Reviewerly/exHarmony huggingface dataset:
Projectional Editors for JSON-Based DSLs

Augmenting text-based programming with rich structured interactions has been explored in many ways. Among these, projectional editors offer an enticing combination of structure editing and domain-specific program visualization. Yet such tools are typically bespoke and expensive to produce, leaving them inaccessible to many DSL and application designers. We describe a relatively inexpensive way to build rich projectional editors for a large class of DSLs—namely, those defined using JSON. Given any such JSON-based DSL, we derive a projectional editor through (i) a language-agnostic mapping from JSON Schemas to structure-editor GUIs and (ii) an API for application designers to implement custom views for the domain-specific types described in a schema. We implement these ideas in a prototype, Prong, which we illustrate with several examples including the Vega and Vega-Lite data visualization DSLs.
WhatsNext: Guidance-enriched Exploratory Data Analysis with Interactive, Low-Code Notebooks

Computational notebooks such as Jupyter are popular for exploratory data analysis and insight finding. Despite the module-based structure, notebooks visually appear as a single thread of interleaved cells containing text, code, visualizations, and tables, which can be unorganized and obscure users' data analysis workflow. Furthermore, users with limited coding expertise may struggle to quickly engage in the analysis process. In this work, we design and implement an interactive notebook framework, WhatsNext, with the goal of supporting low-code visual data exploration with insight-based user guidance. In particular, we (1) re-design a standard notebook cell to include a recommendation panel that suggests possible next-step exploration questions or analysis actions to take, and (2) create an interactive, dynamic tree visualization that reflects the analytic dependencies between notebook cells to make it easy for users to see the structure of the data exploration threads and trace back to previous steps.
Recovering Multi-prime RSA Keys with Erasures and Errors

Since Heninger and Shacham first proposed a key recovery algorithm from random partial RSA private key bits obtained from cold boot attacks, the algorithm has been further investigated in many studies. However, the noisy leakage models assumed in the existing work all fall short of reality, and the studies are all based on CRT-RSA only. In this study, we target Multi-Prime RSA, which is supported by PKCS#1 v2.1 and allows faster decryption than CRT-RSA, and give the theoretical bound of the key recovery algorithm under the Binary Erasure Symmetric Error model. We also propose a more realistic Binary Erasure Asymmetric Error model, under which we give the theoretical bound of the key recovery algorithm as well. Finally, the theoretical bounds of the algorithm are explained in the context of information theory.
Cryptanalysis of Human Identification Protocol with Human-Computable Passwords

In this paper we demonstrate effective attacks on Human Identification Protocol with Human-Computable Passwords (HIPHCP) presented in ISPEC'22. The protocol, which was designed to allow fast user identification, is vulnerable to both the active and the passive attacks, where the significant amount of the secret key can be learned by the adversary. This subsequently allow to compromise the full secret key via brute-forcing the remaining secret bits.
Gradual Structure Editing with Obligations

Structure editors have long promised to facilitate a continuous dialogue between programmer and system-one uninterrupted by syntax errors, such that vital program analyses and editor services are always available. Unfortunately, structure editors are notoriously slow or difficult to use, particularly when it comes to modifying existing code. Prior designs often struggle to resolve the tension between maintaining a program's hierarchical structure and supporting the editing affordances expected of its linear projection. We propose the paradigm of gradual structure editing, which mitigates this tension by allowing for temporary disassembly of hierarchical structures as needed for text-like editing, while scaffolding these interactions by generating syntactic obligations that, once discharged, guarantee proper reassembly. This paper contributes the design and evaluation of a gradual structure editor called teen tylr. We conducted a lab study comparing teen tylr to a text editor and a traditional structure editor on structurally complex program editing tasks, and found that teen tylr helped resolve most usability problems we identified in prior work, though not all, while achieving competitive performance with text editing on most tasks. We conclude with a discussion of teen tylr's remaining limitations and design implications for future code editors and parsers.
The IoT Codex: A Book of Programmable Stickers for Authoring and Composing Embedded Computing Applications

The Internet-of-Things (IoT) promises to enhance everyday objects with computing, but rarely enables directly authoring or composing that behavior. Lightweight IoT approaches attach identifiers (e.g., RFID tags) to objects to enable networked services. Typically these tags are passive, and so, depend on activity recognition and predefined context. This limits interaction to invoking predetermined behavior. Instead, this work presents The IoT Codex: a lightweight approach to customizing everyday objects with IoT by enabling interactive attachable IDs (aIDs) to compose software-supported behavior in situ. This work contributes 1) paper engineering techniques to construct aIDs that embody state, and 2) a tangible, end user programming (EUP) language for customizing IoT within symbolic and idiosyncratic contexts. Here, we provide preliminary validation of our approach with an empirically informed design space, sample applications, and a small co-design workshop. In doing so, we offer preliminary evidence for tangible, end user programming to enable meaningful control over IoT services.
Image restoration with point-spread function regularization and active learning

