Reviewerly/exHarmony huggingface dataset:
Fully Automatic Segmentation of Kidney and Tumor Based on Cascaded U-Nets

Fully automatic segmentation of kidney and its lesions is an important step to obtain accurate clinical diagnosis and computer aided decision support system.In this paper, a method of automatic segmentation of kidney and renal tumor in CT abdominal images using cascade 3D U-Net convolutional neural network (3D cU-Nets) is presented.We trained and cascaded two 3D U-Nets for the joint segmentation of kidney and renal tumor.In the first step, we trained a 3D U-Net to segment kidney as the ROI input for the second 3D u-net.The second 3D U-Net only segmented the lesion from the renal ROI predicted in step 1.
MULTIVARIATE T2 CONTROL CHART BASED ON JAMES-STEIN AND SUCCESSIVE DIFFERENCE COVARIANCE MATRIX ESTIMATORS FOR INTRUSION DETECTION

The intrusion detection is a process to monitor the events taking place in a computer system or network and analyse the monitoring results to find signs of intrusion. The multivariate control chart, which is often used in the intrusion detection system, is Hotelling's T2. In this study, the Hotelling's T2 chart performance for intrusion detection is improved using the successive difference covariance matrix to estimate the covariance matrix and James-Stein estimator to estimate the mean vector. The control limits of the proposed chart are calculated using kernel density estimation. The performance of the proposed method, using T2 based on kernel density estimation control limit, outperforms the other control chart approaches in both training and testing dataset.
On Visually-Grounded Reference Production: Testing the Effects of Perceptual Grouping and 2D/3D Presentation Mode

When referring to a target object in a visual scene, speakers are assumed to consider certain distractor objects to be more relevant than others. The current research predicts that the way in which speakers come to a set of relevant distractors depends on how they perceive the distance between the objects in the scene. It reports on the results of two language production experiments, in which participants referred to target objects in photo-realistic visual scenes. Experiment 1 manipulated three factors that were expected to affect perceived distractor distance: two manipulations of perceptual grouping (region of space and type similarity), and one of presentation mode (2D vs. 3D). In line with most previous research on visually-grounded reference production, an offline measure of visual attention was taken here: the occurrence of overspecification with color. The results showed effects of region of space and type similarity on overspecification, suggesting that distractors that are perceived as being in the same group as the target are more often considered relevant distractors than distractors in a different group. Experiment 2 verified this suggestion with a direct measure of visual attention, eye tracking, and added a third manipulation of grouping: color similarity. For region of space in particular, the eye movements data indeed showed patterns in the expected direction: distractors within the same region as the target were fixated more often, and longer, than distractors in a different region. Color similarity was found to affect overspecification with color, but not gaze duration or the number of distractor fixations. Also the expected effects of presentation mode (2D vs. 3D) were not convincingly borne out by the data. Taken together, these results provide direct evidence for the close link between scene perception and language production, and indicate that perceptual grouping principles can guide speakers in determining the distractor set during reference production.
Secure Desktop Computing in the Cloud

As companies begin to transition the computation that employees perform on their desktop and the management of the desktop computing infrastructure to the cloud, the need for securing such cloud-hosted user computing tasks and environments become paramount. In this paper, we present Venia, a secure cloud-based desktop computing platform designed to protect against both external and internal threats. Accessible to end-users through a thin Remote Desktop Protocol (RDP) client Venia isolates end-user's applications and data into containers and subjects the interactions with and among the containers to security policies. Following a principle of least privilege, Venia security policies control user's access to containers, network and file system interaction of the containers, cross-container data sharing and also enables collection of detailed logs for auditing purpose. Venia has been deployed to a 3rd party test environment where it demonstrated that end-users can perform the tasks they need on a daily basis, without introducing greater risk to the overall organization, and its currently undergoing security and performance evaluation by an independent evaluation team.
Determinant of The Bitcoin Prices as Alternative Invesment in Indonesia

The aims of this study to know the determinant that affect bitcoin prices and how bitcoin prices response to the shock from GDP (Gross Domestic Product), inflation, exchange rate, JCI (Jakarta Composite Index. The method that was used in this research was quantitative analysis, with data analysis tools Vector Error Correction Model (VECM). Data used in this research was secondary data taken from Bank Indonesia, Bitcoincharts, and Yahoo Finance. The results of this study showed that (1) inflation in short term and in long term has negative significant effect on bitcoin prices, exchange rate in long term has positive significant effect on bitcoin price. In short term and in the long term GDP and JCI do not have significant effect on bitcoin prices (2) The results of IRF shows bitcoin prices respond negatively shock from GDP and exchange rate, while shock from inflation and JCI responded posifively by bitcoin prices.
Interactive Image Contents Search Based on High Dimensional Information Theory

