Reviewerly/exHarmony huggingface dataset:
Computationally derived cytological image markers for predicting risk of relapse in acute myeloid leukemia patients following bone marrow transplantation

Allogenic hematopoietic stem cell transplant (HCT) is a curative therapy for acute myeloid leukemia (AML). Relapse after HCT is the most common cause of treatment failure and is associated with poor prognosis. Early identification of which patients are at elevated risk of relapse may justify use of aggressive post-HCT treatment options, potentially preventing relapse and treatment failure. In this study, our goal was to predict relapse after HCT in AML patients using quantitative features extracted from digitized Wright-Giemsa stained posttransplant aspirate smears. We collected 39 aspirate specimens from a cohort of 39 AML patients after HCT, of which 25 experienced relapse, while 14 did not. Our approach comprised the following main steps. First, a deep learning model was developed to segment myeloblasts, a cell type in bone marrow that accumulates and characterizes AML. A total of 161 texture and shape descriptors were then extracted from these segmented myeloblasts. The top eight predictive features were identified using a Wilcoxon rank sum test over 100 iterations of 3-fold cross validation. A model was subsequently built employing these features and yielded an average area under the receiver operating characteristic curve of 0.80&plusmn;0.05 in cross validation. The top eight features include four Haralick texture features and four fractal dimension features. The texture features appear to characterize chromatin patterns in myeloblasts while the fractal features quantify morphological irregularity and complexity of myeloblasts, in alignment with findings previously reported for AML patients post-treatment.
AUTO-INSURANCE FRAUD DETECTION: A BEHAVIORAL FEATURE ENGINEERING APPROACH

Every year, billions of dollars are lost in the auto insurance industry due to fraud, which forces insurance premium prices to go up annually. Although fraud detection solutions have been developed to fix the fraud detection problem, they all still face the same well-known problems of imbalanced data. There is need for a centralized claims database to gather a holistic view of fraudulent characteristic behavior. This paper proposes a data pre-processing technique, particularly a fraud behavior feature engineering approach, to improve the overall performance of prediction models. The behavior being assessed is be based on the RFM model along with an additional behavior analysis related to policy expiration. Furthermore, an ensemble feature selection and modeling is used to deal with the high dimensionality problems that the feature engineering approach brings along with it, as well as the class imbalance problems. The proposed approach shows a 56.2% increase in the F1-meaure, compared against the previous published stat-of-the-art results.
The Application of Cloud Computing in Government Management

Abstract Nowadays, the realization of government functions has been more and more inseparable from the network, at the same time, the public also hope the government to provide more and more convenient and fast related services, interactive functions can also advance with The Times. At the same time, the government wants to make public services more efficient. In the context of big data and cloud computing, the openness and sharing of government-owned data has an extremely important impact on the society. In the process of government management, the traditional management way already can’t adapt to the needs of a large number of data exchange, but now the government management slightly to the research of innovative technology still lags behind, the rise of cloud computing provides a new way for the government management innovation, this article to introduce the cloud computing government management for research purposes, to provide a new way for the government management reform.
Hardware Acceleration of Machine Learning Beyond Linear Algebra

Specialized hardware for machine learning allows us to train highly accurate models in hours which would otherwise take days or months of computation time. The advent of recent deep learning techniques can largely be explained by the fact that their training and inference rely heavily on fast matrix algebra that can be accelerated easily via programmable graphics processing units (GPU). Thus, vendors praise the GPU as the hardware for machine learning. However, those accelerators have an energy consumption of several hundred Watts. In distributed learning, each node has to meet resource constraints that exceed those of an ordinary workstation—especially when learning is performed at the edge, i.e., close to the data source. The energy consumption is typically highly restricted, and relying on high-end CPUs and GPUs is thus not a viable option. In this work, we present our new quantum-inspired machine learning hardware accelerator. More precisely, we explain how our hardware approximates the solution to several NP-hard data mining and machine learning problems, including k-means clustering, maximum-a-posterior prediction, and binary support vector machine learning. Our device has a worst-case energy consumption of about 1.5 W and is thus especially well suited for distributed learning at the edge.
THE EFFECTS OF BATANG KANDIS RIVER FLOOD CONTROL IN PADANG CITY-PALAPA METROPOLITAN URBAN AREA

