Reviewerly/exHarmony huggingface dataset:
MLtool: A Tool to Automate the Construction, Evaluation, and Selection of Machine Learning Models

The use of Machine Learning has intensified in recent years, gaining notoriety in the most diverse applications. Thus, there is a growing demand for professionals in this area, which has favored the entry of countless inexperienced users in the labor market. The path from the initial step to the use of algorithms and Machine Learning techniques in production environment takes considerable time, even from experts, due to the realization of interactive and manual tasks required for building predictive models that provide the desired results. In view of this, this work presents a tool to automate the construction, evaluation and selection of predictive models considering Machine Learning algorithms and parameters values more appropriate for each situation. The stages of feature selection, standardization, resampling, and training in the construction of models were considered in the tool. The proposed tool deal with the treatment of the problem of data unbalancing, as needed, as well as the execution control of the steps involved in the process of creating predictive models. The results obtained demonstrate that the order and choice of the steps and the values chosen for the algorithm parameters affect the final results of the generated models.
Multiple News Headlines Generation using Page Metadata

Multiple headlines of a newspaper article have an important role to express the content of the article accurately and concisely. A headline depends on the content and intent of their article. While a single headline expresses the whole corresponding article, each of multiple headlines expresses different information individually. We suggest automatic generation method of such a diverse multiple headlines in a newspaper. Our generation method is based on the Pointer-Generator Network, using page metadata on a newspaper which can change headline generation behavior. This page metadata includes headline location, headline size, article page number, etc. In a previous related work, ensemble of three different generation models was performed to obtain a single headline, where each generation model generates a single headline candidate. In contrast, we use a single model to generate multiple headlines. We conducted automatic evaluations for generated headlines. The results show that our method improved ROUGE-1 score by 4.32 points higher than baseline. These results suggest that our model using page metadata can generate various multiple headlines for an article In better performance.
OH LORD, WON'T YOU BUY ME A MERCEDES BENZ OR, THERE IS A THERE THERE

An overview of the current situation of scholarly research and publication on the Internet, with suggestions for further developments.
Toward a Better Story End: Collecting Human Evaluation with Reasons

Creativity is an essential element of human nature used for many activities, such as telling a story.Based on human creativity, researchers have attempted to teach a computer to generate stories automatically or support this creative process.In this study, we undertake the task of story ending generation.This is a relatively new task, in which the last sentence of a given incomplete story is automatically generated.This is challenging because, in order to predict an appropriate ending, the generation method should comprehend the context of events.Despite the importance of this task, no clear evaluation metric has been established thus far; hence, it has remained an open problem.Therefore, we study the various elements involved in evaluating an automatic method for generating story endings.First, we introduce a baseline hierarchical sequenceto-sequence method for story ending generation.Then, we conduct a pairwise comparison against human-written endings, in which annotators choose the preferable ending.In addition to a quantitative evaluation, we conduct a qualitative evaluation by asking annotators to specify the reason for their choice.From the collected reasons, we discuss what elements the evaluation should focus on, to thereby propose effective metrics for the task.
A quick algorithm for evaluation of minimum zone circles from polar coordinate data

It is very important to select the initial four candidate points of the minimum zone circles to improve the computational efficiency. According to the radius of the measured points, three models of error evaluation are proposed and corresponding algorithms are given for evaluating the minimum zone circles based on the lower envelope lines, four-point intersection principle, and bisection method. The points constructing the lower envelope lines can be used for the evaluation of the maximum inscribed circle. Several examples have been carried out to validate the validity of the proposed algorithm.
Digital Video Stabilization with Preserved Intentional Camera Motion and Smear Removal

Videos captured through camera may have unwanted jitters and vibrations. Most of the video algorithms, available in literature stabilizes the video against all the disturbances. It may happen that user may be recording video using intentional hand motion or by moving the camera fitted on a vehicle with very slow motion to capture scene or target. Then this recorded image sequence will have both intentional and unintentional motion. Conventional video stabilization algorithm will reject this intentional motion also. In this paper, a computer vision method is presented to segregate unintentional and intentional motion (both translation and rotational). Then unintentional jitter motion is filtered out and compensated to get smooth image sequence. This high frequency jitter motion also introduces motion smear. The authors have also addressed this issue and developed the algorithm to remove/minimize the motion smear. A novel approach is presented to estimate motion vectors using Affine modeling and SURF features detection to stabilize the video. Point Spread Function is parameterized using estimated intentional global motion vector to remove motion smear simultaneously while stabilizing the video. Promising video results have been obtained for digital video stabilization keeping intentional motion and removing motion smear.
Innovation and institutional quality: Evidence from OECD countries

