Reviewerly/exHarmony huggingface dataset:
Comparison with State-of-the-Art: Traps and Pitfalls

When a new metaheuristic is proposed, its results are compared with the results of the state-of-the-art methods. The results of that comparison are the outcome of algorithms' implementations, but the origin, names, and versions of the implementations are usually not revealed. Instead, only papers that introduced state-of-the-art are cited. That approach is generally wrong because algorithms are usually described in articles in a way that explains the idea that is hidden behind them but omits the technical details. Therefore, developers have to fill in these details, and they might do so in different ways. The paper shows that even implementations made by one author who is the creator of an algorithm give results which differ considerably from one another. Therefore, for the comparison purpose, the best possible implementation should be identified and used. To illustrate how details that are hidden in the code of implementations influence the quality of the results, sources of quality differences are tracked down for selected implementations. It was found that sources of the differences are hidden in auxiliary code and also stem from implementing a different version of the algorithm which undergoes development. These findings imply best practice recommendations for researchers, implementation developers, and authors of the algorithms.
First Year Practice of Social Implementation Type PBLs as Compulsory Subjects and its Educational Effects

This paper reports the introduction of PBLs for social implementation as compulsory subjects, the efforts in first year practice, and the analysis of their effects. In the first half-year for the fourth grade students in KOSEN, “Social Implementation Project I” is placed to learn the importance of finding issues through case studies of social Implementations and to understand the relationship between technology and society. In the latter half-year, “Social Implementation Project Ⅱ” is placed, in which students work on social implementation as a team in collaboration with partners inside or outside of our campus. It shows the fact which the student’s self-evaluation of generic skills was improved by the student’s experience of PBLs. The fact that the student’s PROG scores were improved by the experience of PBLs suggests that PBLs for social implementation have the effect of improvement of the student’s competency. It shows that the analysis in this paper provides quantitative evidence for the effects of PBL for social implementation on human resource development.
Self-directed training of person reidentification with synthetic data

This paper examines the utility of a self-directed feedback training method for machine learning models trained on synthetic data. This method aims to improve the speed of data generation and training by generating small batches of training data and observing the classification performance for each class. The classification accuracy is then used to adjust subsequent training classes and data generation limiting the total generation and training time while achieving optimal performance. Synthetic generation of images provides a viable approach to training machine learning models when real data is sparse. Synthetic data removes the intensive and error-prone manual process of human data labeling through automatic tagging. This is particularly valuable for re-identification tasks where unique objects need to be identified from multiple cameras with different orientations, lighting, or focal characteristics. We construct an artificial re-identification scene using 3D modeling software and generate images with a number of human avatar objects taken from different orientations, backgrounds, and lighting conditions. Automatic tagging and bounding generates re-identification metadata allowing unique avatars to be recognized by a metric learning neural network. As the network improves, the classes with lowest performance prompt the generator to supply additional images to improve the classifier accuracy. This allows the rendering engine to focus on the dominant error cases. This process will be compared against the rendering/training time and accuracy of the same system trained without self-directed feedback training.
Variation of the expectation and quantiles of the mixture of ordered distributions under imprecise choice of prior

We consider families of probability distributions indexed by the parameters from an interval in the real line. We merely assume that the increase of parameter results in the increase of corresponding distribution with respect to the standard stochastic order. We investigate consequences of imprecise choice of prior distribution on the parameters of the model. Namely, we assume a fixed prior whereas the true one belongs to a nonparametric neighbourhood of it. We determine sharp lower and upper bounds on the differences between the expectations and quantiles of the mixtures with respect to the true and assumed mixing distributions in various scale units.
An Automatic Acquisition Method of Detection Data for Substation Equipment

Live detection and testing items are an indispensable method for diagnosing the status of substation equipment and discovering hidden dangers of substation equipment defects. At present, the input of detection data is mainly by manual, and there are problems such as large workload, low efficiency, and error-proneness. An automatic acquisition method of detection data for substation equipment is proposed in this paper. This method captures the screen images of electric power meters, then uses the OCR (Optical Character Recognition) method to automatically identify the detection data from the screen images, and input them into the testing report automatically. The experimental results show that the method has high accuracy, and can greatly improve the efficiency of automatic acquisition of detection data.
Reform practice of Information Theory course