Floods often occur in Lubuk Buaya residential housing in Padang city, which has a population of 6,500 citizens per km2. These natural phenomena happen due to sea level water rises and the change in the new river mouth of the Batang Kandis river. The construction of a 500 m floodway and a 700 m dike of Batang Kandis river were proposed to control flooding. The floodway was then damaged during the earthquake on September 30th, 2009, and wiped out by a flood on March 24th, 2016. Theoretically, to solve flood conditions downstream of Batang Kandis river with 70 km² of watershed area, the floodway is expected to be able to discharge 307 m3/s. This study uses a numerical simulation using HEC-RAS software with three scenarios. The first scenario uses the existing condition of the river width of 50 m and a depth of 1 m, it experienced an inundated area of 0.60 km2 for 5 hours. The second scenario is done by increasing 2.50 m embankment height, which gave the result of 2x700 m2 flood inundation in several locations. The third scenario also carried out dredging of the river up to the elevation of 0.00 m above sea level, and there was no flood inundation. Thus, the third scenario is the best result that has been carried out to control the floods downstream of Batang Kandis river
BNS-CNN: A Blind Network Steganalysis Model Based on Convolutional Neural Network in IPv6 Network

There still exists the difficulties in the feature extraction and few approaches can detect multiple network steganographic algorithms currently in the IPv6 network. A unified network steganalysis model based on convolutional neural network, abbreviated as BNS-CNN, is proposed to detect multiple network storage steganographic algorithms. After preprocessing the network traffic, the model divides them by field to preserve the integrality of traffic feature to the maximum extent to build a matrix. Multiple convolution kernels and the K-max pooling are effectively combined to perform the feature extraction to speed up the model convergence; the full connection layer is designed to improve the ability of feature integration and boosts up the robustness of the model. Compared with the traditional network steganalysis method, the model can automatically extract data features and identify multiple storage covert channels at the same time. The experimental results show that the detection accuracy of BNS-CNN model is as high as 99.98% with low time complexity and favorable generalization performance.
A Classification of Virtual Reality Technology: Suitability of Different VR Devices and Methods for Research in Tourism and Events

The purpose of this study is to review previous literature on Virtual Reality in tourism and events and suggest suitable VR devices for diverse research purposes and methods of each study. This paper provides a classification of VR devices based on physical connectivity, tracking system, and user behaviour. Then, a systematic overview of recent VR research in tourism and event and recommendations of suitable VR devices for each study were provided. Twenty one academic articles were reviewed by two VR engineers and one tourism/event expert. The current analysis can be used as a guideline for researchers to identify the right VR technology to collect and measure more relevant data, which in turn will increase the validity of research in general. This paper offers managerial implications for industry practitioners as well.
Discrete Infomax Codes for Supervised Representation Learning

For high-dimensional data such as images, learning an encoder that can output a compact yet informative representation is a key task on its own, in addition to facilitating subsequent processing of data. We present a model that produces discrete infomax codes (DIMCO); we train a probabilistic encoder that yields k-way d-dimensional codes associated with input data. Our model maximizes the mutual information between codes and ground-truth class labels, with a regularization which encourages entries of a codeword to be statistically independent. In this context, we show that the infomax principle also justifies existing loss functions, such as cross-entropy as its special cases. Our analysis also shows that using shorter codes reduces overfitting in the context of few-shot classification, and our various experiments show this implicit task-level regularization effect of DIMCO. Furthermore, we show that the codes learned by DIMCO are efficient in terms of both memory and retrieval time compared to prior methods.
A Survey for Sentiment Analysis and Personality Prediction for Text Analysis

