Reviewerly/exHarmony huggingface dataset:
RepFair-GAN: Mitigating Representation Bias in GANs Using Gradient Clipping

Fairness has become an essential problem in many domains of Machine Learning (ML), such as classification, natural language processing, and Generative Adversarial Networks (GANs). In this research effort, we study the unfairness of GANs. We formally define a new fairness notion for generative models in terms of the distribution of generated samples sharing the same protected attributes (gender, race, etc.). The defined fairness notion (representational fairness) requires the distribution of the sensitive attributes at the test time to be uniform, and, in particular for GAN model, we show that this fairness notion is violated even when the dataset contains equally represented groups, i.e., the generator favors generating one group of samples over the others at the test time. In this work, we shed light on the source of this representation bias in GANs along with a straightforward method to overcome this problem. We first show on two widely used datasets (MNIST, SVHN) that when the norm of the gradient of one group is more important than the other during the discriminator's training, the generator favours sampling data from one group more than the other at test time. We then show that controlling the groups' gradient norm by performing group-wise gradient norm clipping in the discriminator during the training leads to a more fair data generation in terms of representational fairness compared to existing models while preserving the quality of generated samples.
Emotion detection of social data: APIs comparative study

The development of emotion detection technology has emerged as a highly valuable possibility in the corporate sector due to the nearly limitless uses of this new discipline, particularly with the unceasing propagation of social data. In recent years, the electronic marketplace has witnessed the establishment of a large number of start-up businesses with an almost sole focus on building new commercial and open-source tools and APIs for emotion detection and recognition. Yet, these tools and APIs must be continuously reviewed and evaluated, and their performances should be reported and discussed. There is a lack of research to empirically compare current emotion detection technologies in terms of the results obtained from each model using the same textual dataset. Also, there is a lack of comparative studies that apply benchmark comparison to social data. This study compares eight technologies; IBM Watson NLU, ParallelDots, Symanto-Ekman, Crystalfeel, Text to Emotion, Senpy, Textprobe, and NLP Cloud. The comparison was undertaken using two different datasets. The emotions from the chosen datasets were then derived using the incorporated APIs. The performance of these APIs was assessed using the aggregated scores that they delivered as well as the theoretically proven evaluation metrics such as the micro-average of accuracy, classification error, precision, recall, and f1-score. Lastly, the assessment of these APIs incorporating the evaluation measures is reported and discussed.
The way we cite: common metadata used across disciplines for defining bibliographic references

Current citation practices observed in articles are very noisy, confusing, and not standardised, making identifying the cited works problematic for hu-mans and any reference extraction software. In this work, we want to investigate such citation practices for referencing different types of entities and, in particular, to understand the most used metadata in bibliographic refer-ences. We identified 36 types of cited entities (the most cited ones were articles, books, and proceeding papers) within the 34,140 bibliographic references extracted from a vast set of journal articles on 27 different subject ar-eas. The analysis of such bibliographic references, grouped by the particular type of cited entities, enabled us to highlight the most used metadata for de-fining bibliographic references across the subject areas. However, we also noticed that, in some cases, bibliographic references did not provide the essential elements to identify the work they refer to easily.
Provable concept learning for interpretable predictions using variational autoencoders

In safety-critical applications, practitioners are reluctant to trust neural networks when no interpretable explanations are available. Many attempts to provide such explanations revolve around pixel-based attributions or use previously known concepts. In this paper we aim to provide explanations by provably identifying \emph{high-level, previously unknown ground-truth concepts}. To this end, we propose a probabilistic modeling framework to derive (C)oncept (L)earning and (P)rediction (CLAP) -- a VAE-based classifier that uses visually interpretable concepts as predictors for a simple classifier. Assuming a generative model for the ground-truth concepts, we prove that CLAP is able to identify them while attaining optimal classification accuracy. Our experiments on synthetic datasets verify that CLAP identifies distinct ground-truth concepts on synthetic datasets and yields promising results on the medical Chest X-Ray dataset.
Iterative Learning for Semi-automatic Annotation Using User Feedback

With the advent of state-of-the-art models based on Neural Networks, the need for vast corpora of accurately labeled data has become fundamental. However, building such datasets is a very resource-consuming task that additionally requires domain expertise. The present work seeks to alleviate this limitation by proposing an interactive semi-automatic annotation tool using an incremental learning approach to reduce human effort. The automatic models used to assist the annotation are incrementally improved based on user corrections to better annotate the next data. To demonstrate the effectiveness of the proposed method, we build a dataset with named entities and relations between them related to the crime field with the help of the tool. Analysis results show that annotation effort is considerably reduced while still maintaining the annotation quality compared to fully manual labeling.
The Effects of Grammar Mastery and Critical Thinking on Students’ Speaking Skill