ABSTRACT Large-scale astronomical surveys can capture numerous images of celestial objects, including galaxies and nebulae. Analysing and processing these images can reveal the intricate internal structures of these objects, allowing researchers to conduct comprehensive studies on their morphology, evolution, and physical properties. However, varying noise levels and point-spread functions can hamper the accuracy and efficiency of information extraction from these images. To mitigate these effects, we propose a novel image restoration algorithm that connects a deep-learning-based restoration algorithm with a high-fidelity telescope simulator. During the training stage, the simulator generates images with different levels of blur and noise to train the neural network based on the quality of restored images. After training, the neural network can restore images obtained by the telescope directly, as represented by the simulator. We have tested the algorithm using real and simulated observation data and have found that it effectively enhances fine structures in blurry images and increases the quality of observation images. This algorithm can be applied to large-scale sky survey data, such as data obtained by the Large Synoptic Survey Telescope (LSST), Euclid, and the Chinese Space Station Telescope (CSST), to further improve the accuracy and efficiency of information extraction, promoting advances in the field of astronomical research.
RunEx: Augmenting Regular-Expression Code Search with Runtime Values

Programming instructors frequently use in-class exercises to help students reinforce concepts learned in lecture. However, identifying class-wide patterns and mistakes in students' code can be challenging, especially for large classes. Conventional code search tools are insufficient for this purpose as they are not designed for finding semantic structures underlying large students' code corpus, where the code samples are similar, relatively small, and written by novice programmers. To address this limitation, we introduce RunEx, a novel code search tool where instructors can effortlessly generate queries with minimal prior knowledge of code search and rapidly search through a large code corpus. The tool consists of two parts: 1) a syntax that augments regular expressions with runtime values, and 2) a user interface that enables instructors to construct runtime and syntax-based queries with high expressiveness and apply combined filters to code examples. Our comparison experiment shows that RunEx outperforms baseline systems with text matching alone in identifying code patterns with higher accuracy. Furthermore, RunEx features a user interface that requires minimal prior knowledge to create search queries. Through searching and analyzing students' code with runtime values at scale, our work introduces a new paradigm for understanding patterns and errors in programming education.
On delineating forward- and backward-graded knowledge structures from fuzzy skill maps

Forward-graded and backward-graded structures of knowledge are two important classes of knowledge structures. Spoto and Stefanutti (2020) establish necessary and sufficient conditions for skill maps to delineate these structures. We introduce fuzzy skills to describe varying levels of proficiency in skills and extend the theoretical results of Spoto and Stefanutti (2020) for delineating forward- and backward-graded knowledge structures using fuzzy skill maps. The paper establishes necessary and sufficient conditions for fuzzy skill maps to delineate a backward-graded simple closure space, a forward-graded knowledge space, and a forward-graded simple closure space. Furthermore, the competence-based local independence model (CBLIM) with fuzzy skills is introduced and its unidentifiability is discussed.
Towards a Visual Language for Sketched Expression of Software IDE Commands

Given that touch- and pen-enabled screens are becoming more ubiquitous - not just in mobile devices, but also in laptop computers and large interactive display walls - we are exploring the potential for using digital ink sketches as a novel interaction modality that would allow users to express commands by drawing on top of an application's user interface with a pen or stylus. We hypothesize that such sketch-based interaction could be particularly useful when several people are collaborating at a large wall-mounted display where a mouse and keyboard are not in convenient reach, e.g. when software engineers undertake a code review. As a step towards building a prototype to evaluate that vision, this paper reports on two studies with professional software engineers to elicit and rate sketched expressions of a variety of commands in an integrated software development environment (IDE). We describe the experimental setup and discuss how the findings can help to define a sketchbased command language for IDEs.
Code Stories for Software Repurposing

Software reuse has the potential to save programmers time and effort through leveraging existing code. Today however, code reuse is typically limited to programmers reusing code that does exactly what they want to do, through APIs and modules. Research in analogical reasoning suggests that problem-solving can be enhanced by reusing logic from previously-solved, analogous problems. This type of problem-solving reuse in software is rare due to difficulties programmers face when understanding code. Specifically, programmers have difficulty understanding the intentions behind design choices of the original developer. I hypothesize that by being presented the story behind a piece of software's development, programmers will be better able to 1) understand the source code and 2) draw analogies between the source code's functionalities and those of their target code.
Support for Long-Form Documentation Authoring and Maintenance