In the last few years’ researchers started to pay attention for social media using personality prediction and sentiment analysis but due to the high costs and the privacy of these datasets this paper will present a comparative study on Sentiment Analysis and Personality Prediction through Social Media. First, what is meant by Social Media? It is a place which is designed to allow people (share, comment, post and …) for their beliefs and thoughts through websites and applications. The data gathered form social media users are using to represent the user behavior based on the different situations of everyday life. With machine learning techniques can be used to understand the mood. For that we can predict the human behavior through social media using machine learning techniques (clustering techniques, classification techniques). In this paper, clustering techniques will be discussed such as K-means clustering, hierarchical clustering, DBSCAN clustering. The personality assessment will help the market place and organizations to find the suitable person to a specific job and vice versa. For this reason, the sentiment analysis techniques can facilitate the personality assessment through the texts and the emotions that he has expressed on the social media websites.
Distributed software development in agile projects: a model for the promotion of social interactions

Agile methods address the challenge of an unpredictable world, relying on people and their creativity, flexible team self-organization with incentives for participation, social cooperation and less formalization being increasingly used in larger projects. The issues related to communication, trust, coordination, and social interactions among project stakeholders impact Information Technology team dynamics. This article investigates what the constructs are that lead to the promotion of social interaction in DSD environments. We conducted this qualitative and exploratory research using an interpretative approach to interview 35 project professionals. This paper contributes to the theory by delivering a model for the promotion of social interactions in distributed agile projects. We introduced the interaction coordination variable that emerged from the field, aiming to encourage and direct relationships between team members so that they are aligned with the mutual benefits of the project. These benefits include improving communication, knowledge sharing, and a sense of responsibility for the product. The study also draws the attention of practitioners to the promotion of social interactions in agile DSD environments.
Accurate 3D coordinate measurement using holographic multipoint technique

In this contribution we introduce an imaging based measurement setup, that is able to very accurately measure 3D relative positions between moving light sources. The system consists of two highspeed cameras, one equipped with a telecentric, the other with an endocentric lens. To improve accuracy of image based position detection, each lens is upgraded with a computer-generated-hologram (CGH) to replicate a single object point into a predefined pattern of spots on image plane. By averaging the centers of all replications, noise and other error contributions can be reduced. We will show how to apply image processing using two different approaches. The first approach is based on a tracking algorithm running on CPU reaching 330 fps. The second is a FPGA implementation to process whole images with a speed of 390 fps. Furthermore, we will demonstrate how threedimensional calibration can be done using the Nanomeasurement and Nanopositioning Machine NPMM-200. For the calibration, a three-dimensional multivariate polynomial is used. The standard deviations of residual error in object space for a calibration in a volume of 100 mm &times; 100 mm &times; 24 mm are &sigma;<sub>x</sub> = 0.367 &mu;m, &sigma;<sub>y</sub> = 0.373 &mu;m and &sigma;<sub>z</sub> = 0.437 &mu;m (polynomial order = 9).
A case study on active aging policies and programs of middle-aged and elderly people at The Seoul50Plus Foundation

