Reviewerly/exHarmony huggingface dataset:
The benefits and struggles of FAIR data: the case of reusing plant phenotyping data

Abstract Plant phenotyping experiments are conducted under a variety of experimental parameters and settings for diverse purposes. The data they produce is heterogeneous, complicated, often poorly documented and, as a result, difficult to reuse. Meeting societal needs (nutrition, crop adaptation and stability) requires more efficient methods toward data integration and reuse. In this work, we examine what “making data FAIR” entails, and investigate the benefits and the struggles not only of reusing FAIR data, but also making data FAIR using genotype by environment and QTL by environment interactions for developmental traits in potato as a case study. We assume the role of a scientist discovering a phenotypic dataset on a FAIR data point, verifying the existence of related datasets with environmental data, acquiring both and integrating them. We report and discuss the challenges and the potential for reusability and reproducibility of FAIRifying existing datasets, using metadata standards such as MIAPPE, that were encountered in this process.
Concurrent Pipeline Stages Optimization for Embedded Keyword Spotting

Voice interaction has rapidly become the preferred option for human-machine interactions, especially for domotics and wearable applications. In such contexts, the availability of accurate, yet resource-efficient keyword-spotting (KWS) systems for identifying user commands and queries is paramount. In this work, we focus on optimizing the development of KWS pipelines for voice user interfaces running on commercial embedded systems that can be deployed at the edge of the Internet of Things. Specifically, we investigate the joint optimization of the frontend and ConvNet stages of the KWS information processing pipeline, demonstrating how a holistic approach can significantly improve the quality of results of standard methods that apply optimizations on each stage of the pipeline independently. As the main contributions, we first define and discretize the design space to identify the most significant hyperparameters and then introduce a framework to search the dominant pipeline implementations in the accuracy-latency objective space. Porting and testing the explored solutions on an ARM Cortex-A72 CPU core embedded into a Raspberry Pi 4 board, the collected results show substantial improvements in accuracy (1.8% best-case, 0.82% average) and latency (best-case 27.36%, average 15.32%) compared to state-of-the-art approaches.
Secure Software Development in Google Colab

Software is said to be secure if it has been developed and designed with security as its top priority. This entails implementing secure coding practices, performing vulnerability testing and repair, as well as continuing to monitor and maintain the product to ensure the program remains secure over time. Since it may lessen the likelihood of security breakdowns, protect sensitive data and infrastructure, and maintain users' and clients' trust, secure software is essential. As technology advances and more aspects of our lives rely on it, the need for secure software has become more critical to preserving the safety and security of our digital environment. In this paper, we discuss vulnerabilities in software development such as SQL Injection and XSS Attacks, analyze the insecure cases related to them, and demonstrate the prevention approaches which are implemented in Google Colab with relevant libraries.
An Efficient Anonymous Identity Authentication based on CP-ABE and Consortium Blockchain for IoV

In recent years, with the rapid development of the IoT, the IoV as a branch of IoT has gradually entered the public’s attention. The vehicle identity authentication technology that can ensure the security of the vehicle communication process in IoV, is the basis for IoV to provide effective services to users. In order to improve the efficiency and security of the authentication of IoV, we design an anonymous identity authentication system based on CP-ABE and blockchain. Our scheme adopts the authentication policy combining dynamic attributes and static attributes, the certification authority assigns pseudonyms to vehicles, realizes fine-grained anonymous authentication in IoV, reduces redundant authentication, improves time efficiency and protects user’s privacy. Meanwhile, authentication policy is prevented from being maliciously tampered in the process of identity authentication by the blockchain. Finally, the security analysis and performance comparison show that our scheme is both secure and efficient.
Revisiting Deep Learning for Variable Type Recovery

Compiled binary executables are often the only available artifact in reverse engineering, malware analysis, and software systems maintenance. Unfortunately, the lack of semantic information like variable types makes comprehending binaries difficult. In efforts to improve the comprehensibility of binaries, researchers have recently used machine learning techniques to predict semantic information contained in the original source code. Chen et al. implemented DIRTY, a Transformer-based Encoder-Decoder architecture capable of augmenting decompiled code with variable names and types by leveraging decompiler output tokens and variable size information. Chen et al. were able to demonstrate a substantial increase in name and type extraction accuracy on Hex-Rays decompiler outputs compared to existing static analysis and AI-based techniques. We extend the original DIRTY results by re-training the DIRTY model on a dataset produced by the open-source Ghidra decompiler. Although Chen et al. concluded that Ghidra was not a suitable decompiler candidate due to its difficulty in parsing and incorporating DWARF symbols during analysis, we demonstrate that straightforward parsing of variable data generated by Ghidra results in similar retyping performance. We hope this work inspires further interest and adoption of the Ghidra decompiler for use in research projects.
How Well Static Type Checkers Work with Gradual Typing? A Case Study on Python

