Reviewerly/exHarmony huggingface dataset:
Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization

This paper explores three simple data manipulation techniques (synthesis, augmentation, curriculum) for improving abstractive summarization models without the need for any additional data.We introduce a method of data synthesis with paraphrasing, a data augmentation technique with sample mixing, and curriculum learning with two new difficulty metrics based on specificity and abstractiveness.We conduct experiments to show that these three techniques can help improve abstractive summarization across two summarization models and two different small datasets.Furthermore, we show that these techniques can improve performance when applied in isolation and when combined.
A data replication strategy for document-oriented NoSQL systems

Cloud providers aim to maximise their profits while satisfying tenant requirements, e.g., performance. The relational database management systems face many obstacles in achieving this goal. Therefore, the use of NoSQL databases becomes necessary when dealing with heterogeneous workloads and voluminous data. In this context, we propose a new data replication strategy that balances the workload of nodes and dynamically adjusts the number of replicas while the provider profit is taken into account. Result analysis shows that the proposed strategy reduces the resource consumption, which improves the provider profit while satisfying the tenant performance requirement.
On the Challenges of Evaluating Compositional Explanations in Multi-Hop Inference: Relevance, Completeness, and Expert Ratings

Building compositional explanations requires models to combine two or more facts that, together, describe why the answer to a question is correct.Typically, these "multi-hop" explanations are evaluated relative to one (or a small number of) gold explanations.In this work, we show these evaluations substantially underestimate model performance, both in terms of the relevance of included facts, as well as the completeness of model-generated explanations, because models regularly discover and produce valid explanations that are different than gold explanations.To address this, we construct a large corpus of 126k domain-expert (science teacher) relevance ratings that augment a corpus of explanations to standardized science exam questions, discovering 80k additional relevant facts not rated as gold.We build three strong models based on different methodologies (generation, ranking, and schemas), and empirically show that while expert-augmented ratings provide better estimates of explanation quality, both original (gold) and expertaugmented automatic evaluations still substantially underestimate performance by up to 36% when compared with full manual expert judgements, with different models being disproportionately affected.This poses a significant methodological challenge to accurately evaluating explanations produced by compositional reasoning models.
Emotion Recognition using Feed Forward Neural Network &amp; Naïve Bayes

In this paper we analyze and predict the emotion of a user by recognizing his/her face. Face recognition is a software application which is used to identify a particular person; it will be mostly useful in security applications to secure our data. Now a day we are using face unlock in mobiles to unlock our phones. We need to know the emotions of a person in some situations. Though we can recognize his emotion through his tone of voice, it would be more helpful if get to know his emotions. This can be much helpful in finding out a criminal by finding out his emotion whether he is feeling nervous or not which expresses his/her fear by this. In order to analyze his/her emotion firstly we need to recognize his/her face, so we need to use face recognition method and then implement emotion analysis. Here we use different algorithms to implement emotion analysis such as CNN. We will have the dataset with pixels and emotion this will be the training data. Then we will be initially taking the picture and then convert them to pixels these will be acting as the testing data. We then use an algorithm to predict these pixels emotion which is nothing but predicting the emotion of the picture taken.
Challenge and Prospects for Fault Diagnosis of Power Battery System for Electrical Vehicles Based on Big-data

Abstract：Battery fault diagnosis techniques are regarded as significant means for guaranteeing safe operation of electric vehicles (EVs).Precise and effective techniques not only can improve safety and reliability of EVs but also accelerate the progress of EVs' market.Firstly, focusing on the battery management system and thermal management system, the latest research progress of battery state estimation and cooling technology in ensuring the safe operation of EVs is reviewed; Secondly, advanced technical means of data transmission security of battery system operation are introduced respectively at the vehicle local level and the vehicle terminal cloud network level; Additionally, from the perspective of big data, the fault diagnosis technology is summarized into three aspects: multi-scale data fusion, fault identification, and fault pre-alarming, and the advantages and disadvantages of the current technology are analyzed; Finally, in view of the difficulties faced by current fault diagnosis technology, the future research trend of EV's fault diagnosis method combining big data and artificial intelligence technology is prospected.
Optimal Path Smoothing with Log-aesthetic Curves Based on Shortest Distance, Minimum Bending Energy or Curvature Variation Energy

