Reviewerly/exHarmony huggingface dataset:
The Establishment and Optimization of Public Emotion Network Communication Model using Deep Learning

This work aims to construct and optimize the public emotion network communication model under the artificial intelligence (AI) environment and provide a new research direction for exploring public emotion network communication with intelligent human–computer interaction (HCI). The main challenge of this research is the lack of interactivity of intelligent human–computer expression design, and the accuracy of emotion recognition is not high. First, the theoretical analysis of network public opinion is made, and the network public opinion calculation method and emotion recognition method based on deep learning are proposed. On this basis, the public emotion network communication model is established, and the model’s communication mechanism and rules are described in detail. Next, the emotion calculation of human–computer expression interaction and the emotion expression mode of the humanoid robot are proposed, which provides an experimental basis for HCI and public emotion network communication analysis. According to the data of email network and microblog network in the real network, the network degree distribution map is established. The statistical results show that email and microblog networks belong to power law distribution, and the aggregation coefficient is relatively high. Microblog network degree changes slowly, and more points are scattered in the middle area. In a practical sense, when the positive and negative emotions in the public emotion network communication model are input into the bionic robot, the robot can show happy and lost expressions. The results show that compared with the traditional convolutional neural network (CNN) model or recurrent neural network (RNN) model, the RNN–CNN structure model of RNN combined with CNN reduces the waiting time by about 20% and improves the algorithm accuracy by at least 3.1%. The public emotion network communication model based on deep learning and intelligent HCI can accurately reflect the public emotion state in the network, which provides a practical basis for the application of AI technology in network public opinion judgment.
BAN-Cap: A Multi-Purpose English-Bangla Image Descriptions Dataset

As computers have become efficient at understanding visual information and transforming it into a written representation, research interest in tasks like automatic image captioning has seen a significant leap over the last few years. While most of the research attention is given to the English language in a monolingual setting, resource-constrained languages like Bangla remain out of focus, predominantly due to a lack of standard datasets. Addressing this issue, we present a new dataset BAN-Cap following the widely used Flickr8k dataset, where we collect Bangla captions of the images provided by qualified annotators. Our dataset represents a wider variety of image caption styles annotated by trained people from different backgrounds. We present a quantitative and qualitative analysis of the dataset and the baseline evaluation of the recent models in Bangla image captioning. We investigate the effect of text augmentation and demonstrate that an adaptive attention-based model combined with text augmentation using Contextualized Word Replacement (CWR) outperforms all state-of-the-art models for Bangla image captioning. We also present this dataset's multipurpose nature, especially on machine translation for Bangla-English and English-Bangla. This dataset and all the models will be useful for further research.
Automatic Inference of BGP Location Communities

We present a set of techniques to infer the semantics of BGP communities from public BGP data. Our techniques infer communities related to the entities or locations traversed by a route by correlating communities with AS paths. We also propose a set of heuristics to filter incorrect inferences introduced by misbehaving networks, sharing of BGP communities among sibling autonomous systems, and inconsistent BGP dumps. We apply our techniques to billions of routing records from public BGP collectors and make available a public database with more than 15 thousand location communities. Our comparison with manually-built databases shows our techniques provide high precision (up to 93%), better coverage (up to 81% recall), and dynamic updates, complementing operators' and researchers' abilities to reason about BGP community semantics.
Sentiment Analysis of Amazon Customers for Mangaldeep Sadhvi Agarbatti using Python

In today’s competitive world, customers are playing the role of king as business firms are customizing their products and services as per the requirements of the customers. New start-ups are coming and existing firms are trying vigorously hard to compete and survive in this challenging environment. Therefore, Customer reviews play a vital role in deciding the future endeavours of the company for making them satisfied with their products and services. In this paper an effort is being made to analyze the customer reviews for Mangaldeep Sadhvi Agarbatti using Python’s NLTK (Natural Language Toolkit). The data set has been prepared by web-scraping the Amazon website using Python’s BeautifulSoup library. The data has been processed to find the polarity of customers by analyzing the content and the title of the reviews. The result has been compared with the ratings provided by the customers. Further, performance of two Machine Learning classification algorithms viz. Logistic regression and Decision Tree Classification is also compared.
Subordination Algebras as Semantic Environment of Input/Output Logic

