Reviewerly/exHarmony huggingface dataset:
Traffic Density Estimation at Intersections via Image-Based Object Reference Method

This study aims to estimate traffic density at intersections using images. The problem involves focusing on two components: the number of vehicles and the area of the region. Counting vehicles is relatively straightforward with various modern techniques available. However, estimating the region’s area is more challenging due to the lack of specific camera configurations for calculations. The proposed technique utilizes traffic vehicles as the reference object to calculate the intersection’s total area dynamically. The study presents two approaches for identifying the region’s area based on the object reference technique and an experimental system architecture for real-time application.
EVB - Electronic Voting System Based on Blockchain Technology

Blockchain is one of the prominent technologies, the trend of blockchain in IoT application in free and fair elections creating positive changes for the public sector. IoT integration in blockchain-based electronic voting systems provides secure device authentication and real-time monitoring, ensuring security, transparency, and accuracy. We proposed solution for building an online voting system, called EVB, leverages Ethereum as a smart contract on a digital platform. Experimental results demonstrate the scalability potential of EVB by comparing processing performance and costs across different blockchain layers, including layer 1 - Ethereum, layer 2 - Polygon, and sidechain - BSC. The utilization of Merkle tree enhances security, controls user numbers, and reduces costs significantly. Additionally, the computational complexity of Optimistic Rollup suggests that switching to layer 2 is unnecessary. Account abstraction is applied to support user payments, thereby facilitating transaction fee support and laying the groundwork for the expansion of the EVB voting system on IoT environment.
Enhanced Complex Human Activity Recognition System: A Proficient Deep Learning Framework Exploiting Physiological Sensors and Feature Learning

Human activity recognition is the process of identifying daily living activities of a person using sensor attributes and intelligent learning algorithms. Identifying complex human activities is tedious, as capturing long-term dependencies and extracting efficient features from the raw sensor data is challenging. This letter proposes an efficient and lightweight hybrid deep learning model for recognizing complex human activities using physiological electromyography (EMG) sensors and enhanced feature learning. The proposed convolutional neural networks - long short-term memory (CNN-LSTM) incorporates multiple 1-D convolution layers for spatial feature extraction and then feeds the generated feature maps to the recurrent layers to identify long-term temporal dependencies. Incorporating a physiological sensor-based raw EMG dataset and minimal preprocessing, we trained and tested our proposed model and achieved the highest accuracy of 84.12% and an average accuracy of 83%. The proposed model outperformed the benchmark models with optimal performance margins and generalized the patterns in significantly less computational time than other deep learning models.
The Seasonality of Bitcoin

The cryptocurrency market has grown significantly and is no longer considered a small or negligible sector. As the crypto market continues to evolve, researchers are increasingly interested in conducting in-depth analyses, for example, one of the most fascinating phenomena in the world - seasonality effects. We examine the distribution of the daily returns, proposing a simple seasonality strategy based on holding BTC only for two hours per day. Moreover, this paper extends research on potential seasonal patterns related to Bitcoin, focusing on whether these patterns are influenced by factors such as current market trends or the level of volatility in the market.
Nurturing Neural Networks

Most challenging problem associated with neural networks is, the proportionality of resources required for training with number of parameters associated with that model. Many efficient architectures have been proposed over years for solving specific problems but even the most profound architectures may not be extrapolated to problems in the same domain without much difficulty. This paper works at introducing a new criterion for building neural networks from a preliminary stage and expanding it progressively through the training stages.
A Blockchain-Based IoT System for Secure Attendance Management

Attendance plays a crucial role within any organization. Traditionally, attendance records have been primarily maintained on paper in numerous businesses, colleges, and institutions, leading to a higher likelihood of errors or incomplete records. Additionally, manual management of attendance is often challenging and time-consuming. Therefore, the objective of this paper is to create an intelligent attendance system that automatically monitors and manages someone’s attendance. The entire system is built using a microcontroller, incorporating a fingerprint sensor for attendance verification. To ensure transparency and security, the attendance data will be stored on a Blockchain network. By implementing this system, the amount of manual work required by individuals in any organization will be significantly reduced. Furthermore, the utilization of the Blockchain network will enable people to access attendance information in a transparent manner.
The Effect of ARIAS Model Learning on Mathematical Writing Skills Reviewed from Self-Efficacy

