Reviewerly/exHarmony huggingface dataset:
Exploring Low-level and High-level Transfer Learning for Multi-task Facial Recognition with a Semi-supervised Neural Network

Facial recognition tasks like identity, age, gender, and emotion recognition received substantial attention in recent years. Their deployment in robotic platforms became necessary for the characterization of most of the non-verbal Human-Robot Interaction (HRI) scenarios. In this regard, deep convolution neural networks have shown to be effective on processing different facial representations but with a high cost: to achieve maximum generalization, they require an enormous amount of task-specific labeled data. This paper proposes a unified semi-supervised deep neural model to address this problem. Our hybrid model is composed of an unsupervised deep generative adversarial network which learns fundamental characteristics of facial representations, and a set of convolution channels that fine-tunes the high-level facial concepts for the recognition of identity, age group, gender, and facial expressions. Our network employs progressive lateral connections between the convolution channels so that they share the high-abstraction particularities of each of these tasks in order to reduce the necessity of a large amount of strongly labeled training data. We propose a series of experiments to evaluate each individual mechanism of our hybrid model, in particular, the impact of the progressive connections on learning the specific facial recognition tasks and we observe that our model achieves a better performance when compared to task-specific models.
Fast Search and Estimation of Bayesian Nonparametric Mixture Models Using a Classification Annealing EM Algorithm

Bayesian nonparametric (BNP) infinite-mixture models provide flexible and accurate density estimation, cluster analysis, and regression. However, for the posterior inference of such a model, MCMC algorithms are complex, often need to be tailor-made for different BNP priors, and are intractable for large datasets. We introduce a BNP classification annealing EM algorithm which employs importance sampling estimation. This new fast-search algorithm, for virtually any given BNP mixture model, can quickly and accurately calculate the posterior predictive density estimate (by posterior averaging) and the maximum a-posteriori clustering estimate (by simulated annealing), even for datasets containing millions of observations. The algorithm can handle a wide range of BNP priors because it primarily relies on the ability to generate prior samples. The algorithm can be fast because in each iteration, it performs a sampling step for the (missing) clustering of the data points, instead of a costly E-step; and then performs direct posterior calculations in the M-step, given the sampled (imputed) clustering. The new algorithm is illustrated and evaluated through BNP Gaussian mixture model analyses of benchmark simulated data and real datasets. MATLAB code for the new algorithm is provided in the supplementary materials. Supplementary materials for this article are available online.
A Personalised Approach to Adaptive Tutoring using Machine Learning and Natural Language Processing

The Adaptive Learning Application (ALA) is an intelligent tutoring system that identifies the manner in which a student assimilates information, and accordingly evolves itself to generate personalised lesson plans that optimise learning. The ALA is built using a combination of a Weighted Support Vector Machine, a custom-built algorithm amalgamating versions of the FP-Growth and Genetic algorithms, and Natural Language Processing techniques. Thus, it uses both supervised and unsupervised learning to learn two things in parallel - how to quantifiably identify the types of topics a student finds difficult, and the best way in which to teach a topic of a particular difficulty level. Natural Language Processing techniques including but not limited to POS-tagging and TF-IDF scores are used to evaluate how much the student has learnt. The results are propagated back into a feedback loop to facilitate learning.
Quantifying the Effect of Cognitive Bias on Security Decision-Making for Authentication Methods

The main challenge that can impact the effectiveness of authentication mechanisms is human error (unintentional threats). Irrational judgment associated with human error is often linked to a unique attribute called cognitive bias (CB). CB is a tendency to think irrationally in certain situations and make irrational judgment. The appearance of CB in human decisions is considered one of the implications of system usability. In the security filed, usability is recognized as one of the main issues that affect an individual’s security decisions. Clearly, security decision-making is a result of three overlapping factors: security, usability and CB. In this paper, we quantify security decision making by providing a holistic view on how these factors affect the security decision. For this purpose, an experiment was conducted involving 54 participants who performed multiple security tasks related to authentication. An eye-tracking machine was used to record cognitive measurements that were used for decision analysis. Multi Criteria Decision Analysis (MCDA) approach was then used to evaluate the participants’ decisions. The result showed that participants security decisions are varied depends on the authentication method. For instance, picture type was the authentication method least influenced by CB. Low system usability is one of the major causes of CB in decisions. This was not the case for the picture password method. The different levels of usability associated with the picture method resulted in low impact of CB on participants’ security decision. This finding point to investigating how picture-based authentication methods are capable of handling the issue of the CB.
Subspace Based Face Recognition: A Literature Survey

