Reviewerly/exHarmony huggingface dataset:
Performance Evaluation of Different Algorithms for Handwritten Isolated Bangla Character Recognition

Handwritten character recognition is one of the most emerging fields within the optical character recognition area. Bangla handwritten character recognition is a complex task, it is challenging due to extensive size and diversity within the alphabets. Currently, convolutional neural network (CNN) has been proven to have the ability to classify complex dataset. The convolutional neural network does not require any predefined feature extraction method, but it requires a large dataset to gain accuracy. This work proposes a convolutional neural network model for classifying Bangla handwritten alphabets and compares the performance with the other widely used models for classification. Each model is trained with a large dataset, which is augmented to have diversity in data and features. After training, we have tested the models with 7500 sample images and it shows an accuracy of 97.87% for the proposed model. In this work, we also find out the weights of the CNN network for best performance and used that weights to evaluate the performance from other data set for cross-validation of our model. The weighted model accuracy for two different independent data set is 95.23% and 94.22%.
Timos Sellis Speaks Out on Research in Australia and Greece

Welcome to ACM SIGMOD Record's series of interviews with distinguished members of the database community. I'm Marianne Winslett, and today we are in Snowbird, Utah, USA, site of the 2014 SIGMOD and PODS conference. I have here with me Timos Sellis, who is a professor at the Royal Melbourne Institute of Technology1. Before that, he was at the National Technical University of Athens and the University of Maryland. He is an ACM Fellow and an IEEE Fellow, and he has a VLDB 10-Year Paper Award. His PhD is from Berkeley.
System for automatic soil sampling by AUV equipped with multilink manipulator

The development and research of the new synthesis method of the control system for an autonomous underwater vehicle (AUV) with a multilink manipulator is described in the paper. This system provides an automatic soil sampling and the taking of geological samples of a sea bottom surface. In automatic mode, the developed system defines the location of a bottom surface relative to the AUV by means of the onboard multibeam Doppler lag. During the vehicle immersion, the system evaluates the complexity of the bottom relief in the working area and makes decision on the suitability of the said relief for the trouble-free implementation of the specified manipulative tasks. Also, the proposed system realises the formation algorithm for spatial trajectories of the multilink underwater manipulator's working tool and defines the AUV spatial orientation and position at which the performing of manipulation operations occurs most efficiently and safely.
Improving Handwritten Character Recognition by Using a Ranking-Based Feature Selection Approach

In handwriting recognition, because of the large variability of the writers, the selection of a suitable set of features is a challenging task. This has led to the development of a large variety of feature sets, which, in many cases, contain a large number of attributes, causing performance problems in terms of classification results and computational costs. In this paper, we considered a widely used set of features in handwriting recognition, to verify if it is possible to improve the classification results for handwriting recognition by using a reduced set of features. To this aim, we adopted a feature ranking based approach and tested several univariate measures. The experiments, performed on two real-world databases, confirmed the effectiveness of our proposal.
Planning a Digital Scholarship Center

Orzech, M. J. Planning a Digital Scholarship Center. Lightning talk at Digital Scholarship Centers Discussion Group: Working with Library Administration & Colleagues to Support Digital Scholarship. ALA MidWinter, Seattle, WA, January 26, 2019.
Model Federation based on Role Modeling

Modeling approaches could be a powerful solution for specification, design and analysis. At a system level, models must take into account many system concerns. Thus, several system modeling approaches are based on several viewpoints expressed in Domain Specific Modeling Languages. Cyber threat analysis takes place within this modeling context with the need for several DSMLs to address several viewpoints of the system. So, the analysis of this domain is supported by DSML interoperability to perform simulation or other algorithms. Therefore, in this paper, we present an approach to face DSML interoperability based on role modeling. The Role4All framework is based on a metamodel including the Role concept. The Role4All language provides the capacity to define shared semantics between the DSMLs. Role4All and role modeling avoid model transformations and promote a federation approach between several DSMLs. The federation mechanisms of Role4All are illustrated in the cyber threat modeling framework to emphasize information gathering and the updates of the role model.
Expanding the Awareness and Use of Library Collections Through Social Media

The Library of Congress houses more than 164 million items in various formats, languages, and subjects. Found among its treasures are the international collections. The Library's four area studies divisions—African and Middle Eastern (AMED), Asian, European, and Hispanic—reading rooms provide access to many of these resources. In 2016, the four area studies divisions launched a collaborative social media program to encourage the use of the library's international collections by domestic and global online audiences. The program adopted the 4 Corners of the World blog and the Library of Congress International Collections Facebook page as interactive social media tools. This chapter will examine the interdivisional initiative through its purpose, target audience, content focus, platform selection, management and operation, audience interaction, and current status. Next, the chapter will discuss challenges and opportunities facing the four divisions. Finally, the chapter will offer recommendations for other libraries that are interested in establishing a similar program.
Exploiting JCVM on Smart Cards Using Forged References in the API Calls