Shape grammar implementation tools play an important role in the generation of designs.Most of the available tools were created to allow the application of shape grammar rules without restrictions.This is largely because shape grammars are context free, as such, the application of a rule is not controlled during derivation.This is also the reason some generate designs that have parts that are too small for the human eye to see.This work presents a tool that allows the addition of bag context to shape grammar rules such that it automatically allows when a rule should be applied based on a defined range.Bag context represents information that is not part of a developing design but instead evolves separately during a derivation.This helps to generate an infinite number of images that are similar but not identical.This tool could offer a wide range of application areas such as aiding the teaching of shape grammar or bag context shape grammar implementation in formal language classes or higher learning in general, a framework to support designers for the development of an improved bag context shape grammar interpreter tool, and others.
TTPA: a two tiers PSO architecture for dimensionality reduction

Particle swarm optimisation (PSO) is a popular nature inspired computing method due to its fast and accurate performance, exploration and exploitation capability, cognitive and social behaviour and has fewer parameters to adjust. Recently, an improved binary PSO (IBPSO) was proposed by Chuang et al. (2008) to avoid getting trapped in local optimum and they have shown that it outperforms all other variants of PSO. Even though many variants of PSO exists independently to improve the performance of PSO, to escape from local optimum and to deal with dimensionality reduction, there still needs an integrated approach to handle it. Hence, in this paper, two tiers PSO architecture (TTPA) is proposed to find the maximum classification accuracy with minimum number of selected features. The proposed method is used to classify nine benchmarking gene expression datasets. The results show the merits of TTPA.
Folding-BSD Algorithm for Binary Sequence Decomposition

The Internet of Things (IoT) revolution leads to a range of critical services which rely on IoT devices. Nevertheless, they often lack proper security, becoming the gateway to attack the whole system. IoT security protocols often rely on stream ciphers, where pseudo-random number generators (PRNGs) are an essential part of them. In this article, a family of ciphers with strong characteristics that make them difficult to be analyzed by standard methods is described. In addition, we will discuss an innovative technique of sequence decomposition and present a novel algorithm to evaluate the strength of binary sequences, a key part of the IoT security stack. The density of the binomial sequences in the decomposition has been studied experimentally to compare the performance of the presented algorithm with previous works.
ANALISIS SENTIMEN OPINI PUBLIK BAHASA INDONESIA TERHADAP WISATA TMII MENGGUNAKAN NAÏVE BAYES DAN PSO

Sentiment analysis is a text analysis to identify an opinion. Public opinion or reviews are important for visitors who will travel to tourist because of the reviews will produce information about the destination. One of the tourist attractions in Indonesia that Taman Mini Indonesia Indah one of the tourist attractions that has extraordinary. Reviews about Taman Mini Indonesia Indah make it difficult for visitors to draw conclusions about information about Taman Mini Indonesia Indah. Therefore a classification method in study with Naïve Bayes and PSO methods. In study using data as many 50 positive reviews and 50 negative reviews where the results of accuracy obtained using the Naïve Bayes method of 70% while the accuracy of using Naïve Bayes and PSO is 94,02%.
Strategi Kepala Sekolah dalam Meningkatkan Literasi Membaca Siswa di SMA Negeri 1 Kota Kediri

This study discusses how the potential for literacy needed by education. Students’ literacy ability is closely related to the guidance of reading skills analytically, critically, and reflectively. However, the fact of learning at school this time has not been able to realize it well. Principals as leaders and at the same time policyholders must have the right strategy. This is a great homework for all educational institutions in Indonesia, including in the city of Kediri. This research uses qualitative methods with descriptive approaches, data collection methods in the form of, observation, interviews, and documentation. Checking the validity of the data using triangulation. Data analysis through steps, compile, manage, and combine all data obtained from the field so that it becomes a conclusion. The results of the study show that the principal’s strategy in improving reading literacy is for students, first, the level of habituation. Second, student literacy is directed at religious reading literacy. Third, forming a literacy team. This has not been said to be maximum because the school has obstacles. First, student reading facilities are the only libraries. Second, the delay in students can make students’ abilities, skills and insights different from a student who is disciplined.
Schwa Deletion in Hindi Language Speech Synthesis

Schwa deletion is important factor for conversion of Grapheme to Phoneme. In Hindi language each consonant has weak vowel. This weak vowel is called as inherent schwa. These schwa is deleted some cases in pronunciation. Written form and speech forms are different in Indian language. Schwa plays important role in speech form. Deletion and retention of weak vowel decides how words are pronounced. Words morphology is main factors that affects pronunciation. In current paper, we describe schwa handling, deletion and retention rules. Based on different rule we developed schwa deletion algorithm. This algorithm has been tested over 6000 high frequency words. We received accuracy result up to 80%. Based on result an application has been developed to provide user interface for the text processing component of text to speech system.
Vision based Patient Fall Detection using Deep Learning in Smart Hospitals