Python has become increasingly popular and widely used in many fields. Dynamic features of Python provide much convenience for developers. However, they can also cause many type-related bugs undetected until runtime, which increases the cost of maintenance. Static type checking is essential to find bugs early, and the introduction of gradual typing and type annotations makes it easier to perform static type analysis. However, it remains to be investigated how well gradual typing improves real bug detection. Therefore, we conducted a comprehensive study on three widely used checkers: MyPy, PyRight, and PyType. We used a benchmark containing 10 popular Python projects with 40 real type-related bugs. First, we performed static type checking on the projects with and without type annotations to evaluate the effectiveness of finding real bugs. Second, we manually analyzed the missing bugs and investigated the reasons. The results show that the three tools can detect 29 of the 40 studied bugs after annotating, while only 14 bugs are detected before annotating. We also found that type annotations can substantially improve the ability of static type checkers to detect real bugs. A detailed analysis of bugs missed by the checkers shows that: (i) the accuracy of type analysis is challenged when it comes to programs with complicated dynamic features, such as dynamically changing object’s attributes, even with annotations; (ii) the inaccurate type annotations can undermine the ability of static type checkers to detect real bugs; (iii) static type checkers have different checking strategies in some cases, which has an impact on real bug detection. Our study can not only enable developers to better understand static type checking and make better use of them but also guide future research.
MAPS: A Noise-Robust Progressive Learning Approach for Source-Free Domain Adaptive Keypoint Detection

Existing cross-domain keypoint detection methods always require accessing the source data during adaptation, which may violate the data privacy law and pose serious security concerns. Instead, this paper considers a realistic problem setting called source-free domain adaptive keypoint detection, where only the well-trained source model is provided to the target domain. For the challenging problem, we first construct a teacher-student learning baseline by stabilizing the predictions under data augmentation and network ensembles. Built on this, we further propose a unified approach, Mixup Augmentation and Progressive Selection (MAPS), to fully exploit the noisy pseudo labels of unlabeled target data during training. On the one hand, MAPS regularizes the model to favor simple linear behavior in-between the target samples via self-mixup augmentation, preventing the model from over-fitting to noisy predictions. On the other hand, MAPS employs the self-paced learning paradigm and progressively selects pseudo-labeled samples from ‘easy’ to ‘hard’ into the training process to reduce noise accumulation. Results on four keypoint detection datasets show that MAPS outperforms the baseline and achieves comparable or even better results in comparison to previous non-source-free counterparts. The code is available at https://github.com/YuheD/MAPS.
Analysis of Semarang City Government's Mitigation Efforts to Reduce Flood Disaster Risk to Support National Security

Disasters are closely linked to human security, which of course is also closely linked to national security. Se Bang Fai is known as a flood-prone district. This study aims to analyze the flood control efforts carried out by the Semarang city government. Disaster is a serious threat to the social life of the existing society and finally to the stability of the nation. The threat that the citizens of Indonesia feel is the frequency of disasters that occur in almost all of Indonesia. Semarang, which is the capital of Central Java province, is vulnerable to flooding. Floods in Sepon City started to occur frequently between 2017 and 2021. Disaster reduction is one of the efforts of the Sepon City Government to reduce the risk of flood disasters. One of the mitigation efforts implemented by the Semarang city government is the construction of the West Tidal Canal and the East Tidal Canal. The construction of 2 rivers: the East Canal and the West Canal is expected to reduce the flood disaster in Sam Nua District. The government must prepare a flood preparedness plan so that the government knows what to do to reduce future flood disasters.
SISTEM INFORMASI PERUMAHAN PURIMAS CITRA GEMILANG 2 BERBASIS MOBILE

The development of information technology is increasing rapidly. Such developments affect the speed of obtaining information. Unfortunately, there are still many needs for good and comfortable housing that has not used a good information system to facilitate housing management and data collection such as in Purimas Citra Gemilang 2 Housing, a housing that has facilities to meet the needs of its residents. In managing housing, there are often obstacles in terms of data collection management such as monthly contribution payments, registration of prospective new residents, and recording of housing residents. This is because the recording is still manual. The use of technology, especially mobile-based applications, can optimize housing implementation by providing an integrated data management system that can be accessed via smartphones. Modern housing needs an application that can support the population system. On that basis, it is hoped that the application "Housing Information System Purimas Citra Gemilang 2 Based on Mobile", can help manage housing more efficiently and effectively. The app's features are for managing homeowner data, monthly dues payments, and social activities. The stages in the design of this system are analysis, design, implementation and testing. The waterfall method is used in the design of a housing data collection information system. Housing residents can use this system as a means of communication between officers and residents and to assist housing managers in carrying out social activities and housing data collection.
Exploration of Autonomous Cooperative Learning Model in Mathematics Teaching in Secondary Vocational Schools