This paper presents a novel style of attack which compromises the applet isolation implemented by modern smart cards built on the Java Card platform. System calls (APIs) implemented by all cards tested during our research – from several different manufacturers – fail to perform (sufficient) checks on the ownership of the objects provided by applets, compromising the security of the applet firewall. The practical impact of these vulnerabilities is platform-specific; we show that disclosure of critical private data including secure channel protocol keys is possible on some cards, and that even Secure Elements – with dedicated hardware support for memory isolation – fail to prevent memory disclosure of objects owned by the Java Card Runtime Environment, despite preventing all other known state-of-the-art logical attacks. We demonstrate that physical attacks can also be used to exploit this vulnerability on some smart cards, removing the need for an attacker to first install an applet on the card. Finally, we propose a potential countermeasure for preventing these classes of attacks.
Placement-and-Profit-Aware Association Rules Mining

Previous approaches on association rule mining in recommendation have already achieved promising performances. However, to the best of our knowledge, they seldom simultaneously take the profit and placement factor into consideration. In E-commerce recommendation scenario, the order of the recommendation reflects as placement. In this paper, we propose a novel placement-and-profit-aware association rule mining algorithm to maximize profit as well as maintaining recommendation accuracy. We also propose two metrics: Expectation of Profit (EOP), which measures the overall profit, and Expectation of Click rate (EOC), which measures the user experience. Experiments on SPMF dataset show that the proposed algorithm can improve the EOP significantly with only slight decrease in EOC.
The effects of crypto-currencies on financial market and monetary policy

The emergence of crypto-currency breaks the concept of existing currency. Against this backdrop, there is a move to issue Central Bank Digital Currency. In addition to the regulation on crypto-currency, it is to distribute the crypto-currency example directly so as not to confuse the financial policy with private crypto-currency. This study examines the effects of crypto-currency on financial markets, especially its effects on monetary policy, and discusses implications for central bank’s issuing digital currency
Writing Training Classroom Action Research (CAR) in Supporting Teacher Quality and Professional Teacher ]Pelatihan Menulis Artikel Peneltitian Tindakan Kelas (PTK) dalam Menunjang Kualitas Pembelajaran dan Profesional Guru]

The implementation of community service aims to provide tips for writing classroom action research articles so that this activity can motivate teachers to write and improve their abilities in researching and writing articles where the benefits can be implemented in the classroom. Facts on the ground indicate that the obligation of teachers to write PTK is a "horror" and burdensome for teachers. That is because the teacher is not used to writing and researching. Hope after community service in both schools, the teacher can overcome problems in class and make the research culture the best solution in school.
20 YEARS OF EXPERIENCE IN CATHODOLUMINESCENCE RESEARCH AT THE PGI-NRI

The use of catholuminescence analysis in scientific research at the PGI-NRI has a 20-year history. The method, combined with other analytical techniques, has become an effective tool in widely understood mineralogical and petrographic investigation reported in numerous publications.
A deep learning method for volumetric breast density estimation from processed full field digital mammograms

Breast density is an important factor in breast cancer screening. Methods exist to measure the volume of dense breast tissue from 2D mammograms. However, these methods can only be applied to raw mammograms. Breast density classification methods that have been developed for processed mammograms are commonly based on radiologist Breast Imaging and Reporting Data System (BI-RADS) annotations. Unfortunately, such labels are subjective and may introduce personal bias and inter-reader discrepancy. In order to avoid such limitations, this paper presents a method for estimation of percent dense tissue volume (PDV) from processed full field digital mammograms (FFDM) using a deep learning approach. A convolutional neural network (CNN) was implemented to carry out a regression task of estimating PDV using density measurement on raw FFDM as a ground truth. The dataset used for training, validation, and testing (Set A) includes over 2000 clinical cases from 3 different vendors. Our results show a high correlation of the predicted PDV to raw measurements, with a Spearman’s correlation coefficient of r=0.925. The CNN was also tested on an independent set of 97 clinical cases (Set B) for which PDV measurements from FFDM and MRI were available. CNN predictions on Set B showed a high correlation with both raw FFDM and MRI data (r=0.897 and r=0.903, respectively). Set B had radiologist annotated BI-RADS labels, which agreed with the estimated values to a high degree, showing the ability of our CNN to make a distinction between different BI-RADS categories comparable to methods applied to raw mammograms.
Modern problems of university education positioning of the Chuvash Republic

The article deals with the modern understanding of the university and its role in the educational process, the essential features and criteria of the university, the idea of the reference university are given. The authors emphasize that the educational environment of the modern university is an important resource for the formation and development of the student's personality. The university acts as a social institution, performs socio-cultural tasks of modern society development and acquires new qualities.
FEMALE STUDENT ACHIEVEMENT USING EDMODO IN THE ELEVENTH-GRADE ECONOMIC GEOGRAPHY CURRICULUM IN OMAN