With the emergence of new concepts like smart hospitals, video surveillance cameras should be introduced in each room of the hospital for the purpose of safety and security. These surveillance cameras can also be used to provide assistance to patients and hospital staff. In particular, a real-time fall of a patient can be detected with the help of these cameras and accordingly, assistance can be provided to them. Different models have already been developed by researchers to detect a human fall using a camera. This paper proposes a vision based deep learning model to detect a human fall. Along with this model, two mathematical based models have also been proposed which uses pre-trained YOLO FCNN and Faster R-CNN architecture to detect the human fall. At the end of this paper, a comparison study has been done on these models to specify which method provides the most accurate results.
Facial feature discovery for ethnicity recognition

WIREs Data Mining and Knowledge DiscoveryVolume 10, Issue 2 e1351 ErratumFree Access Facial feature discovery for ethnicity recognition This article corrects the following: Expression of Concern: Facial feature discovery for ethnicity recognition Cunrui Wang, Qingling Zhang, Wanquan Liu, Yu Liu, Lixin Miao, Volume 9Issue 1Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery First Published online: August 2, 2018 First published: 26 December 2019 https://doi.org/10.1002/widm.1351AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat In Wang et al. (2019), a disclaimer has been added to the published version to clarify some issues regarding the participants of this study. Here is the text that was added: The study was conducted with the approval of Dalian University and written, informed consent was obtained from each study participant who understood their photographs would be used for non-profit scientific research. We apologize for this error. REFERENCE Wang, C., Zhang, Q., Liu, W., Liu, Y., & Miao, L. (2019). Facial feature discovery for ethnicity recognition. WIREs Data Mining Knowl Discov., 9, e1278. https://doi.org/10.1002/widm.1278 Volume10, Issue2March/April 2020e1351 ReferencesRelatedInformation
Computerized Face Detection and Tracking

Item revelation has been an unprecedented test from the past couple of years. In PC vision human face area is a basic research topic. It is required for some PC applications like HCI, perception, human-robot joint effort, etc. In this field facial following is finding greater advancement of use in security and prosperity applications to recognize distinctive situations1 . This following zone can be used to control or talk with robots. Perceiving human faces in a video is a fantastic testing issue. These setups may take after edge of view, establishment control, and diverse edifications. This is a result of high variety of setups that may occur. The multifaceted idea of the face results in a particular dimension of issue for speedy area and following. In this paper we use two philosophies for recognizing a face and track it always. In a general sense video groupings give a larger number of information than a still picture. It is reliably a testing undertaking to pursue a target thing in a live video. We experience challenges like edification; present assortment and hindrance in pre-planning stages. In any case, this is can be overpowered by disclosure of the target article unendingly in each and every edge. Face following by Kanade Lucas Tomasi computation that is used to pursue face reliant on arranged features.Automatic face detection and tracking is a challenging part and here the proposed method is improvised on that.
Use of KNN Classifier for Emotion Recognition Based on Distance Measures

Human-computer interaction (HCI), in recent times, is gaining a lot of significance. The systems based on HCI have been designed for recognizing different facial expressions. The application areas for face recognition include robotics, safety, and surveillance system. The emotions so captured aid in predicting future actions in addition to providing valuable information. Fear, neutral, sad, surprise, happy are the categories of primary emotions. From the database of still images, certain features can be obtained using Gabor Filter (GF) and Histogram of Oriented Gradient (HOG). These two techniques are being used while extracting features for getting the expressions from the face. This paper focuses on the customized classification of GF and HOG using the KNN classifier.GF provides texture features whereas HOG finds applications for images exhibiting differing lighting conditions. Simplicity and linearity of KNN classifier appeals for its use in the present application. The paper also elaborates various distances used in KNN classifiers like city-block, Euclidean and correlation distance. This paper uses Matlab implementation of GF, HOG and KNN for extracting the required features and classification, respectively. Results exhibit that the accuracy of city- block distance is more .
Self-regulated learning in higher education: the need to thrive

The intention of the present research was to assess the level of self-regulated learning among information technology employees from two state universities in Jordan. The study surveyed 133 employees, by completing the self-regulated learning at work questionnaire (SRLWQ), which was developed by Fontana et al. (2015). Results of the study indicated strong levels for all stages of self-regulated learning. Results also showed no significant differences in self-regulated learning based on gender. However, significant differences were found for experience, where employees with more years of experience exhibited higher levels of self-regulated learning than those with fewer years of experience. The study provided recommendations both for theory and practice.
Panoptical View of the Sentiment Analysis Techniques