It has become a necessity than a mere requirement in computer vision and image analysis technology over years, for image database identifications and identity verifications through iris scan, color, occlusions, texture of the face images. Also we could see security cameras installed under surveillance in sensitive areas such as Airports, Security on defense system, ATMs, in schools and colleges, banks, offices etc. In order to recognize the image of a person and identify, there are plethora of approaches. However, there are many problems which are still unsolved in the face recognition. This paper provides a till-date survey of the image face recognition showcasing the complete review of the research underwent on linear face recognition approaches and throw light upon certain non-linear approaches for the future case of study. The survey is made only with principal component analysis as the key on algorithms which are analyzed for its pros and cons in the purview of databases and classifiers. The survey shows the linear phase with least complex to the most complex issues with solutions and challenges faced by many authors.
English Out-of-Vocabulary Lexical Evaluation Task

Unlike previous unknown nouns tagging task, this is the first attempt to focus on out-of-vocabulary (OOV) lexical evaluation tasks that does not require any prior knowledge. The OOV words are words that only appear in test samples. The goal of tasks is to provide solutions for OOV lexical classification and predication. The tasks require annotators to conclude the attributes of the OOV words based on their related contexts. Then, we utilize unsupervised word embedding methods such as Word2Vec and Word2GM to perform the baseline experiments on the categorical classification task and OOV words attribute prediction tasks.
Optimized Prediction Model to Diagnose Breast Cancer Risk and Its Management

Breast malignancy is the second biggest disease that results in fatal condition for women population. Research endeavors have revealed with expanding affirmation that the support vector machines (SVMs) have more noteworthy precise conclusion capacity. In this paper, breast disease determination is dependent on a SVM-based technique that has been proposed. Investigations have been directed on various preparing test allotments of the Wisconsin breast malignancy dataset (WBCD), which is generally utilized among scientists who use machine learning strategies for breast disease conclusion. The working of the technique is assessed by utilizing characterization precision, particularity positive and negative prescient qualities, collector working trademark bends, and perplexity lattice. The outcomes demonstrate that the most elevated grouping precision (99%) is achieved for the SVM.
Enhancing the quantum cost of Reed-Muller Based Boolean quantum circuits using genetic algorithms

Abstract There is a direct equivalence between Boolean functions represented in Reed-Muller logic and Boolean Quantum Circuits. Different polarity Reed-Muller expansions will give different Boolean quantum circuits with different cost for the same Boolean function. For a given Boolean function with n variables there are 2 n possible expansions. Searching for the expansion that gives a Boolean quantum circuit with minimum quantum cost within the search space is a hard problem for large n. This paper will use genetic algorithms to find the fixed/mixed polarity Reed-Muller expansion that gives a Boolean quantum circuit with minimum quantum cost to optimize the circuit realization of a given Boolean function.
The Design of Treadmill with Distributed Generation Function

Physical health is very important to economic development, and with the improvement of the living standard, indoor treadmills are more and more popular. However, traditional treadmill drive system has many drawbacks. In order to meet the requirements of low-carbon environmental protection, it is necessary to develop an electromagnetic damping control system capable of distributed generation to improve the shortcomings of traditional treadmill drive system. Therefore, we designed a treadmill that uses PWM mode to adjust the electromagnetic damping, which can guarantee the accuracy of damping adjustment during the movement process. This system integrated intelligent charging management and data networking function together, which can greatly enhance the practicability of the treadmill. If a large number of such treadmills are brought together and the electricity generated from the treadmills is so large that could be connected to the grid, which will effectively alleviate the energy shortage.
Tweet-Based Sentiment Analyzer

People, these days, express their opinions regarding any particular topic or issue widely on social media. One such popular social media platform among masses is twitter with over 320 million monthly users. Users also express their thoughts on any political announcements or decisions taken by a particular party. Analyzing these tweets on a specific topic can help in determining what people think about measures undertaken by the government. It will give an idea on how many percent of people are in favor of any announcement, and how many of them stand against it. This will in turn provide areas of improvement for the ruling or opposition party. This paper thus aims on finding sentiments of tweets on a political leader, some party or announcements like a union budget. This can further be generalized to any particular measure undertaken by any organization.
Research of Lung Cancer-Assisted Diagnosis Algorithm Based on Multi-scale Convolution Kernel Network

