Reviewerly/exHarmony huggingface dataset:
White Blood Cell Detection Depending on Binary Analysis Based on Red, Blue and Hue Components

The automatic detection of white blood cells remains an unresolved problem in medical imaging. white blood cell image analysis has included researchers from the fields of medicine and computer vision alike. In this paper, a new algorithm for white blood cell detection is proposed. This algorithm is based on the binary conversion of red and blue identity compounds and gradient after defining the values of specific areas while deleting unwanted small areas depending on the median filter. In the experimental results from white blood cell images, the proposed algorithm was compared with several other algorithms for detection. A quality scale was used that compares manual cell counts with the automatic detection of algorithms where the proposed algorithm obtained a high distinction accuracy reaching 98 percent, as compared to other methods.
LibGuides: Library Course Reserves: Sending / Publishing Lists

Library Course Reserves use Leganto. Created by Ex Libris, Leganto is an online application that supports the creation and management of course resource reading lists.
Two Structures of Serial Verb Constructions

The purpose of this study is to investigate serial verb constructions (hereafter, SVCs), and provide how two verbs combine to represent a single event. Examining the argument structures of verbs, we propose that SVCs fall into two types: one is total argument sharing, and the other is partial argument sharing. These different types of sharing should have a distinct merger considering the argument structures. When two verbs in an SVC share their all arguments (internal and external arguments), namely total sharing, the serialization of two verbs takes place at the level of (categorial) head (head-head merger). In the case of partial sharing, the merger occurs at a phrase level (phrase-phrase merger): that is, after each verb meets their internal argument requirement. Previously asserted arguments about the relations between two verbs (namely, adjunction and complementation) are reexamined, and this study will employ the labeling algorithm to resolve their shortcomings.
DAMPAK MEDIA PEMBELAJARAN TERHADAP NILAI BELAJAR PESERTA DIDIK KELAS 6 DI SD NEGERI LEDOK 06 SALATIGA

The focus of this research is to investigate the impact of learning media towards the students’ value in grade six. This research was conducted in Ledok Elementary School State 6 in Salatiga by using survey with eighteen students as the number of the sample. Survey method was applied by the researcher in this research by giving questionnaire for students grade six. The result of this research showed that the use of learning media performed the strong impact towards the students’ learning achievement. In the development of elementary students, the students were in the a concrete operational phase. Moreover, the students need something real or all the learning process which involved the sensory in this phase. The use of learning media were effective to make the students became more enthusiastic and more active in the learning activity
A Hybrid Approach to Learning Endocrine Physiology: Investigating the Efficacy of Using “MicroVids” to improve student learning

Introduction With advances in technology, many lectures are now recorded and uploaded online for re‐viewing by students. However, lectures are long ranging from 50–120 minutes and students need to devote a large amount of time in order to review lecture, which can deter students from viewing recorded lectures. Therefore, we proposed creating short videos (MicroVids) reviewing only one specific concept from lecture and embedding the MicroVid into the printed notes via QR code, so that as students review the printed notes, they have a quick and easily accessible option for viewing professor‐explained concepts without searching the entire recorded lecture for the desired material. Objectives To investigate the efficacy of utilizing MicroVids as a tool to improve student learning of the endocrine system. Methods A series of 8 MicroVids were created reviewing concepts in the endocrine section of Anatomy and Physiology 1. The MicroVids were uploaded to YouTube and the URLs for each video was linked to a QR code that students could either click on to access the MicroVid (electronic notes) or scan with a QR code reader while reviewing their notes. Students (n=18, experimental group) taking an AP1 class during the Fall 2019 semester were given 8 MicroVids as supplemental material. After one week of access to each group’s MicroVids, students were given a survey to gather student feedback. Exam results from endocrine questions on their summative assessment was compared a control group of students taking AP1 the same semester as those that received Microvids and also compared to students who took AP1 Fall 2018. Outcome This approach garnered an overwhelming positive feedback. Two major themes from surveyed students were: 1) the MicroVids were easily accessible and 2) they helped students capture information they missed from their lecture notes. In the class that received the Microvids, 94% of the students watched the videos and 61% of students reported that it helped them succeed in their summative assessment. Additionally, while there was no statistically significant difference in exam grades between the class of students who watched the MicroVids versus the students of the control group, there was a modest improvement on the grasp of the endocrine system between poor test performers in the experimental group as compared to the poor test performers in the control group (an average 2.8 percentage points difference). While modest, the observation that poor test performers in the experimental group outperformed their control counterparts on their assessment is consistent with other reports that demonstrated similar positive learning outcomes when an active learning strategy was implemented in the classroom.
THE USE OF PICTURE SERIES IN SPEAKING SKILL (A DESCRIPTIVE QUALLITATIVE STUDY AT SECOND YEAR STUDENTS OF SMPN 15 BANDA ACEH)