Focusing the effect of innovations on economic growth, the literature has not adequately cared about what determines the innovations or innovative capacity. However, policy makers and business leaders have accepted the need for creating platforms and institutions that promote innovative activities since it was accepted that innovations were the basic key to economic growth. This study focuses on the effect of institutions or institutional quality on the innovations. In this study where OECD countries have been selected as the sampling (1996â€“2015 period) and World Bankâ€™s Worldwide Governance Indicators represent institutional quality while the number of patent application represents the innovation, the effect of institutional quality on the innovations has been examined through the methods of panel data analysis. Innovation is positively related to voice and accountability, political stability and rule of law while it is negatively related to control of corruption. Moreover, there has been no relationship determined between government effectiveness and regulatory quality and innovation. Findings of this study suggest that it is highly difficult to state what is the net effect of institutional quality on the innovations.&#x0D; Keywords: Governance, innovation, institutions, institutional quality, patent, panel data analysis.
Emu: Enhancing Multilingual Sentence Embeddings with Semantic Specialization

We present Emu, a system that semantically enhances multilingual sentence embeddings. Our framework fine-tunes pre-trained multilingual sentence embeddings using two main components: a semantic classifier and a language discriminator. The semantic classifier improves the semantic similarity of related sentences, whereas the language discriminator enhances the multilinguality of the embeddings via multilingual adversarial training. Our experimental results based on several language pairs show that our specialized embeddings outperform the state-of-the-art multilingual sentence embedding model on the task of cross-lingual intent classification using only monolingual labeled data.
Usability beyond the Home Page: Bringing Usability into the Technical Services Workflow

Usability and user experience research methodology, defined by usability.gov as a group of techniques that seek to “gain a deep understanding of users, what they need, what they value and their limitations” have been applied regularly in libraries to websites and public services. Yet, little information exists in the literature about acquisitions, collections, or electronic resource management librarians using such techniques for their own work. This paper will use a case study at the University of North Carolina Greensboro’s University Library to demonstrate how usability techniques can be implemented to help solve such common problems for technical services as improving access to and discoverability of library resources.
Weakly-Supervised Opinion Summarization by Leveraging External Information

Opinion summarization from online product reviews is a challenging task, which involves identifying opinions related to various aspects of the product being reviewed. While previous works require additional human effort to identify relevant aspects, we instead apply domain knowledge from external sources to automatically achieve the same goal. This work proposes AspMem, a generative method that contains an array of memory cells to store aspect-related knowledge. This explicit memory can help obtain a better opinion representation and infer the aspect information more precisely. We evaluate this method on both aspect identification and opinion summarization tasks. Our experiments show that AspMem outperforms the state-of-the-art methods even though, unlike the baselines, it does not rely on human supervision which is carefully handcrafted for the given tasks.
Towards a Development Methodology for Adaptable Collaborative Audience Response Systems

The use of Audience Response Systems (ARSs) for tech-enhanced learning scenarios has proven to address issues occurring within higher education, e.g. the missing interaction between the lecturer and the students. Since the majority of these systems relies on a single supported didactic concept and therefore has a limited set of provided functions, ARSs are currently restricted to support classic content-based as well as enquiry-based learning. The support of more advanced didactic concepts in order to investigate studio-based learning is currently not possible due to the lacking collaborative and cooperative functionality. This paper presents a unified (meta-)model which is able to express various scenarios, targeting the holistic support of content-based, enquiry-based and studio-based learning. The created model is evaluated within a user study to reason about the applicability of its underlying concept as well as the defined function blocks. In addition, this paper purposes ideas for a future graphical editor, which will support the modeling process, and provides concrete details for a possible implementation of a system on top of the (meta-)model.
Bayesian Regularization Neural Network Model for Stock Time Series Prediction

