Reviewerly/exHarmony huggingface dataset:
ShineOn: Illuminating Design Choices for Practical Video-based Virtual Clothing Try-on

Virtual try-on has garnered interest as a neural rendering benchmark task to evaluate complex object transfer and scene composition. Recent works in virtual clothing try-on feature a plethora of possible architectural and data representation choices. However, they present little clarity on quantifying the isolated visual effect of each choice, nor do they specify the hyperparameter details that are key to experimental reproduction. Our work, ShineOn, approaches the try-on task from a bottom-up approach and aims to shine light on the visual and quantitative effects of each experiment. We build a series of scientific experiments to isolate effective design choices in video synthesis for virtual clothing try-on. Specifically, we investigate the effect of different pose annotations, self-attention layer placement, and activation functions on the quantitative and qualitative performance of video virtual try-on. We find that Dense-Pose annotations not only enhance face details but also decrease memory usage and training time. Next, we find that attention layers improve face and neck quality. Finally, we show that GELU and ReLU activation functions are the most effective in our experiments despite the appeal of newer activations such as Swish and Sine. We will release a well-organized code base, hyperparameters, and model checkpoints to support the reproducibility of our results. We expect our extensive experiments and code to greatly inform future design choices in video virtual try-on. Our code may be accessed at https://github.com/andrewjong/ShineOn-Virtual-Tryon.
Human-Centric AI: The Symbiosis of Human and Artificial Intelligence

Well-evidenced advances of data-driven complex machine learning approaches emerging within the so-called second wave of artificial intelligence (AI) fostered the exploration of possible AI applications in various domains and aspects of human life, practices, and society [...]
Identification of Medicinal Plant Leaves Using Convolutional Neural Network

Abstract Medicinal plants (herbs) are plants that are known to have certain compounds which are nutritious for health. In Indonesia there are 30,000 types of plants and 7000 of them are classified as medicinal plants (herbs). The human body is complex and organic, while chemical medicines contain chemicals that are inorganic and pure. Therefore, chemical medicine are considered not very suitable for consumption by the human body, which if consumed continuously can even be bad for human health. However, some chemical drugs are actually symptomatic (temporary) so they must be taken for life by patients with certain diseases. Therefore a system is needed to be able to help the community to recognize medicinal plants better, in this case the medicinal plants are focused on the introduction of medicinal leaves. In this study identification of medicinal plant leaves was carried out using the Convolutional Neural Network method. This research will build a system of identification of medicinal plant leaves by using Convolutional Neural Networks. Using training data that is carried out in a computer set and then implemented in mobile-based software to recognize the types and benefits of medicinal plant leaves identified.
Metalinguistic Awareness in Technical Communication

The paper discusses the socio-communicative function of the engineer’s foreign language training where the content should be based on materials of the global latest technological advances and understanding the essence of production culture in different countries. The structure and content of foreign languages training is revealed taking into account the potential of multilingualism, technocommunication and metalinguistic awareness. Foreign language training contributes to the formation of linguistic, communicative and metacognitive skills. International modern requirements to the students’ foreign language training at an engineering university are actualized, its contradictions and features of pedagogical forms, methods and tools are identified. The content and structure of multilingualism, technocommunication and metalinguistic awareness of the students’ foreign languages training at an engineering university are revealed. A methodology for the implementation of foreign language training in combination with multilingualism, technocommunication and metalinguistic awareness is devoted. The effectiveness of the proposed methodology was proved through the realization of group international study trips where students showed an understanding of technology by means of foreign languages and a metalinguistic awareness focusing on the cultural traditions of the region and local features of production. A conscious approach to deeper linguistic knowledge, cultures of different countries and technologies according to the high language and communications requirements in the field of science and technology, combining linguistic and engineering thinking in the human mind for a more complete understanding of the essence and content of engineering education is argued.
Towards Accurate Scene Text Detection with Bidirectional Feature Pyramid Network