The purpose of the research is to obtain empirical data and analyze about whether or not there are the effects of grammar mastery and critical thinking on students’ speaking skill. The reseach was conducted at the state senior high school in Serang. The research method was survey with multiple-correlation technique. 80 students out of the state senior high school, grade X were chosen to be samples. Data collections include 1 questionnaire and 2 multiple-choice tests. The scale used for questionnaire was Likert Scale. The validity was calculated by using Person-Product Moment, and reliability obtained from Cronbach Alpha is 0.713. Research findings show three points. Firstly, there are significant effects of grammar mastery and critical thinking towards students’ speaking skill where multiple-correlation coefficient (R) is 0.696 in significance grade 5% ( = 0,05) further Fobserved is higher than Ftable (36.202 &gt; 2.735). Secondly, there was a significant effect of grammar mastery and students’ speaking skill even though the effect is slightly weak where ry1 is 0.365, tobserved=3.725&gt;ttable=1.994 with Sig score is lower than significance grade (0.000 &lt; 0.05). Thirdly, there is a significant effect of critical thinking and students’ speaking skill where ry2 is 0.626, tobserved=7.242&gt;ttable=1.994 with Sig score lower than the significance value (0.000 &lt; 0.05).
On the Expressivity and Applicability of Model Representation Formalisms

A number of first-order calculi employ an explicit model representation formalism for automated reasoning and for detecting satisfiability. Many of these formalisms can represent infinite Herbrand models. The first-order fragment of monadic, shallow, linear, Horn (MSLH) clauses, is such a formalism used in the approximation refinement calculus. Our first result is a finite model property for MSLH clause sets. Therefore, MSLH clause sets cannot represent models of clause sets with inherently infinite models. Through a translation to tree automata, we further show that this limitation also applies to the linear fragments of implicit generalizations, which is the formalism used in the model-evolution calculus, to atoms with disequality constraints, the formalisms used in the non-redundant clause learning calculus (NRCL), and to atoms with membership constraints, a formalism used for example in decision procedures for algebraic data types. Although these formalisms cannot represent models of clause sets with inherently infinite models, through an additional approximation step they can. This is our second main result. For clause sets including the definition of an equivalence relation with the help of an additional, novel approximation, called reflexive relation splitting, the approximation refinement calculus can automatically show satisfiability through the MSLH clause set formalism.
Ship classification from overhead imagery using synthetic data and domain adaptation

In this paper, we revisit the problem of classifying ships (maritime vessels) detected from overhead imagery. Despite the last decade of research on this very important and pertinent problem, it remains largely unsolved. One of the major issues with the detection and classification of ships and other objects in the maritime domain is the lack of substantial ground truth data needed to train state-of-the-art machine learning algorithms. We address this issue by building a large (200k) synthetic image dataset using the Unity gaming engine and 3D ship models. We demonstrate that with the use of synthetic data, classification performance increases dramatically, particularly when there are very few annotated images used in training.
The Effectiveness of Affective and Social Strategies to Teach Speaking for Students with High and Low self Confidence

The English subject in formal school becomes one of the important material in Indonesia. The purpose of learning a foreign language here is; to make the students be able to communicate in using English not only in written form but also orally. This research is quantitative, where the findings are presenting in the graphic and diagram with the calculation of results. The findings show, there are pre-test, materials delivering and post-test into two class. It shows in the pre-test, IPS I is better than IPS II, it can be seen by the score and the performance in the video. During the pre-test, IPS I is more variety in uttering the language, they can enhance the utterances although it still not be perfect. Treatment is done after pre-test, in this activity, the researcher gives materials in greeting, how to make good utterances and how to enhance the utterances during speaking so that the time while the test can be more efficient. After that, in post-test activity, IPS I is still dominant in a better score and perform better because they can use phatic and enhance their utterances and make their time test more efficient. Finally, that research has the suggestion for the teacher in teaching speaking if using social and affective strategies are effective to develop the ability of the students in oral language.
Anytime Multi-arm Task and Motion Planning for Pick-and-Place of Individual Objects via Handoffs

Automation applications are pushing the deployment of many high DoF manipulators in warehouse and manufacturing environments. This has motivated many efforts on optimizing manipulation tasks involving a single arm. Coordinating multiple arms for manipulation, however, introduces additional computational challenges arising from the increased DoFs, as well as the combinatorial increase in the available operations that many manipulators can perform, including handoffs between arms. The focus here is on the case of pick-and-place tasks, which require a sequence of handoffs to be executed, so as to achieve computational efficiency, asymptotic optimality and practical anytime performance. The paper leverages recent advances in multi-robot motion planning for high DoF systems to propose a novel multi-modal extension of the dRRT* algorithm. The key insight is that, instead of naively solving a sequence of motion planning problems, it is computationally advantageous to directly explore the composite space of the integrated multi-arm task and motion planning problem, given input sets of possible pick and handoff configurations. Asymptotic optimality guarantees are possible by sampling additional picks and handoffs over time. The evaluation shows that the approach finds initial solutions fast and improves their quality over time. It also succeeds in finding solutions to harder problem instances relative to alternatives and can scale effectively as the number of robots increases.
Blood Classification Technique using Deep Learning