“The basis of information theory” is the core content of electronic information engineering, and it is a very important fundamental course for electronic information engineering. Under the background application-oriented undergraduate education in Shanghai Polytechnic University, this paper presents the reform practice of this course from the aspects of course orientation, teaching content design, teaching methods and teaching conditions construction. It also demonstrates teaching practice results, and puts forward further improvement suggestions.
Influence Factor Analysis of the Development of Professional Pharmacy Teachers in An Application-Oriented University Based on Logistic Regression Model

The survey method was combined with interview method to empirically analyze the influence factors of the development of professional pharmacy teachers in an application-oriented university in Anhui Province via the Logistic regression model. The regression results show that variables such as education background, professional title, working age, enterprise experience and related qualification certificate have significant influences on the development of professional pharmacy teachers in an application-oriented university in Anhui Province. Therefore, the abovementioned factors should be considered in the development of professional pharmacy teachers in this university in order to improve teaching level and student satisfaction.
Detecting Face Masks Using Deep Learning to Control Public Hygiene, Safety and COVID-19 Spreading

The Novel Coronavirus or CoViD-19 has been spreading rapidly around the globe since 2019 and is causing a massive impact on humans; lakhs of people have died because of this virus. It is declared an epidemic by WHO due to its impact and lack of antiviral drugs. This disease is contagious and is easily transmissible by some basic mistakes, like coming in contact with the coughing sneezing droplets of an infected person, and symptoms are like normal flu. During these days and even after this lockdown ends it is advised to wear a mask or cover their face but in crowded places like hospitals and supermarkets and it is not easy to track whether a person is wearing a mask or not and manual checking is not practical as it increases labor. In this paper, propose a mask detector with the help of a deep learning facial classification system to detect whether a person is wearing a mask or not so it can be attached to a CCTV to ensure the entry of only people with a mask.
One-Step Modal Logics, Intuitionistic and Classical, Part 2

Hodes (2021) “looked under the hood” of the familiar versions of the classical propositional modal logic K and its intuitionistic counterpart (see Plotkin & Sterling 1986). This paper continues that project, addressing some familiar classical strengthenings of K (D, T, K4, KB, K5, Dio (the Diodorian strengthening of K) and GL), and their intuitionistic counterparts (see Plotkin & Sterling 1986 for some of these counterparts). Section 9 associates two intuitionistic one-step proof-theoretic systems to each of the just mentioned intuitionistic logics, this by adding for each a new rule to those which generated IK in Hodes (2021). For the systems associated with the intuitionistic counterparts of D and T, these rules are “pure one-step”: their schematic formulations does not use □ or ♢. For the systems associated with the intuitionistic counterparts of K4, etc., these rules meet these conditions: neither □ nor ♢ is iterated; none use both □ and ♢. The join of the two systems associated with each of these familiar logics is the full one-step system for that intuitionistic logic. And further “blended” intuitionistic systems arise from joining these systems in various ways. Adding the 0-version of Excluded Middle to their intuitionistic counterparts yields the one-step systems corresponding to the familiar classical logics. Each proof-theoretic system defines a consequence relation in the obvious way. Section 10 examines inclusions between these consequence relations. Section 11 associates each of the above consequence relations with an appropriate class of models, and proves them sound with respect to their appropriate class. This allows proofs of some failures of inclusion between consequence relations. (Sections 10 and 11 provide an exhaustive study of a variety of intuitionistic modal logics.) Section 12 proves that the each consequence relation is complete or (for those corresponding to GL) weakly complete, that relative to its appropriate class of models. The Appendix presents three further results about some of the intuitionistic consequence relations discussed in the body of the paper.
Explicit Nonlinear Mapping for Manifold Learning with Neighborhood Preserving Polynomial Embedding

Compared with the linear projection assumption used previously, a polynomial mapping provides high-order approximation to the unknown nonlinear mapping and therefore is more accurate for data samples lying on nonlinear manifolds.
The influence of GeoGebra media use to student’s mathematics problem solving ability