Scene text detection, this task of detecting text from real images, is a hot research topic in the machine vision community. Most of the current research is based on an anchor box. These methods are complex in model design and time-consuming to train. In this paper, we propose a new Fully Convolutional One-Stage Object Detection (FCOS)-based text detection method that can robustly detect multioriented and multilingual text from natural scene images in a per pixel prediction approach. Our proposed text detector employs an anchor-free approach, unlike state-of-the-art text detectors that do not rely on a predefined anchor box. In order to enhance the feature representation ability of FCOS for text detection tasks, we apply the Bidirectional Feature Pyramid Network (BiFPN) as the backbone network, enhancing the model learning capacity and increasing the receptive field. We demonstrate the superior performance of our method on multioriented (ICDAR-2015, ICDAR-2017 MLT) and horizontal (ICDAR-2013) text detection benchmark tasks. Moreover, our method has an f-measure of 88.65 and 86.32 for the benchmark datasets ICDAR 2013 and ICDAR 2015, respectively, and 80.75 for the ICDAR-2017 MLT dataset.
Healthcare fraud detection using primitive sub peer group analysis

Abstract Healthcare fraud is a significant problem greatly affecting the quality of healthcare services. Manual auditing of insurance claims extends to the delay in finding fraudulent behaviors causing huge financial loss and also putting the patients' health conditions at risk. Since the past decade, the automation of fraud detection using machine learning techniques has become a prominent research topic. Several automated fraud detection systems using machine learning techniques have been proposed so far. However, developing a healthcare fraud detection system that is adaptive to the systematic changes is still missing. Therefore, in this article, we develop primitive sub peer group analysis (PSPGA) for identifying the suspicious behaviors in health insurance claims. PSPGA is inspired by peer group analysis, a popular unsupervised learning technique, which identifies suspicious behaviors based on local pattern analysis. PSPGA distinguishes between the concept drifts and the sudden drifts and flags the sudden drifts as fraudulent. Moreover, PSPGA makes the fraud detection system adaptive to the concept drifts by considering the updates for peer groups over time.
Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

Abstract In real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.
Multinetwork Collaborative Feature Learning for Semisupervised Person Reidentification

Person reidentification (Re-ID) aims at matching images of the same identity captured from the disjoint camera views, which remains a very challenging problem due to the large cross-view appearance variations. In practice, the mainstream methods usually learn a discriminative feature representation using a deep neural network, which needs a large number of labeled samples in the training process. In this article, we design a simple yet effective multinetwork collaborative feature learning (MCFL) framework to alleviate the data annotation requirement for person Re-ID, which can confidently estimate the pseudolabels of unlabeled sample pairs and consistently learn the discriminative features of input images. To keep the precision of pseudolabels, we further build a novel self-paced collaborative regularizer to extensively exchange the weight information of unlabeled sample pairs between different networks. Once the pseudolabels are correctly estimated, we take the corresponding sample pairs into the training process, which is beneficial to learn more discriminative features for person Re-ID. Extensive experimental results on the Market1501, DukeMTMC, and CUHK03 data sets have shown that our method outperforms most of the state-of-the-art approaches.
Sparse linear discriminant analysis for multiview structured data

Classification methods that leverage the strengths of data from multiple sources (multiview data) simultaneously have enormous potential to yield more powerful findings than two-step methods: association followed by classification. We propose two methods, sparse integrative discriminant analysis (SIDA), and SIDA with incorporation of network information (SIDANet), for joint association and classification studies. The methods consider the overall association between multiview data, and the separation within each view in choosing discriminant vectors that are associated and optimally separate subjects into different classes. SIDANet is among the first methods to incorporate prior structural information in joint association and classification studies. It uses the normalized Laplacian of a graph to smooth coefficients of predictor variables, thus encouraging selection of predictors that are connected. We demonstrate the effectiveness of our methods on a set of synthetic datasets and explore their use in identifying potential nontraditional risk factors that discriminate healthy patients at low versus high risk for developing atherosclerosis cardiovascular disease in 10 years. Our findings underscore the benefit of joint association and classification methods if the goal is to correlate multiview data and to perform classification.
US presidential election 2020 prediction based on Twitter data using lexicon-based sentiment analysis