We establish a novel connection between two research areas in non-classical logics which have been developed independently of each other so far: on the one hand, input/output logic, introduced within a research program developing logical formalizations of normative reasoning in philosophical logic and AI; on the other hand, subordination algebras, investigated in the context of a research program integrating topological, algebraic, and duality-theoretic techniques in the study of the semantics of modal logic. Specifically, we propose that the basic framework of input/output logic, as well as its extensions, can be given formal semantics on (slight generalizations of) subordination algebras. The existence of this interpretation brings benefits to both research areas: on the one hand, this connection allows for a novel conceptual understanding of subordination algebras as mathematical models of the properties and behaviour of norms; on the other hand, thanks to the well developed connection between subordination algebras and modal logic, the output operators in input/output logic can be given a new formal representation as modal operators, whose properties can be explicitly axiomatised in a suitable language, and be systematically studied by means of mathematically established and powerful tools.
Introduction to Apache NetBeans IDE

This chapter presents basic and fundamental knowledge about and implementations of the Apache NetBeans IDE 12. The Apache NetBeans IDE is one of the most current and updated IDEs and is widely implemented in a wide spectrum of Java applications. The Apache NetBeans IDE is actually written in Java and runs everywhere a Java Virtual Machine is installed, including Windows, Mac OS, Linux and Solaris. The Apache NetBeans Platform is a broad Swing-based framework on which a programmer can base large desktop applications. The IDE itself is based on the NetBeans Platform. The Platform contains APIs that simplify the handling of windows, actions, files and many other things typical in applications. The Apache NetBeans IDE is an open-source integrated development environment, and it supports development of all Java application types, such as Java Standard Edition including JavaFX, Java Mobile Edition, Web, Enterprise JavaBean and mobile applications, out of the box.
Object-level change detection for autonomous sensemaking

For many intelligence sources, reliable independent algorithms exist for interpreting the data and reporting relevant information to analysts. However, achieving the necessary cross-source data fusion from these sources and algorithmic outputs to achieve true sensemaking can be challenging. This is especially true at the individual object level, given the sources' highly variable spatiotemporal resolutions and uncertainties. We have developed a framework for merging automatic target recognition (ATR) algorithms and their outputs to produce a sensor-agnostic means of object level change detection to establish the necessary patterns-of-life for big picture sensemaking, activity-based intelligence, and autonomous decision making.
Measuring Lexical Sophistication in Written and Spoken Production by EFL Learners

Using a Tool for the Automatic Analysis of LExical Sophistication (TAALES), this study analyzed lexical sophistication in written and spoken production by EFL Asian learners with different CEFR (Common European Framework of Reference) proficiency levels in the International Corpus Network of Asian Learners of English (ICNALE). The present study aims to examine whether indices of lexical sophistication explain the variability of learners’ CEFR proficiency levels in writing and speaking and whether there is any difference between the two production modalities. The results of regression analyses showed that the prediction model including six indices explained 15.4% of the variance in CEFR proficiency levels in writing. In speaking, the prediction model including one index accounted for 1.2% of the variance in the CEFR proficiency levels. Finally, lexical sophistication predicted learners’ CEFR proficiency levels more reliably in writing than in speaking.
Stylistic Fingerprints, POS-tags and Inflected Languages: A Case Study  in Polish

In stylometric investigations, frequencies of the most frequent words (MFWs) and character n-grams outperform other style-markers, even if their performance varies significantly across languages. In inflected languages, word endings play a prominent role, and hence different word forms cannot be recognized using generic text tokenization. Countless inflected word forms make frequencies sparse, making most statistical procedures complicated. Presumably, applying one of the NLP techniques, such as lemmatization and/or parsing, might increase the performance of classification. The aim of this paper is to examine the usefulness of grammatical features (as assessed via POS-tag n-grams) and lemmatized forms in recognizing authorial profiles, in order to address the underlying issue of the degree of freedom of choice within lexis and grammar. Using a corpus of Polish novels, we performed a series of supervised authorship attribution benchmarks, in order to compare the classification accuracy for different types of lexical and syntactic style-markers. Even if the performance of POS-tags as well as lemmatized forms was notoriously worse than that of lexical markers, the difference was not substantial and never exceeded ca. 15%.
Real Time Face Detection using Raspberry Pi 3B+

Human face detection is one of the best technologies in today’s world and it’s being used various platforms like security and surveillance that identifies the human face in a digital image format. In today’s digital lifestyle security plays a major role, current technology uses a smartphone, identity Cards, Fingerprints, and others for security, which is used to carry with us always and also likely to spread contagious diseases. Here comes an alternate way of security technology is a human face which is unique and easy to identify human, which helps in smart door lock and attendance system etc. This paper describes the software and hardware of the face detection system using the raspberry pi 3B+ module and coding a program using a python programming language. And the method used is the HOG (histogram of oriented gradients) descriptor with the SVM (Support Vector Machines) classifier which processes better results than other techniques. The main goal of this paper is to detect a human face in real-time and store the face as an image format in the database and also real-time face recognition. Overall, of this Face detection kit, we aim at designing the system affordable, user-oriented, and with superior performance.
Gemcitabine response prediction in the adjuvant treatment of resected pancreatic ductal adenocarcinoma using an AI histopathology platform.