본 논문의 목적은 중장년층의 활동적 노화를 지원하기 위하여 첫째, 서울특별시50플러스 정책 및 사업사례를 분석하고, 둘째, 분석 결과에 근거하여 활동적 노화와 평생학습을 추진하기 위한 정책 및 사업의 방향을 제안하는 것에 있다. 연구방법은 단일 사례연구를 활용하였으며, 사례의 분석틀은 정책 및 사업의 배경-환경, 이념-목표, 주체-대상, 과정-영역의 4가지로 접근하였다. 분석 자료는 서울특별시50플러스 정책 및 사업 관련 연구보고서와 관련 연구 자료, 재단 실무자의 간담회 발표 자료를 활용하였다. 연구결과는 다음과 같다. 정책 및 사업의 배경·환경에서는 고령사회로의 환경 변화 대응은 정책의 필수적 요소로 나타났다. 정책이념-목표에서는 ‘새로운 인생비전 창조’의 이념과 활동적 노화를 위한 일자리창출, 사회적 참여, 인생설계 측면의 3가지 측면의 정체성 접근이 주로 이루어지고 있다. 주체-대상측면에서는 정책의 실행 주체는 서울50플러스재단, 50플러스캠퍼스, 50플러스센터의 체계적인 3단계로 구성되어 있으나 대상은 50+세대(50~64세)로만 제한되어 있고, 희망자만 참여하는 문제점을 가지고 있다. 과정-영역 분석에서는 중장년층의 특성에 맞추어 교육지원, 일·창업 지원, 상담정보 제공, 신노년문화 창조 활동 등의 프로그램이 연계적으로 부가가치를 높이도록 설계되어 있다. 향후 제언으로는 정책 및 사업은 중장년층의 특성에 맞추고, 지역의 산업과 대학 등과 연계하여 추진하며, 대상자의 확대가 필요하고, 사업 간 가치를 높이기 위한 연계가 필요하다.The purpose of this study was to analyze the policy and programs of the Seoul50Plus Foundation and to propose policies and programs to promote active aging of middle-aged and elderly people. A single case study was utilized as a research method. The analysis framework of case was approached with four frameworks: the context of policy and program, ideology-goal analysis of policy and program, supplier and customer analysis of policy and program, and process and domain analysis of policy and program. The analysis data included the homepage content of the Seoul50Plus Foundation, related 50+ policy and program data, active aging research papers at home and abroad, and presentation and meeting materials. The results of the research showed that the policy and program background and environment were essential elements for responding to environmental changes in the aging society. In terms of the philosophy-goal, job creation, social participation, and approach to life design were systematically carried out for the ideology of creating a new life vision and promoting active aging. In terms of subject-target, the subject of policy implementation is systematically composed of three stages: 50+ foundation, 50+ campus, and 50+ center. The target was limited to 50+ generations(age 50~64) and had a problem that is limited to applicants. In the process-area analysis, programs such as educational support, work and entrepreneurship support, counseling information, and new-age cultural creation activities were designed to increase added value according to the characteristics of the middle-aged people. In conclusion, the future Seoul50Plus Foundation s policy and lifelong learning programs need to be systematically promoted through cooperation with businesses and universities, expanding the target audience, matching the characteristics of the middle-aged and elderly, and linkage among programs.
Application Principle of Digital Animation Technology in Experimental Teaching

With the continuous development and improvement of social science and technology, the “learning-oriented” teaching method is proposed, the traditional experimental teaching methods in colleges and universities are insufficient to meet the needs of experimental teaching. By studying the role and advantages of digital animation technology in experimental teaching, analyzing the application principle of digital animation in laboratory teaching, it is proposed to strengthen the application of digital animation technology in experimental teaching, give full play to the advantages of digital animation technology, and improve the quality of experimental teaching to promote students’ learning efficiency in experimental teaching.
An Improved One-bit OFDM Receiver Based on Model-Driven Deep Learning

Accurate channel estimation and signal detection are very difficult for orthogonal frequency division multiplexing (OFDM) receiver under the limit of one-bit complex quantization which can greatly reduce power loss and systematic complexity. In this paper, we propose an improved one-bit OFDM receiver based on model-driven deep learning (DL). Different from the conventional one-bit receiver based on autoencoder architecture of DL, our proposed one-bit receiver consists of channel estimation module and signal detection module, and each of which is constructed by a deep neural network after using traditional communication method as initialization. Simulation results show that our scheme is superior to AE-OFDM based on autoencoder in view of bit error rate (BER) performance.
Age and origin of the Paramillos de Uspallata Pb-Zn-Ag vein deposit in the Cuyo basin, Argentina: Constraints from structural controls and isotopic evidence