Abstract The problem of this research was the low ability of grade V elementary students’ mathematics problem solving in Baitul Jannah Islamic elementary school. The objective of this research was to analyze the average of students’ mathematics problem solving abilities by using GeoGebra media and by using conventional learning. This was an experimental research. Population was all grade V students in classrooms V.A with GeoGebra learning for experiment and classroom V.B with conventional learning for control in Baitul Jannah Islamic elementary school. Students were given with five mathematics test problems in essay that had been tested for their validities and reliabilities. Data were analyzed by using t-test. The result showed that the average of students’ mathematics problem solving who used GeoGebra learning were higher compared to those with conventional learning.
Abstractive Text Summarization with LSTM using Beam Search Inference Phase Decoder and Attention Mechanism

Volume of textual data has been being increased tremendously for last few years. Such increase is because of rapid growth in technical era. Especially, in the field of product review, lengthy and large number of reviews are available. An automated tool is required, which can transform lengthy review into brief summary; containing overall idea behind the review. Such automatic text summarization may appear as a boon for humanity. Abstractive text summarization is very useful in summary generation. It considers context of the information and generates non-verbatim sentences. In this paper, we are going to focus on using beam search decoder during inference phase with linear normalization and LSTM in encoder-decoder sequence-to-sequence model. Attention mechanism is used to improve processing speed by focusing on certain part of the review sentence. Result shows that the summary generated using beam search decoder is more precise than summary generated from normally used greedy decoder.
The design and evaluation of interactive learning media using augmented reality (AR) technology for equilibrium phase diagram

This research study is about the design and evaluation of interactive learning media using Augmented Reality (AR) application for learning the Equilibrium Phase Diagram. Augmented reality gives better approaches for users to rapidly getting information which comes “alive” instantly to help and attract the students to learn equilibrium phase diagram effectively. Augmented Reality also was found helpful in reducing the problems of visualization in the topic of Equilibrium Phase Diagram. In order to ensure the learning process is successful, the instructional design model: ADDIE Hybrid Model, has been used in the development of the interactive learning media using augmented reality to learn the equilibrium phase diagram. There were five phases involved to make sure that the process of before, during and after using the interactive learning media with augmented reality applications has achieved the objectives.
A deep-learning approach to pupillometry

The Pupillary Light Reflex (PLR) refers to the change in pupil size due to changes in illumination. The PLR is used by clinicians for the non-invasive assessment of the pupillary pathway. Typically, Infrared (IR) illumination based pupillometers are used to measure the PLR. Researchers have explored the problem of robust pupil detection and reconstruction with algorithms based on traditional computer vision techniques. These techniques do not generalize well when tested with visible light (VL) images. The current study presents a novel approach to pupillometry that uses deeplearning (DL) methodology which is applied to VL images. We used public iris datasets (e.g., UBIRISv2) and data augmentation techniques to train our models for robustness. Noise in the images can be due to different lighting conditions, iris colors, pupil shapes, etc. Ellipses were fit to the pupil images and the parameters were extracted. We evaluated a UNet model and its quantized version. A. non-deep learning model (PuRe) was also evaluated. This study also reports the accuracy of these models with real-world experimental data. This work is the first step toward a VL smartphone-based pupillometer that is fast, accurate, and relies on on-device computing. Such a device can be useful in areas where internet access is unavailable and, more importantly, can be used in the field by paramedics for telemedicine purposes.
Hyper-Parameter Optimization for Deep Learning by Surrogate-based Model with Weighted Distance Exploration

To improve deep neural net hyper-parameter optimization we develop a deterministic surrogate optimization algorithm as an efficient alternative to Bayesian optimization. A deterministic Radial Basis Function (RBF) surrogate model is built to interpolate previously evaluated points, and this surrogate model is incrementally updated in each iteration. The stochastic algorithm CMA-ES is used to search the acquisition function based on the surrogate. The acquisition function at a point is based on a weighted average of the surrogate at x and the minimum distance from x to a previously evaluated point. We evaluate the proposed algorithm RBF-CMA on hyper-parameter optimization tasks for deep convolutional neural networks on datasets of CIFAR-10, SVHN, and CIFAR-100. We show that RBF-CMA achieves a promising performance especially when the search space dimension is high in comparison to other algorithms including GP-EI, GP-LCB, and SMBO.
Extraction of Metadata from Images