Sentiment analysis is a technique of analyzing public opinion on a problem. The presidential election in the United States is a hot issue that will affect various aspects of the world. The goal of this analysis is to forecast the outcome of the US presidential election and to compare these results with the actual results of the polls. The sentiment analysis used in this study is the lexicon-based sentiment analysis. The method used in this research is data collection, data preprocessing, data mapping and sentiment analysis. The data in this study were obtained from Twitter taken one week before the United States presidential election was held. The model used in this research is VADER sentiment analysis. The data cleaning mechanism in this study uses a method in text mining, where the data is first cleaned of various things that are not considered important in the analysis. Furthermore, the data that can be used as material for analysis is saved again to make it easier to read the data. In the analysis, tweets from users are mapped and counted by the state of the United States of America. The result of this research is a prediction for the Democratic Party to win 22 votes over the Republican Party which received 19 votes. The results from the BBC show that the Democratic Party won with 24 votes, and the Republican Party only got 20 votes. With these results, the VADER sentiment analysis model can produce predictions following the actual results of the US presidential election.
From DevOps to NoOps: Is It Worth It?

With the rise of the adoption of microservice architecture due to its agility, scalability, and resiliency for building the cloud-based applications and their deployment using containerization, DevOps were in demand for handling the development and operations together. However, nowadays serverless computing offers a new way of developing and deploying cloud-native applications. Serverless computing also called NoOps, offloads management and server configuration (operations work) from the user to the cloud provider and lets the user focus only on the product developments. Hence, there are debates regarding which deployment strategy to use. This research provides a performance comparison of a cloud-native web application along with three different function benchmarks in terms of scalability, reliability, and latency when deployed using DevOps and NoOps deployment strategy. NoOps deployment in this work is achieved using Google Cloud Function and OpenWhisk, while DevOps is achieved using the Kubernetes engine. This research shows that neither of the deployment strategies fits all the scenarios. The experimental results demonstrate that each type of deployment strategy has its advantages under different scenarios. The DevOps deployment strategy has a huge performance advantage (almost 72% lesser 90 percentile response time) for simple web-based requests and requests accessing databases while compute-intensive applications perform better with NoOps deployment. Additionally, NoOps deployment provides better scaling-agility as compared to DevOps.
New Advances in Second Language Acquisition Methodology in Higher Education

This article summarizes new advances, as described by current research, in the methodology of teaching Business English as a lingua franca (BELF) in the era of mobile learning and provides the reader with hands-on strategies that are useful for BELF classes and applicable in distance learning. The primary objectives of this literature review are to explore the fundamental approaches which should help practitioners in their course preparation, development, and teaching. The paper provides the reader with the most up-to-date strategies for teaching BELF and brings ideas on how to utilize these principles in a mobile learning (m-learning) environment. The methods include a literature review of available articles exploring the research topic, i.e., BELF and its pedagogy, which was performed by finding relevant studies in the Web of Science and Scopus databases. The results indicate that there are three fundamental approaches recommended by the current research on the teaching of BELF, namely task-based activities/case studies, exploitation of authentic materials, and blended learning implementation. In summary, the paper provides the readers with an update on current approaches for teaching BELF in higher education when utilizing modern tools for foreign language learning, such as m-learning, blended learning, and hybrid learning.
Understanding the Role of Objectivity in Machine Learning and Research Evaluation

This article makes the case for more objectivity in Machine Learning (ML) research. Any research work that claims to hold benefits has to be scrutinized based on many parameters, such as the methodology employed, ethical considerations and its theoretical or technical contribution. We approach this discussion from a Naturalist philosophical outlook. Although every analysis may be subjective, it is important for the research community to keep vetting the research for continuous growth and to produce even better work. We suggest standardizing some of the steps in ML research in an objective way and being aware of various biases threatening objectivity. The ideal of objectivity keeps research rational since objectivity requires beliefs to be based on facts. We discuss some of the current challenges, the role of objectivity in the two elements (product and process) that are up for consideration in ML and make recommendations to support the research community.
Key-Value Pair Searhing System via Tesseract OCR and Post Processing