When creating a software project of any significant size or complexity, developers must write about the code in some form. While this is a common practice, tooling support for creating, and perhaps more significantly, maintaining these documents remains limited, despite these documents benefiting later users of the corresponding code. In order to combat some of these known challenges, we developed Sodalite – a context-aware, Visual Studio Code extension for writing and maintaining code-related documents. Sodalite represents a holistic approach to the documentation authoring, maintaining, and using life cycle by suggesting relevant code to attach to the document using common code-writing templates, evaluating how up-to-date the document is using the code in the editor, automatically attempting to identify out-of-date information, and, based on how successful the system is, notifying the user of how trustworthy the document seems to be given their version of the related code. In our preliminary evaluation of Sodalite, we found that users of the system were able to successfully author documents about their code and the system's out-of-date link checking was accurate 86.5% of the time.
Blockchain Adapted to IoT via Green Mining and Fractal Proof of Work

Blockchain applications continue to grow in popularity, but their energy costs are clearly becoming unsustainable. In most cases, the primary cost comes from the amount of energy required for proof-of-work (PoW). Here we study the application of blockchains to the IoT, where most devices are underpowered and would not support the energy cost of proof of work. PoW was originally intended for two main uses: block moderation and protecting the blockchain from tampering. For IoT we propose to replace the expensive moderation of PoW with the proposed energy-efficient green mining [6]. The blockchain will be protected by a fractal difficulty PoW. One of the results of this paper is the proof that the average mining time in fractal PoW actually depends only on the average difficulty. This crucial property will allow low-difficulty PoWs to be reserved for devices with low computational capacity, while higher difficulties will be reserved for devices with the highest computational power. The consequence is to give equal opportunity to objects with low computational power compared to objects with high computational power.
Potential of Digital Technologies and Artificial Intelligence in the Management of Personalized Learning

The development of digital technologies opens up new opportunities for managing personalized learning using intelligent data analysis methods. The purpose of a comprehensive analysis of the data obtained in the learning process is to create a controlled learning environment that takes into account the individual needs, goals and personal characteristics of students. In the course of our study, various approaches to the organization of the educational process based on the use of intellectual technologies were analyzed. Based on the study of best practices in the use of new digital tools for modeling learning processes based on data analysis, the report presents the developed automated system of intellectual support for personalized learning.
Location and Time Based Access Security Control Scheme for Mobile Cloud Computing

With mobile cloud data storage, mobile users can offload personal and business data to the cloud for flexibility and save money. However, data outsourcing involves high levels of confidentiality and data protection risks. To address the aforementioned mobile cloud data storage problem, Attribute-Based Encryption (ABE) is proposed due to the flexibility of the encryption strategy and fine-grained access control. However, existing attribute-based multi-authority encryption schemes still require a trusted central authority to issue system parameters and generate secret user keys. In this article, we propose a multi-authority attribute-based access control system that ensures user anonymity, protects user identities from malicious permissions, and supports permission coexistence. Our system uses some location range restrictions with ABE as access policies, and subsequently grants authorization to users whose current location and time align with these policies. To verify security and assess the validity of our proposition, we employ the Scyther tool for verification and conduct experiments to validate its correctness.
A computer vision-approach for activity recognition and residential monitoring of elderly people

In this study, we explore a human activity recognition (HAR) system using computer vision for assisted living systems (ALS). Most existing HAR systems are implemented using wired or wireless sensor networks. These systems have limitations such as cost, power issues, weight, and the inability of the elderly to wear and carry them comfortably. These issues could be overcome by a computer vision based HAR system. But such systems require a highly memory-consuming image dataset. Training such a dataset takes a long time. The proposed computer-vision-based system overcomes the shortcomings of existing systems. The authors have used key-joint angles, distances between the key joints, and slopes between the key joints to create a numerical dataset instead of an image dataset. All these parameters in the dataset are recorded via real-time event simulation. The data set has 7 80,000 calculated feature values from 20,000 images. This dataset is used to train and detect five different human postures. These are sitting, standing, walking, lying, and falling. The implementation encompasses four distinct algorithms: the decision tree (DT), random forest (RF), support vector machine (SVM), and an ensemble approach. Remarkably, the ensemble technique exhibited exceptional performance metrics with 99 % accuracy, 98 % precision, 97 % recall, and an F1 score of 99 %.
Mobile Learning: The Using Learning Media of Science Teacher Students and Their Perspectives on Mobile Learning