This study investigates the effectiveness of using Edmodo to teach the economic geography curriculum in Oman to improve eleventh-grade female students' learning outcomes, using an experimental pre-posttest design, with experimental and control groups of 24 and 28 female students (respectively).An achievement test of economic geography was used to collect data.The results showed that using Edmodo significantly increased female students' achievement.
Software Engineering Guidelines for Scientists - A Practical Handout for the Developing Researcher

In research facilities, scientists often develop software. Most of them do not have any specific education in software development. Usually they had programming courses at university or they self-taught some programming skills. Therefore their knowledge about software engineering and adjacent topics is quite limited. To support scientists, we created a set of software engineering guidelines. These guidelines give advice in different fields of software development (e.g., requirements management, design and implementation, change management). To make it easy to start with them, we developed a simple classification scheme taking aspects into account like expected software size or software lifetime. This scheme is useful to filter the guidelines and to fit them to the right context. Besides providing written guidelines and explanations, we created check list in different formats (e.g., Markdown, Word) to offer scientist a light-weight and easy-to-use tool. In this talk, we provide an overview about the concept of the guidelines and report about experiences introducing them at the German aerospace center (DLR) – a large research facility in Germany. At DLR around 2000 to 3000 persons develop software in part or full time.
Low Cost and Fast Development of 3D Printed Gloves for 10 Degrees of Freedom Gesture Recognition

The decreasing cost allows easy access and diffusion of 3D printers even for domestic use in the same way as 2D printers.The present work proposes the development of a sensorial glove in 3D printing, featuring low cost, easy reproduction and replacement.A 3D desktop printer, that was able to extrude different plastic materials, was used.In order to generate the geometric shape that best suited the hand anatomy, the 3D CAD design was based on hand photos from the top and the sagittal section.The design of the glove includes the sensor housings, which are pockets within which the sensor can slide during joint bending.The wiring of 10 flex sensor and the acquisition board designed for a Lycra glove were easily applied to the printed glove without modification.The glove in 3D printing was able to control virtual or mechanical hands, which provides for surgical, military, space and civil applications.The possibility to achieve waterproofing allows the use in applications that require contact with solvents or water.A standard test applied to six healthy subjects demonstrated that the proposed glove achieves performances, in terms of repeatability, reproducibility and reliability, comparable to that of the other literature gloves.
영어로 진행하는 멀티미디어 영문법 수업 설계와 실행을 위한 교사 협력 연구

The aim of this paper is to explore the benefits of and challenges to teacher collaboration in designing and implementing multimedia-assisted English...
Towards Realistic Simulations of Arbitrary Cross-Cloud Workloads

The research undertakings in cloud computing often require designing new algorithms, techniques, and solutions requiring large-scale cloud deployments for comprehensive evaluation. Simulations make a powerful and cost-effective tool for testing, evaluation, and repeated experimentation for new cloud algorithms. Unfortunately, even though cloud federation and hybrid cloud simulations are explored in the literature, Cross-Cloud simulations are still largely an unsupported feature in most popular cloud simulation frameworks. In this paper, we present a Cross-Cloud simulation framework, which makes it possible to test scheduling and reasoning algorithms on Cross-Cloud deployments with arbitrary workload. The support of Cross-Cloud simulations, where individual application components are allowed to be deployed on different cloud platforms, can be a valuable asset in selecting appropriate mixture of cloud services for the applications. We also implement a Cross-Cloud aware reasoner using our Cross-Cloud simulation framework. Simulations using both simple applications and complex multi-stage workflows show that the Cross-Cloud aware reasoner can substantially save cloud usage costs for most multi-component cloud applications.
How Users Gaze and Experience on Digital Humanities Platform?: A Model of Usability Evaluation

Digital humanities platform has been developing rapidly. Using eye-tracking in usability evaluation can make a difference in user experience. In this paper we propose a gaze-experience model for usability evaluation on digital humanities platform, and select the “Digital Dunhuang” platform as the case study. A user experiment was carried out to verify the application value of the model through a large amount of eye-tracking and user experience data collection and analysis. We found that the features of eye-tracking (such as fixation and saccade) and those of user experience (such as satisfaction, efficiency and effectiveness) had correlations. Implications were also put forward to improve the usability of digital humanities platform and can be extended to similar platforms.
Teaching for Impact