Abstract Name: Rachmawati Student No.: 0806102050044 Study Program: English Language Education Title :The Use of Picture Series in Speaking Skill (A Descriptive Quallitative Study at Second Year Students of SMPN 15 Banda Aceh) Keywords: Picture Series, Speaking Skill. The objectives of this study are to describe how the teacher uses picture series in teaching speaking skill and whether picture series make the students active in speaking skill. The design of this study is descriptive qualitative research at second year students of SMPN 15 Banda Aceh. The subjects are an English teacher and the students of VIII-2 class. This study used some instruments: observation, interview, questionnaire, and document. The finding are classified into two categories which are the use of picture series in speaking skill by the teacher and whether picture series make the students active in speaking skill. From the findings, the writer found that the teacher implemented some of the procedures of picture series activities in teaching speaking skill, and the students were active in speaking skill, they could express their ideas through picture, and also spoke bravely in front of class in teaching and learning process. Based on the findings, some suggestions are given to increase the use of picture series in speaking skill at SMPN 15 Banda Aceh. It is suggested that the teacher has to pay attention about using picture series in teaching and learning process. It should be appropriate between lesson plan and procedures of picture series activities in the classroom. This is done to make the students to get clear explanation and understanding materials well.
Intelligent Remote Respiratory Monitoring System Based on Deep Learning

Based on depth learning, a wearable elastic band embedded with grating strips is tied to the chest or abdominal cavity of a patient. Due to volume changes generated during breathing, the position changes of grating strips in the elastic band are caused. Accurate detection of the position changes of grating strips through grating sensors can realize monitoring of respiratory process. Firstly, the application program is developed based on LabVIEW virtual software platform, and the remote monitoring and control functions are realized by using the network mode based on Browser/Server (B/S). Then, the impedance signals and blood oxygen saturation of human body are collected through simulation experiments for system test verification. The system realizes the wireless transmission and monitoring of respiratory signals, which enables patients to get rid of the bondage of electrode connection and does not affect sleep quality. With the characteristics of small volume and low power consumption, it provides a new idea for community medical remote monitoring.
Topic Extraction and Classification for Questions Posted in Community-Based Question Answering Services

This paper presents methods of simultaneously performing topic/keyword extraction and unsupervised classification for questions posted in community-based question answering services (CQA) or Q&A websites, using topic models and hybrid models. Large-scale experiments on two kinds of data, one called category data and the other called subtyping data, show the effectiveness of our methods. The purity and correct rate show that the topic models outperform clustering methods, hybrid models outperform topic models in question classification, and the adoption of term frequency-inverse document frequency is effective for the subtyping data. Manual evaluations with the extracted keywords show the effectiveness of the topic models in topic extraction.
Iteration Practice Guidelines towards Agile Innovation

As more information actively sharing and flowing due to the recent development in information and communication technologies, changes in the customer requirements to artifacts have been accelerating. In such market circumstances, companies are focusing on agile methods, which are design methods for software development. These methods continuously adjust artifacts based on customer requirements and create sustainable value. By means of applying agile methods not only to software developments but also to the broader artifact developments, the Agile Innovation (AI) expected to be realized. The process in agile methods includes the iteration phase which embraces the changes in customer requirements. To lead the agile development to the success, team managements that enable the efficient iteration phase is the most important. In previous research, agile modeling (AM), which is the methodology to support the modeling in the iteration phase, is known as the means of critical team management. By applying the AM to the modeling in the iteration phase, the team will work efficiently in the whole of the iteration phase. Thus, if we can extend the conventional AM for software to AM for a broad sense of artifacts including products and services, etc., designers can realize AI more efficiently. This paper aims to extend the conventional AM for software to a new AM for a broad sense of artifacts and reconfigures the new AM that enables to apply to the AI processes.
So You Think Digital Is the Future. Your Internet Data Are Rotting

Civil registration practice usually requires records to be kept for a very long time, or even in perpetuity. Digitization has raised the matter of the (non-)longevity of digital records because of frequent, ongoing changes in hard-and software. This has not been on the radar of the enthusiasts of digital ID. It is not-for example-addressed in the World Bank-initiated 'Principles on Identification' (endorsed by more than two dozen other organizations). Record management has been identified as a vulnerability of the Aadhaar system in India in which more than a billion people have been enrolled. The currently boisterous expectations re digital ID threaten to negatively impact civil registration, including its irreplaceable legal purpose and the importance of the preservation of records of legal identity. The article concludes with an illustration using a World Bank-funded project for Tonga, the Pacific Island country with just over 100,000 inhabitants.
Occluded illegally parked vehicle detection and long term tracking (Conference Presentation)