&lt;p class="JRPMAbstractBodyEnglish"&gt;&lt;span lang="IN"&gt;This study aimed to determine the influence of &lt;/span&gt;&lt;span lang="IN"&gt;Assurance&lt;/span&gt;&lt;span lang="EN-ID"&gt;,&lt;/span&gt;&lt;span lang="IN"&gt; Relevance&lt;/span&gt;&lt;span lang="EN-ID"&gt;,&lt;/span&gt;&lt;span lang="IN"&gt; Interest&lt;/span&gt;&lt;span lang="EN-ID"&gt;,&lt;/span&gt;&lt;span lang="IN"&gt; Assessment&lt;/span&gt;&lt;span lang="EN-ID"&gt;,&lt;/span&gt;&lt;span lang="IN"&gt; Satisfaction &lt;/span&gt;(&lt;span lang="IN"&gt;ARIAS&lt;/span&gt;)&lt;span lang="IN"&gt; learning models on students' mathematical writing skills reviewed from self-efficacy. The type of research conducted in this study is &lt;/span&gt;&lt;span lang="EN-ID"&gt;a &lt;/span&gt;&lt;span lang="IN"&gt;quasi-experimental research with a post&lt;/span&gt;-&lt;span lang="IN"&gt;test nonequivalent control group design. The population in this study was students majoring in mathematics education at Bina Bangsa University, then two classes &lt;/span&gt;are &lt;span lang="IN"&gt;selected &lt;/span&gt;at random&lt;span lang="IN"&gt; as research subjects. The data was obtained from mathematical writing ability tests and self-efficacy questionnaires, then processed with ANOVA (Analysis of Variance) tests. The results showed significant differences in mathematical writing skills between students who obtained ARIAS learning models with students who received regular learning, and there were significant differences in students' mathematical writing skills reviewed on their self-efficacy. &lt;/span&gt;&lt;span lang="EN"&gt;T&lt;span class="ts-alignment-element"&gt;he &lt;/span&gt;implication i&lt;span class="ts-alignment-element"&gt;s &lt;/span&gt;k&lt;span class="ts-alignment-element"&gt;nown &lt;/span&gt;t&lt;span class="ts-alignment-element"&gt;hat &lt;/span&gt;s&lt;span class="ts-alignment-element"&gt;tudents &lt;/span&gt;w&lt;span class="ts-alignment-element"&gt;ith &lt;/span&gt;h&lt;span class="ts-alignment-element"&gt;igh &lt;/span&gt;s&lt;span class="ts-alignment-element"&gt;elf-confidence &lt;/span&gt;h&lt;span class="ts-alignment-element"&gt;ave &lt;/span&gt;b&lt;span class="ts-alignment-element"&gt;etter &lt;/span&gt;w&lt;span class="ts-alignment-element"&gt;riting &lt;/span&gt;s&lt;span class="ts-alignment-element"&gt;kills &lt;/span&gt;t&lt;span class="ts-alignment-element"&gt;han &lt;/span&gt;s&lt;span class="ts-alignment-element"&gt;tudents &lt;/span&gt;w&lt;span class="ts-alignment-element"&gt;hose &lt;/span&gt;c&lt;span class="ts-alignment-element"&gt;onfidence &lt;/span&gt;i&lt;span class="ts-alignment-element"&gt;s &lt;/span&gt;l&lt;span class="ts-alignment-element"&gt;ow.&lt;/span&gt;&lt;/span&gt;&lt;/p&gt;
Breaking the Domination of the Word over the Human Spirit

