Reviewerly/exHarmony huggingface dataset:
An Introduction to the Minimum Information about a Digital Specimen (MIDS) Digitisation Standard

Digitisation is the process of converting analogue data about physical specimens to digital representation that includes electronic text, images and other forms. The term has been used diversely within the natural science collections community, and between different digitisation initiatives, the outputs can be quite different. Digitisation of individual specimens provides explicit and precise details about each object curated in a collection. This digitisation is based on diverse aims, the needs of specific projects and the specific practices and workflows in different institutions, so the digitised output has a wide range of uses. Capturing and presenting such data from future digitisation in standard formats is essential so that data can be more easily understood, compared, analysed and communicated via the Internet. By harmonising a framework that clarifies what is meant by different levels of digitisation (MIDS level), as well as the minimum information to be captured at each level, it becomes easier to consistently measure the extent of digitisation achieved over time and to set priorities for the remaining work. Similarly, ensuring that enough data are captured, curated and published is essential so they are useful for the widest possible range of future research, teaching and learning purposes. The Minimum Information about a Digital Specimen (MIDS) specification aims to address these problems. MIDS is a 'minimum specification', which means that the information specified as necessary at a each MIDS level is the minimum expected to be made digitally available following each major stage of digitisation. More is not precluded. From September 2020, MIDS specification work is now the work topic of an approved TDWG Task Group.
Mobile Robotic Navigation System With Improved Autonomy Under Diverse Scenarios

Mobile robots integrate a combination of physical robotic elements for locomotion and artificial intelligence algorithms to move and explore the environment. They have the ability to react and make decisions based on the perception they receive from the environment to fulfill the assigned navigation tasks. A crucial issue in mobile robots is to address the energy consumption in the robot design strategy for prolonged autonomous operation. Therefore, the battery charge level is an input variable that is commonly monitored and evaluated at all times, in this type of robots, in order to influence the decision-making with the least user intervention, during the navigation phase. Hence, the robot is capable to complete its tasks successfully. To achieve this, a navigation approach based on a fuzzy Q-Learning architecture for decision-making in combination with a module of artificial potential fields for path planning is introduced. The exhibited behavior of a six-legged robot obtained under this approach, demonstrates the robot’s ability of moving from a starting point to a destination point, considering the need to go to the charging station or to remain static, if necessary.
IMPLEMENTATION OF DATA MINING TO DETERMINE THE ASSOCIATION BETWEEN BODY CATEGORY FACTORS BASED ON BODY MASS INDEX

The development of the increasing flow of globalization in the field of science and technology as well as increased income has resulted in reduced physical activity of the community which results in diverging diet and physical activity which makes a person not pay attention to his body shape. This method of calculating the Body Mass Index can be used to determine a person's body shape. There are several factors that can affect the value of the Body Mass Index, including individual factors, consumption patterns, and lack of physical activity which leads to a sedentary lifestyle. These factors are made into 69 itemset which will be used as the basis for questions in the questionnaire to collect a dataset which will later be processed using the FP Growth algorithm and looking for association rules that have the highest support x confidence value. From the 490 calculation data, the results are categorized into 10, each of which is Men with a Very Thin BMI with the highest support x confidence value of 39.56%, Men with a Thin BMI of 55.90%, Men with a Normal BMI of 70%, men with a fat BMI of 49.23%, men with an obese BMI of 41.34%, women with a very thin BMI of 41.37%, women with a thin BMI of 37.21%, Normal BMI is 68.83%, women with obese BMI are 41.65%, and women with obese BMI are 42.91%.
Metadata-Based Detection of Child Sexual Abuse Material

Child Sexual Abuse Media (CSAM) is any visual record of a sexually explicit activity involving minors. Machine learning-based solutions can help law enforcement identify CSAM and block distribution. Yet, collecting CSAM imagery to train machine learning models has ethical and legal constraints. CSAM detection systems based on file metadata offer several opportunities. Metadata is not a record of a crime and, therefore, clear of legal restrictions. This paper proposes a CSAM detection framework consisting of machine learning models trained on file paths extracted from a real-world data set of over 1 million file paths obtained in criminal investigations. Our framework includes guidelines for model evaluation that account for data changes caused by adversarial data modification and variations in data distribution caused by limited access to training data, as well as an assessment of false positive rates against file paths from common crawl data. We achieve accuracies as high as 0.97 while presenting stable behavior under adversarial attacks previously used in natural language tasks. When evaluating the model on publicly available file paths from common crawl data, we observed a false positive rate of 0.002, showing that the model operating in distinct data distributions maintains low false positive rates.
Publishers’ and editors’ perceptions and use of electronic textbook