In recent years, the number of patients with lung cancer has risen steadily, becoming the first malignant tumor in men and the second malignant tumor in women. Researchers at home and abroad have found that pulmonary nodule-assisted diagnosis can detect pulmonary nodules early and effectively reduce the risk of lung cancer. Therefore, deep learning has become a new hotspot in the diagnosis of pulmonary nodules. The research content of this paper is as follows: In this paper, we extract features of lung nodules with geometric features, gray value features, texture features and use support vector machine (SVM) and extreme learning machine (ELM) to train and classify the lung nodules. The convolutional neural network (CNN) deep learning method was used to extract the features of CT images of lung nodules, to establish a characteristic model of CT images of pulmonary nodules, and to classify the benign and malignant lung nodules. This paper presents a method for computer-aided diagnosis of pulmonary nodules based on improved CNN. This method uses the convolutional neural network (CNN) to extract the features of CT images of lung nodules and establishes the feature model of CT images of pulmonary nodules. The multi-scale convolution kernel depth learning is used to prove the advancement of improved algorithms.
Iris Image Error Correction Techniques

In today's world, security has gotten paramount. Many biometric methods like facial expression recognition system and Iris recognition system have been developed. Iris biometry helps in identifying an individual in a more intuitive and natural manner. Iris recognition focuses on recognizing the identity of individuals using the textural based characteristics. But, due to various reasons corruption of texture features of iris often occurs during denoising and deblurring. In this paper we propose maltlab algorithms to overcome these defects as well as extract the features to receive a defect less image. To verify the algorithm white noise is added to the iris dataset and the calculations are done as required. This process shows that denoising and deblurring can improve the quality of the iris image evidently.
A Reliable Gait Analysis Using Fuzzy Logic

Abstract In this paper, an artificial intelligence technique which is fuzzy logic is used as an online prediction of a patient (especially Alzheimer patient) motion status. It depends on the input variables: GPS, GPRS, accelerometer, gyroscope, temperature, heartbeat rate, and the motion status (standing, walking, and running) as an output variable. Fuzzy logic can detect the motion status based on 385 rules for all inputs and output variables. A low-cost smart shoe with embedded positioning tracking is implemented based on sensors and microcontrollers to help Alzheimer patients. The first phase of the experimental work is based on GPS, GPRS, GSM, accelerometer, heartbeat rate sensor, temperature sensor, and a microcontroller, mounted on the shoe side to navigate according to the pedestrian or patient movement. All sensors data are collected by the microcontroller and are transmitted to the GSM receiver in SMS message. The GSM module is connected to the PC via a USB serial port. Transmitting data is periodically sent upon a specific time. The detected data is stored in the database as a function of time. The data includes the position of the patient (latitude and longitude) located on the Google map software, health status (heartbeat rate), gyroscope and accelerometer (x, y, z) of movement as a function of time.
Facial Recognition: Traditional Methods vs. Methods Based on Deep Learning

The present study shows the quantitative analysis of the evaluation of traditional methods of facial recognition versus the evaluation of methods based on deep learning, the contribution of this study lies in the conditions of the dataset to be used, which unlike the regular paradigms, it contains a limited size of images to train what the researcher has called a hostile training environment, which will serve as a reference to determine what technique and/or algorithm can work to recognize faces in which you do not have a lot of information to be processed and with a poor quality of the face image to be recognized, factors that in recent years are recurring in the search requests of missing persons or in the absence of the quality of certain security cameras.
Credit card fraud identification based on unbalanced data set based on fusion model

With the rapid development of bank credit card business, banks are facing increasingly serious credit card fraud problems. In order to carry out the credit card business and effectively prevent fraud risks, this paper analyzes the research results at home and abroad, comprehensively considers the availability, security and timeliness of the model. For the unbalanced data set, using machine learning methods, three kinds of anti-balance are proposed. Fraud model. Firstly, data preprocessing is performed by using undersampling method. Secondly, Lasso-Logistic, XGBoost, and the above two models are used to model and predict. Finally, the three methods are compared and optimized. This paper analyzes fraudulent behavior through three kinds of anti-fraud systems, which has positive and important significance for improving the accuracy and effectiveness of credit card fraud detection.
Why Johnny Can (and Should) Write Essays

