Reviewerly/exHarmony huggingface dataset:
Prediction of Short-Term Breast Cancer Risk Based on Deep Convolutional Neural Networks in Mammography

This work investigated a novel framework of predicting short-term breast cancer risk by using a deep learning approach in mammography. A dataset of 675 negative screening cases were applied. 333 cases were cancer diagnosed at next screening, while 342 cases remained negative. In order to stratify these patients into high and low cancer risk group, we first used an automatically method to segment bilateral matched central regions from right and left mammography respectively. Then, three AlexNet, GoogLeNet and ResNet based deep learning models were established with ten-fold cross validation method for both difference image of bilateral matched central regions and two whole regions of bilateral breasts respectively. Using AlexNet-, GoogLeNet- and ResNet-based risk model, areas under ROC curves (AUC) are 0.56, 0.62 and 0.64 for central regions and 0.59, 0.57 and 0.65 for whole regions, respectively. When combining prediction scores of three deep learning models with a multi-agent fusion algorithm, AUCs are 0.67 and 0.67 for central regions and whole regions respectively. When fusing scores of central region-based risk model and whole region-based risk model, AUC significantly increases to 0.71 ( p &lt; 0.01). By dividing 675 cases into five subgroups based on sorting results of risk scores, the odds ratios had an significant increasing trend as the scores increased ( p = 0 003). This study demonstrates feasibility of applying deep learning technology to assist investigating novel markers in mammography for helping assessment of short-term breast cancer risk and improving the efficiency of breast cancer screening in the future.
Face Recognition Based on Histogram Equalization and LBP Algorithm

In the recent time bioinformatics take wide field in image processing. Face recognition which is basically the task of recognizing a person based on its facial image. It has become very popular in the last two decades, mainly because of the new methods developed and the high quality of the current visual instruments. There are different types of face recognition algorithms, and each method has a different approach to extract the image features and perform the matching with the input image.&#x0D; In this paper the Local Binary Patterns (LBP) was used, which is a particular case of the Texture Spectrum model, and powerful feature for texture classification. The face recognition system consists of recognizing the faces acquisition from a given data base via two phases. The most useful and unique features of the face image are extracted in the feature extraction phase. In the classification the face image is compared with the images from the database.&#x0D; The proposed algorithm for face recognition in this paper adopt the LBP features encode local texture information with default values. Apply histogram equalization and Resize the image into 80x60, divide it to five blocks, then Save every LBP feature as a vector table.&#x0D; Matlab R2019a was used to build the face recognition system. The Results which obtained are accurate and they are 98.8% overall (500 face image).
Quantum Algorithms for Portfolio Optimization.

We develop the first quantum algorithm for the constrained portfolio optimization problem. The algorithm has running time $\widetilde{O} \left( n\sqrt{r} \frac{\zeta \kappa}{\delta^2} \log \left(1/\epsilon\right) \right)$, where $r$ is the number of positivity and budget constraints, $n$ is the number of assets in the portfolio, $\epsilon$ the desired precision, and $\delta, \kappa, \zeta$ are problem-dependent parameters related to the well-conditioning of the intermediate solutions. If only a moderately accurate solution is required, our quantum algorithm can achieve a polynomial speedup over the best classical algorithms with complexity $\widetilde{O} \left( \sqrt{r}n^\omega\log(1/\epsilon) \right)$, where $\omega$ is the matrix multiplication exponent that has a theoretical value of around $2.373$, but is closer to $3$ in practice. We also provide some experiments to bound the problem-dependent factors arising in the running time of the quantum algorithm, and these experiments suggest that for most instances the quantum algorithm can potentially achieve an $O(n)$ speedup over its classical counterpart.
Depth Camera Using High-Transmittance Off-Axis Dual Electro-Optical Irises

A depth camera prototype with high-transmittance off-axis dual electro-optical (DEO) irises is proposed. Our DEO irises are switchable through the polymer dispersed liquid crystal, and exhibit the advantages of low cost, easy fabrication, and higher transmittance compared to the traditional liquid crystal shutters. The transmittance of our device is near 75% when the driving voltage reaches about 30 V. The principle of depth measurement of our system is based on a pair of stereo images, which are captured through the left and right irises, respectively. The depth is calculated from the shifts of features between the two images. The DEO iris design, fabrication process, and depth camera prototype are demonstrated, and experimental results using our depth camera verify reliable performance for the depth measurement. Our DEO iris system therefore provides potential applications in depth sensing.
Efektivitas blended learning terintegrasi model pemetaan Bloom–Rederker–Guerra (B–R–G) memberdayakan self-regulated learning peserta didik

