Reviewerly/exHarmony huggingface dataset:
A Study on Career Indecision and College Students' Mobile Phone Addiction Tendency

People's production and life are increasingly dependent on mobile phones.As a group of college students who use mobile phones heavily, the impact of mobile phone dependence (MPD) is more prominent.Mobile phone addiction, also known as mobile phone syndrome, is similar to addictive behavior.It refers to a pathological behavior in which individuals cannot control their impulse to use mobile phones, have obvious withdrawal symptoms, and damage their physiological, psychological, and social functions.Mobile phone addiction has obvious external manifestations.We can judge whether an individual has mobile phone addiction from the frequency, duration, intensity of using mobile phone, and whether it has a negative impact on his body, mind, and life.In daily life, the most obvious symptom of mobile phone addiction is "the machine does not leave the hand".When the individual is cut off from the mobile phone, the individual will become restless.Fu Guifang et al. (2015) found that the detection rate of College Students' mobile phone addiction was 33.06%, and college students with mobile phone addiction had varying degrees of mental health problems.College Students' mobile phone addiction will hinder the formation of their correct thinking concept, lead to the decline of physical function, the increase of psychological problems and the weakening of cognitive ability.Mobile phone addiction is a high-risk factor for poor sleep quality and psychological problems of college students (Zhang Ling et al., 2017).Relevant studies have pointed out that mobile phone addiction has a negative predictive effect on subjective well-being (Wang Yueqin and Zhang Yu, 2015).College students' mobile phone addiction seriously affects classroom learning efficiency (XuGuocheng et al., 2014).Mobile phone addiction can distract students' attention, increase students' depression and further affect their academic performance (SEO, D.G., et al., 2016).Mobile phone addiction will also affect college students' future college life.Some studies have shown that freshmen with high degree of mobile phone addiction will have poor adaptation to college life after becoming sophomores.There is a significant negative correlation between mobile phone addiction and college adaptation.However, sophomores with low college adaptation will have poor mental health status (i.e.subjective well-being, anxiety and depression) after being promoted to sophomores.Mobile phone addiction plays an intermediary role between college adaptation and mental health status (Guohua Zhang, Xue Yang, et al, 2020).Mobile phone addiction should be paid attention to by schools, parents and college students themselves, Exploring the mechanism of mobile phone addiction is helpful to provide enlightenment for the intervention of mobile phone addiction.
In the End: Shchedrin, Sologub, and Terminal Provinciality

This chapter shows how, in the nineteenth-century discourse of provintsiia, Mikhail Saltykov-Shchedrin and Fyodor Sologub act as end points. They evoke what one might call a “terminal provinciality.” At the same time, their texts point forward to how the provinces trope will make itself felt in the twentieth century. Shchedrin's <italic>The Golovlyov Family</italic> and Sologub's <italic>Petty Demon</italic> are separated in time by approximately two decades (<italic>The Golovlyov Family</italic> was serialized between 1875 and 1880; <italic>Petty Demon</italic> came out between 1892 and 1902); Shchedrin's text is set on an estate, Sologub's in an unnamed provincial town. But both these settings seem to be the end of the line—places where narrative itself is so congealed in a mire of sticky <italic>provintsial 'nost'</italic> that forward movement has become virtually impossible.
Abstract 1393: Automated adeno/squamous-cell NSCLC classification from diagnostic slide images: A deep-learning framework utilizing cell-density maps

Abstract Background: The most common form of lung cancer, non-small cell lung cancer (NSCLC), is further categorized into two major histopathological subtypes: ~40% Adenocarcinoma (LUAD), and ~30% squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions, but requires costly pathologist review. Here we present an automated algorithm to differentiate LUAD and LUSC diagnostic whole slide images (WSIs). Methods: 488 subtyped NSCLC high-resolution diagnostic WSIs were obtained from TCGA sources. Adjacent normal regions were identified and excluded from analysis. Cancer cell-density maps were created based on cell counts within discrete patches. These maps were then binned into ten ranges of cell counts (20-30 cells per patch, 30-40, etc. up to &amp;gt;110 cells per patch). 2D color patches were transformed into 1D descriptive vectors using the inception v3 deep learning framework. Samples were randomly split into 70% training and 30% testing sets. Ten LUAD/LUSC linear SVM classifiers (one for each cell-density bin) were trained on such transformed data. Subtype prediction in unseen testing samples was achieved by averaging subtype predictions from the 10 subsequent models. Results: 338 TCGA diagnostic WSIs (164 LUAD and 174 LUSC) were used to train, and 150 (71 LUAD and 79 LUSC) were used to test. The proposed system achieved an AUC of 0.9068 in test samples, corresponding to a classification accuracy of 83.33%. The (heretofore excluded) adjacent normal regions were classified correctly almost as accurately as tumor regions (74.7%). Conclusions: This fully-automated histopathology subtyping method generates maps of regions-of-interest within WSIs, providing novel spatial information on tumoral organization. For example, our results on test data show tumor patches of size 100 square microns with 60 to 100 cells distinguish LUAD from LUSC better than other cell-density ranges. Moreover, adjacent normal tissue may provide additional insights into tumorigenesis mechanisms. Citation Format: Mustafa I. Jaber, Liudmila Beziaeva, Christopher W. Szeto, John Elshimali, Shahrooz Rabizadeh, Bing Song. Automated adeno/squamous-cell NSCLC classification from diagnostic slide images: A deep-learning framework utilizing cell-density maps [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1393.
Learning Rate Scheduling Policies