Currently e-learning editions are actively conquering the educational environment, but there are some challenges and obstacles in the process. Therefore, the purpose of the article is to determine the perceptions of publishers and ezine publishers, as well as to clarify their role in the book digitization process. We believe this will highlight the advantages and disadvantages of using e-textbooks, obtain interesting and useful data for future promoters and publishers, help to systematize terminology. The article explores the perceptual characteristics of e-learning publications by publishers and publishers. For this purpose, a survey was conducted of 50 people who work in educational publishing houses. The questionnaire method was applied and a sample was formed as a material collection strategy, in particular a partial quantitative sample was applied. In addition, statistical observation was applied as a method of mathematical statistics, descriptive statistics to summarize indicators, and a content analysis method was used to provide quantitative data on the phenomenon under study. It was concluded that when creating new e-textbooks, factors should be taken into account: high quality, professional need (clearly define the target audience) and set a reasonable price. The survey results indicate that modern publishers are aware of the importance of the main features of electronic textbooks (hyperlinks, illustrations and high-quality multimedia), which gives hope that users will receive such publications in the near future.
Nighttime Stereo Depth Estimation using Joint Translation-Stereo Learning: Light Effects and Uninformative Regions.

Nighttime stereo depth estimation is still challenging, as assumptions associated with daytime lighting conditions do not hold any longer. Nighttime is not only about low-light and dense noise, but also about glow/glare, flares, non-uniform distribution of light, etc. One of the possible solutions is to train a network on night stereo images in a fully supervised manner. However, to obtain proper disparity ground-truths that are dense, independent from glare/glow, and have sufficiently far depth ranges is extremely intractable. To address the problem, we introduce a network joining day/night translation and stereo. In training the network, our method does not require ground-truth disparities of the night images, or paired day/night images. We utilize a translation network that can render realistic night stereo images from day stereo images. We then train a stereo network on the rendered night stereo images using the available disparity supervision from the corresponding day stereo images, and simultaneously also train the day/night translation network. We handle the fake depth problem, which occurs due to the unsupervised/unpaired translation, for light effects (e.g., glow/glare) and uninformative regions (e.g., low-light and saturated regions), by adding structure-preservation and weighted-smoothness constraints. Our experiments show that our method outperforms the baseline methods on night images.
Students' Attendance Monitoring System with SMS Notification

Students’ attendance in a university is commonly being monitored by lecturers and the Academic Affairs Division (AAD) as it may help to identify students’ problems at an early stage. This project aims to monitor the students who have the possibility to be absent from classes for more than the permissible percentage. Thus, the purpose of this research is to develop a students’ attendance monitoring system with Short Message Services (SMS) notification (SAMS). This system helps the AAD to manage the absenteeism report from lecturers and it automatically sends the information through SMS notification to the parents and the students themselves. The system has been developed using the Waterfall Model methodology that consists of five phases which are analysis, design, implementation, testing, and documentation. The results from the usability testing show that SAMS can help lecturers to monitor students’ absenteeism more easily and efficiently. Furthermore, the integration of the system with SMS is very useful as it can directly notify the parents regarding their children’s attendance problem.
Near and Far Collision Attack on Masked AES

Collision attack is an effective method in the field of side-channel analysis to crack cryptographic algorithms, and masking can be used as a countermeasure. Most collision attacks only utilize the traces that will collide. In this paper, we propose a collision attack method that exploits not only traces tending to collide, but also non-colliding traces. It can bring higher efficiency and reduce the number of needed traces significantly. In addition, our method is a random-plaintext collision attack method instead of a chosen-plaintext attack. The experimental results show that our proposed approach is better than the existing collision-correlation attack proposed by Clavier et al. at CHES 2011 [11]. To achieve a high key recovery success rate at 80%, we use at least 60% less traces than collision-correlation attack.
Surfacing Misconceptions Through Visualization Critique

Students of visualization come to formal education with an abundance of personal experience. However, one's exposure to graphics through media and education may not be sufficiently diverse to appreciate the nuance and complexity required to design and evaluate effective representations. While many introductory courses in visualization address best practices for visual encoding of data based on perceptual characteristics, as cognitive scientists, we place equal value on representational decisions based on communicative context: how the representation is intended to be used. In this pedagogical activity, we aim to surface learners' preconceived notions about what makes a visualization effective. Here we describe the structure and context of an introductory-level visualization activity, how it might be conducted in individual or group settings, our experience with the common misconceptions the activity can reveal, and conclude with recommendations on how they might be addressed.
Outlier Detection at GBIF Using DBSCAN