The 21st century learning paradigm requires teachers to be able to integrate using technology, information, and communication (ICT) in learning. Technological, Pedagogical, Content, and Knowledge (TPACK) is a framework in designing new learning models by integrating technology in teaching. This research is a quasi-experimental study with the research design of The One Group Pretest-posttest which aims to test the effectiveness of an integrated blended learning environment in the Bloom-Redekker_Guerra mapping model in empowering self-regulated learning of students. The study was conducted in the XI MIA 1 class of Gowa 10 Senior High Schools totaling 36 students in the odd semester of the academic year 208/2019. Self-regulated lerning (metacognitive skills) of students is measured using an essay test that is integrated with the achievement test. Data were analyzed using descriptive techniques. The results showed that the integrated blended learning environment of the Bloom-Rederker-Guerra (B – R – G) mapping model was effective in empowering the self-regulate learning of students. Therefore, the integration of ICT in learning is seen as important to do so that the learning independence of students can be trained.
Effectiveness of Administrative Staff Working in the Study Program State Polytechnic Banjarmasin in Using Integrated Information System (SIMPADU)

The importance of the work effectiveness of each employee is expected to have a positive impact on improving the quality of human resources in an organization. In the field of education, one effort in increasing the effectiveness of employee work can be done by developing an information technology system in the educational institution. The purpose of this research is to test the difference of working effectiveness of the administrative staff of study programs in Polytechnic Banjarmasin before and after the implementation of SIMPADU. The effectiveness of the work examined in several indicators items, namely: production, efficiency, quality, flexibility, and job satisfaction. This research used a quantitative approach. The sample was obtained from all the administrative staff of study programs in Polytechnic Banjarmasin, 16 people was chosen by using census technique. The data collected through questionnaire were analyzed by using paired samples t test in order to prove the acceptance and rejection of the hypotheses. The results of analysis show that there is significant difference of working effectiveness of the administrative staff of study programs before and after the implementation of SIMPADU in Polytechnic Banjarmasin. This study connects also the results of research on the theoretical and managerial implications. The managerial implications Polytechnic Banjarmasin recommend that can improve work effectiveness through providing trainings both inside and outside the campus environment. Providing trainings to face the technological change will improve the quality of employees so as to provide satisfactory performance.
Italian and Spanish Students’ Perception on Use of Technology in Classrooms of Classics in Secondary School

espanolUna sociedad que cada vez es mas tecnologica debe explorar nuevas formas de aprendizaje. De hecho, las tecnologias digitales tienen un impacto en la educacion a traves del desarrollo de entornos de aprendizaje mas flexibles adaptados a las necesidades de una sociedad de alta movilidad. Para preparar a los jovenes en su futura incorporacion al mercado laboral, las recientes reformas educativas en Italia y en Espana han centrado su atencion en las competencias digitales de los estudiantes. Por ello, estamos interesados en investigar como el alumnado de estudios clasicos en escuelas secundarias italianas y espanolas perciben el uso de las nuevas tecnologias en el aula. Es relevante investigar acerca de los clasicos en la escuela porque tradicionalmente estas materias se perciben como menos utiles para el trabajo. Utilizamos una metodologia de estudio de caso cualitativa y recopilamos datos a traves de entrevistas semiestructuradas con estudiantes de ambos paises. Se utilizo el programa OpenCode para analizar los datos. Las principales categorias de contenido fueron: uso de recursos tecnologicos en el aula, metodos de ensenanza, competencias latinas y griegas utiles para la colocacion laboral. Los resultados mostraron que los estudiantes, especialmente los italianos, perciben una falta de uso de la tecnologia en el aula y el uso de la ensenanza tradicional expositiva prevalece. Los que usan la tecnologia en el aula no son conscientes de la utilidad para su trabajo futuro. Concluimos con sugerencias para estudiantes de lenguas y cultura clasicas. EnglishA society that is becoming increasingly digital must explore new ways of learning. In fact, digital technologies have an impact on the education through the development of more flexible learning environments adapted to the needs of a high-mobility society. To prepare future citizens to the needs of the digital labor market, recent educational reforms in Italy and Spain focused their attention on students’ Digital Competences. Therefore, we are interested in probing how students of classical studies in Italian and Spanish high schools perceive the use of new technologies in the classroom. It is important to investigate classics at school, because traditionally this program is perceived as less useful for employment. We used a qualitative case study methodology and collected data through semi-structured interviews with students from both nationalities. OpenCode software was used to analyse the data. The main content categories identified were: use of technological resources in the classroom, teaching methods, useful Latin and Greek competences for job placement. The results showed that students, especially Italians, perceive a lack of technology usage in the classroom due to teacher-centered instruction prevailing. Moreover, students who use technology in the classroom are not aware of its usefulness in their future job. Conclusions will offer suggestions to students of classics.
A No-Reference Image Quality Assessment For Detecting Illumination Alteration