Optical character recognition systems make it possible to extract text from images. In many cases, this may be sufficient, but there are cases where key-value pairs are required. In this paper, we investigate the use of the open source Tesseract OCR system, to extract text data from images, and perform a key-value pair search. Image noise needs to be minimized with image processing algorithms before recognition. It is necessary to perform so-called post processing procedures on the output of the Tesseract. These post-processors can transform the result of the recognition performed by the OCR system. Those can improve the accuracy of the information extracted during the transformation, for example with the help of regular expressions. The key value pair search is performed after these procedures.
Temporal logic learning-based anomaly detection in metroplex terminal airspace operations

Abstract The airspace system is a complex dynamical system with complicated controlled interactions between its constituent subsystems – terminal airspace, en-route airspace, and ground. Of these, air traffic management in the multi-airport (metroplex) terminal airspace is one of the most complicated subsystems to manage, especially due to the interactions between proximal airports. Analyzing anomalous behaviors in the metroplex is emerging as a key problem in understanding air traffic management complexity and safety. Although physics-based approaches have been studied in-depth for this application, newfound interest has been observed to use recorded time-series air traffic surveillance and airport operations datasets for this purpose. In this paper, we propose a machine learning-based anomaly detection algorithm that generates mathematical models to detect anomalies in metroplex operations. Several machine learning algorithms have been developed to detect anomalies using only air traffic surveillance data, but there is a significant scope of improvement by including airport operational characteristics as well, since integrating such closely-controlled metroplex operational datasets allows the developed models to effectively detect anomalies. The key contribution of this paper is in allowing anomaly detection models to recursively update so as to adapt to changes in metroplex operations. The proposed algorithm is demonstrated with real air traffic surveillance and airport operations datasets at LaGuardia, John F. Kennedy, and Newark airports, thereby detecting anomalies for operations in the New York metroplex.
Big Data classification: techniques and tools

An enormous volume of data, known as Big Data, of varied properties, is continuously being generating from several sources. For efficient and consequential use of this huge amount of data, automated and correct categorization is very important. The precise categorization can find the correlations, hidden patterns, and other valuable insights. The process of categorization of mixed heterogeneous data is known as data classification and is done based on some predefined features. Various algorithms and techniques are proposed for Big Data classification. This chapter attempts to discuss various technicalities of Big Data classification, comprehensively. To start with, the basics of Big Data classifications such as need, types, patterns, phases, approaches, etc. are explained aptly. Different classification techniques, including traditional, evolutionary, and advanced machine learning technique, are discussed with suitable examples, along with citing their advantages and disadvantages. Finally, a survey of various open-source and commercial libraries, platforms, and tools for Big Data classification is presented.
Heralding the Future of Federated Learning Framework: Architecture, Tools and Future Directions

In today's era, the exponential growth of data and its management is a matter of concern. Machine learning has shown its efficacy in multiple application areas. But machine learning on decentralized data was a hectic task since last decade. A novel technology has gained much importance in recent days i.e., federated learning which deals with training on decentralized and distributed data along with preservation of its privacy. Smartphone data being privacy-sensitive is used for locally training a global model which further is aggregated to generate an updated global model which again is distributed among multiple clients. This paper focuses on presenting the efficacy of federated learning by epitomizing an architecture showing the working mechanism of the technology. Further, this paper exhibits an intersection of on-device machine learning, privacy preservation technology and edge computing i.e., federated learning. Also, we have used TensorFlow Federated, an open source platform to simulate federated learning tasks for MNIST and extended MNIST (E-MNIST) datasets. Further, the results contain the loss and accuracy parameters for ten iterations repeated for six optimizer states (Opt <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sub> ) for each dataset. The peak accuracy that we achieved for MNIST and E-MNIST datasets are 0.843 and 0.853 respectively by using federated averaging algorithm. Further, the minimum loss value that we obtained for MNIST and E-MNIST datasets are 0.652 and 0.646 respectively. The execution time for implementing the algorithm for each dataset is presented in a graphical manner. Finally, certain application areas where federated learning technology has aided are scrutinized.