The Paramillos de Uspallata deposit, located in the Cuyo rift basin, is a Pb–Zn–Ag vein deposit hosted by Mesozoic Triassic volcano-sedimentary sequences. In the present study, new geological and isotopic data is used to constrain the age, source and structural controls of the ore forming fluids. The isotopic data suggest a Middle Cretaceous age for the mineralization and that mineralizing fluids were formed from a mixture of sources that includes the Permian volcanic rocks from the basin basement and the Triassic basalts, with Pb (and Sr) isotopic composition typical of the upper crust. Regional structural analysis allowed to conclude that the veins formed within large-scale reactivated structures that affect the basin basement. Local structural analysis showed that the mineralization was controlled by extensional and shear-extensional WNW-ESE to NW-SE and shear E-W trending structures. Kinematic analyses performed in the veins evidenced a transtensional environment with a NE extension direction and E-W dextral shear. In this scenario fluids were channeled by a strike-slip-faulting system connected with a deep weakness zone during the beginning of the Andean Orogeny. This study provides new insights into the genesis and the geodynamic scenario of Pb–Zn–Ag vein deposit.
Providing Access to Old Greek Documents Using Keyword Spotting Techniques

Keyword spotting is an alternative methodology for document indexing based on spotting words directly on images without the use of a character recognition system. In this paper, an overview of recent techniques and available databases for keyword spotting is presented focusing on the specific characteristics of old Greek machine-printed and handwritten documents. These documents are import treasures of cultural heritage and a valuable source of information for scholars. Indexing of such content is a very challenging task considering the additional problem of having many character classes as well as a variety of different diacritic mark combinations that may appear above or below Greek characters. All steps of a keyword spotting system are highlighted, namely, preprocessing for image binarization, enhancement and segmentation, feature representation, matching and word retrieval, in order to report the efficiency of current keyword spotting approaches when applied to old Greek documents.
Deep Monocular Video Depth Estimation Using Temporal Attention

Monocular video depth estimation (MVDE) plays a crucial role in 3D computer vision. In this paper, we propose an end-to-end monocular video depth estimation network based on temporal attention. Our network starts by a motion compensation module where the spatial temporal transformer network (STN) is utilized to warp the input frames using the estimated optical flow. Next, a temporal attention module is used to combine features from the warped frames, while emphasizing the temporal consistency. A monocular depth estimation network is used to estimate the depth from the temporally combined features. Experimental results demonstrate that our proposed framework achieves better performance compared to the state-of-the-art single image depth estimation (SIDE) networks, as well as existing MVDE methods.
Effects of Past Exercise Experience on Life Skills : A Cross-national Study of Chinese and Japanese University Students

This study aimed to compare the relationship between exercise experience from primary school to the present and current life skills among Chinese and Japanese university students. The participants were 331 Chinese university students (n=181 men ; n= 150 women) and 327 Japanese university students (n=156 men ; n= 171 women). The cross-sectional survey included a questionnaire with items regarding participants' exercise experience from elementary school to the present and the Daily Life Skills Scale (Shimamoto and Ishii, 2006). A t-test was used to examine the between-group differences in the scores of daily life skills between Chinese and Japanese students. The results showed that Chinese students reported higher scores in the life skills used mainly in personal situations, while Japanese students reported higher scores in the skills used generally in interpersonal situations. Further, the positive correlation between exercise experience and life skills was larger in Japanese students than Chinese students. Specifically, the correlation demonstrated that the number of exercise experiences during high school was positively correlated with the affinity, etiquette to others, and skills used in interpersonal situations, which is a subscale, and the total score of the Life skills. This result suggests that the extracurricular activities that target exercise in Japanese school have a beneficial impact on the development of daily Life skills.
A Comparative Study of the Interaction Features of Barrage and Forum in Social Learning Course

Forum as an effective way for learners and instructors to communicate has been widely used in online learning, but there are still some unsolved problems in online learning, such as loneliness and lack of motivation.With the development of the technology of barrage, some teaching videos try to add the barrage function.As a virtual synchronous interaction way based on situation, interaction on barrage is different from the traditional online discussion and question-answering areas.In this paper, the content analysis method is used to analyze a Japanese teaching course on Bilibili website.It is found that there were differences in the number, length, interactive objects and interactive content between barrage and forum.This conclusion provides some valuable ideas for solving the problems of loneliness and lack of motivation in distance learning.