This research aimed to: 1) investigate the using learning media in mobile learning of science teacher students, and 2) investigate the perspectives of science teacher students regarding mobile learning media. The sample consisted of 18 third- year science teacher students, at Phra Nakhon Si Ayutthaya Rajabhat University, the second semester, the academic year 2022, who were studying the subject of creating and utilizing scientific learning media and were selected using a purposive sampling. The research instruments included an observation form and focus group interview form. Collecting data involved observing teacher students' behavior during instruction and group discussions. Data collection was the content analysis. The findings of the study revealed that students utilized mobile learning media such as applications, online forum, simulation programs, virtual programs, websites, and online videos. Utilizing media was intended to assist students in achieving their learning objectives. According to student feedback, mobile learning media could motivate students to study and create an enjoyable learning environment and help students achieve better learning results. The advantage of this study is useful element for science teacher students or teachers of all disciplines to bring these media as guidelines for learning management for students according to the objective of mobile learning media.
Instruction Model of Electrical Motor Control for Up/Re-skill New-staff's APM Argo company

This paper has proposed the training of electrical motor control system for practice new staff at industrial APM Argo CO.,LTD. in Chombueng district Ratchaburi province. Due to work skill of new staff there aren't enough. As the problem, effect to efficiency working in process such as more spent work time, team work not as well. Thus, this paper has studied the instruction model of motor control up/re-skill for new staff of APM Argo company. With instruction model have designed it can support the new staff by training. From the study, found that the efficiency of the instruction model was effective according to the Meguigans quality criterion valued at 1.154, in which standard more than 1.00. that imply was this instruction model had high efficiency. By the way, the quality of the instruction model as a high appropriation, with a total mean of 4.10 and a standard deviation of 0.62 respectively.
Application of Augmented Reality with On- The-Job Training for The Quality Control Workforce

The product testing process in the electronics industry is an essential part that verifies the product's quality before it is delivered to the customer. However, the workforce often has limited knowledge and skills for working in the product testing process, resulting in high breakdown time. Therefore, this article presents the development of augmented reality (AR) technology combined with on-the-job training to enhance the workforce's skills for the product testing process. AR was designed to simulate a step-by-step product testing environment, supplementing the real world. It offers a comparison between the correct and incorrect testing processes and allows for product zooming and rotation to provide clear details of the operation. Additionally, a sound button was added to explain each step-in detail. During daily product testing, the workforce can use AR through their phones to scan the target paper provided by the trainer. The virtual environment would then be demonstrated, and the workforce can adjust the testing process steps according to their abilities. After implementing the AR application, the workforce answered a survey indicating that the AR technology was easy to use and helped them better understand the testing process. Comparing traditional training and AR, the error rate was reduced by 0.6% using AR. The results also showed a decrease in errors from product testing and helped reduce the breakdown time caused by the faulty product testing process.
Reinforcement Learning application on Gamification

Machine Learning, a component of Artificial Intelligence, has gained increasing importance and contribution to our society. This paper presents one of many machine learning methods, Reinforcement Learning, through gamification of a simple maze game. The purpose of this paper is to visualize the basic concept of reinforcement learning and strategy which the reader may be able to apply. The paper compares two model-free learning algorithms: on-policy and off-policy approaches using SARSA and Q-Learning models. Our experiments will demonstrate the difference between on-policy and off-policy by training the agent on mazes of different scales and complexity. Moreover, this paper will also illustrate the effect of Epsilon Greedy Policy. The maze is generated and visualized using Pygame. The result is presented in a table and graph plot for comparisons.
Tweets Sentiment Analysis Using Machine Learning and Modified BERT Methods

Sentiment Analysis is a progressed shape of learning in factual examination that employments computational strategies to extricate information from crude information. It includes analyzing comments to decide whether they have positive, negative, or impartial sentiments. Social media stages such as Facebook and Twitter are habitually utilized by individuals, and Twitter, in specific, can be compelling in deciding people's considerations or sentiments. The nation employments numerous machine learning strategies where crude information from Twitter is collected and compared to preparing models that can classify positive, negative or unbiased suppositions. A subjective examination can analyze the social arrange and extricate vital data from the collected information. The implemented system is evaluated in terms of precision, recall and fl-score for model designed by Naïve Bayes model, SVM model and modified BERT approach. After the implementation the modified BERT yields better performance. The obtained precision, recall and f1-score value is 98.0 %, 99.0 % and 98.0 % respectively for positive tweets and 79.0 %, 71.0% and 74.0 5 for negative tweets. The produced results shows that the modified BERT model is more suitable for tweets sentiment analysis rather than another model.