We propose a method of detecting and tracking occluded illegally parked vehicles. The method used a deep learning framework that can detect and track moving vehicles. To obtain the long term tracking of stationary vehicles the process must be capable of withstanding large changes in lighting, weather and large amounts of occlusion from passing vehicles. A modified dense SIFT descriptor algorithm has been developed. This compares the current frame with the background and removes objects in motion. The tracking of the occluded illegally parked vehicle is achieved by YOLO version 3 algorithm, combined with a predictive filter. For each illegally parked vehicle, the occluded portion is not used for feature point matching. Based on the matching result, the occluded illegal vehicle can be tracked. Our approach tested performance on a public database(i-LIDS) and the results indicate the method produces a very high accuracy compared to other published work.
Practices of Teaching English to Young Learners: Voices from Islamic Primary Schools

This research is aimed at finding out the design teaching English to young learners and the teachers good practice of teaching English to young learners. The descriptive qualitative research is considered to be an appropriate design of this research. The fifth grade English teachers of SDI Qurrotaa’yun Ngunut and MI Al Azhaar Bandung Tulungagung are the subjects of the study. The data were collected by conducting observation, interview, and documentation. This research revealed that the English teachers at SDI Qurrotaa’yun Ngunut and MI Al Azhaar Bandung did good practice in TEYL, as follow; (1) English Teachers did good preparation, (2) providing various activities in teaching-learning process, (3) making the students active in the class, (4) using various kinds of assessments, (5) creating positive language environment. In conclusion, the English teachers of SDI Qurrotaa’yun Ngunut and MI Al Azhaar Bandung already implemented the principles of good teaching English for young learners. The result of this study is beneficial for English teachers to implemented good TEYL principles in their teaching. For other researchers this research was done in Islamic school background, therefore they can conduct similar research related to the TEYL in the public schools or other backgrounds.
A decentralizáció és a blockchain-technológia felhasználási lehetőségei gépi látás és mesterséges intelligencia használatával a katonai szervezetekben

Military science has faced new challenges at the end of the 20th century with the emergence of the Internet. Challenges and threats to traditional security got a new interpretation with a new concept as cybersecurity, which leads to an organic transformation of military engineering and IT. As the Internet has fundamentally changed the way the world works, new technologies have emerged on the network that can revolutionise the multitude of industries. Such innovation is Distributed Ledger Technology (DLT) and Blockchain Technology supplemented with Artificial Intelligence and computer vision. The potential uses of the blockchain represent a multitude of military technical scientific challenges. The technology makes it possible to co-operate freely with cryptographic procedures on distributed networks without state control, but can also serve military and defence management purposes.
Using bayes error rate estimation to analyze feature spaces (Conference Presentation)

This study wishes to apply a non-parametric estimate of the Bayes Error Rate (BER) to current Air Force problems related to target classification. Whether they be neural networks, autoencoders, or other architectures, classifiers are commonly assessed through confusion matrices and associated statistics, or visualizations of feature spaces like t-SNE plots. However, these methods depend on the test data used to assess the performance of the network, not the robustness of the classifier itself. This research incorporates a different statistic that estimates the BER, or the probability of misclassification given some data, to serve as an upper bound for potential classifier performance. This estimate leverages a Friedman-Rafsky test statistic: the number of cross labels in a minimum-spanning tree (MST) through points in the feature space. The first part of this study examines the behavior of the BER estimate over a general learning process, such as different epochs of the training process in a neural network. The second part of the study examines whether certain factors affect the separability of synthetic aperture radar (SAR) images of targets of interest. Given the fact that it is often difficult and expensive to survey real targets and generate SAR images, 3-D CAD models are frequently used to generate synthetic SAR images. However, given that many resources are devoted to perfecting these models, this study applies the BER estimate to examine whether minute changes to CAD models affect separability in the image domain. The results seem to indicate that, if the topology of the target is maintained in the CAD domain, low-fidelity versions of targets (with 25% the number of faces of highly accurate models), exhibit separability and ability to be correctly classified identical to their high-fidelity counterparts. The BER estimation also shows promising applications in other domains, serving as a way to describe the underlying structure of feature spaces intuitively but effectively.
What to design in the ruins of capitalism? Designing Kinship through the lens of interspecies relationships and Indigenous Futurisms