Content-Based Image Retrieval aims at developing techniques that support effective searching and browsing of large image repositories based on image features. The current Content-Based Image Retrieval suffer from the semantic gap. it often leads to distraction in the search. To overcome these disadvantages, Combine the theory of high dimensional information property, we propose different dimensional space's vector angel cosine algorithm based on high dimensional information theory. The experimental results demonstrate the performance of the proposed algorithm.
Domain Representation for Knowledge Graph Embedding

Embedding entities and relations into a continuous multi-dimensional vector space have become the dominant method for knowledge graph embedding in representation learning. However, most existing models ignore to represent hierarchical knowledge, such as the similarities and dissimilarities of entities in one domain. We proposed to learn a Domain Representations over existing knowledge graph embedding models, such that entities that have similar attributes are organized into the same domain. Such hierarchical knowledge of domains can give further evidence in link prediction. Experimental results show that domain embeddings give a significant improvement over the most recent state-of-art baseline knowledge graph embedding models.
Evaluasi e-Tracer Study menggunakan HOT (Human-Organization-Technology) Fit Model

Tracer Study is one of the methods used by several universities, especially in Indonesia to get feedback from alumni. Along with the development of AMIK Indonesia's e-Tracer Study, it needs to be evaluated for use by students and alumni. Evaluation of Tracer Study from the usability aspect can be done to find out a system that can be used by users in the effectiveness, efficiency, and satisfaction. One method that can be used to conduct observation and evaluation is the HOT (Human-Organization-Technology) Fit Model. The method in this study consisted of three stages namely; 1) literature review, 2) discuss theories about information system evaluation models and fit, and 3) analysis and identification of models, and data analysis tools used are SPSS and SmartPLS. From the results of the current research achievements, it can be concluded that in theory, this study has added a reference to the use of the HOT-FIT model for subsequent researchers or other parties in understanding the measurement of the successful implementation of the system, it can be used as a material consideration for the student sector as the manager of the e-system -Tracer Study. There is a deletion of 1 deleted indicator, the LO3 indicator. From 12 hypotheses there are 3 rejected hypotheses, namely IQ → STR, IQ → SU, and LO → NB. the relationship between variables IQ → STR, IQ → SU, and LO → NB did not have a significant effect, based on data analyzed from 124 respondents, 79% stated they use of e-Tracer Study felt very good, 11% felt quite good, and 10% feeling unwell.Keywords:Evaluation, e-Tracer Study, HOT (Human-Organization-Technology) Fit Model
Dependency-Gated Cascade Biaffine Network for Chinese Semantic Dependency Graph Parsing

The Chinese Semantic Dependency Graph (CSDG) parsing breaks the limitation of the syntactic or semantic tree structure dependency system with a richer representation ability to express more complex language phenomena and semantic relationships. Most of the existing CSDG parsing systems used transition-based approach. It needs to define a complex transition system and its performance depends heavily on whether the model can properly represent the transition state. In this paper, we adopt neural graph-based approach which using Biaffine network to solve the CSDG parsing task. Furthermore, considering that dependency edge and label have the strong relationship, we design an effective dependency-gated cascade mechanism to improve the accuracy of dependency label prediction. We test our system on the SemEval-2016 Task 9 dataset. Experiment result shows that our model achieves state-of-the-art performance with 7.48% and 6.36% labeled F1-score improvement compared to the previous best model in TEXTBOOKS and NEWS domain respectively.
Situational Cognition of College English Education Based on Image Block Gain Optimization in Pseudo-haze State

Situational cognition can help students to construct their knowledge to a great extent. In order to solve the problem of lack of situational cognition in College English teaching, this paper studies the validity of students' situational cognition in College English teaching based on the method of image block gain optimization. First, this paper analyses the general situation of situational cognition capacity in College English teaching in China at present, and puts forward the function of device image in constructing situational cognitive competence in teaching. Then, it divides device image into blocks under pseudo-haze conditions, and proposes the optimization method of block gain. Finally, on the basis of block gain, it makes an empirical test of situational cognitive competence in College English teaching. The empirical results show that image block gain optimization can effectively improve the construction of situational cognition capacity in College English teaching. With the help of this study, some new and useful ideas can be traced for the development of computer science and college English teaching, and also stimulate the further improvement of College English education in China.
Hierarchical Intention Enhanced Network for Automatic Dialogue Coherence Assessment

