Reviewerly/exHarmony huggingface dataset:
Automatic Breast Cancer Detection and Classification using Deep Learning Techniques

Breast cancer is considered to be the main cause of loss of life in women of every nation. The quick identification of any changes in normal breast helps in diagnosing the breast cancer by radiologist in a short span of time. Appropriate breast cancer diagnostic device with high efficiency will assist the health care Physicians to monitor the prognosis and treatment in a timely manner. In our work, we used Wisconsin Diagnosis Breast Cancer database to classify begin and malignant forms of breast cancer. Supervised studying algorithms like-Support Vector Machine (SVM) along with kernels like Linear and Neural Networks (NN) were used for comparison. The performances of these models were analysed the place Neural Network method gives high ‘accuracy’ and ‘precision’  in contrast to the Support Vector Machine, and seems to be quick and efficient method in the diagnostic classification of breast cancer.
Research Guides: NBL Virtual background: Libraries logo

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A Toolbox for Perception and Action Experiments Using the Vizard VR Platform

Virtual Reality (VR) is gaining momentum in vision science due to its unprecedented potential to study the behavior of active participants in naturalistic but still well-controlled environments. This approach is further facilitated by the growing availability of VR devices with built-in eye tracking, allowing simultaneous recording of gaze and movement behavior. Rendering engines such as Vizard, Unity, and Unreal have made it simple to create virtual environments, but most come ill-equipped out of the box to handle the typical structure of a sensorimotor experiment, such as looped presentation of structured behavioral 'trials' and capture of continuous behavioral data. This leads to code duplication and re-implementation of the same components by multiple labs and hampers open science and reproducibility efforts. Recently, some research groups have published toolboxes for the Unity platform that facilitate the creation of behavioral experiments. Based on experience from our lab, we here present a similar toolbox for the Vizard VR platform, which is widely used in perception and action research. Using our toolbox, a researcher can implement a behavioral experiment including eye and hand movement recording with comparatively little Python code. The experimental structure can be generated programmatically or imported from a file, and the toolbox takes care of randomizing, presenting, and recording trials. Results are saved in standardized file formats such as CSV and JSON for ease of analysis. Additionally, our toolbox allows for easy online recording and calibration of gaze and motion tracking data using any hardware device supported by Vizard. We highlight the structure and central features of our toolbox using the example of a goal-directed reaching task in VR. This is accompanied by the release of the toolbox code as open source.
ExplainEx: An Explainable ArtificialIntelligence Framework for InterpretingPredictive Models

Artificial Intelligence (AI) systems are increasingly dependent on machine learningmodels which lack interpretability and algorithmic transparency, and hence may not betrusted by its users. The fear of failure in these systems is driving many governments todemand more explanation and accountability. Take, for example, the “Right ofExplanation” rule proposed in the European Union in 2019, which gives citizens theright to demand an explanation from AI-based predictions. Explainable ArtificialIntelligence (XAI) is an attempt to open up the “black box” and create more explainablesystems which create predictive models whose results are easily understandable tohumans. This paper describes an explanation model called ExplainEx whichautomatically generates natural language explanation for predictive models byconsuming REST API provided by ExpliClas open-source web service. The classificationmodel consists of four main decision tree algorithms including J48, Random Tree,RepTree and FURIA. The user interface was designed based on Microsoft.NetFramework programming platform. At the background is a software engine automatinga seamless interaction between Expliclas API and the trained datasets, to providenatural language explanation to users. Unlike other studies, our proposed model is both a stand-alone and client-server based system capable of providing globalexplanations for any decision tree classifier. It supports multiple concurrent users in aclient-server environment and can apply all four algorithms concurrently on a singledataset, returning both precision score and explanation. It is a ready tool forresearchers who have datasets and classifiers prepared for explanation. This workbridges the gap between prediction and explanation, thereby allowing researchers toconcentrate on data analysis and building state-of-the-art predictive models.
Emotion analysis method for elderly living alone based on CNN-BGRU neural network

On the basis of affective computing technology and deep learning, this paper proposes a hybrid neural network model based on CNN-BGRU to solve the problem of accurate classification. In this algori...
Neuroinformatic Tool to Study High Dimensional Dynamics with Distributed Delays in Neural Mass Models