Abstract A case is made for regimenting talk of properties and relations, alongside propositions and modalities, in a formal language. Focusing on properties, a number of desiderata of such a formal language are developed. It is shown that these desiderata are jointly inconsistent, using an analog of the inconsistency of naive set comprehension for properties. One of the desiderata is singled out as the least important one. The remaining desiderata motivate the introduction of a formal language which is a version of higher-order logic in the form of relational type theory. This language is introduced in such a way that its sentences are meaningful, with the relevant metasemantic constraints involving connections to natural language expressions and stipulative logical principles.
A Formal Language

Abstract A restricted version of the higher-order logic motivated in the previous chapter is introduced. The language includes variables in the position of sentences, and quantifiers binding them; these propositional quantifiers can be used to formalize talk of propositions. Similarly, the language includes variables in the position of sentential operators and quantifiers binding them, which can be used to formalize talk of modalities. The language also provides Boolean connectives and lambda abstraction, and an extension provides a connective for propositional identity. Proof systems for these languages are defined, and a schematic comprehension principle is derived. A general class of possible worlds models are introduced, and a soundness result is established for a suitable subclass. It is noted that model-theoretic consistency results can be seen as relative consistency proofs, and so do not rely on the existence of set-theoretic models.
Building Your Consortium of Support

Abstract Chapter 9 introduces the reader to the nuts and bolts of grant proposal budgets and the all-important consortium of support. As the author points out, once a descriptive and persuasive project abstract is developed, the next step is in honing a detailed and credible budget that demonstrates one’s cognizant understanding of financial principles. This chapter offers concrete examples and guidelines that undergird all successful budgets. Moreover, the author delves deeply into the development of the critical consortium of support that is required prior to submitting any budget. Essentially, it provides concrete evidence regarding the cost-sharing resources one already has in place. This is the essential foundation of a dynamic project budget. Those preliminary cost-sharing partners sign on to the efficacy of one’s grant proposal, in essence vetting the project. Furthermore, the author explores the principle of money attracting money, making it much easier to obtain financial support from those larger more significant funders once there is a diverse and dynamic consortium of preliminary support in place. In this chapter you will find a real-life story about one musician’s successful grant awards and the winding path she took to the acquisition of funding. Lastly, this chapter includes principles and strategies for building effective collaborations with funders, and the skillful networking required to develop dynamic funding consortia.
ReactCOP: Modular and Scalable Web Development with Context-Oriented Programming

We present a library named ReactCOP that extends React's capabilities with support for Context-Oriented Programming. The library lets developers manage behavioral variations in React applications through layers, and adapt the application's behavior dynamically based on different contexts.
Sui Move: Modern Blockchain Programming with Objects

This paper presents Sui Move, a new smart contract language for programming blockchains using objects as an abstraction.
A Monadic Framework for Name Resolution in Multi-phased Type Checkers

An important aspect of type checking is name resolution --- i.e., determining the types of names by resolving them to a matching declaration. For most languages, we can give typing rules that define name resolution in a way that abstracts from what order different units of code should be checked in. However, implementations of type checkers in practice typically use multiple phases to ensure that declarations of resolvable names are available before names are resolved. This gives rise to a gap between typing rules that abstract from order of type checking and multi-phased type checkers that rely on explicit ordering.
Partial Gradual Dependent Type Theory

Gradual typing supports imprecise types in the type system, allowing incremental migration from untyped code to typed in the same language. Through the gradual typing approach, our ongoing work proposes a new theory based on the Martin-Löf type theory called Partial Gradual Dependent Type Theory. PGTT supports a gradual step from non-dependently typed code to dependently typed, enhancing the functionality of code reasoning while preserving the usability of widely used non-dependent type systems. PGTT restricts entirely unknown types and only permits dynamic terms on the type indices, making it naturally maintain better properties than existing gradual dependent type theories. It allows us to simplify runtime type checks into type parameter checks and elaborate the surface language into a static, dependently typed language, thereby reducing the performance overhead associated with gradual typing.
Experiences of Autistic Twitch Livestreamers: “I have made easily the most meaningful and impactful relationships”