With strong nonlinear characterization ability, a BP neural network can effectively describe the characteristics of nonlinear time series.However, there are still some limitations, such as the ease of falling into a local optimum.Aiming at this problem, the Bayesian regularization optimization algorithm was used to improve the BP neural network.Under the premise of minimizing the objective function, the algorithm adjusts the weight update function through the conditional probability density and the prior probability of the historical data.Thus, the generalization capability of BP neural network will be enhanced.After an empirical study on stock time series prediction, we found that the improved network could prominently increase the prediction ability, while the ability of volatility prediction was better than that of other traditional algorithms.
A Bayesian network approach for the identification of relationships between drivers of chlordecone bioaccumulation in plants

Plants were sampled from four different types of chlordecone-contaminated land in Guadeloupe (West Indies). The objective was to investigate the importance of biological and agri-environmental parameters in the ability of plants to bioaccumulate chlordecone. Among the plant traits studied, only the growth habit significantly affected chlordecone transfer, since prostrate plants concentrated more chlordecone than erect plants. In addition, intensification of land use has led to a significant increase in the amount of chlordecone absorbed by plants. The use of Bayesian networks uncovers some hypothesis and identifies paths for reflection and possible studies to identify and quantify relationships that explain our data.
Population Size in Differential Evolution

In this paper we examined how the population size affects the performance of the differential evolution algorithm. First, we tested the original differential evolution algorithm, and then the improved self-adaptive differential evolution algorithm, on ten benchmark functions, that have been proposed for the CEC 2019 competition. We used six different population sizes. Afterwards, we tested the newly created algorithm with population reinitialization. The results show that the population size affects the algorithm’s efficiency, and that we need to tune it to obtain the best results. In the paper, we demonstrate that the newly created algorithm with reinitialization gives better, or at least comparable, results than the two algorithms without reinitialization.
Application of Balancing Techniques with Ensemble Approach for Credit Card Fraud Detection

Fraud detection is an important application area of data mining. Due to the development in technology, there is an increase in the number of frauds nowadays. Detecting the frauds is one of the greatest challenges in organizations. A major challenge that comes while handling frauds in datasets is that the datasets are highly imbalance in nature. The fraud instances are below 1% as compared to normal transactions. In this paper, our focus is on the comparison of various available balancing techniques in conjunction with classifiers and identified the best combination. The dataset taken is the standard credit card data of a European bank. We have applied different balancing techniques like Down Sampling, Up Sampling, Regular SMOTE, Borderline SMOTE, SVM SMOTE and ADASYN. For comparison purpose, we have taken classifiers as bagging and boosting models. Results have shown that balancing dataset using SVM SMOTE followed by Random Forest classifier gave the best results with F-score value of 0.85.
Object Tracking Based on Channel Attention

Object tracking is one of the important research topics in computer vision. Despite the great progress in this area, effectively and efficiently tracking object in videos still remains challenging especially in scenarios of rapid movement of objects, illumination changes, appearance of similar objects in the background. In this work, we propose an object tracking method by improving the state-of-the-art tracker MDNet based on channel attention, which can distinguish among similar objects by suppressing the background and highlighting the object. We integrate the channel attention module and group normalization into MDNet network. To validate the effectiveness of the proposed tracker, we compare it with a number of existing state-of-the-art trackers in terms of success rate and precision on OTB-100, OTB-50 and CVPR 2013 datasets. The test results have demonstrated the effectiveness and improvement of the proposed tracking algorithm.
Innovating in University Teaching Through Classroom Interaction

Previous research has shown that the best educational practices are built on the basis of interaction in the classrooms, regardless of their level. However, the dialogue among the students themselves and between them and the teacher is still more scarce than desirable, especially in the university context. Taking this weakness into account, the authors of this contribution propose a Teaching Innovation Project (TIP) as a means of confronting the reality of this matter and advancing in it through an interdisciplinary collaboration involving 16 teachers, who participate as external observers, representing all the Faculties from the University of Cantabria (Spain). Here, we present the design of the TIP that we are currently developing, the working methodology and an advance of the first partial results. Specifically, each of the 16 people involved in this TIP will externally evaluate 6 teachers from their own field of knowledge. The aim is to identify varied interaction practices throughout the university. To develop this process we have designed three observation scales: one for students, another for the teacher in action and a third for the external observer. Data will be collected between October 2018 and May 2019. The ultimate goal is to promote innovation in university teaching through interaction in the classroom in order to achieve the active learning of the students. We hope to contribute to inspire other universities that may be interested in following our steps.
Lattices From Tight Frames and Vertex Transitive Graphs