e16295 Background: Adjuvant chemotherapy improves survival following resection of pancreatic ductal adenocarcinoma (PDAC). A modified fluorouracil/irinotecan/oxaliplatin regimen (mFOLFIRINOX) has demonstrated improved disease free survival and overall survival, though gemcitabine-based monotherapy and gemcitabine plus capecitabine are alternatives in less fit patients. Though there are several proposed biomarkers to guide treatment decisions (GATA6, hENT1, and GemPred), no biomarker is used to guide treatment selection in clinical practice. Consequently, we sought to develop an artificial intelligence-derived signature of features from digital images of routine histopathology specimens that could identify patients susceptible to routine chemotherapeutic agents. Methods: 139 whole-slide digitized histological slides corresponding to 102 resected PDAC tumors from TCGA-PAAD were used in this study. This dataset corresponded to patients that had received either gemcitabine-backbone or 5 FU-backbone chemotherapy as their first-line adjuvant treatment. We extracted nuclei images from tissue regions using segmentation models and computed geometric features of these nuclei which we then correlated with Disease Specific Survival (DSS) in order to construct a signature associated with treatment benefit. This signature was compared against two board certified pathologists using the grade of the digital slides images to classify patients into above or below average DSS buckets. Results: Among quantitative geometric features, a set of area and ellipse features describing nuclei geometry correlated most with response to gemcitabine (R̃0.4). The cox proportional hazards model using these geometric nuclei features was found to be predictive of response to gemcitabine and achieved a C-index (95% CI) of 0.69 (0.58, 0.79). The pathologist-based baseline model for above and below average DSS had a median DSS of 443 and 461 days respectively. Using the average expected lifetime as the threshold, the model divides patients receiving gemcitabine into two histological subtypes with median DSS of 586 and 394 days respectively (p &lt; 0.05). The model appeared specific to gemcitabine. Among patients receiving 5-FU (n = 10) there was no statistical significance in median DSS between the subtypes and a c-index of 0.63 (0.27, 1.0). Conclusions: An artificial intelligence approach utilizing only routine histopathology can identify features that correlate with treatment outcomes in PDAC with classification performance (c-index:0.69) superior to the validated AJCC treatment prediction tool (0.59).
perspective of parents and students towards online learning as a sustainable platform for future learning

Emergence of COVID-19 pandemic has created a need to shift the education and learning from regular classroom teaching to online technology-based learning. Without the adoption of this mode, education would not be accessible to students during this period. This mode of learning has its own pros and cons from the point of view of parents and students both. Delivery and conduction of classes through online mode is a challenge for both students and parents. The purpose of this study was to conduct an online survey regarding parents and student’s perception and experience towards online learning as a medium of education for future. The sample was divided into two parts consisting of 100 students from schools, undergraduate and post graduate level comprising of 64% males and 36% females and 100 parents comprising 50% males and 50% females. Online survey method was used for the purpose of data collection. The findings show that the Online learning has ease down the financial burden on parents in some areas such as the students are not required to avail transport facilities, uniform is also not mandatory while taking online classes, curtailment of expenditure on canteens etc.
An Effective Fusion Method to Enhance the Robustness of CNN

With the development of technology rapidly, applications of convolutional neural networks have improved the convenience of our life. However, in image classification field, it has been found that when some perturbations are added to images, the CNN would misclassify it. Thus various defense methods have been proposed. The previous approach only considered how to incorporate modules in the network to improve robustness, but did not focus on the way the modules were incorporated. In this paper, we design a new fusion method to enhance the robustness of CNN. We use a dot product-based approach to add the denoising module to ResNet18 and the attention mechanism to further improve the robustness of the model. The experimental results on CIFAR10 have shown that our method is effective and better than the state-of-the-art methods under the attack of FGSM and PGD.
URL Based Phishing Website Detector

In the current scenario most popular cyber crime today is phishing, Phishing is a social engineering-based attack where the phisher targets to retrieve the user’s legitimate confidential details to exploit and it is commonly done through sending mail or any other electronic communication in an automated fashion.
Mobile Learning for Malay Language among Foreign Workers: A Preliminary Study