We present perspectives from 10 autistic Twitch streamers regarding their experiences as livestreamers and how autism uniquely colors their experiences. Livestreaming offers a social online experience distinct from in-person, face-to-face communication, where autistic people tend to encounter challenges. Our reflexive thematic analysis of interviews with 10 participants showcases autistic livestreamers’ perspectives in their own words. Our findings center on the importance of having streamers establishing connections with other, sharing autistic identities, controlling a space for social interaction, personal growth, and accessibility challenges. In our discussion, we highlight the crucial value of having a medium for autistic representation, as well as design opportunities for streaming platforms to onboard autistic livestreamers and to facilitate livestreamers communication with their audience.
An Optimal Structure-Aware Code Difference Framework with MaxSAT-Solver

The Abstract Syntax Tree (AST) serves as a pivotal representation of program codes, offering a structured and hierarchical view of the program’s syntax. When developers modify code, the underlying AST also evolves to reflect these changes. Tree-diff algorithms, such as truediff and Gumtreediff, are developed to compare different versions of the AST and identify the modifications made between them. However, these heuristics are based on certain vertex matching methods that do not ensure optimality and preciseness. In this study, I propose a novel tree-diff approach that utilizes a MaxSAT (Maximum satisfiability) solver to address this issue. By encoding potential vertex matches and edges with associated costs as a tree-diff SAT problem, the MaxSAT solver effectively minimizes the edit distance and reveals the optimal vertex matching plan.
Nanopass Attribute Grammars

Compilers for feature-rich languages are complex; they perform many analyses and optimizations, and often lower complex language constructs into simpler ones. The nanopass compiler architecture manages this complexity by specifying the compiler as a sequence of many small transformations, over slightly different, but clearly defined, versions of the language that each perform a single straightforward action. This avoids errors that arise from attempting to solve multiple problems at once and allows for testing at each step.
JaMaBuild: Mass Building of Java Projects

Many large-scale Java empirical studies require not only source code but also resulting binaries such as JAR files. Pre-compiled datasets quickly become obsolete, and the creation of a custom corpus for every study is tedious. We present a prototype of JaMaBuild, a tool and a framework for mass building of Java projects from source. Given a list of projects and optional settings, it downloads the projects, filters them by user-definable criteria, builds them using Maven or Gradle, and collects outputs such as JAR files and build logs. Our tool can also be used for local build breakage studies.
Remote Just-in-Time Compilation for Dynamic Languages

Cloud platforms allow applications to meet fluctuating levels of demand through automatic horizontal scaling. These deployment models are characterized by short-lived applications running in resource-constrained environments. This amplifies the overhead of dynamic languages with just-in-time (JIT) compilation. Dynamic-language runtimes suffer from a warmup phase and resource-usage peaks caused by JIT compilation. Offloading compilation jobs to a dedicated server is a possible mitigation for these problems. We propose leveraging remote JIT compilation as a means to enable coordination between the independent instances. By sharing compilation results, aggregating profiles, and adapting the compiler and compilation policy, we strive to improve the peak performance and further reduce the warmup time of these applications. Additionally, an implementation on top of the Truffle framework enables us to bring these benefits to many popular languages.
Unleashing the Power of Implicit Feedback in Software Product Lines: Benefits Ahead

Software Product Lines (SPLs) facilitate the development of a complete range of software products through systematic reuse. Reuse involves not only code but also the transfer of knowledge gained from one product to others within the SPL. This transfer includes bug fixing, which, when encountered in one product, affects the entire SPL portfolio. Similarly, feedback obtained from the usage of a single product can inform beyond that product to impact the entire SPL portfolio. Specifically, implicit feedback refers to the automated collection of data on software usage or execution, which allows for the inference of customer preferences and trends. While implicit feedback is commonly used in single-product development, its application in SPLs has not received the same level of attention. This paper promotes the investigation of implicit feedback in SPLs by identifying a set of SPL activities that can benefit the most from it. We validate this usefulness with practitioners using a questionnaire-based approach (n=8). The results provide positive insights into the advantages and practical implications of adopting implicit feedback at the SPL level.
Dynamic Library Compartmentalization