We show that real tight frames that generate lattices must be rational. In the case of irreducible group frames, we show that the corresponding lattice is always strongly eutactic. We use this observation to describe a construction of strongly eutactic lattices from vertex transitive graphs. We show that such lattices exist in arbitrarily large dimensions and discuss some examples arising from complete graphs, product graphs, as well as some other notable examples of graphs. In particular, some well-known root lattices and those related to them can be recovered this way. We discuss various properties of this construction and also mention some potential applications of lattices generated by incoherent systems of vectors.
An AR/TUI-supported Debugging Teaching Environment

This paper presents research on the potential application of Tangible and Augmented Reality (AR) technology to computer science education and the teaching of programming in tertiary settings. An approach to an AR-supported debugging-teaching prototype is outlined, focusing on the design of an AR workspace that uses physical markers to interact with content (code). We describe a prototype which has been designed to actively scaffold the student's development of the two primary abilities necessary for effective debugging: (1) the ability to read not just the code syntax, but to understand the overall program structure behind the code; and (2) the ability to independently recall and apply the new knowledge to produce new, working code structures.
Rectified Softmax Loss With All-Sided Cost Sensitivity for Age Estimation

In Convolutional Neural Network (ConvNet) based age estimation algorithms, softmax loss is usually chosen as the loss function directly, and the problems of Cost Sensitivity (CS), such as class imbalance and misclassification cost difference between different classes, are not considered. Focus on these problems, this paper constructs a rectified softmax loss function with all-sided CS, and proposes a novel cost-sensitive ConvNet based age estimation algorithm. Firstly, a loss function is established for each age category to solve the imbalance of the number of training samples. Then, a cost matrix is defined to reflect the cost difference caused by misclassification between different classes, thus constructing a new cost-sensitive error function. Finally, the above methods are merged to construct a rectified softmax loss function for ConvNet model, and a corresponding Back Propagation (BP) training scheme is designed to enable ConvNet network to learn robust face representation for age estimation during the training phase. Simultaneously, the rectified softmax loss is theoretically proved that it satisfies the general conditions of the loss function used for classification. The effectiveness of the proposed method is verified by experiments on face image datasets of different races.
Augmented Reality (AR) on Mobile Application for Learning Bahasa Melayu among Primary Students

The aim of the study is to develop mobile learning for Bahasa Melayu based on mobile augmented reality (AR) application.The AR technology will enhance the understanding of the subject.This improves the user's sensorial experience of the real world and provides a new way of interacting between the real world and virtual worlds with computer-generated objects.Quantitative methods were mostly preferred in this study.The respondents are primary school students' age 7 to 12 years old to master Bahasa Melayu especially in these two subtopics such as collective nouns and proverbs.An Instructional Design that is ADDIE model embedded in developing the mobile learning application.Research suggests that AR technology will enable public school students to adopt the basic principles of Bahasa Melayu in an effective learning environment.The study concludes with specific suggestions and recommendations on AR learning and recommends further studies on this interesting subject.
Barriers Affecting the Diffusion of Business-to-Consumer Online Retailing Acceptance in Kingdom of Saudi Arabia

Objectives:The study is to identify the impediments affecting business-to-consumer (B2C) online shopping in the Kingdom of Saudi Arabia.This research concentrates on the demographic survey of the buyers and their influence on buyer sensitivity to internet purchasing obstructions.Methods/analysis: This study is a descriptive survey design.Quantitative data is collected using an online questionnaire.The respondents were asked questions about their demographic profile and their observed impediments to internet purchasing.The sample size is 240 respondents who had engaged in online shopping only once or never engaged in online shopping.Frequency counts, mean score, ranking and chi-square test were the statistical techniques deployed to test the hypotheses using SPSS statistical software.Findings: The most important observed impediments to internet purchasing were identified as security and privacy risks in online shopping, the English language barrier, trusting tangible products, not using a credit card and the issue of distrust and credibility.It was found that the barrier of security and privacy risks in online shopping is independent of the educational status of interviewees.The hurdle of believing tangible goods does not depend on the age of interviewees.Moreover, the barrier of distrust and credibility is dependent on the online shopping experience of customers.Novelty/improvement: This study can help online retailers to understand the barriers affecting consumers' online buying behavior, which can help retailers to create their selling approach to change prospective buyers into real buyers.