Reviewerly/exHarmony huggingface dataset:
A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science

Abstract We present visual analysis methods for the evaluation of tomographic fiber reconstruction algorithms by means of analysis, visual debugging and comparison of reconstructed fibers in materials science. The methods are integrated in a tool (FIAKER) that supports the entire workflow. It enables the analysis of various fiber reconstruction algorithms, of differently parameterized fiber reconstruction algorithms and of individual steps in iterative fiber reconstruction algorithms. Insight into the performance of fiber reconstruction algorithms is obtained by a list‐based ranking interface. A 3D view offers interactive visualization techniques to gain deeper insight, e.g., into the aggregated quality of the examined fiber reconstruction algorithms and parameterizations. The tool was designed in close collaboration with researchers who work with fiber‐reinforced polymers on a daily basis and develop algorithms for tomographic reconstruction and characterization of such materials. We evaluate the tool using synthetic datasets as well as tomograms of real materials. Five case studies certify the usefulness of the tool, showing that it significantly accelerates the analysis and provides valuable insights that make it possible to improve the fiber reconstruction algorithms. The main contribution of the paper is the well‐considered combination of methods and their seamless integration into a visual tool that supports the entire workflow. Further findings result from the analysis of (dis‐)similarity measures for fibers as well as from the discussion of design decisions. It is also shown that the generality of the analytical methods allows a wider range of applications, such as the application in pore space analysis.
Testing a Minimalist Grammar Parser on

Stabler’s (2013) top-down parser for Minimalist grammars has been used to account for off-line processing preferences across a variety of seemingly unrelated phenomena cross-linguistically, via complexity metrics measuring “memory burden”. This paper extends the empirical coverage of the model by looking at the processing asymmetries of Italian relative clauses, as I discuss the relevance of these constructions in evaluating plausible structure-driven models of processing difficulty.
Evaluasi Website Kuliah Online STMIK Amikom Purwokerto Menggunakan Metode Heuristik (Studi Kasus Mata Kuliah Enterprise Resource Management)

Online lecture is one alternative to support distance learning. STMIK Amikom Purwokerto has implemented the online lecture system. In order to support effective learning while studying online using website media, an effective web interface is needed. However, there were complaints from users when accessing web lectures online. One of them is the user does not understand the overall use of functions on the existing system. Therefore heuristic evaluation is needed to find out how useful the system is in supporting online lectures. The results show that based on the calculation results using heuristic evaluation, the severety rating with a scale of 3 means that the online learning website at STMIK Amikom Purwokerto needs to be throughly improved from both system functionality and interface design.
USING SMALL GROUP DISCUSSION TO IMPROVE STUDENTS’ READING ACHIEVEMENT ON NARRATIVE TEXT

Objective of this research was to find out significant difference of reading achievement between students who are taught using Small Group Discussion and those are taught using conventional method. The research used quantitative approach by applying quasi experimental design of nonequivalent group design. Population of this research was the grade eight students of SMP Negeri 2 Pasarwajo in school year of 2017/2018. Number of populations were 118 students. Samples of this research were class VIII.A as experimental class which consisted of 30 students and VIII.B as the control class which consisted of 29 students. Instrument of this research was test and it was analyzed using Independent Sample T Test. This research concludes that there was a significant difference of reading achievement between the class whom is taught by using Small Group Discussion method and the class whom is taught by using conventional method.
Optimal ball recycling

Balls-and-bins games have been a successful tool for modeling load balancing problems. In this paper, we study a new scenario, which we call the ball-recycling game, defined as follows:Throw m balls into n bins i.i.d. according to a given probability distribution p. Then, at each time step, pick a non-empty bin and recycle its balls: take the balls from the selected bin and re-throw them according to p.This balls-and-bins game closely models memory-access heuristics in databases. The goal is to have a bin-picking method that maximizes the recycling rate, defined to be the expected number of balls recycled per step in the stationary distribution.We study two natural strategies for ball recycling: Fullest Bin, which greedily picks the bin with the maximum number of balls, and Random Ball, which picks a ball at random and recycles its bin. We show that for general p, Random Ball is Θ(1)-optimal, whereas Fullest Bin can be pessimal. However, when p = u, the uniform distribution, Fullest Bin is optimal to within an additive constant.
ENGAGING LEARNERS THROUGH EMOTION IN ARTIFICIALLY INTELLIGENT ENVIRONMENTS

Appears in: EDULEARN19 Proceedings Publication year: 2019Pages: 5661-5668ISBN: 978-84-09-12031-4ISSN: 2340-1117doi: 10.21125/edulearn.2019.1380Conference name: 11th International Conference on Education and New Learning TechnologiesDates: 1-3 July, 2019Location: Palma, Spain
Asking the Right Question: Inferring Advice-Seeking Intentions from Personal Narratives