Under the background of teaching innovation, secondary vocational education has also received more attention.In the current mathematics teaching practice in secondary vocational schools, the teaching mode of combining unified teaching with students' independent learning is often adopted.Although this teaching mode has certain teaching advantages, it also has many teaching disadvantages.In the final analysis, because the students in secondary vocational schools often have weak mathematical foundation, they often can't keep up with the progress of mathematics teaching in the process of unified teaching, As a result, there are many problems and puzzles in mathematics autonomous learning.At the same time, mathematics itself is too abstract and obscure, which in the long run will make many students lose their desire and motivation to learn mathematics.At this time, the independent cooperative learning mode came into being.Through scientific grouping of students, cooperative learning enables students to achieve something on the basis of jointly discussing mathematical problems and dealing with mathematical problems, and ensures that each student can not be disconnected or left behind in mathematical learning.Cooperative learning is a relatively innovative and effective teaching method in current mathematics teaching practice in secondary vocational schools.
The experience of using virtual mobility, as a window to approach knowledge in the field of physics, among students of the Universidad Tecnológica de Bolívar (Colombia), Universidad Nacional de San Luis (Argentina) and the Universidad Tecnológica de Jalisco (Mexico)

The experience was carried out with the courses of mechanical physics of the UTB (Colombia), physics at the Technological University of Jalisco (Mexico) UTJ and physics 1 of the Faculty of Engineering and Agricultural Sciences (FICA) of the National University of San Luis (Argentina) UNSL. The purpose was to carry out work with international peers with the students of the 3 participating universities, promote interculturality, understanding and appropriation of scientific concepts that support the training without implying mobility or a considerable investment. To develop the strategy, meetings were held to review content, schedules, and activities. The sessions included icebreaker activities; master classes conducted by the two teachers with groups from both universities. In some sessions, tests were carried out to compare the level of learning of the students on the topics presented. The sessions were complemented with demonstrations, simulations and the development of team exercises, as well as supporting readings selected by each one of the teachers. The results of the strategy showed that all the participants were able to have new academic and intercultural experiences, improving their global vision of physics, through the use of laws, principles and theorems of mechanical physics, through mirror classes and collaborative work done.
Rushing for security: a document analysis on the sources and effects of time pressure on organizational cybersecurity

Purpose A growing body of research has identified time pressure as a key driver of cybersecurity (CS) risks and vulnerabilities. To strengthen CS, organizations use CS documents (e.g. best practices, guidelines and policies) intended to strengthen CS. The purpose of this paper is to provide an overview of how specifically time pressure is addressed by CS documents. Design/methodology/approach The authors conducted a systematic search for CS documents followed by a content analysis of the identified documents. First, the authors carried out a systematic Web search and identified 92 formal and informal CS documents (e.g. security policies, procedures, guidelines, manuals and best practices). Second, they systematically analyzed the resulting documents ( n = 92), using a structured approach of data familiarization and low-/high-level coding for the identification and interpretation of themes. Based on this analysis, the authors formulated a conceptual framework that captures the sources and effects of time pressure along the themes of industry, operations and users. Findings The authors developed a conceptual framework that outlines the role of time pressure for the CS industry, threats and operations. This provides a shared frame of reference for researchers and practitioners to understand the antecedents and consequences of time pressure in the organizational CS context. Research limitations/implications While the analyzed documents acknowledge time pressure as an important factor for CS, the documents provide limited information on how to respond to these concerns. Future research could, hence, consult with CS experts and policymakers to inform the development of effective guidelines and policies on how to address time pressure in the identified areas. A dedicated analysis within each area will allow to investigate the corresponding aspects of time pressure in-depth along with a consideration for targeted guidelines and policies. Last, note that a differentiation between CS document types (e.g. formal vs informal and global vs regional) was beyond the scope of this paper and may be investigated by future work. Originality/value This study makes three main contributions to the CS literature. First, this study broadens the understanding of the role of time pressure in CS to consider the organizational perspective along the themes of industry, threats and operations. Second, this study provides the first comprehensive assessment of how organizations address time pressure through CS documents, and how this compares to existing research in academic literature. Third, by developing a conceptual framework, this study provides a shared frame of reference for researchers and practitioners to further develop CS documents that consider time pressure’s role in secure behavior.
Solving the Global Optimization Problem with Swarm Intelligence