Designing Kinship through the lens of Indigenous Futurism and collaboration:In my talk, I will look at designing through the lens of indigenous futurisms. How do Indigenous people imagine different futures while critically addressing contemporary issues? How can we embrace a subversive mode of storytelling and representation in order to create more meaningful work and undermine dominant ideologies? How is kinship (re)imagined in Indigenous media productions? I will further suggest that socially relevant design should be based on collaboration. As creative content creators and designers, it is not only important what we envision and create, but also how we design and who we include in the process.
AN EFFECT ANALYSIS OF THE PARALLEL MIGRATING BIRDS OPTIMIZATION ALGORITHM PARAMETERS

Migration is the process of sending selected solutions from a sub-population to the neighboring sub-population at specified intervals in parallel metaheuristic algorithms (PMAs). Topology, migration rate (MR), migration interval (MI), migration policy and communication model are the factors which characterize the nature of migration. Identification of relationship between migration parameters and an accurate selection of such parameter values increase the performance of PMAs. The number of sub-populations (NS) denotes the number of different populations in which algorithm can perform simultaneous searches. In this study, Migrating Birds Optimization (MBO) Algorithm, no migration performed, was applied for four different NS values. Additionally, Parallel Migrating Birds Optimization (PMBO) Algorithm is executed using five MR values, five MI values and four NS values and obtained fitness values are provided. According to the results, PMBO algorithm outperforms MBO in 99% of case studies. Therefore, the contribution of migration to the performance of the algorithm is evidently demonstrated. Furthermore, the values obtained during the iterations are shown on graph to investigate the effect of MI and MR changes on search performance of algorithms. As MI decreases, it is confirmed that the algorithm produces good results in early steps of iterations, making faster searches. MR has a greater effect on performance if MI is kept low. If MI increases, the changes in MR have less affect. Additionally, the effect of MI, MR, NS values and their correlation on fitness value is analyzed with analysis of variance (ANOVA). According to the analysis, MI is identified to be the most significant factor. The least significant factor is NS. Combinations of such parameters are analyzed and it was shown that MR*MI combination has the most significant effect on performance.
Design method of service contents to realize service chains and continuous improvement

In the context of recent servitization of manufacturing characterized by IoT, Bigdata, and AI, we need data-driven front-loading design of product with service and engineering support for it. This paper proposes TriCyPSS (Triple Cycles for Product Service System) that supports developing service content from the viewpoints of service chain and continuous improvement among customer company, my company, and partner company. Using the TriCyPSS method, we can investigate not only service content but also plans of data utilization and information circulation based on its structure. This paper also details notations of the proposed model using the i* (eye-star), which is a methodology of system modeling in requirement engineering.
From the EIC: Robust Machine Learning

<fig orientation="portrait" position="float" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <graphic orientation="portrait" position="float" xlink:href="henke-2984228.tif"/> </fig> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Machine learning techniques</b> have become pervasive through many technical fields but an obstacle for employment is often the criterion of robustness. While machine learning can be a great means to improve upon the quality of traditional optimization techniques in uncritical scenarios (e.g., a customized online search result that proposes to a consumer a more or less well-fitting new product advertisement), it may be prohibitive to employ when directly embedded in critical decision flows (e.g., a self-driving car that needs to decide whether to engage an emergency brake). In the latter case, robustness is one mandatory constraint. Robustness can have many facets; some of them are covered by this timely special issue that represents the state of the art from a design and test point of view. Many thanks to the Guest Editors Theocharis Theocharides, Muhammad Shafique, Jungwook Choi, and Onur Mutlu for editing this special issue that includes two keynote articles, a survey on “Robust Machine Learning Systems: Challenges, Current Trends, Perspectives, and the Road Ahead,” and six technical articles.
A random finite set formalism for multiple hypothesis tracking

This paper is generally concerned with mathematical formalisms to support theory and algorithm developments of multiple hypothesis tracking (MHT), as a class of solutions to multiple target tracking (MTT) problems based on targetwise detections. In particular, this paper presents a new perspective on random set (RFSet) formalism to support a form of MHT, in which an unknown number of targets is modeled by a RFSet of continuous-time stochastic processes, rather than a single stochastic process defined on the space of finite sets in a given target state space, while generally multiple sensors provide noisy and cluttered target detections without any explicit indications of origins. The focus is on a clearcut approach to avoid any complication resulting from diagonal sets in direct-product spaces when a space of finite subsets of a state space is defined as its quotient space, instead of a subspace of the space of closed subsets in the state space with Fell-Matheron topology.