Previous functional magnetic resonance imaging studies in bipolar disorder (BD) have evidenced changes in functional connectivity (FC) in brain areas associated with emotion processing, but how these changes vary with mood state and specific clinical symptoms is not fully understood.We investigated resting-state FC between a priori regions of interest (ROIs) from the default-mode network and key structures for emotion processing and regulation in 27 BD patients and 27 matched healthy controls. We further compared connectivity patterns in subgroups of 15 euthymic and 12 non-euthymic patients and tested for correlations of the connectivity strength with measures of mood, anxiety, and rumination tendency. No correction for multiple comparisons was applied given the small population sample and pre-defined target ROIs.Overall, regardless of mood state, BD patients exhibited increased FC of the left amygdala with left sgACC and PCC, relative to controls. In addition, non-euthymic BD patients showed distinctive decrease in FC between right amygdala and sgACC, whereas euthymic patients showed lower FC between PCC and sgACC. Euthymic patients also displayed increased FC between sgACC and right VLPFC. The sgACC–PCC and sgACC–left amygdala connections were modulated by rumination tendency in non-euthymic patients, whereas the sgACC-VLPFC connection was modulated by both the current mood and tendency to ruminate.Our results suggest that sgACC-amygdala coupling is critically affected during mood episodes, and that FC of sgACC play a pivotal role in mood normalization through its interactions with the VLPFC and PCC. However, these preliminary findings require replication with larger samples of patients.
An Integrated System for Connecting Cybersecurity Competency, Student Activities and Career Building

For educators, preparing students who are able to solve cybersecurity problems requires not only a curriculum that provides students with interdisciplinary knowledge but also activities that develop their skills and competencies to solve problems that call for an interdisciplinary approach. To facilitate this process, we have developed a system called Cyberpassport, which integrates students’ academic and career goals with cybersecurity co-curricular activities. This system is designed for students to search and register for cybersecurity activities, and track their own progress, for advisors to support their mentoring efforts, and for activity hosts such as faculty members or industry professionals to facilitate connection with interested students. Usability testing was conducted to test the application’s functionality as well as user experience and interest. Preliminary results indicate that users found the system easy to use and beneficial for a career in cybersecurity. Furthermore, this is the first mobile events app developed that aligns with the skills and competencies defined by the National Initiative for Cybersecurity Education (NICE) cybersecurity workforce framework.
Automatic traveltime picking by global path tracing

PreviousNext No AccessFirst International Meeting for Applied Geoscience & Energy Expanded AbstractsAutomatic traveltime picking by global path tracingAuthors: Dongliang ZhangFei W. TongSong HanYi LuoConstantine TsingasHongwei LiuDongliang ZhangSaudi AramcoSearch for more papers by this author, Fei W. TongSaudi AramcoSearch for more papers by this author, Song HanAramco Beijing Research CenterSearch for more papers by this author, Yi LuoSaudi AramcoSearch for more papers by this author, Constantine TsingasSaudi AramcoSearch for more papers by this author, and Hongwei LiuSaudi AramcoSearch for more papers by this authorhttps://doi.org/10.1190/segam2021-3577750.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail AbstractFirst arrival picking is a challenging problem for noisy seismic datasets. The stability and smoothness criteria of the picked first arrival are not satisfied for datasets with shingles and interferences from unexpected and backscattered events. To improve first arrival picking, we propose an automatic first arrival picking method using global path tracing to find a global solution for first arrival picking with the condition of smoothness on the traced path. The proposed methodology is composed of data preconditioning, global path tracing, and final addition to compute the total travel time. We propose two ways to precondition the dataset, including amplitude and amplitude ratio with a pilot. The 2D global path tracing comprises of two steps, namely, accumulation of energy on the potential path and backtracking of the optimal path with a strain factor for smoothness. Tests on field data showed that the proposed automatic first arrival picking by global path tracing can provide stable and smooth picked first arrivals.Keywords: near surface, traveltime, tomographyPermalink: https://doi.org/10.1190/segam2021-3577750.1FiguresReferencesRelatedDetails First International Meeting for Applied Geoscience & Energy Expanded AbstractsISSN (print):1052-3812 ISSN (online):1949-4645Copyright: 2021 Pages: 3561 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 01 Sep 2021 CITATION INFORMATION Dongliang Zhang, Fei W. Tong, Song Han, Yi Luo, Constantine Tsingas, and Hongwei Liu, (2021), "Automatic traveltime picking by global path tracing," SEG Technical Program Expanded Abstracts : 1846-1850. https://doi.org/10.1190/segam2021-3577750.1 Plain-Language Summary Keywordsnear surfacetraveltimetomographyPDF DownloadLoading ...
An Empirical Study of Popular Content Management System - Wordpress vs Drupal Vs Joomla