With the availability of high processing capability hardwares at less expensive prices, it is possible to successfully train multi-layered neural networks. Since then, several training algorithms have been developed, from algorithms which are statically initialized to algorithms which adaptively change. It is observed that to improve the training process of neural networks, the hyper-parameters are to be fine tuned. Learning Rate, Decay rate, number of epochs, number of hidden layers and number of neurons in the network are some of the hyper-parameters in concern. Of these, the Learning rate plays a crucial role in enhancing the learning capability of the network. Learning rate is the value by which the weights are adjusted in a neural network with respect to the gradient descending towards the expected optimum value. This paper discusses four types of learning rate scheduling which helps to find the best learning rates in less number of epochs. Following these scheduling methods, facilitates to find better initial learning rate value and step-wise updation during the later phase of the training process. In addition the discussed learning rate schedules are demonstrated using COIL-100, Caltech-101 and CIFAR-10 datasets trained on ResNet. The performance is evaluated using the metrics, Precision, Recall and F1-Score. The results analysis show that, depending on the nature of the dataset, the performance of the Learning Rate Scheduling policy varies. Hence the choice of the scheduling policy to train a neural network is made, based on the data.
The Model Made Me Do It! A Cautionary Tale From a Security Control Baseline Tool Developer

Even the best written specifications can be complicated documents to read and understand. Normative prose is often supported by tables and diagrams intended to clarify the specification. What happens when a tool developer interprets those clarifying features as a different model than the prose intends? What does this say about relying on derived data models in tools that support the specification? A cautionary tale involving security control baselines from National Institute of Standards and Technology Special Publication 800-53 provides some answers — and insights.
Deep filter bridge for malaria identification and classification in microscopic blood smear images

Malaria is a major global infectious health disease threat. The process of identifying and quantifying malaria is one of the most challenging tasks in the field of microscopy image processing due to variations in sample preparation and uncertainty of cell classes. However, motivated by the challenges, we present a novel simplified deep learning model; deep filter bridge, combining multi-rolling stacked denoising autoencoder (SAE) and fisher vector (FV) to automatically classify the different types of single cells in microscopic blood smear images as either infected or uninfected. The results indicate that the proposed model SAE kernels can extract representative malaria features from large unlabelled data and extreme learning machine (ELM) is used as a final ensemble base classifier to improve the learning speed of the algorithm. We have experimentally evaluated performance based on 39000 single cell elements in a ten-fold cross-validation, obtaining average classification F-score and accuracy at 98.36% and 98.12% respectively, on the microscopic blood smear image datasets.
Emotion Recognition in Speech Processing using Fast Fourier Transform

The idea of acquiring the state of human emotions from one’s speech, we have gathered required data that makes one to understand the concept behind this process. Human emotions can be predicted by his/her facial expressions or by the tone of their voice. Reading the facial expressions is one of the major tasks involved in image processing. Likewise, each emotion holds different tone in one's voice. It requires a various emotional tone frequency to calculate and analyse the emotions. We need to fetch approximate frequencies of emotions. It’s the challenging task as each speaker has various pitches while speaking and frequencies of the same person varies in his emotion. Another main issue is the noise in the input while a person is speaking, due to less quality recordings or surrounding environment. List of basic emotions are Happy, angry, sad, bored, surprised, disgust, fear. For this project the prior important concept is speech recognition. The machine must be capable of reading the input in form as speech and must be capable of analysing various contents. The input given is converted into wav format. At the same time machine must be also capable of fetching the frequencies. The calculation is performed using many methodologies that are defined.
Application of VR Technology in Criminal Law Education Practice Teaching and Countermeasures Analysis

