabilities.
Building S-STEM scholars' knowledge and skills through technical and career-development seminars

Abstract This paper first presents an overview of the objectives and the accomplishments of the S-STEM ACCESS project and then focuses on a specific ACCESS program activity - the technical and career-development seminars and panels - which support ACCESS scholars' knowledge and skills development and contribute toward achieving several project objectives. The technical seminars exposed students to state-of-the-art topics and industry practices related to the rapidly evolving field of cybersecurity. The career-development seminars included topics on obtaining security clearances, understanding different cybersecurity workforce career paths, as well as on pursuing graduate degrees and research-centered careers. Since seminars and panels were open to all West Virginia University (WVU) students, they helped publicize the ACCESS program and the B.S. and Area of Emphasis (AoE) in Cybersecurity and therefore also served as a recruitment mechanism. The results of the external evaluation showed that the seminars and panels were of exceptional value to students who unanimously gave them high positive scores. Qualitative responses to the survey and focus group input revealed that students appreciated the seminars for providing opportunities to connect with professionals and peers, to learn about potential career paths, to gain the knowledge and confidence needed to successfully secure an internship position, and other benefits.
Generating arbitrary analytically solvable two-level systems

Abstract We present a new approach for generating arbitrary analytically solvable two-level systems. This method offers the ability to completely derive all analytically solvable Hamiltonians for any analytical evolutions of two-level systems. To demonstrate the effectiveness of this approach, we reconstruct the Rosen–Zener model and generate several new exact solutions. Using this approach, we present the exact evolution of the semi-classical Rabi model with new analytical properties. The parameters used to generate Hamiltonians have direct physical interpretations within the Bloch sphere, the quantum speed limit, and the geometric phase. As a result, the physical properties of the generated Hamiltonian are highly controllable, which plays a significant role in the fields of quantum control, quantum computing, and quantum information.
Sand Painting Generation Based on Convolutional Neural Networks

Neural style transfer is an algorithm that transfers the style of one image to another image and converts the style of the second image while preserving its content. In this paper, we propose a style transfer approach for sand painting generation based on convolutional neural networks. The proposed approach aims to improve sand painting generation via neural style transfer, which can address the problem of blurred objects. Furthermore, it can reduce background noise caused by neural style transfers. First, we segment the main objects from the content image. Subsequently, we perform close–open filtering operations on the content image to obtain smooth images. Subsequently, we perform Sobel edge detection to process the images and obtain edge maps. Based on these edge maps and the input style image, we perform neural style transfer to generate sand painting images. Finally, we integrate the generated images to obtain the final stylized sand painting image. The results show that the proposed approach yields good visual effects from sand paintings. Moreover, the proposed approach achieves better visual effects for sand painting than the previous method.
In-Situ Bending Moment Visualization of a Structure Using Augmented Reality and Real-Time Object Detection

Agile technology
Generative AI-Powered Chatbots

Imagine having a concept for a literary work but being unable to see it take shape because of a plethora of reasons: paucity of time, writer's block, linguistic barriers, among others. Gen AI promises to be a supportive partner in these creative endeavours. With the synergy of mind and machine, literary texts are born. This chapter attempts to provide insights into the comparison between the nuances of human-AI co-creative partnerships through the crafting of short stories. Using the lens of Collaborative Autoethnography, two seasoned educators collaboratively reflect on cognitive, behavioural, and affective dimensions of co-creating with ChatGPT, a chatbot developed by Open AI. The chapter analyses the co-creation process followed by each of the authors and brings out the commonalities and specificities of the individual lived experience to achieve a narrative piece of work. The insights of the co-creation process would be beneficial to educators, curriculum designers, technology specialists, students and those who intend to use AI-powered chatbots as co-creators.
Central Bank Digital Currency (CBDC) in Pakistan

This chapter explores the implications and prospects of Central Bank digital currencies (CBDC) in Pakistan, focusing on its potential to transform the financial ecosystem. The emergence of blockchain and distributed ledger technologies has incited significant interest in digital currencies, leading central banks worldwide to research and develop CBDCs. In Pakistan, the implementation of CBDC presents opportunities for modernizing the financial infrastructure, enhancing financial inclusion, and fostering economic growth. The study employs a mixed-method approach, integrating qualitative and quantitative analyses. It examines the types of CBDCs, namely wholesale and retail, and their potential impacts on payment safety and efficiency. A thorough literature review provides insights into the multifaceted consequences of CBDCs on payment systems, monetary policy, and financial stability. Additionally, the research explores the challenges of CBDC implementation in Pakistan, including cybersecurity risks, technological limitations, and potential threats to financial stability. The findings highlight the need for comprehensive research, international collaboration, and a robust regulatory framework to mitigate risks and maximize the benefits of CBDCs. This study contributes to the ongoing discourse on digital currencies and their role in shaping the future of money and banking in emerging economies like Pakistan.
Between Partial Translation and Summary Translation in Translating the Sundanese Folklore into English

The strategies of partial translation and summary translation are related but not exactly the same. There are some characteristics indicating each of them. This research article is about the translation of the most popular Sundanese folklore, which is written in the Sundanese language and translated into English. It deals with literary translation as the data source is folklore, and folklore is included as literary work. Using content analysis of the descriptive qualitative research method, this research aims to identify what information in the source text is partially translated and which information is summarized. The theory used is one of the pragmatic strategies given by Chesterman: the strategy of partial translation. The results show that the strategies used in identifying the missing information are partial translation, summary translation, and the combination of partial translation and summary translation. The partial translation is used to avoid repetition, eliminate very detailed information or complex information, and change explicit information into implicit information. The summary translation is used to create a short summary of information involving the key points or messages given in the source text by adopting the syntactic form of the target text based on the grammatical rules.