In the modern world, the absence of a business website means loss of a significant share of the tentative market. A business website helps businesses become round the clock accessible to the world. In the past, website development required a significant amount of time, computing knowledge, and skills. Content Management System (CMS) is a new website creation toolkit to address these problems. A CMS converts the complex process of website development into a simple workflow. A CMS requires no prior knowledge/experience and significantly reduces the time of developing a website. Today, many CMS platforms are available for users. This increase in the number of CMS platforms has made it difficult for a user to choose the best one. The objective of this study is to provide a comprehensive comparison of the available CMS platforms. This study performs a comparison of the three most popular CMS platforms (i.e. WordPress, Joomla, and Drupal) using a comprehensive set of criteria. For this purpose, a convenient sample of 110 students from the Computer Science Department and Management Sciences Department was used. These students had used one or more of the CMS platforms to develop websites. The results reveal that WordPress is the dominating CMS platform due to its technical capabilities and ease of use.
Unified Distillation: Supervising the Student to Make Fewer Mistakes

Abstract Knowledge distillation has become a popular task in modern deep learning applications by performing knowledge transfer from a cumbersome neural network commonly called “teacher model” to a much smaller network called “student model”. In traditional knowledge distilling process, there are usually two objectives for training the student model, namely hard target and soft target. However, sometimes it’s hard to find a trade-off between them. We unify the two objectives into one making it easier to perform knowledge distillation, and propose a novel distilling method called “Unified Distillation” to supervise the student to make fewer mistakes. The model can correct the wrong predictions according to the hard target, and maintain the advantage of knowledge distillation. Although our method can be used in almost all fields suitable for knowledge distillation, we choose neural machine translation as a study object for its complexity. We conducted experiments on three neural machine translation tasks, using a finetuned language model BERT as the teacher, and a Transformer base model as the student. The experimental results indicate that our method is better than traditional knowledge distillation method.
STANDARDS IN E-LEARNING. SCORM

The COVID-19 pandemic has sparked interest in e-learning. In the days before the pandemic, it was seen as an opportunity, and today it is seen as an inevitability. Our tasks now are not just to prove its applicability, but to look for methods that will get the most out of electronic and distance forms of training. The most frequently emphasized advantages of e-learning and its distance variation are accessibility, reusability, flexibility, interactivity, openness, personalization, economy and others. These advantages of e-learning over classical face-to-face education are possible thanks to the application of interoperability standards. This paper discusses the SCORM standard, which is the most commonly used one in e-learning.Methodology: A review of literary sources was made: articles in peer-reviewed journals, official websites of standardizing institutions, monographs.Results: SCORM is the most popular standard today. It is essentially a reference model for describing a learning object. This model does not refer to the pedagogical aspect of learning, it defines the way of communication between the learning content and the e-learning system. The latest edition is SCORM 2004. Developed by ADL - Advanced Distributed Learning Initiative. SCORM combines three specifications:1. Content packaging.2. Run-Time Environment3. Sequencing and NavigationSCORM compliant learning content is created by using special development software.Conclusion: The creation of standardized learning content facilitates the transfer of learning units in different courses and platforms and makes it possible to build repositories of learning objects. The SCORM standard is imposed due to its advantages:• All popular learning content management systems are SCORM compliant;• Packages are easily created through special programs, no specific technical knowledge is required;• Provides personalized training by assigning different organizations to resources and activities.
Artificial Neural Networks based Smart Routine Plan

Our daily routines have been drastically altered as a result of the breakout of COVID-19, making it difficult for most of us to cope with boredom, stress, and unpredictability. A smart routine planner was born, an application that helps users set realistic goals and prepare for them. By monitoring user efficiency, eating and sleeping habits, exercise routines, hobbies, and other leisure activities, we want to create a perfect balance between work and pleasure.
Gone with the pandemic: effects of COVID-19 on academic performance in Colombia