This study aims to understand the learning process and language acquisition of Communicative Malay Language (CML) among foreign workers in Malaysia. A total of 10 foreign workers who were randomly selected in Kuala Lumpur and volunteered to participate were interviewed individually. The interviews were recorded, transcribed, translated and interpreted to enable the researcher to conduct content analysis. The analysis revealed that most of the respondents were not adequately supplied with general knowledge about the country, and especially the language, when they arrived in Malaysia for the first time. Although only half of them specifically reported the utilization of mobile applications in learning CML, all of the respondents were aware of mobile applications in general and agreed that this would be a good instrument to help them learn the language, especially the listening aspect.
The Andes and Hotspots: Mutual Evidence

Earth and Space Science Open Archive This preprint has been submitted to and is under consideration at Geochemistry, Geophysics, Geosystems. ESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary.Learn more about preprints preprintOpen AccessYou are viewing the latest version by default [v2]The Andes and Hotspots: Mutual EvidenceAuthorsRex HerbertPilgeriDSee all authors Rex Herbert PilgeriDCorresponding Author• Submitting AuthorRetirediDhttps://orcid.org/0000-0003-3715-5084view email addressThe email was not providedcopy email address
The W Boson Mass: 80.433 GeV: A Result of a Composite H, W, Z Bosons Model: Other Results: H, Z, Proton, Neutron and Dark Matter Masses

The present article develops a model initially published in ref. [1] and completed in ref. [2].This is a quasi-classical quantum model of composite particles with ultra-relativistic (UR) constituents (leptons and quarks). The model is used to calculate the mass energy of three composite particles: an UR tauonium, an UR bottomonium and an UR leptoquarkonium. The result is that these three hypothetic particles have masses close to 125 GeV: the Higgs boson mass energy. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; These results are recalled in the present article. Then the model is extended to calculate the mass energy of the W and Z bosons assumed to be composite particles, as well as those of the proton and the neutron. For the W boson, the model gives two values: one has a mass strictly equal to that measured recently at Fermilab (80.433 GeV), higher than the values measured so far. The other model value is according to the other measurements of the W boson mass. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Finally the model provides a hypothesis on dark matter.
Teachers’ Difficulties in Enhancing EFL Students’ Reading Motivation

One of English teachers’ major concerns in teaching reading is students’ low reading motivation. Studies show that most of EFL learners are not highly enthusiastic when the teachers have them read English texts. Likewise, there have been limited efforts done by the teachers to address this problem. This study was therefore aimed to investigate the levels of students’ reading motivation, teachers’ difficulties in enhancing students’ reading motivation, and teacher effort to overcome the problems of motivating students to read. The participants were three EFL teachers and 12 students of junior secondary school in East Java. The data were collected through semi-structured interviews and observation. The result of semi-structured interview revealed that most of students had low reading motivation due to their understanding of difficult words in the reading text. Added to this, the major difficulty of teachers to enhance students’ reading motivation is related to increasing the interest, dedication and confidence of students in solving difficult vocabularies while reading English texts. This major difficulty reported by the teachers is in agreement with students’ report on their low reading motivation due to their limited vocabulary. Then, the result of observation showed that rewards had different effects on students’ reading motivation and the teachers should improve their efforts to enhance students’ reading motivation. Hence, several strategies such as reward, metacognitive reading strategy, reciprocal teaching method, reading aloud to improve students’ reading motivation were also suggested.
POTENTIAL POSSIBILITIES OF USING INFORMATION AND COMMUNICATION TECHNOLOGIES IN PROFESSIONAL TRAINING OF FUTURE EDUCATORS INSTITUTIONS

The article raises issues of introduction of ICT in the educational process of higher education institutions. The phenomenon of the concept of ICT is substantiated and the peculiarities of the application of information and communication technologies in the professional training of future educators as a factor in improving the educational process in higher education institutions are outlined. The potential opportunities for the use of ICT in the professional training of future educators of preschool institutions contribute to improving students' knowledge of various educational tasks and solving pedagogical situations; practicing the skills of higher education seekers in the use of ICT in further professional activities; providing a personality-oriented, differentiated, integrated approach to the training of future educators in higher education institutions; motivating students to lifelong learning and self-education. The following theoretical research methods were used: analysis and generalization of scientific sources in order to determine the essence of the basic concepts of research; generalization and systematization of the experience of leading teachers to form their own views on the issue; generalization and formulation of research conclusions. Positive practice of application of ICT in the educational process of higher education institutions are noted by all teachers-innovators; the use of ICT can motivate students to educational activities, promote their professional development, stimulate self-education; introduction and active use of ICT tools by teachers in the educational process of the university, implementation of individual research tasks, development of ICT tools for their own professional activities form the readiness to use ICT in working with preschool children.