Sentiment analysis (SA) is a rapidly evolving field that aims at computationally categorizing the opinions of people about a particular product, movie, brand or anything that can be opined. It has changed the way the information is perceived and utilized by big business groups, brands and marketing agencies by demonstrating that the computational recognition of a sentimental expression is feasible. The fruition of Internet based applications has generated huge amount of personalized views on the Web. These reviews exist in different forms like social Medias, blogs, Wiki or forum websites. The boom of search engines like Yahoo and Google has flooded users with copious amount of relevant reviews about specific destinations, which is still beyond human comprehension. Sentiment Analysis poses as a powerful tool for users to extract the needful information, as well as to aggregate the collective sentiments of the reviews. This research will explore and compare the various techniques used for Sentiment Analysis in the last decade.
Tuning the False Positive Rate / False Negative Rate with Phishing Detection Models

Phishing attacks have risen by 209% in the last 10 years according to the Anti Phishing Working Group (APWG) statistics [19]. Machine learning is commonly used to detect phishing attacks. Researchers have traditionally judged phishing detection models with either accuracy or F1-scores, however in this paper we argue that a single metric alone will never correlate to a successful deployment of machine learning phishing detection model. This is because every machine learning model will have an inherent trade-off between it’s False Positive Rate (FPR) and False Negative Rate (FNR). Tuning the trade-off is important since a higher or lower FPR/FNR will impact the user acceptance rate of any deployment of a phishing detection model. When models have high FPR, they tend to block users from accessing legitimate webpages, whereas a model with a high FNR will allow the users to inadvertently access phishing webpages. Either one of these extremes may cause a user base to either complain (due to blocked pages) or fall victim to phishing attacks. Depending on the security needs of a deployment (secure vs relaxed setting) phishing detection models should be tuned accordingly. In this paper, we demonstrate two effective techniques to tune the trade-off between FPR and FNR: varying the class distribution of the training data and adjusting the probabilistic prediction threshold. We demonstrate both techniques using a data set of 50,000 phishing and 50,000 legitimate sites to perform all experiments using three common machine learning algorithms for example, Random Forest, Logistic Regression, and Neural Networks. Using our techniques we are able to regulate a model’s FPR/FNR. We observed that among the three algorithms we used, Neural Networks performed best; resulting in an higher F1-score of 0.98 with corresponding FPR/FNR values of 0.0003 and 0.0198 respectively.
Readability and Understandability Scores for Snippet Assessment: an Exploratory Study

Code search engines usually use readability feature to rank code snippets. There are several metrics to calculate this feature, but developers may have different perceptions about readability. Correlation between readability and understandability features has already been proposed, i.e., developers need to read and comprehend the code snippet syntax, but also understand the semantics. This work investigate scores for understandability and readability features, under the perspective of the possible subjective perception of code snippet comprehension. We find that code snippets with higher readability score has better comprehension than lower ones. The understandability score presents better comprehension in specific situations, e.g. nested loops or if-else chains. The developers also mentioned writability aspects as the principal characteristic to evaluate code snippets comprehension. These results provide insights for future works in code comprehension score optimization.
Scientific Paper Peer-Reviewing System With Blockchain, IPFS, and Smart Contract

In general, peer reviewing is known as an inspection of a work that is completed by one or more qualified people from the same profession and from the relevant field to make the work more error-free, readable, presentable, and adjustable according to the pre-published requirements and also considered as the primary metric for publishing a research paper, accepting research grants, or selecting award nominees. However, many recent publications are pointing to the biasness and mistreatment in the peer-review process. Thus, the scientific community is involved to generate ideas to advance the reviewing process including standardizing procedures and protocols, blind and electronic reviewing, rigorous methods in reviewer selection, rewarding reviewers, providing detailed feedback or checklist to reviewers, etc. In this chapter, the authors propose a decentralized and anonymous scientific peer-reviewing system using blockchain technology. This system will integrate all the above concern issues and eliminate the bias or trust issues interconnected with the peer-reviewing process.
Introducing JSON

Many applications communicate by exchanging JSON objects instead of XML documents. Chapter 7 introduces JSON, tours its syntax, demonstrates JSON in a JavaScript context, and shows how to validate JSON objects in the context of JSON Schema.