Virtual reality technology is widely used in many fields. VR technology takes computer technology as the core, generates virtual three-dimensional environment, and carries out real-time interaction with the help of sensing helmets, data clothes and other equipment, which can fully mobilize people’s senses and make people feel there. With the rapid development of China’s economy, VR technology is no longer limited to military, aerospace, medical and other fields. Under the idea that the Party and the country strongly advocate education first, and with the input of policies and social capital, VR has gradually entered the field of education. However, this article is concerned with the shortcomings of the application in the practical teaching of criminal law education, and puts forward relevant alternative treatment methods corresponding to VR technology.
Applying attribute-based encryption on mobile devices

The 21st century has witnessed the rapid development of small and convenient mobile devices such as smartphones, tablets, game players, sensor nodes, etc. The rise of such mobile devices indicates the increase of colossal data transmission through the Internet and online services along with the challenges of data security. It is common to think of a solution to protect sensitive data from unauthorized users, and the most popular solution is to use encryption. While many research activities in functional encryption have widely been applied to network devices, computers, and applications, mobile devices still attract much attention to security issues due to the limitations of system resources, connectivity, data transmission and power consumption that malicious users can exploit to launch attacks. Especially, mobile devices have become a principal tool to share data on the Internet through online services, such as Facebook, Youtube, DropBox, Amazon, Online Games, etc. This paper presents a study of the Attribute-Based Encryption (ABE) scheme that exploits user attributes to build the secret key and the ciphertext. ABE encryption is specified by a set of attributes or a policy defining attributes that users possess. The paper also describes a few implementations of ABE applied in the cryptography community and the challenges of integrating ABE into real-world applications. Finally, the paper proposes an implementation of ABE for Android mobile devices. This implementation associated with the Kerberos protocol can be applied to secured data sharing applications. The Kerberos protocol aims at providing mutual authentication for the client server model. Experiments have evaluated the proposed ABE implementation on Android mobile devices along with the Kerberos system. The evaluation also includes ABE performance with discussions and lessons learned.
Supplemental Material: Crustal material recycling induced by subduction erosion and subduction-channel exhumation: A case study of central Tibet (western China) based on P-T-t paths of the eclogite-bearing Baqing metamorphic complex

Compositional mapping images of one garnet, Triassic paleo-geographic facies of Qiangtang, summarized published Paleozoic and Proterozoic ages in Tibetan Plateau and Himalaya, mineral compositions, and chronology data of the Baqing metamorphic rocks.
The approach of using ontology as a pre-knowledge source for semi-supervised labelled topic model by applying text dependency graph

Multiple topics discovering from text is an important task in text mining. In the past, the supervised approaches fail to explore multiple topics in text. The topic modelling approach, such as: LSI, pLSI, LDA, etc. are considered as an unsupervised method which supports to discover distributions of multiple topics in text documents. The labelled LDA (LLDA) model is a supervised method which enables to integrate human labelled topics with the given text corpus during the process of modelling topics. However, in real applications, we may not have enough high qualified knowledge to properly assign the topics for all documents before applying the LLDA. In this paper, we present two approaches which have taken the advantage of dependency graph-of-words (GOW) in text analysis. The GOW approach uses frequent sub-graph mining (FSM) technique to extract graph-based concepts from the text. Our first approach is the method of using graph-based concepts for constructing domain-specific ontology. It is called GC2Onto model. In our second approach, the graph-based concepts are also applied to improve the quality of traditional LLDA. It is called LLDA-GOW model. We combine two GC2Onto and LLDA-GOW models to leverage the multiple topic identification as well as other mining tasks in the text.
Credit Card Fraud Detection in Data Mining u sing XGBoost Classifier

In today's economy, credit card (CC) plays a major role. It is an inevitable part of a household, business &amp; global business. While using CCs can offer huge advantages if used cautiously and safely, significant credit &amp; financial damage can be incurred by fraudulent activity. Several methods to deal with the rising credit card fraud (CCF) have been suggested. Both such strategies, though, are meant to prevent CCFs; each of them has its own drawbacks, benefits, and functions. CCF has become a significant global concern because of the huge growth of e-commerce and the proliferation of payment online. Machine learning (ML) algo as a data mining technology (DM) was recently very involved in the detection of CCF. There are however several challenges, including the absence of publicly available data sets, high unbalanced size, and different confusing behavior. In this paper, we discuss the state of the art in credit card fraud detection (CCFD), dataset and assessment standards after analyzing issues with the CCFD. Dataset is publicly available in the CCFD data set used in experiments. Here, we compare two ML algos of performance: Logistic Regression (LR) and XGBoost in detecting CCF Transactions Real Life Data. XGBoosthas an inherent ability to handle missing values. When XGBoost encounters node at lost value, it tries to split left &amp; right hands &amp; learn all ways to the highest loss. This is when the test runs on the data. The experimental results show an effective use of the XGBoost classifier. Technique of performance is widely accepted metric based on exclusion: accuracy &amp; recall. Also, the comparison between both approaches displayed based on the ROC curve.
National Survey on Educommunication in Spanish Universities During the COVID-19 Pandemic: Methodology and Results