The Quality assessment of a face image is a topic of great interest for biometric applications where images with bad samples decrease the system performance and increase authentication errors, especially in biometric passport applications that use only a single image for enrollment. Thus, in order to have a useful biometric authentication system, the quality of the biometric sample images must be controlled. This paper presents a no-reference quality assessment method which detects the illumination problem using Symmetric Based Features along with BLIINDS Based Features. The experimental results on the AR database recorded an accuracy of 90.3 % by using Stochastic Gradient Descent (SGD) classifier.
GAME EDUKASI AKUNTANSI SEBAGAI SARANA PENINGKATAN MINAT BELAJAR SISWA (STUDI KASUS : SMK NEGERI 8 BANDAR LAMPUNG)

The learning process will be a matter of accounting requirements. The subject of accounting adjusting journal entries is a subject that is very difficult to understand for student class X because in the learning there are abstract calculation elements and do not have a formula as well as lessons that use other calculations and students feel bored with conventional learning. The purpose of this study was to design and build educational game applications as a means of increasing student learning interest.Data collection techniques in this research is to use interviews, questionnaires, and review of the literature. Sources of data / subjects are students of SMK N 8 Bandar Lampung. Methods of testing conducted on aspects ie Usability, Functionality, Efficiency, and Portability. Accounting application development educational game built using Construct 2 software that will run on android platform.The results of testing the quality of game applications obtained values from usability aspects obtained 96%, 99% aspect functionality, efficiency aspects showed no memory shortages and CPU usage by applications by 47%, and the portability aspect of the application can only be installed and run on the device with Android version of Jelly Bean and above.
Robust Sentence Classification by Solving Out-of-Vocabulary Problem with Auxiliary Word Predictor

In recent years, deep learning methods have achieved outstanding performances in sentence classification. However, many sentence classification models do not consider the out-of-vocabulary (OOV) problem, which generally appears in sentence classification tasks. Input units smaller than words, such as characters or subword units, have been considered the basic unit for sentence classification to cope with the OOV problem. Although this approach naturally solves the OOV problem, it has obvious performance limitations because a character by itself has no meaning, whereas a word has a definite meaning. In this paper, we propose a neural sentence classification model that is robust to the OOV problem, even though the proposed model utilizes words as the basic unit. To this end, we introduce the unknown word prediction (UWP) task as an auxiliary task to train the proposed model. Owing to joint training of the proposed model with the objectives of classification and UWP, the proposed model can represent the meanings of entire sentences robustly even if a sentence includes a number of unseen words. To demonstrate the effectiveness of the proposed model, a number of experiments are conducted using several sentence classification benchmarks. The proposed model consistently outperforms two baselines over all four benchmark datasets in terms of the classification accuracy.
Frameworks for Studying Social Media Interaction: A Discussion on Phenomenology and Poststructuralism

During the past decade or so, much HCI research has, according to many researchers, become increasingly phenomenologically oriented. Some have gone so far as to argue that HCI is now in a phenomenological paradigm. But how does such a paradigmatic view work for understanding interaction in the ever growing sphere of social media? Prima facie it may look as it would work only well since social media has become increasingly richer in terms of the sheer range of phenomenologically possible user experiences provided by new and emerging interaction technologies. However, through a critical reading of three major phenomenological works in HCI, we argue that phenomenology as traditionally applied in HCI points indirectly to alternative approaches for engaging in much of contemporary social media research due to its associated semiotic and relationally oriented contents. One possible family of approaches for analyzing such content can be found, as we will argue, in poststructuralist theory. We propose an increased engagement with poststructuralist semiotics grounded in Jacques Derrida's philosophy and exemplify how this could contribute to the study of social media in the context of HCI.
Machine Teaching by Domain Experts: Towards More Humane,Inclusive, and Intelligent Machine Learning Systems

This paper argues that a possible way to escape from the limitations of current machine learning (ML) systems is to allow their development directly by domain experts without the mediation of ML experts. This could be accomplished by making ML systems interactively teachable using concepts, definitions, and similar high level knowledge constructs. Pointing to the recent advances in machine teaching technology, we list key technical challenges specific for such expert-centric ML systems, and suggest that they are more humane and possibly more intelligent than traditional ML systems in many domains. We then argue that ML systems could also benefit greatly from being built by a community of experts as much as open source software did, creating more inclusive systems, in terms of enabling different points-of-view about the same corpus of knowledge. Advantages of the community approach over current ways to build ML systems, as well as specific challenges this approach raises, are also discussed in the paper.
A Contrastive Study of Pre- and Post-legislation Interaction Design for Communication and Action About Personal Data Protection in e-Commerce Websites