Liye Fu, Jonathan P. Chang, Cristian Danescu-Niculescu-Mizil. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Research on Warehouse Object Detection Algorithm Based on Fused DenseNet and SSD

Aiming at the problem that the object detection accuracy of SSD algorithm in warehouse environment is not too high, a warehouse object detection algorithm based on improved SSD that fusion of DenseNet and SSD is proposed (Dense-SSD). Firstly, a large number of images containing cargos, trays and forklifts in real warehouse environment are collected through the camera, and the collected images are labeled to building the warehouse object dataset. Further the based network of the improved algorithm based on SSD pipeline is adapted with DenseNet, the Dense-SSD is trained from scratch on the PASCAL VOC and self-built warehouse object dataset, respectively. Finally, the trained models are tested on the above two datasets respectively. Experimental results show that the proposed method can reach 77.62% mAP on the PASCAL VOC, which is higher than SSD by 5.15 points. And the Dense-SSD can reach 93.85% mAP on the self-built warehouse object dataset while the model size is only 62.9 MB, which is higher than SSD by 1.43 points. Meanwhile, the model size is reduced by 31.8 MB than SSD.
Teknik Bagging Dan Boosting Pada Algoritma CART Untuk Klasifikasi Masa Studi Mahasiswa

Undergraduate Students data in academic information systems always increases every year. Data collected can be processed using data mining to gain new knowledge. The author tries to mine undergraduate students data to classify the study period on time or not on time. The data is analyzed using CART with bagging techniqu, and CART with boosting technique. The classification results using 49 testing data, in the CART algorithm with bagging techniques 13 data (26.531%) entered into the classification on time and 36 data (73.469%) entered into the classification not on time. In the CART algorithm with boosting technique 16 data (32,653%) entered into the classification on time and 33 data (67,347%) entered into the classification not on time. The accuracy value of the classification of study period of undergraduate students using the CART algorithm is 79.592%, the CART algorithm with bagging technique is 81.633%, and the CART algorithm with boosting technique is 87.755%. In this study, the CART algorithm with boosting technique has the best accuracy value.
Hitting Times and Probabilities for Imprecise Markov Chains

We consider the problem of characterising expected hitting times and hitting probabilities for imprecise Markov chains. To this end, we consider three distinct ways in which imprecise Markov chains have been defined in the literature: as sets of homogeneous Markov chains, as sets of more general stochastic processes, and as game-theoretic probability models. Our first contribution is that all these different types of imprecise Markov chains have the same lower and upper expected hitting times, and similarly the hitting probabilities are the same for these three types. Moreover, we provide a characterisation of these quantities that directly generalises a similar characterisation for precise, homogeneous Markov chains.
A Convolutional Neural Network Model Based on Improved Softplus Activation Function

Although the Softplus function was used as the activation function of the convolutional neural network, its image recognition rate is not good. To solve this problem, this paper proposes a convolutional neural network model based on the improved Softplus activation function. According to the principle that “the activation function whose average output is close to zero can improve the learning performance of the neural network”. First, the output value of the original function is shifted downward to alleviate the offset phenomenon of the Softplus activation function. Then the offset function output is multiplied by a parameter to adjust the slope and the saturation point of the function. This is used to alleviate the gradient disappearance/overflow phenomenon that occurs during training. We use significantly the well-known TensorFlow as the deep learning framework. The convolutional neural network architecture consists of three convolutional layers with the max-pooling and one fully-connected softmax layer. Comparison experiments with the currently used activation functions are done on the MNIST, CIFAR-10 and CIFAR-100 datasets. The experimental results show that the improved activation function in this paper has a better recognition rate than the original function and other commonly used activation functions.
Responding to Cybersecurity Challenges: Securing Vulnerable U.S. Emergency Alert Systems

Emergency alert systems (EASs) in the United States (US) form part of the nation’s critical infrastructure. These systems rely on aging platforms and suffer from a fragmented interconnected network of partnerships. Some EASs have an easily identifiable vulnerability: one can access their management website via the Internet. Authorities must secure these systems quickly. Other concerns also exist, such as the lack of policies for reporting vulnerabilities. To begin to assess EASs in the US, we used Shodan to evaluate the availability of these websites in six southeastern states. We found 18 such websites that one could access via the Internet and that required only requiring user credentials to login into. Next, we searched for published policies on reporting vulnerabilities; we found no vulnerability-disclosure policies for any system we identified. To identify, prioritize, and address EAS vulnerabilities, we present a list of technical and management strategies to reduce cybersecurity threats. We recommend integrated policies and procedures at all levels of the public-private-government partnerships and system resilience as lines of defense against cybersecurity threats. By implementing these strategies, EASs in the US will be positioned to update critical infrastructure, notify groups of emergencies, and ensure the distribution of valid and reliable information to at-risk populations.
Image steganalysis based on convolutional neural network and feature selection

