Reviewerly/exHarmony huggingface dataset:
Openness and Computational Reproducibility in Plant Pathology: Where We Stand and a Way Forward

Open research practices have been highlighted extensively during the last 10 years in many fields of scientific study as essential standards needed to promote transparency and reproducibility of scientific results. Scientific claims can only be evaluated based on how protocols, materials, equipment, and methods were described; data were collected and prepared; and analyses were conducted. Openly sharing protocols, data, and computational code is central to current scholarly dissemination and communication, but in many fields, including plant pathology, adoption of these practices has been slow. We randomly selected 450 articles published from 2012 to 2021 across 21 journals representative of the plant pathology discipline and assigned them scores reflecting their openness and computational reproducibility. We found that most of the articles did not follow protocols for open science and failed to share data or code in a reproducible way. We propose that use of open-source tools facilitates computationally reproducible work and analyses, benefitting not just readers but the authors as well. Finally, we provide ideas and suggest tools to promote open, reproducible computational research practices among plant pathologists. [Formula: see text] Copyright © 2023 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .
Internet of Things (IoT) Product Design and Modeling Amikom Purwokerto Hand Sanitizer (AMPUH)

In previous research, an automatic hand sanitizer prototype tool was made to prevent COVID-19. The product design in this research is quite complicated and too large. Then, in other studies, a prototype tool has also been made to measure body temperature. The study also had an inefficient prototype design. So, it can be concluded that no product design model is efficient, simple, and has its charm. In this research, the concept design method consists of several stages: planning, measurement, design, and implementation. To identify the production needs that will be used in making AMPUH product design, researchers carry out measurement stages based on a schematic series (Schematic) of the product to be made. AMPUH product designs are made with shapes and models that have been adapted to their components without reducing the aesthetic value, compatibility, complexity, and design applicability. The advantage of this product design is that it always looks at the compatibility with the components that will be installed in it so that it is certain that both in terms of form and function will work well. The drawback of this product design may still leave a little free space in it to minimize the remaining free space and repair.
Vision Based Sidewalk Navigation for Last-mile Delivery Robot

Navigating delivery robot along the sidewalk safely and robustly in a campus environment is extremely challenging due to the narrow motion space, appearance changes and unstable GPS localization signal under canopies of trees, etc. To that end, we have completed a systematic implementation for delivery robot sidewalk navigation, where a robust vision based navigation algorithm has been proposed. And it consists of three main modules: sidewalk segmentation, costmap generation and motion planning. More Specifically, the first module is to find the drivable area of the surrounding environment, where an image-based segmentation neural network has been developed to extract where the robot can traverse. Since it only takes as input immediate and local sensory data, thus releasing the high dependence on a prior map. Then, an inverse perspective mapping follows to generate a bird-eye-view of the drivable area and constructs the local occupancy grid map intuitively. Next, two different motion planners, control-based primitives (Dynamic Window Approach) and state-based primitives (state lattice planner), have been adopted to generate a trajectory candidate for navigating the robot along the sidewalk. Both simulation and real-world sidewalk navigation experiments have been conducted to test and evaluate their performance. The results show that our algorithm can precisely extract the sidewalk area for traversing, and the state-based primitive planner demonstrates superior performance in terms of trajectory length and time cost, achieving 14.3% and 18.7% improvement compared with control-based primitive planner.
Accelerating Change Amidst Stability

The publication of Issue 1 from Volume 100 of the Journal invites discussion of the path by which this landmark has been achieved. Over many years, the Journal has served as a key source for curated works about the teaching and learning of chemistry─and these activities continue apace as the 100th volume is reached. At the same time, major changes in academic journal publishing have improved the processes used in producing the Journal. The past 25 years, in particular, have seen robust advances for publishing technology, and this recent history is recounted here.
Reflexive communication as a condition for the manifestation and development of educational agency of master students