Dialogue coherence across multiple turns is still an open challenge. The entity grid model is arguably the most popular approach for coherence modeling. However, it heavily relies on the distribution of entities across adjacent sentences but ignores the emotional context embedded in non-entity text and fails to model long dependencies between speech intentions. These limitations become even more severe when applied to dialogue domain since sentences in dialogue are short, informal and colloquial, thereby, less entities could be extracted and less coherence information could be expressed in these grids. To address the limitations of entity gird methods and incorporate the structure knowledge of dialogue, we propose a new neural network architecture, Hierarchical Intention Enhance Network, to integrate semantic context and speech intention in both utterance and dialogue levels to hierarchically model the global coherence without any entity grids. Our proposed model outperforms the state-of-the-art entity-grid based coherence model on text discrimination task by 17.13% increase in accuracy, confirming the effectiveness of our hierarchical modeling in dialogue context and the crucial importance of intention information in dialogue coherence assessment.
Steady State Analysis and Optimization for AUV Using Washout Filter Root Locus Correction Method

Autonomous underwater vehicles (AUVs) are widely used in the field of ocean engineering. Steady state analysis of a AUV is considered a necessary part in the design process. A rear dual propeller vector propulsion AUV is developed in this paper. Motivated by improving the AUV stability, a washout filter-based feedback optimization methodology for AUV is presented. First, with the AUV structural parameters, a AUV four degree-of-freedom mathematical model of the motion space is calculated to provide a mathematical basis for rock-bottom steady state control systems. Second, the transfer function of the filter is derived according to the pole-zero and root locus map of the AUV system. The washout filter correction model is established based on the root locus map and is connected in series with the AUV space-state model. The simulation analysis result shows that the stability of a system is greatly increased after the whole system is connected to the filter. Finally, the AUV thrust pool test and sailing test are carried out. The experimental results show that the proposed method can improve the thrust stability of a system. This method can be applied to AUV thrust control systems.
A Gaussian Process-based Self-Organizing Incremental Neural Network

This paper proposes a Gaussian process-based self-organizing incremental neural network (GPINN) to address the density estimation problem of online unsupervised learning. First, we adopt Gaussian process models with adaptive kernels that map the distribution of the neighbors of each node to its link relationship. Second, combining GPINN and kernel density estimation, we derive the bandwidth matrix updating rule for adapting to the generated network. We theoretically analyze the advantages of the proposed approach in determining threshold regions over using distance measures. The experimental results on both synthetic data sets and real-world data sets show that our method achieves remarkable improvement in density estimation accuracy for large noisy data.
Finding Optimum Wavelet Decomposition Level Using Autocorrelation

Wavelet transform is used to carry out time-frequency analysis including filtering of noises and reconstruction of the signal.Wavelet decomposition at a low level for denoising of signal is not very impactful in reducing noise.Alternatively, a high-level of wavelet decomposition may eliminate useful information carried by the signal.This study hinges on finding the optimum level of wavelet decomposition when performing denoising using wavelet transform.Optimum decomposition level is estimated based on parameters: mean and autocorrelation of residual in wavelet denoising.The importance of the paper is significantly the reduction of noise in the received signals whilst maintaining sufficient signal quality to allow the information carried by the signal to be read.
Improving the Latency of Python-based Web Applications

This paper describes the process of optimizing the latency of Python-based Web applications.The case study used to validate the optimizations is an article sharing system, which was developed in Django.Memcached, Celery and Varnish enabled the implementation of additional performance optimizations.The latency of operations was measured, before and after the application of the optimization techniques.The optimization of the application was performed at various levels, including the transfer of content across the network and the back-end services.HTTP caching, data compression and minification techniques, as well as static content replication using Content Delivery Networks, were used.Partial update of the application's pages on the frontend and asynchronous processing techniques were applied.The database utilization was optimized by creating indexes and by taking advantage of a NoSQL solution.Memory caching strategies, with distinct granularities, were implemented to store templates and application objects.Furthermore, asynchronous task queues were used to perform some costly operations.All of the aforementioned techniques favorably contributed to the Web application's latency decrease.Since Django operates on the back-end, and optimizations must be implemented at various levels, it was necessary to use other tools.
Accelerating Lossy Compression on HPC Datasets via Partitioning Computation for Parallel Processing