Review Article| January 01 2020 Why Johnny Can (and Should) Write Essays: A Case for an Essay-centric Writing Curriculum Crafting Presence: The American Essay and the Future of Writing Studies. By Wallack, Nicole B.Utah State University Press, 2017. 230 pages. Jenny Spinner Jenny Spinner Jenny Spinner is associate professor of English at Saint Joseph’s University in Philadelphia, where she teaches creative nonfiction and journalism and serves as director of the writing center. Her essays and essay criticism have appeared in Fourth Genre, Brevity, Writing on the Edge, Pedagogy, and Assay, and on NPR’s All Things Considered, among others. She is the author of Of Women and the Essay: An Anthology from 1655 to 2000 (2018) and coauthor, along with her twin sister Jackie Spinner, of Tell Them I Didn’t Cry (2006). Search for other works by this author on: This Site Google Pedagogy (2020) 20 (1): 185–191. https://doi.org/10.1215/15314200-7879206 Cite Icon Cite Share Icon Share Facebook Twitter LinkedIn MailTo Permissions Search Site Citation Jenny Spinner; Why Johnny Can (and Should) Write Essays: A Case for an Essay-centric Writing Curriculum. Pedagogy 1 January 2020; 20 (1): 185–191. doi: https://doi.org/10.1215/15314200-7879206 Download citation file: Zotero Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search Books & JournalsAll JournalsPedagogy Search Advanced Search The text of this article is only available as a PDF. Copyright © 2020 by Duke University Press2020 Article PDF first page preview Close Modal Issue Section: Review You do not currently have access to this content.
Development of decision support system for subsidized electricity purchase using Naive Bayes method to improve company reputation (Case study in PT. PLN (Persero) Malang)

Abstract Nowadays there are a lot of customers who are categorized as capable households (Rumah Tangga Mampu). The problem is, electricity rates increased to IDR 1,352,00/KWH for customers with average capacity (900 Volt Ampere). Central government spending the proportion of electricity subsidies tends to rocketed up from 2.5% in 2005 to 4.7% in 2012 APBN. For average electricity subsidies consume around 7% of central government spending. This condition shows that electricity subsidies are quite burdensome to the central government budget (Biro Analisa Anggaran dan Pelaksanaan APBN-Sekjen DPR-RI). Problems related to electricity consumed for the national industry gives impact on fluctuations in subsidies and inefficiencies at PT PLN require a comprehensive solution. In implementing the electricity subsidy distribution program that is right on target and foster the performance of PT. PLN in charge, it is necessary to hold a new breakthrough based on technology by creating a systemized system for new registration and re-registration for electricity consumers by bringing the data that has been determined by PT PLN. Next, the consumers will be trained to accessthe system to determine whether the consumer is eligible for subsidies or not. To overcome the problem the system is proposed the solution through the implementation of Naive Bayes. Based on the result, the system is feasible tocategorizefor whom the customer get subsidized electricity rate or not. The precise objectives and the system are able to improve the accuracy in selecting consumer tariff subsidy of electricity to the consumer the right target for PT PLN.
Objective Assessment of Noise Presence in Diagnostic Radiographic Images

Objective evaluation of the effect of noise on diagnostic image quality of radiographic (X-ray) images provides an opportunity for autonomous elimination of noise and optimisation of overall quality of such imagery that meets the requirements of radiologists. Four objective measures of the effect of noise on the quality of diagnostic images are described in this paper. The aim of the metrics is to correspond as closely as possible to the quality assessments made by radiological diagnosticians. Of the proposed metrics, analysis of local coherence gives the best results, primarily for calcified tissue images, such as spine, hand and knee images.
A Hybrid Binary Classifier for Pattern Classification

In this work, we propose a hybrid binary classifier which combines a decision tree with a support vector machine. The proposed hybrid model has the advantages of improved accuracy and easy interpretability. The model will be useful for feature selection cum classification tasks in real-world supervised learning problems. Numerical evidence is also provided using 25 standard data sets from various fields to assess the performance of the model. Performance of the proposed hybrid binary classifier is quite better when compared to individual classifiers.
End-to-End Measure for Text Recognition

Measuring the performance of text recognition and text line detection engines is an important step to objectively compare systems and their configuration. There exist well-established measures for both tasks separately. However, there is no sophisticated evaluation scheme to measure the quality of a combined text line detection and text recognition system. The F-measure on word level is a well-known methodology, which is sometimes used in this context. Nevertheless, it does not take into account the alignment of hypothesis and ground truth text and can lead to deceptive results. Since users of automatic information retrieval pipelines in the context of text recognition are mainly interested in the end-to-end performance of a given system, there is a strong need for such a measure. Hence, we present a measure to evaluate the quality of an end-to-end text recognition system. The basis for this measure is the well established and widely used character error rate, which is limited - in its original form - to aligned hypothesis and ground truth texts. The proposed measure is flexible in a way that it can be configured to penalize different reading orders between the hypothesis and ground truth and can take into account the geometric position of the text lines. Additionally, it can ignore over-and under-segmentation of text lines. With these parameters it is possible to get a measure fitting best to its own needs.