The learning process at school and at a university usually allows students to remain a passive “material” of pedagogical influences; it both does not require any student initiative and prevents its manifestation. This paper describes the experience of creating conditions for students to take a more active position regarding the process of their professional training: to realize their interests and requests for the learning process, to reflect on personal results for a semester and academic year, to cooperate with teachers to improve the educational process. The authors describe three communicative formats (goal-setting session, analytical session, comprehensive semester exam) and the results of their use in the organization of the educational process. The main feature of these communication formats is the posing of questions by each of the participants (to themselves or to other participants in the interaction), the exchange of these questions, and their joint discussion highlighting the problems that need to be solved. The described experience shows that such an organization of interaction between students, both among themselves and with teachers, helps students to better understand their own educational needs and the possibilities of the postgraduate program, set realistic goals for the next semester, and think about how to make their professional training as effective as possible. As a result, some master students take part in the educational process improvement together with teachers. This allows suggesting that as a result of the interaction organized in this way, students acquire the educational agency experience.
Penerapan Online Analytical Processing (OLAP) dalam Pengelolaan Data Karyawan

At this time many companies manage employee data manually from the process of recording employee data, employee absences which can result in data errors. Because of several things, it is necessary to have a system that can record every incoming data as well as a method that can control it so as not to experience excess or deficiency. The method used is the Online Analytical Processing (OLAP) method. The Online Analytical Processing (OLAP) method is an approach method to provide answers to requests for dimensional analysis processes quickly, the result to be achieved is to be able to design applications that are expected to provide convenience for companies to carry out the process of recording employee data, employee attendance to presentation. reports quickly, precisely and accurately
Implementasi Teorema Bayes Untuk Diagnosa Penyakit Hawar Daun Bakteri (Kresek) Dan Penyakit Blas Tanaman Padi

Rice is a plant that has an important role for the majority of the population in Indonesia. Rice plants have a fairly high carbohydrate content. Therefore, rice cultivation is very important to be maintained. The government is targeting rice yields in 2013 to reach 72.06 million tons of dry milled grains and a surplus of 10 million tons in 2015. But in fact, this has not been realized due to disease attacks on rice plants which are quite detrimental to farmers. This is due to the lack of knowledge among Indonesian farmers about the problem of rice plant diseases. Farmers often have difficulty in consulting with an expert because of the travel time to the agricultural office which is quite far. In this expert system research, the Bayes theorem calculation method determines the probability of an event based on the effects obtained through observations. The result of the analysis of this method is an expert system analysis to diagnose diseases in rice plants along with the probability value of the diagnosed disease, which shows the level of system confidence in the disease by Bayes calculation that the conclusion with the highest value of 0.574 is Blast Disease
Handling big tabular data of ICT supply chains: a multi-task, machine-interpretable approach

Due to the characteristics of Information and Communications Technology (ICT) products, the critical information of ICT devices is often summarized in big tabular data shared across supply chains. Therefore, it is critical to automatically interpret tabular structures with the surging amount of electronic assets. To transform the tabular data in electronic documents into a machine-interpretable format and provide layout and semantic information for information extraction and interpretation, we define a Table Structure Recognition (TSR) task and a Table Cell Type Classification (CTC) task. We use a graph to represent complex table structures for the TSR task. Meanwhile, table cells are categorized into three groups based on their functional roles for the CTC task, namely Header, Attribute, and Data. Subsequently, we propose a multi-task model to solve the defined two tasks simultaneously by using the text modal and image modal features. Our experimental results show that our proposed method can outperform state-of-the-art methods on ICDAR2013 and UNLV datasets.
Post-Quantum PHY-Layer Authentication for Secure Initial Access in V2X Communications

One of the greatest challenges in vehicle-to-everything(V2X) communications lies in protecting vehicle authentication during initial access against advanced quantum cyber-physical attack (CPA), as well as maintaining ultra-reliable low-latency communications with each other and the infrastructure. In this paper, we propose a vehicle initial access authentication system which exploits post-quantum encryption and signal processing technique to defend against both active and passive quantum CPA. The system encodes/decodes the physical layer bits of messages using quasi-cyclic moderate-density parity-check code to avoid quantum passive cyber attacks while eliminating the influence of high-power active cyber attacks on bits by employing the independent component analysis based signal separation technique. We define the system failure probability to measure the reliability, by considering both the cyber and physical plane influence, such as asynchronous access delay caused by mobility and communications, wireless channel fading and the uncertain bit generation induced by traffic hazards. Simulations show the effectiveness of our proposed system against attacks and indicate how the adverse cyber-physical effects, either being intentional or unintentional, can be avoided subtly to reduce system failures.
Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchange

Federated learning (FL) has been recognized as one of the most promising solutions for distributed machine learning (ML). In most of the current literature, FL has been studied for supervised ML tasks, in which edge devices collect labeled data. Nevertheless, in many applications, it is impractical to assume existence of labeled data across devices. To this end, we develop a novel methodology, Cooperative Federated unsupervised Contrastive Learning (CF-CL), for FL across edge devices with unlabeled datasets. CF-CL employs local device cooperation where data are exchanged among devices through device-to-device (D2D) communications to avoid local model bias resulting from non-independent and identically distributed (non-i.i.d.) local datasets. CF-CL introduces a push-pull smart data sharing mechanism tailored to unsupervised FL settings, in which, each device pushes a subset of its local datapoints to its neighbors as reserved datapoints, and pulls a set of datapoints from its neighbors, sampled through a probabilistic importance sampling technique. We demonstrate that CF-CL leads to (i) alignment of unsupervised learned latent spaces across devices, (ii) faster global convergence, allowing for less frequent global model aggregations; and (iii) is effective in extreme non-i.i.d. datasettings across the devices.
MTF<sup>2</sup>N: Multi-Channel Temporal-Frequency Fusion Network for Spectrum Prediction

Radio spectrum prediction is of great significance for dynamic spectrum management and alleviating spectrum congestion. Based on the real spectrum dataset, this paper constructs a multi-channel temporal-frequency fusion network (MTF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N) for spectrum prediction. The network consists of two parts: first, it uses CNN to extract the latent features of the occupancy state of multi-channel and multi-slot; then, it uses the latent features of the spectrum occupancy state to predict occupancy state through the memory property of LSTM. Experiments show that the network model designed in this paper can achieve comparable performance to the LSTM network in short-term spectrum prediction. In the long-term spectrum prediction, MTF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N is more accurate than LSTM, Seq2seq and GRU networks since MTF <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N integrates more channel and time slot correlation features. The prediction accuracy of 200 prediction time slots for 40 channels in GSM1800DOWN service band reaches 92.26% and more robust performance is obtained.
Full-Volume 3D Fluid Flow Reconstruction With Light Field PIV

Particle Imaging Velocimetry (PIV) is a classical method that estimates fluid flow by analyzing the motion of injected particles. To reconstruct and track the swirling particles is a difficult computer vision problem, as the particles are dense in the fluid volume and have similar appearances. Further, tracking a large number of particles is particularly challenging due to heavy occlusion. Here we present a low-cost PIV solution that uses compact lenslet-based light field cameras as imaging device. We develop novel optimization algorithms for dense particle 3D reconstruction and tracking. As a single light field camera has limited capacity in resolving depth (z-dimension measurement), the resolution of 3D reconstruction on the x-y plane is much higher than along the z-axis. To compensate for the imbalanced resolution in 3D, we use two light field cameras positioned at an orthogonal angle to capture particle images. In this way, we can achieve high-resolution 3D particle reconstruction in the full fluid volume. For each time frame, we first estimate particle depths under a single viewpoint by exploiting the focal stack symmetry of light field. We then fuse the recovered 3D particles in two views by solving a linear assignment problem (LAP). Specifically, we propose an anisotropic point-to-ray distance as matching cost to handle the resolution mismatch. Finally, given a sequence of 3D particle reconstructions over time, we recover the full-volume 3D fluid flow with a physically-constrained optical flow, which enforces local motion rigidity and fluid incompressibility. We perform comprehensive experiments on synthetic and real data for ablation and evaluation. We show that our method recovers full-volume 3D fluid flows of various types. Two-view reconstruction results achieves higher accuracy than those with one view only.
Relational data embeddings for feature enrichment with background information