Geographic outliers at GBIF (Global Biodiversity Information Facility) are a known problem. Outliers can be errors, coordinates with high uncertainty, or simply occurrences from an undersampled region. Often in data cleaning pipelines, outliers are removed (even if they are legitimate points) because the researcher does not have time to verify each record one-by-one. Outlier points are usually occurrences that need attention. Currently, there is no outlier detection implemented at GBIF and it is up to the user to flag outliers themselves. DBSCAN (a density-based algorithm for discovering clusters in large spatial databases with noise) is a simple and popular clustering algorithm. It uses two parameters, (1) distance and (2) a minimum number of points per cluster, to decide if something is an outlier. Since occurrence data can be very patchy, non-clustering distance-based methods will fail often Fig. 1. DBSCAN does not need to know the expected number of clusters in advance. DBSCAN does well using only distance and does not require some additional environmental variables like Bioclim. Advanatages of DBSCAN : Simple Easy to understand Only two parameters to set Scales well No additional data sources needed Users would understand how their data was changed Simple Easy to understand Only two parameters to set Scales well No additional data sources needed Users would understand how their data was changed Drawbacks : Only uses distance Must choose parameter settings Sensitive to sparse global sampling Does not include any other relevant environmental information Can only flag outliers outside of a point blob Only uses distance Must choose parameter settings Sensitive to sparse global sampling Does not include any other relevant environmental information Can only flag outliers outside of a point blob Outlier detection and error detection are different. If your goal is to produce a system with no false positives, it will fail. While more complex environmentally-informed outlier detection methods (like reverse jackknifing (Chapman 2005)) might perform better for certain examples or even in genreal, DBSCAN performs adequately on almost everything despite being very simple. Currently I am using DBSCAN to find errors and assess dataset quality. It is a Spark job written in Scala (github). It does not run on species with lots of (&amp;gt;30K) unique latitude-longitude points, since the current implementation relies on an in-memory distance matrix. However, around 99% of species (plants, animals, fungi) on GBIF have fewer than &amp;gt;30K unique lat-long points (2,283 species keys / 222,993 species keys). There are other implementations ( example) that might scale to many more points. There are no immediate plans to include DBSCAN outliers as a data quality flag on GBIF, but it could be done somewhat easily, since this type of method does not rely on any external environmental data sources and already runs on the GBIF cluster.
Generative Melody Composition with Human-in-the-Loop Bayesian Optimization

Deep generative models allow even novice composers to generate various melodies by sampling latent vectors. However, finding the desired melody is challenging since the latent space is unintuitive and high-dimensional. In this work, we present an interactive system that supports generative melody composition with human-in-the-loop Bayesian optimization (BO). This system takes a mixed-initiative approach; the system generates candidate melodies to evaluate, and the user evaluates them and provides preferential feedback (i.e., picking the best melody among the candidates) to the system. This process is iteratively performed based on BO techniques until the user finds the desired melody. We conducted a pilot study using our prototype system, suggesting the potential of this approach.
Scientific Discourse of Populism: Aspects of Economy and Tax Policy

Introduction. Insufficient level of the economic growth perceiving amongst population of even economically developed countries, growth of inequality of incomes and welfare caused the opening for the opportunities for exploitation of controversial narratives by non-systematic politicians of different spectra in order to obtain electoral dividends, that stipulates the initiation of scientific discourse on the sources, reasons and consequences of populism. Problem Statement. A populist impact in many of European and world countries as well as considerable challenges for economic and political sustainability stipulate the necessity for scientific cognition of that essence for further elaboration of receipts for minimization of negative and implementation of possible positive outcomes. Purpose. To generalize, to structure and to systematize the studies of the political and economic foundations of populism and its manifestations in the context of the formation and implementation of economic and fiscal (tax) policy. Materials and Methods. A methodological consensus as general method introduced on defining populism as ideology, policy and social movement grounded on coordination on the structural elements contained in the definitions of different research schools is implemented; a comparative analyses of Western European and Ukrainian populism; a factsheet and statistical analyses for substantiation of evidence-based tax populism in Ukraine. Results. The article reviews the publications of researchers on the problem of populism - its definition, analysis of social and economic foundations, as well as the impact on the fiscal and tax policies elaboration. On the basis of generalization of broad international research approached framework regarding the analysis of populist tax policy, the definition of tax populism is proposed. The reasons for the prevailing influence of right populism on the current tax policy in Ukraine. Introduced concept of elaboration of sustainable system of institutional and organizational-analytical measures grounded on the modernization of institutes of general public interest in the field of development and strengthening of political process coordination and economic policy. Conclusions. Generalized scientific, informational, analytical and statistical data about the objective essence of economic and tax populism growing tendencies in the world as well as danger of right-wing tax populism in the Ukraine.
Randomized Overdrive Neural Networks