Summary Steganalysis is to detect whether or not the seemly innocent image hiding message. It is an important research topic in information security. With the development of steganography technology, steganalysis becomes more and more difficult. Some steganalysis methods have been proposed to improve the performance. Most research work concentrates on special steganography information detection and the image steganography features are designed manually. Few research works concentrate on universal steganalysis methods. In this paper, as the first several attempts, a novel image steganalysis method based on deep neural network is proposed. First, image high‐frequency features are extracted with wavelet transformation method because that most image hiding message are high frequency. Second, high‐dimensional image steganography features are extracted with deep neural networks according to the high‐frequency images and informative features combination is selected with a novel feature selection method based on entropy. Then, a parallel SVM model is proposed to build the steganalysis model based on large scale training samples. At last, the efficiency of the proposed method is illustrated through analyzing a practical image steganalysis example.
Fidelity-based unitary operation-induced quantum correlation for continuous-variable systems

We propose a measure of nonclassical correlation [Formula: see text] in terms of local Gaussian unitary operations based on square of the fidelity [Formula: see text] for bipartite continuous-variable systems. This quantity is easier to be calculated or estimated and is a remedy for the local ancilla problem associated with the geometric measurement-induced nonlocality. A simple computation formula of [Formula: see text] for any [Formula: see text]-mode Gaussian states is presented and an estimation of [Formula: see text] for any [Formula: see text]-mode Gaussian states is given. For any [Formula: see text]-mode Gaussian states, [Formula: see text] does not increase after performing a local Gaussian channel on the unmeasured subsystem. Comparing [Formula: see text] in scale with other quantum correlations such as Gaussian geometric discord for two-mode symmetric squeezed thermal states reveals that [Formula: see text] is much better in detecting quantum correlations of Gaussian states.
Event Detection and Classification in Hungarian Natural Texts

The detection and analysis of events in natural language texts plays an important role in several NLP applications such as summarization and question answering.This paper focuses on introducing a machine learningbased approach that can detect and classify verbal and infinitival events in Hungarian texts.First, the multiword noun + verb and noun + infinitive expressions were identified.Then the events are detected and the identified events are classified.For each problem, binary classifiers were applied based on rich feature sets.The models were expanded with rule-based methods.
Hardware/Software Co-designed Peripheral Protection in Embedded Devices

The growth of the cyber-physical systems (CPSs) and the Internet of Things (IoT) in terms of functionality, connectivity, diversity, and size has been significant in the last years. The number of low-cost embedded devices in the field has dramatically increased and even though they enhance our lives in many ways, at the same time they are becoming attractive target for cybercriminals. The variety of these devices makes them vulnerable to new attacks, making the security challenges bigger and more diverse than ever, especially if no protection mechanism are offered. In this work we present a hardware/software co-designed memory protection approach that provides efficient software isolation of tasks, including a novel solution based on an existing concept for fine-grained protection of shared on-chip peripherals. The security extensions are implemented into a RISC-V-based microcontroller and a microkernel-based operating system, have small hardware and software footprint, and do not produce big runtime overhead.
Robust copy-move forgery detection algorithm using singular value decomposition and discrete orthonormal Stockwell transform

(2020). Robust copy-move forgery detection algorithm using singular value decomposition and discrete orthonormal Stockwell transform. Australian Journal of Forensic Sciences: Vol. 52, No. 6, pp. 711-727.
Model-Driven Skills Assessmentin Knowledge Management Systems

Organizations need employees who perform satisfactorily in generic skills, such as teamwork, leadership, problem solving or interpersonal abilities, among others. In organizational environments, employees perform work that is not always visible for supervisors and, thus, they can hardly assess their performance in generic skills. By using a knowledge management system, the users are able to leave a trace of their activity in the system’s records. This research aims to address a computer supported assessment of the user’s generic skills from the perspective of Model-Driven engineering. First, a systematic mapping study is carried out to understand the state of the art. Second, a proposal based on Model-Driven engineering is presented and is then validated through an organizational learning process model. Our results are promising and we are able to conduct a scalable assessment based on objective indicators of the employee’s planning, time management and problem solving skills.
Reading selectively via Binary Input Gated Recurrent Unit