For many machine-learning tasks, augmenting the data table at hand with features built from external sources is key to improving performance. For instance, estimating housing prices benefits from background information on the location, such as the population density or the average income. However, this information must often be assembled across many tables, requiring time and expertise from the data scientist. Instead, we propose to replace human-crafted features by vectorial representations of entities (e.g. cities) that capture the corresponding information. We represent the relational data on the entities as a graph and adapt graph-embedding methods to create feature vectors for each entity. We show that two technical ingredients are crucial: modeling well the different relationships between entities, and capturing numerical attributes. We adapt knowledge graph embedding methods that were primarily designed for graph completion. Yet, they model only discrete entities, while creating good feature vectors from relational data also requires capturing numerical attributes. For this, we introduce KEN: Knowledge Embedding with Numbers. We thoroughly evaluate approaches to enrich features with background information on 7 prediction tasks. We show that a good embedding model coupled with KEN can perform better than manually handcrafted features, while requiring much less human effort. It is also competitive with combinatorial feature engineering methods, but much more scalable. Our approach can be applied to huge databases, creating general-purpose feature vectors reusable in various downstream tasks.
SMATS: Single and Multi Automatic Text Summarization

In today's world, the rapid growth of textual data on internet sites & online resources makes it challenging for human being to assimilate essential information.To handle such issues, text summarization (TS) plays an important role.Through the TS process, a shorter version of the original content is generated to preserve the relevant information.This study suggests a quantitative assessment of models for single and multi-document summarization based on the sentence scoring method.Experimentation of the models has been carried out on DUC datasets.A detailed comparative analysis of the models is reported with respect to the performance of algorithms based on various metrics such as Recall Oriented-Understudy for Gisting Evaluation (ROUGE), Range, Co-efficient of Variation (CV) and Readability score.
Phishing Website Detection Using Ensemble Learning

Phishing is also the most common type of data breach. As a result, it is carried out by sending an email with links that lead to fraudulent websites. This technique is especially targeted to large companies. Usually, the attackers send emails with work-related information. Machine learning is one of the most successful techniques for detecting phishing. This paper analyzed the results of various machine learning techniques for predicting phishing websites. And also describes the various methods that are used to identify phishing websites. Some of these include the SVM classification method, Random Forest method, and AdaBoost method. Ensemble model that combines the SVM, Random Forest, and AdaBoost methods was able to classify a phishing site with an accuracy of 96%
Development of Computer Intelligent Proofreading System from the Perspective of Medical English Translation Application

With the increasing improvement of China's economic status and the increase of foreign exchanges of traditional Chinese medicine, the importance of medical translation has gradually increased. The English translation proofreading system based on phrase and syntax attaches importance to the accuracy calibration of phrase and syntax, fails to solve the problem of poor contextual coherence of English translation, Based on the development and research of computer intelligent proofreading system from the perspective of medical English translation application, this paper provides a scientific teaching idea for English practical teaching, combines translation theory with TCM translation practice, and better guides students' translation practice.
Digit Recognition Using Composite Features With Decision Tree Strategy

At present, check transactions are one of the most common forms of money transfer in the market.The information for check exchange is printed using magnetic ink character recognition (MICR), widely used in the banking industry, primarily for processing check transactions.However, the magnetic ink card reader is specialized and expensive, resulting in general accounting departments or bookkeepers using manual data registration instead.An organization that deals with parts or corporate services might have to process 300 to 400 checks each day, which would require a considerable amount of labor to perform the registration process.The cost of a single-sided scanner is only 1/10 of the MICR; hence, using image recognition technology is an economical solution.In this study, we aim to use multiple features for character recognition of E13B, comprising ten numbers and four symbols.For the numeric part, we used statistical features such as image density features, geometric features, and simple decision trees for classification.The symbols of E13B are composed of three distinct rectangles, classified according to their size and relative position.Using the same sample set, MLP, LetNet-5, Alexnet, and hybrid CNN-SVM were used to train the numerical part of the artificial intelligence network as the experimental control group to verify the accuracy and speed of the proposed method.The results of this study were used to verify the performance and usability of the proposed method.Our proposed method obtained all test samples correctly, with a recognition rate close to 100%.A prediction time of less than one millisecond per character, with an average value of 0.03 ms, was achieved, over 50 times faster than state-of-the-art methods.The accuracy rate is also better than all comparative state-of-the-art methods.The proposed method was also applied to an embedded device to ensure the CPU would be used for verification instead of a high-end GPU.