The Covid-19 pandemic is an unprecedented exogenous shock that has tested the responsiveness of education systems around the world. The international literature that has quantified the effect of school closings due to Covid-19 on school performance is still limited. This literature suggests that the closure of schools reduced the academic performance of the 2020 cohort compared to the previous ones. To date there is only one study by Lichand et al. 2021 that quantifies this effect for a Latin American country even though the countries of this region have been the most affected by the pandemic. This research seeks to fill this gap in the literature by studying the Colombian case using the information available in the databases of the Saber11 examination for the years 2017 to 2020. Using a pool of cross-sections and a school and time fixed effects model, we estimate the effect of the pandemic on the performance of graduating high school students, finding a negative effect that is in line with the existing literature. The significant reduction in the number of students taking the Saber11 exam in 2020, could raise suspicions about a possible selection bias in these results. We also used inverse probability weighting regressions and trimming by specific quantiles of the test score distribution to control for this possible source of bias. The results show that the effect of the Covid-19 pandemic on Saber11 exam overall scores remains negative and statistically significant.
On the Social Information Environment

The article is devoted to the social and environment and information. The social environment, through the transmission of purposeful information blocks, forms an individual, instilling in him the qualities and properties necessary for optimal functioning in a given society and in a given social group. These goals are promoted by a system of constantly working information transmission channels and, first of all, the mass media. As soon as the information reaches the addressee through the media, social information is included in the process of forming views and opinions, attitudes to certain social values. It is shown that after receiving information, a person can change his point of view on certain events or facts. Moreover, this interaction (influence) can be almost imperceptible, the process passes latently, through quantitative accumulation, but over time, this leads to the fact that quantitative accumulation is accompanied by a qualitative change.
The Price of Breaking the Tyranny of Film: The cognitive demand of top-down processes

Film-viewers’ eye-movements seem largely disconnected from their comprehension; eye-movements rarely deviate from focal narrative elements, regardless of comprehension differences (Loschky et al., 2015; Hutson et al., 2017), termed the Tyranny of film. This suggests bottom-up film features overwhelm top-down attentional control. However, viewers’ eye-movements did deviate from focal narrative elements when given a task irrelevant to comprehension, suggesting viewers used volitional top-down control. However, do viewers naturally engage in volitional top-down control during film viewing or it is too cognitively demanding? Additionally, the role of mandatory top-down attention (e.g., culture) was investigated. Specifically, does mandatory attention have a role in film-viewing or are the bottom-up film features too strong? Participants from the US and Japan viewed film clips with different task goals and levels of attentional demand while eye-tracked. Participants had a primary goal-related task of either watching a film clip for comprehension (Comprehension Condition) or drawing a map of the film space from memory (Map Condition). Participants had a secondary task (cognitive load) on half the trials to increase attentional demand. Results show mandatory attentional selection differences, where Japanese participants were relatively more exploratory during film viewing compared to US participants. Additionally, we replicated and extended volitional attention effects of goal manipulation (Hutson, et al., 2017). Specifically, a volitional task irrelevant to comprehension created more exploration in eye-movements compared to participants who were viewing for comprehension. Finally, evidence shows volitional attention is cognitively demanding during film viewing; this was only true for our Japanese participants. Exploratory analyses showed US participants managed their cognitive load by trading off their dual-task paradigm; suggesting a lower cognitive load, which would explain our lack of an effect for US participants. In sum, the Tyranny of film can be broken, but it comes at the price of cognitively demanding tasks.
Promoting scientific research for economic development

The Ceylon Journal of Science is published by the University of Peradeniya, Sri Lanka. Full text available. The journal also has its own website. The Ceylon Journal of Science is a continuation of the Ceylon Journal of Science (Biological Sciences) which is no longer being published as a separate journal. The history of the journal can be found here.From May 2020, Ceylon Journal of Science is indexed in DOAJ.
A novel adjustable reversible data-hiding method for AMBTC-compressed codes using Hamming distance

In this paper, we propose a novel adjustable reversible data hiding method for AMBTC-compressed codes using Hamming distance. First, the original image is compressed by AMBTC technique to obtain two quantisation levels and a bitmap of each block. Next, the scheme converts the bitmap of each block to a decimal number and calculates the frequency of the decimal number to find the maximum frequency of the decimal number as the peak bitmap. In our scheme, if the block is equal to the peak bitmap, then the block is embeddable. The scheme generates a candidate list to collect a different kind of bitmap in which the Hamming distance between the block and the peak bitmap is less than a pre-defined threshold. The length of the secret message is computed from the total number of bitmaps in the candidate list. The scheme converts the secret message to form an indicated index and uses the corresponding indicated bitmap in the candidate list to replace the original bitmap. Experimentally, the proposed scheme has a high image quality, and the information hiding capacity can be adjusted by the threshold.