By processing audio signals in the time-domain with randomly weighted temporal convolutional networks (TCNs), we uncover a wide range of novel, yet controllable overdrive effects. We discover that architectural aspects, such as the depth of the network, the kernel size, the number of channels, the activation function, as well as the weight initialization, all have a clear impact on the sonic character of the resultant effect, without the need for training. In practice, these effects range from conventional overdrive and distortion, to more extreme effects, as the receptive field grows, similar to a fusion of distortion, equalization, delay, and reverb. To enable use by musicians and producers, we provide a real-time plugin implementation. This allows users to dynamically design networks, listening to the results in real-time. We provide a demonstration and code at https://csteinmetz1.github.io/ronn.
Cross-Thought for Sentence Encoder Pre-training

In this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering. Instead of using the original signals of full sentences, we train a Transformer-based sequence encoder over a large set of short sequences, which allows the model to automatically select the most useful information for predicting masked words. Experiments on question answering and textual entailment tasks demonstrate that our pre-trained encoder can outperform state-of-the-art encoders trained with continuous sentence signals as well as traditional masked language modeling baselines. Our proposed approach also achieves new state of the art on HotpotQA (full-wiki setting) by improving intermediate information retrieval performance.
Phase-shift extraction of multiple-frame randomly phase-shifted interferograms by analysis of the amplitude of the analytic signal

We present a powerful phase-shift extraction algorithm for multiple-frame random phase-shifting fringe patterns. The proposed method is based on changing the regularity of the amplitude of a demodulated analytic signal with respect to different phase shifts and a one-dimensional optimization method. Compared with the existing universal phase-reconstruction method, the proposed method is accurate, stable, and efficient. Both numerical simulations and experimental data demonstrate the high accuracy and efficiency of the proposed method.
Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text Corpus

We present the Swiss Parliaments Corpus (SPC), an automatically aligned Swiss German speech to Standard German text corpus. This first version of the corpus is based on publicly available data of the Bernese cantonal parliament and consists of 293 hours of data. It was created using a novel forced sentence alignment procedure and an alignment quality estimator, which can be used to trade off corpus size and quality. We trained Automatic Speech Recognition (ASR) models as baselines on different subsets of the data and achieved a Word Error Rate (WER) of 0.278 and a BLEU score of 0.586 on the SPC test set. The corpus is freely available for download.
Proximal Policy Optimization with Relative Pearson Divergence

The recent remarkable progress of deep reinforcement learning (DRL) stands on regularization of policy for stable and efficient learning. A popular method, named proximal policy optimization (PPO), has been introduced for this purpose. PPO clips density ratio of the latest and baseline policies with a threshold, while its minimization target is unclear. As another problem of PPO, the symmetric threshold is given numerically while the density ratio itself is in asymmetric domain, thereby causing unbalanced regularization of the policy. This paper therefore proposes a new variant of PPO by considering a regularization problem of relative Pearson (RPE) divergence, so-called PPO-RPE. This regularization yields the clear minimization target, which constrains the latest policy to the baseline one. Through its analysis, the intuitive threshold-based design consistent with the asymmetry of the threshold and the domain of density ratio can be derived. Through four benchmark tasks, PPO-RPE performed as well as or better than the conventional methods in terms of the task performance by the learned policy.
INCORPORATING EDMODO AS LEARNING MANAGEMENT SYSTEM OF SUMMATIVE ASSESSMENT IN EFL CLASSROOM

This present study aims to find out how an English teacher carries out Edmodo in summative assessment and to find out students’ perceptions doing online assessment through Edmodo in EFL classroom. This basic interpretive study involved thirty students of one of senior high schools in Karawang. The data were collected from the subject of this research directly even via google form and whatsapp by using some instruments, such as: observation sheet, interview guideline, and questionnaire. The present study used semi-structured interview for three interviewees via whatsapp. The researchers also distributed close-ended questionnaire via google form to thirty students. Then, it was analyzed by using Miles and Huberman’s method of data analysis. The finding indicated that the teacher conducts an online assessment using Edmodo with appropriate procedures. Edmodo leads teacher and students’ technological knowledge and Edmodo renders the test practical and motivating. The students perceived Edmodo well as summative assessment although there was a few negative assumption. Most students would prefer to be evaluated through Edmodo instead of the traditional test or paper-based test. Keywords : keyword Edmodo, online assessment, summative assessment