Like many problems in AI in their general form, supervised learning is computationally intractable. We hypothesize that an important reason humans can learn highly complex and varied concepts, in spite of the computational difficulty, is that they benefit tremendously from experienced and insightful teachers. This paper proposes a new learning framework that provides a role for a knowledgeable, benevolent teacher to guide the process of learning a target concept in a series of curricular phases or rounds. In each round, the teacher's role is to act as a moderator, exposing the learner to a subset of the available training data to move it closer to mastering the target concept. Via both theoretical and empirical evidence, we argue that this framework enables simple, efficient learners to acquire very complex concepts from examples. In particular, we provide multiple examples of concept classes that are known to be unlearnable in the standard PAC setting along with provably efficient algorithms for learning them in our extended setting. A key focus of our work is the ability to learn complex concepts on top of simpler, previously learned, concepts---a direction with the potential of creating more competent artificial agents.
On codes over the finite non chain ring A = F4+vF4; v2 = v and its covering radius of codes with Bachoc weight

In this paper, some lower and upper bounds on the covering radius of codes over the nite non chain ring A = F4 + vF4; v2 = v with respect to Bachoc weight is given. Also, the covering radius of various Block Repetition Codes of same and different length over the nite non chain ring A = F4 + vF4; v2 = v is obtained.In this paper, some lower and upper bounds on the covering radius of codes over the nite non chain ring A = F4 + vF4; v2 = v with respect to Bachoc weight is given. Also, the covering radius of various Block Repetition Codes of same and different length over the nite non chain ring A = F4 + vF4; v2 = v is obtained.
Sparse discriminative least squares regression model

In this paper, we propose a sparse discriminative least squares regression (SDLSR) for multicategory classification. The main mechanism of SDLSR is to apply L1-norm for regularizing the objective function of discriminative least squares regression (DLSR). Through theoretical analysis, it is obtained that SDLSR can be viewed as a relaxation of L1-SVM. Moreover, we theoretically prove that sparsity of objective function can effectively decrease the number of support vectors (SV). To optimize our proposed SDLSR, an efficient iterative algorithm is proposed. The experimental results confirm the effectiveness of our proposed SDLSR.
Looking for economic prosperity without growth

The only way for humanity to solve its environmental problems may be to abandon our quest for continual economic expansion. It&#8217;s time to study what a future of degrowth might look like, some researchers say.
Reparameterizing Distributions on Lie Groups

Reparameterizable densities are an important way to learn probability distributions in a deep learning setting. For many distributions it is possible to create low-variance gradient estimators by utilizing a `reparameterization trick'. Due to the absence of a general reparameterization trick, much research has recently been devoted to extend the number of reparameterizable distributional families. Unfortunately, this research has primarily focused on distributions defined in Euclidean space, ruling out the usage of one of the most influential class of spaces with non-trivial topologies: Lie groups. In this work we define a general framework to create reparameterizable densities on arbitrary Lie groups, and provide a detailed practitioners guide to further the ease of usage. We demonstrate how to create complex and multimodal distributions on the well known oriented group of 3D rotations, SO{3}, using normalizing flows. Our experiments on applying such distributions in a Bayesian setting for pose estimation on objects with discrete and continuous symmetries, showcase their necessity in achieving realistic uncertainty estimates.
Mewujudkan New Public Management Terhadap Indisipliner Mahasiswa Melalui Penerapan AbCD (Absen Cap Driji)

University of Islam Malang (UNISMA) as a public institution has a goal to be achieved. One of the goals that Unisma wants to achieve is to produce superior and qualified students. To achieve this goal, Unisma should apply new policies in terms of New Public Management (NPM). In NPM, Unisma should apply Absen Cap Driji / Absensi Sidik Jari (AbCD) machines as a solution to overcome student indiscipline. Need to know, so far the form of student indiscipline in Unisma has always increased every year.This form of indiscipline, among others; 1) Often come late, 2) Do not do assignments, 3) Often do not enter without reason, 4) D3C (Come, Sit, Listen, and underestimate the lecture. If the form of student indiscipline is unchecked, it is feared that students are increasingly out of control, affecting other students, even Unisma fails to achieve the goal of creating excellent and high quality students. So that AbCD machines are needed in order to implement NPM while minimizing student indiscipline. AbCD is an absent machine that uses fingerprints so as to reduce dishonesty done by students in terms of college attendance. AbCD replaces the previous presence system which is still manual and easily manipulated. This AbCD program is expected to provide benefits in the long term, those are: 1) To discipline Unisma students by applying AbCD machines, 2) To develop IT in Unisma so as not to be left behind, 3) To avoid conflicts between students and students, and students with lecturers at Unisma. The methods used in implementing AbCD machines are: 1) Pre-Program Analysis, 2) Location Survey, 3) Licensing Administration, 4) Procurement of Goods, 5) Material Preparation, 6) Recruitment of Facilitators and Participants, 7) Lecturer debriefing, 8 ) Student Counseling, 9) Utilization of AbCD, and 10) Evaluation. Which of the ten methods of implementation has been completed with the budget and implementation schedule.