Metadata is defined as the information providing data about one or more faces of the data. It is used to abridge basic indication about data which can make pursuing and working with specific data easier. The idea of metadata is often prolonged to involve words or phrases that stand for objects or “objects” in the world, leading to the notion of unit extraction. In this paper, I am proposing extracting the metadata of the files user inputs to the system, this can be achieved using Flask as the web platform and Python programming language, our goal is to make a free and lightweight metadata extractor which is more efficient and user friendly.
Using Unstructured Text Narratives to Correct Work Unit Codes in the Wild

The lack of accurate Work Unit Codes (WUC) in aircraft maintenance records hinders logistical improvements given current practices of manual, free-text narratives which routinely suffer from natural language challenges, misspellings, and undocumented flight line jargon. This work uses recent advances in machine learning (ML) to score the translation of unstructured text narratives against ground truth data to correct WUCs in the field. Throughout this evaluation, a set of experiments compares dictionary development of predictive keywords, statistical feature generation, and modern word embeddings pre-trained on a much larger text corpus to evaluate effectiveness of these methods on free form text from 10+ years of maintainer-entered records. Given the diversity of the labels, we define and implement a two-stage decomposition of the WUC using 2-character prediction, followed hierarchically by further sub-system and location finders. Where possible, meaningful, or explainable rules are extracted to guide future WUC implementations. This work focuses on a dual delivery method – as a production application programming interface (API) for integration into end user clients and production deployment in a multi-customer Business Intelligence solution. This methodology supports maintainers from data collection to maintenance leadership decision making.
New McEliece cryptosystem based on non-permutation equivalent polar codes

In this paper, we propose a new McEliece public-key cryptosystem based on polar codes with non-permutation equivalent. Due to the fact that polar code is a decreasing monomial code, any permutation of columns of its generator matrix can be found. Thus, any McEliece cryptosystem based on only the permutation equivalent of the standard polar code seems to be insecure against some structural attacks. In contrast, our proposed scheme is able to resist against these attacks and achieves good security level against the generic decoding attacks.
Development of Al-Quran sign language classification based on convolutional neural network

Sign language is the main form of communication used by deaf people. Most of their activities, like; speaking, reading, and learning, involved sign languages. For reading Al-Quran, deaf people used Arabic sign language to read the ayah Al-Quran. For them, assistive technologies to aid them in the process of learning and teaching of Al-Quran is very important, since the traditional method is very difficult and challenging. One of the reasons is that, traditionally, teachers need to know Arabic Sign Languages (ArSL) first in order to teach them to learn Al-Quran. Currently, assistive technology, it still considered to be relatively new and not well developed. In Malaysia and Indonesia, most of the developed technologies are mobile app, and web-based device, which both of them required continuous internet connection and only suitable for personal used. Previous research on assistive technologies can be classified into two types of devices. First, a sensor-based device, and second is the image-based device. Both of them have their advantages and disadvantages. For this project, the only image-based device is focused since the scope of this project is limited to supervised machine learning (Convolution neural network, CNN) that developed with accuracy above 80% in training and testing. The accuracy of CNN model can be explained based on the resulting pattern obtained from the training and testing. Next, the resulting pattern can be described as overfitting, underfitting, or optimum. This project shows that, with the appropriate tuning of hyperparameters based on the resulting pattern, the accuracy of the model can be improved. This CNN model is developed from scratch through trial and error tuning method since there are no formal techniques. Lastly, the CNN model is converted into a Tensorflow Lite format, which can ready to be integrated with mobile applications.
Abstract Modeling of System Communication in Constructive Cryptography using CryptHOL

Proofs in simulation-based frameworks have the greatest rigor when they are machine checked. But the level of details in these proofs surpasses what the formal-methods community can handle with existing tools. Existing formal results consider streamlined versions of simulation-based frameworks to cope with this complexity. Hence, a central question is how to abstract details from composability results and enable their formal verification.In this paper, we focus on the modeling of system communication in composable security statements. Existing formal models consider fixed communication patterns to reduce the complexity of their proofs. However, as we will show, this can affect the reusability of security statements. We propose an abstract approach to modeling system communication in Constructive Cryptography that avoids this problem. Our approach is suitable for mechanized verification and we use CryptHOL, a framework for developing mechanized cryptography proofs, to implement it in the Isabelle/HOL theorem prover. As a case study, we formalize the construction of a secure channel using Diffie-Hellman key exchange and a one-time-pad.
Quantifying Trace Element Variations in Chrysocolla by Clustering FEG-EPMA Hyperspectral Maps

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