Swam intelligence is based on the principles of behavior of living creatures. The formalized algorithmic behavior of these creatures allows solving various mathematical problems. This paper concerns the application of swarm intelligence in solving a global optimization problem. The paper gives an example of solving a global optimization problem and presents an analysis of obtained results. This approach can be used in a variety of applications requiring the solution of global optimization problems. #CSOC1120.
Hybrid Temporal Situation Calculus

&lt;p&gt;We present a hybrid discrete-continuous extension of Reiter's temporal situation calculus, directly inspired by hybrid systems in control theory. While keeping to the foundations of Reiter's approach, we extend it by adding a time argument to all fluents that represent continuous change. Thereby, we ensure that change can happen not only because of actions, but also due to the passage of time. We present a systematic methodology to derive, from simple premises, a new group of axioms which specify how continuous fluents change over time within a situation. We study regression for our new hybrid action theories and demonstrate what reasoning problems can be solved. Finally, we show that our hybrid theories indeed capture hybrid automata. &lt;/p&gt;
Demonstration of A Clements-type 16 × 16 Photonic Analog Matrix Processor Based on Silicon Photonics

We fabricated and packaged a Clements-type 16×16 photonic matrix processor based on silicon photonics. By applying circuit parameter learning, we have obtained an R 2 determination coefficient of ~0.859 and demonstrated a clear identity matrix implementation.
A Robust Colon Cancer Detection Model Using Deep- Learning

Colon cancer is the third most common cancer, with a high mortality rate and a high pathological need for early and accurate diagnosis. Histopathology is a golden standard tool for diagnosing almost all types of cancer, including colon cancer. It can be enhanced using deep learning techniques if the key challenges, namely, clean data annotation and inter-observer variability, are addressed. This highlights a need for a noise-robust algorithm with computational feasibility to work on real-world clinical data. In this paper, the experiment uses different deep learning models- ResNet-34, XCiT, SqueezeNet, MobileNet-individually integrated with a data cleaning architecture in an attempt to (1) achieve unmatched accuracy, (2) draw a comparison amongst the performance of these models and (3) point to the best model as a solution to the given problem. MobileNet outperforms all other models as the backbone for the algorithm, achieving the highest accuracy (84.39%), thus boosting the algorithm performance on the Chaovang dataset.
Language Learning Strategies for Enhancing Reading Comprehension among Rural Primary 6 ESL Learners

Teaching strategies for teaching English as a Second Language (ESL) have seen a substantial change in recent years.Since 21 st -century education emphasizes the learning process as opposed to the learning outcome, the significance of Language Learning Strategies (LLS) is undeniable.As a language skill, reading is a vital component of language proficiency since it concerns information and communication.However, pupils encounter various difficulties while comprehending their reading depending on how well they can read, especially those in rural schools.Therefore, this study was conducted to investigate the LLS that rural Primary 6 ESL pupils found to be the most helpful in terms of enhancing their reading comprehension.A survey questionnaire based on the Oxford (1990) Strategy Inventory for Language Learning (SILL) was given out to 30 rural primary 6 pupils in a school in the Perak district through random sampling.Descriptive analysis was employed, utilizing the mean score as an indication.The findings showed that the pupils are likely to use social strategies most and metacognitive strategies least in reading comprehension.It is hoped that with this information, teachers, especially those in rural areas, will help pupils become more aware of their preferred LLS in order to enhance their reading comprehension.
Examining the Plausible Applications of Artificial Intelligence &amp; Machine Learning in Accounts Payable Improvement

Accounts Payable (AP) is a time-consuming and labor-intensive process used by large corporations to compensate vendors on time for goods and services received. A comprehensive verification procedure is executed before disbursing funds to a supplier or vendor. After the successful conclusion of these validations, the invoice undergoes further processing by traversing multiple stages, including vendor identification; line-item matching; accounting code identification; tax code identification, ensuring proper calculation and remittance of taxes, verifying payment terms, approval routing, and compliance with internal control policies and procedures, for a comprehensive approach to invoice processing. At the moment, each of these processes is almost entirely manual and laborious, which makes the process time-consuming and prone to mistakes in the ongoing education of agents. It is difficult to accomplish the task of automatically processing these invoices for payment without any human involvement. To provide a solution, we implemented an automated invoicing system with modules based on artificial intelligence. This system processes invoices from beginning to finish. It takes very little work to configure it to meet the specific needs of each unique customer. Currently, the system has been put into production use for two customers. It has handled roughly 80 thousand invoices, of which 76 percent were automatically processed with little or no human interaction.