The European General Data Protection Regulation (GDPR) has had a major impact on data collection and processing practices. It has also challenged interaction design aiming to support the effectiveness of data owners’ rights, their informed decisions, and their actions regarding how personal information is used by companies, governments, and others. Similar legislation has been issued in various non-European countries, which means that, in this respect, the HCI community has an important role to play for users all over the world. This paper presents the conclusions of a contrastive study with four major e-commerce websites in Portugal, where data protection law has been effective since 2018, and four analogs in Brazil, where the national Data Protection Law (DPL) has been sanctioned but will only be effective in 2020. The purpose of the study is to examine the pre-legislation to post-legislation evolution in the design of interaction for communication and action about personal data protection matters, so as to anticipate some of the threats and opportunities ahead of us. Using concepts and elements of Semiotic Engineering methods and techniques, we found that, within the scope of this study, GDPR seems to have had little impact on what European users can do and experience online, compared to pre-DPL Brazilian users. We discuss some of the possible reasons for this and conclude with thoughts on the role of interaction design in empowering data owners for this new regulation era.
Empirical Evaluation of Multi-task Learning in Deep Neural Networks for Natural Language Processing

Multi-Task Learning (MTL) aims at boosting the overall performance of each individual task by leveraging useful information contained in multiple related tasks. It has shown great success in natural language processing (NLP). Currently, a number of MLT architectures and learning mechanisms have been proposed for various NLP tasks. However, there is no systematic exploration and comparison of different MLT architectures and learning mechanisms for their strong performance in-depth. In this paper, we conduct a thorough examination of typical MTL methods on a broad range of representative NLP tasks. Our primary goal is to understand the merits and demerits of existing MTL methods in NLP tasks, thus devising new hybrid architectures intended to combine their strengths.
The Research of Blockchain Technology for Data Protection in IoT Devices

The article presents a study aimed at determining an effective security model based on blockchain technology for IoT devices.It is completed a modification of the key features of the classical blockchain used for cryptocurrencies for adaptation to work in the network from the devices of the Internet of things, which requires minimal reaction time to the execution of control commands and knew the redemption of energy resources.
SeesawFaceNets: sparse and robust face verification model for mobile platform

Deep Convolutional Neural Network (DCNNs) come to be the most widely used solution for most computer vision related tasks, and one of the most important application scenes is face verification. Due to its high-accuracy performance, deep face verification models of which the inference stage occurs on cloud platform through internet plays the key role on most prectical scenes. However, two critical issues exist: First, individual privacy may not be well protected since they have to upload their personal photo and other private information to the online cloud backend. Secondly, either training or inference stage is time-comsuming and the latency may affect customer experience, especially when the internet link speed is not so stable or in remote areas where mobile reception is not so good, but also in cities where building and other construction may block mobile signals. Therefore, designing lightweight networks with low memory requirement and computational cost is one of the most practical solutions for face verification on mobile platform. In this paper, a novel mobile network named SeesawFaceNets, a simple but effective model, is proposed for productively deploying face recognition for mobile devices. Dense experimental results have shown that our proposed model SeesawFaceNets outperforms the baseline MobilefaceNets, with only {\bf66\%}(146M VS 221M MAdds) computational cost, smaller batch size and less training steps, and SeesawFaceNets achieve comparable performance with other SOTA model e.g. mobiface with only {\bf54.2\%}(1.3M VS 2.4M) parameters and {\bf31.6\%}(146M VS 462M MAdds) computational cost, It is also eventually competitive against large-scale deep-networks face recognition on all 5 listed public validation datasets, with {\bf6.5\%}(4.2M VS 65M) parameters and {\bf4.35\%}(526M VS 12G MAdds) computational cost.
A GRAPH DATABASE APPROACH TO MANAGING DATA WITH UNPREDICTABLE SCHEMA IN ACADEMIC INFORMATION SYSTEMS DEVELOPMENT

Modelling information systems that are based on ever-evolving data, from both the size and the structure point of view proves to be a challenging software engineering undertaking.With a traditional relational database, any change in the data structure will propagate through the entire architecture of the software solution, requesting a lot of work both in the design and the implementation of the software product.With the advances of the graph databases, storing and using schema-less data that specifies relationships at instance level became a handy alternative.This paper presents a proof-of-concept solution for intuitively modelling interconnected elements and using them as a knowledge-base for an information system.By integrating a Neo4J store in a Spring Boot MVC application, we show how we can overcome the software engineering process problems when schema change requests are frequently propagated and how we can induce flexibility in both the data design and application development.