Since the end of 2019, the world is experiencing the COVID-19 pandemic. Many countries have been forced to take exceptional measures, such as closing many economic activities and educational institutions. In this context, many universities have seen their work routine altered. This investigation aimed at studying how students have adapted to this new circumstance and how they have changed their traditional face-to-face methodology to a distance methodology in a few days. The lack of time to prepare the new materials, together with a lack of knowledge of the technical means to carry out this task, has caused an unprecedented situation. The objective of this research is to survey the perceptions of Spanish university students on the application of this teaching methodology and to analyze the difficulties encountered. To achieve this, we relied on a quantitative methodology, based on the administration of a questionnaire, which allowed us to obtain answers that can help establish the necessary future guidelines. Although they have understood that the exceptional circumstances required online teaching, the results show that students tend to prefer face-to-face education.
Automatic topic labelling for text document using ontology of graph-based concepts and dependency graph

Topic labelling is an important task of text mining. It supports assigning proper topic labels to the text documents. In this paper, we present a novel approach of using graph-based concept matching approach in solving automatic topic labelling task. Our proposed model demonstrates that the quality of automatic topic labelling task for text documents can be improved, in comparison with traditional keyword-based concept matching approach. In this paper, we propose a novel approach of automatic ontology-driven topic labelling. Our proposed model is considered as a semi-supervised approach. It uses existed ontologies as the pre-knowledge base for topic identification in text documents. We performed the experiments on the real-world ACM's documents to show the effectiveness of our proposed model in solving topic labelling task. The experimental results on real-world and standard datasets demonstrate that our proposed model can leverage the output accuracy of topic labelling task in text documents.
Henry David Thoreau’s Lifelong Indian Play

Brent Ranalli’s “Henry David Thoreau’s Lifelong Indian Play” explores a project Thoreau began in his childhood: cultivating “an interlinked set of personal qualities that he and his nineteenth-century audience recognized as stereotypically ‘Indian’ virtues.” Thoreau continued throughout his life “to imaginatively enter the world of the Indian, or to view the world through ersatz Indian eyes,” Ranalli argues. This quest would eventually coalesce around the virtue ethics at the core of his mature philosophy. In Ranalli’s reading, many of the most important virtues Thoreau sought to cultivate were recognizably “Indian” traits. Ranalli refers to Scottish common-sense philosopher Dugald Stewart and his Elements of the Philosophy of the Human Mind (1822) to explain the connection between imaginative play (such as Indian role-play) and character education that Thoreau and his nineteenth-century audience implicitly shared.
Interpolation of the Histogramed MR Brain Images for Resolution Enhancement

Magnetic resonance imaging (MRI) is an incredible testing method which provides appropriate anatomical images of the body. For the diagnosis, high resolution MR images are essential to extract the detailed information about the diseases. However, with the measured MR images it’s a challenging issue in extracting the detailed information associated to disease for the posterior analysis or treatment. Usually to improve the resolution of the MR image, histogram equalization process has to be applied. In this paper, interpolation method is applied to improve the resolution of MR brain images for the histogram-ed images. And for the assessment of the skillfulness of introduced method, performance metrics such as peak signal to noise ratio (PSNR) and absolute mean brightness error (AMBE) are measured. The peak of signal for the enhanced images through interpolation will be much better and may have the good variation to the mean brightness error. With this there can be potential to the artificial intelligence for better diagnosis in complex decisive instances.
Live Deliberately, Stay Woke: Thoreau’s Influence on William Melvin Kelley

Mark Gallagher’s “Live Deliberately, Stay Woke: Thoreau’s Influence on William Melvin Kelley” discusses the work of a twentieth-century Black writer who is only now gaining the recognition he deserves. Gallagher conveys how Kelley’s novel A Different Drummer (1962) engages Thoreauvian themes. While a graduate student at Harvard, Kelley became conversant with the overwhelmingly white American literary canon, including the critical discourses enveloping it, while fully aware of their limitations and blinkers when it came to the experiences and perspectives of people of color. In A Different Drummer, the eponymous Thoreau quotation is printed on a frontispiece page, but so also—above this—is the one from Walden clarifying that the better part of what Thoreau’s neighbors call good, he believes to be bad. Gallagher proposes that Kelley reappropriates Thoreau as he is found in R.W.B. Lewis’s classic of the myth-and-symbol school of criticism, The American Adam: Innocence, Tragedy, and Tradition in the Nineteenth Century (1955), in devising his at-once reticent and proud protagonist. Kelley’s identification with Thoreau in A Different Drummer betokens an ironic and iconoclastic refashioning of the myth of American individualism in the African-American literary imagination.