Recurrent Neural Networks (RNNs) have shown great promise in sequence modeling tasks. Gated Recurrent Unit (GRU) is one of the most used recurrent structures, which makes a good trade-off between performance and time spent. However, its practical implementation based on soft gates only partially achieves the goal to control information flow. We can hardly explain what the network has learnt internally. Inspired by human reading, we introduce binary input gated recurrent unit (BIGRU), a GRU based model using a binary input gate instead of the reset gate in GRU. By doing so, our model can read selectively during interference. In our experiments, we show that BIGRU mainly ignores the conjunctions, adverbs and articles that do not make a big difference to the document understanding, which is meaningful for us to further understand how the network works. In addition, due to reduced interference from redundant information, our model achieves better performances than baseline GRU in all the testing tasks.
Pengaruh Pendidikan Kesehatan Media Video dan Poster terhadap Pengetahuan dan Sikap Anak dalam Pencegahan Penyakit Diare

This study aims to determine the effect of health education using video and poster media on the knowledge and attitudes of fourth grade SDN 65 Seluma children in preventing diarrhea. This type of research is a quantitative study with a quasi-experimental approach using a two group pre and post test design. The results showed that the average score of children's knowledge in the video media group before the intervention was 68.00, after the intervention was 86.67, the child's attitude before the intervention was 52.67 after being given the intervention 80.93 with ρ-value 0.000. The average score of knowledge in the media poster group before the intervention was 72.67 after the intervention was 87.33, the attitude of the child before the intervention was 42.20 after the intervention was 65.40 with a ρ-value of 0.000. In conclusion, there is a significant effect of health education using video media and posters on children's knowledge and attitudes in preventing diarrhea disease.&#x0D; &#x0D; Keywords: Health Education, Knowledge, Posters, Attitudes, Videos
Improving prediction accuracy of classification model using cascading ensemble classifiers

Abstract One way to improve the accuracy of predictive modeling is by combining the models. This research tries to study local cascade. It combined one or more base classifier sequentially. In each stage, the probability prediction of the base classifier was inserted to the data. The data then modeled using a decision tree algorithm. This process continued until the data is homogenous. In the original method, the base classifier used was non-ensemble classifier. Our study included bagging, boosting, and random forest as base classifiers. 11 dataset with binary response was used to assess the accuracy of this method. We also compared the accuracy of our method with others that were published between 1996 and 2009. We found that cascading ensemble classifier slightly improve accuracy and performed better for a dataset with numerical predictors.
Exploring flash fiction for the collaborative interpretation of qualitative data

This paper presents some exploratory reflections on flash fiction as a possible method to spark discussion and collaborative interpretation of qualitative research data. A growing body of work in HCI and CSCW examines the potential of techniques used in creative writing and creative fiction to generate design concepts, and narrative data analysis is adopted by social science using creative writing techniques for qualitative data work. Here we discuss our experience of an exercise where flash fiction was used not as a technique in support of design (which has been done before in human-centred computing), but as a means of probing data and facilitating collaborative data work among researchers. We reflect on the experience and outcomes of the exercise and also discuss exploratory ideas regarding how creative writing techniques could be further explored in human-centred computing as a way to probe findings from empirical data, particularly for collaborative teams.
An empirical study on Practice of Requirement Engineering activities in Software Industries In Pakistan Industry

Requirement engineering is an essential ingredient of software production and first level of software development life cycle (SDLC). In product quality perspective, Requirement Elicitation is a difficult and critical task. Different requirement practices have deep relationship with successful projects. This study aims to find out the procedures of requirement elicitation and management in Pakistan's software industry. This document is trying to report some provisions of processes and software products. Our investigations were carried out by a well-known software company of Lahore, Pakistan. We have investigated the company's requirement engineering processes on the basis of best practices of elicitation, requirement analysis, requirement description, system modelling, requirement validation and requirement management. Our findings walked us towards the investigation of best practices used in requirement engineering process. We observed how these practices are implemented and at which level these are being followed by software industry. The aftermaths of questionnaire are critically evaluated and examined to highlight what practices and standards of requirement engineering are being followed. It is observed that most requirement problems that are faced by company are organizational not rather than technical.
In search of a global belief model for discrete-time uncertain processes

To model discrete-time uncertain processes, we argue for the use of a global belief model in the form of an upper expectation that satisfies a number of simple and intuitive axioms. We motivate these axioms on the basis of two possible interpretations for this upper expectation: a behavioural interpretation similar to that of Walley’s, and an interpretation in terms of upper envelopes of linear expectations. Subsequently, we show that the most conservative upper expectation satisfying our axioms coincides with a particular version of the game-theoretic upper expectation introduced by Shafer and Vovk. This has two important implications. On the one hand, it guarantees that there is a unique most conservative global belief model satisfying our axioms. On the other hand, it shows that Shafer and Vovk’s model can be given an axiomatic characterisation, thereby providing an alternative motivation for adopting this model, even outside their framework.