Software is composed of different parts with different goals, each with different needs. Security-wise, this means not all necessarily need the same permissions: it can be beneficial to isolate some code such that it has limited control over the process, following the principle of least privilege. Without any sort of compartmentalization, a vulnerability found in a sensitive part of an application means the entire process can get corrupted, as its entire memory is now open to be read and modified by the attacker.
“Invisible Illness Is No Longer Invisible”: Making Social VR Avatars More Inclusive for Invisible Disability Representation

As social virtual reality (VR) experiences become more popular, it is critical to design accessible and inclusive embodied avatars. At present, there are few, if any, customization features for invisible disabilities (e.g., chronic health conditions, mental health conditions, neurodivergence) in social VR platforms. To our knowledge, researchers have yet to explore how people with invisible disabilities want to self-represent and disclose disabilities through social VR avatars. We fill this gap in current accessibility research by centering the experiences and preferences of people with invisible disabilities. We conducted semi-structured interviews with nine participants and found that people with invisible disabilities used a unique, indirect approach to inform dynamic disclosure practices. Participants were interested in toggling representation on/off across contexts and shared ideas for representation through avatar design. In addition, they proposed ways to make the customization process more accessible (e.g., making it easier to import custom designs). We see our work as a vital contribution to the growing literature that calls for more inclusive social VR.
A Pragmatic Approach to Syntax Repair

Programming languages share a social and formal heritage. These families were historically divided, but share deep roots, and we argue their destined matrimony heralds important consequences for language design and generative language modeling. In our work, we develop a sociotechnical framework for understanding the dynamics of programming and argue it captures many of the social and formal properties of language acquisition and evolution.
Reimagining Machine Learning's Role in Assistive Technology by Co-Designing Exergames with Children Using a Participatory Machine Learning Design Probe

The paramount measure of success for a machine learning model has historically been predictive power and accuracy, but even a gold-standard accuracy benchmark fails when it inappropriately misrepresents a disabled or minority body. In this work, we reframe the role of machine learning as a provocation through a case study of participatory work co-creating exergames by employing machine learning and its training as a source of play and motivation rather than an accurate diagnostic tool for children with and without Sensory Based Motor Disorder. We created a design probe, Cirkus, that supports nearly any aminal locomotion exergame while collecting movement data for training a bespoke machine learning model. During 5 participatory workshops with a total of 30 children using Cirkus, we co-created a catalog of 17 exergames and a resulting machine-learning model. We discuss the potential implications of reframing machine learning’s role in Assistive Technology for values other than accuracy, share the challenges of using “messy” movement data from children with disabilities in an ever-changing co-creation context for training machine learning, and present broader implications of using machine learning in therapy games.
VisPhoto: Photography for People with Visual Impairments via Post-Production of Omnidirectional Camera Imaging

Many people with visual impairments would like to take photographs. However, they often have difficulty pointing the camera at the target. In this paper, we address this problem by proposing a novel photo-taking system called VisPhoto. Unlike conventional methods, VisPhoto generates a photograph in post-production. When the shutter button is pressed, VisPhoto captures an omnidirectional camera image that contains the surrounding scene of the camera. In post-production, the system outputs a cropped region as a “photograph” that satisfies the user’s preference. We conducted an experiment consisting of two parts. First, 24 people with visual impairments took photographs with a genuine iPhone camera app, a conventional method, and VisPhoto. Second, 20 sighted people evaluated the quality of the photographs. The experimental results showed that the participants with visual impairments preferred to use VisPhoto to take photographs of difficult targets, whereas they preferred the conventional method for easy targets. Moreover, we revealed that their preferences for photo-taking methods were influenced by the participants’ needs and values about photography and their confidence in their photographic abilities.