Recently, increasing attention has been paid to data reduction in the high-performance computing (HPC) environment where a large volume of data are produced continually during scientific simulations. A approach called SZ lossy compressor has been one of the best choices for HPC data reduction due to its high compression ratios while meeting data precision requirements. Currently, high compression rate is also strongly demanded because of fairly high data production throughput of many applications. In this work, we aim to accelerate the SZ compressor significantly by developing a parallel model in terms of the wildly-used point-wise relative error bound. It is non-trivial to parallelize SZ because of the strong data dependency in SZ. To address this issue, we develop a pipeline-like method and exploit a series of strategies to parallelize the 'logarithmic transformation' and 'prediction + quantization' stages for SZ. Our evaluation with real-world scientific simulation datasets shows that our design can accelerate the compression rate by over 2.0× in most cases while still guaranteeing the same compression ratio as the original serial version of SZ.
Word Position Aware Translation Memory for Neural Machine Translation

The approach based on translation pieces is appealing for neural machine translation with a translation memory (TM), owing to its efficiency in both computation and memory consumption. Unfortunately, it is incapable of capturing sufficient contextual translation leading to a limited translation performance. This paper thereby proposes a simple yet effective approach to address this issue. Its key idea is to employ the word position information from a TM as additional rewards to guide the decoding of neural machine translation (NMT). Experiments on seven tasks show that the proposed approach yields consistent gains particularly for those source sentences whose TM is very similar to themselves, while maintaining similar efficiency to the counterpart of translation pieces.
Designing a university seminar to professionalize prospective teachers for digitization in chemistry education

Abstract The legal obligation to implement digital tools in teaching and learning at all German schools poses a challenge to all teachers since the majority of teachers feel that they are not well enough prepared to meet the requirements. It is therefore of great importance to professionalize both teaching staff and prospective teachers for implementing digital tools effectively and appropriately. Therefore, within this project we develop and evaluate an innovative university seminar that aims at the professionalization of prospective teachers for digitization in chemistry education by providing the requisite skills. The study is an intervention study that tests the interventions’ effects by using repeated measures. In reference to the TPACK (technological pedagogical content knowledge) model, we explore the potential implications of the seminar to the future teachers’ teaching practices on the levels of self-efficacy, lesson planning and practical implementation. Consequently, this study does not only reflect one important area of teaching practice, but consists of a comprehensive analysis of TPACK knowledge. Additionally, the study also aims to determine potential implications for other teaching programs. The first results of the pilot study show that the seminar participants assess the seminar as very attractive. Moreover, the seminar participation led to a significant positive change regarding the prospective teachers’ attitudes towards the implementation of digital tools in chemistry lessons as well as their TPACK self-efficacy perceptions.
An Efficient Secret Sharing-based Storage System for Cloud-based Internet of Things

Internet of things (IoTs) is the newfound information architecture based on the internet that develops interactions between objects and services in a secure and reliable environment. As the availability of many smart devices rises, secure and scalable mass storage systems for aggregate data is required in IoTs applications. In this paper, we propose a new method for storing aggregate data in IoTs by the use of -threshold secret sharing scheme in the cloud storage. In this method, original data is divided into ‎ blocks ‎that ‎each ‎block ‎is ‎considered ‎as a ‎share. The edge server does not send these shares (blocks) directly (through the secure channel) to cloud service providers ( s). Rather, the edge server hides the shares (blocks) with XORing two secret values and publishes the result. Indeed, with this method, none of s has an amount of block information.‎This scheme is also verifiable, i.e., in the verification phase, each can verify its quasi-share. Moreover, before data retrieval, the edge server checks the correctness of provided quasi-share from s of an authorized group. Also, the proposed scheme is scalable, since new data can be inserted or part of the original data can be deleted, without changing shares. It is worth noting that the proposed scheme is more efficient compared with the other scheme since heavy and complex computation is not required.
Proposal for a web portal managing registration for student accommodation in a dormitory

A proposal for developing a database model and a web portal for managing registration for student accommodation in a dormitory is given in this paper. The portal has two user profiles and corresponding functionalities; they are intended for users/students and administrators, respectively. In the process of application for student accommodation in a dormitory, every student has to provide data referring to GPA, i.e. academic achievement at school, level of education, university, parents’ status, household members, the number of siblings and their school age, and household monthly income. There are also extra conditions that could ensure direct allocation of accommodation at a dormitory. After the application process deadline has expired, the administrator can either accept or decline applications. At the end of the application process, the final ranking list can be published and made accessible. Advantages of using a web portal for the dormitory accommodation election and application process are e.g. better accessibility and the fact that students are exempt from delivering the necessary application documentation personally. In that way, costs are reduced, efficiency at work is increased and the possibility of making errors when entering and processing data is significantly lower.