Abstract: This study aims to develop a system to distinguish blood cells from images. Precise and programmed analysis of blood cell images has been considered as an effective way for the determination of types of blood cells such as Eosinophil, Lymphocyte, Monocyte, Neutrophil. In this work, we extracted different blood cell features such as Eosinophil, Lymphocyte, Monocyte, Neutrophil and then applied convolutional neural network based models for the detection of types of blood cells with photographs involved in structured analysis. It described the innovative solution that provides efficient classification detection and deep learning with convolutional neural networks (CNNs) has achieved great success in the classification of various types of blood cells. A variety of neuron-wise and layer-wise visualization methods were applied using a CNN, trained with a publicly available blood cells given image dataset. So, it observed that neural networks can capture the colors and textures of lesions specific to respective type, which resembles human decision-making. And this model to deploy Django web framework.. We experi
Credit Card Fake Revealing Analysis and Prediction of Algorithms

Abstract: Credit cards are frequently used in conjunction with the Internet to make payments. The project primarily aims to detect credit card fraud in the real world. Today, our lives have become more and more dependent on online transactions. As technology advances, the number of fraud cases also increases, and ultimately, a fraud detection algorithm needs to be developed to accurately find and eradicate fraudulent activity. Some Machine Learning algorithms can be applied to collect data to address this problem. These machine learning algorithms ultimately optimize the accuracy of the result data. Technique performance is evaluated based on accuracy, f1 score, and precision and recall. Then, processing some of the technique provided attributes identifies the fraud detection and provides the visualization of the graphical model. Classification based algorithms such as logistic regression, random forest and KNN for processing highly unbalanced datasets.
Identical Image Extraction from PDF Document Using LBP (Local Binary Patterns) and RGB (Red, Green and Blue) Color Features

Abstract: Content based image retrieval (CBIR) has become a main research area in multimedia applications. In the literature, there is lot of papers focusing on the content-based image retrieval in order to extract the semantic information within the query concept. The content based image retrieval aims to find the similar images from PDF document against a query image. Generally, the similarity between the representative features of the query image and PDF images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. In this project we have developed a method to extract the images present in PDF documents based on appropriate features extracted from query image such as color and local binary pattern features to find perfect matching image is present in PDF or not. Based on similarity analysis model we will display the query input image is present in PDF documents or not. If query matching image is present in the PDF documents then we will display the list corresponding PDF documents using suitable applications to show the presence.
Microservices Enabled E-Commerce Web Application

Abstract: Microservices are emerging as a new computing paradigm which is a suitable complementation of cloud computing. Microservices decompose traditional monolithic applications into a set of fine-grained services which can be independently developed, tested and deployed. In this work we have developed an e-commerce web application using microservices architecture. The drawbacks of monolithic architecture are overcome by microservices in building the e-commerce web application. The web application is developed using MERN stack technology. The services developed in the application are containerized using Docker containers. The containerized services are then managed using Kubernetes which is an orchestration platform. Skaffold is used in the application to sync local code files with the cold files in Docker. The events emitted by services are managed using an event bus called NATS streaming server.
Hyperloop

Abstract: The modification and modernization of renewable source of energy in the transport segment has brought upon the existence a modern technology of transportation through vacuum reducing forces. Hyperloop Transportation System are proposed high-speed systems that have chambers that are shot at high speeds of about 1000km/hr through a dragged-out hollow cylinder. As initiated by Elon Musk along with a group of engineers in 2013 to provide the fastest means of transportation. The system works on the principal of electromagnetic force of attraction. The transportation system contains a tube that is at low pressure which is surrounded by thin air which allows the case to travel thoroughly through the cylindrical tube.
Why the Automation of Regulation Can Obstruct Business Processes

Abstract The growing regulatory pressure on increasingly digitized businesses, for example to combat the growing number of corporate fraud cases, can have an obstructive effect on the execution of automated business processes. Such security-related obstructions occur when the implementation of regulations, that is, the enforcement of so-called safety properties, blocks the execution of business processes – in particular, the so-called liveness property of process completion. Those obstructions exemplify the conflicting goals between business processes and classic IT security. Automation is no panacea against regulation acting obstructive. Adequate and effective application of regulation builds the basis against fraud. However, there is no way to avoid automation when IT methods are used to generate competitive advantages. An obstruction results from introducing IT security on business processes, particularly authorization and further security policies such as separation of duties. This chapter leads to this problem and describes how to widen this restricted behavior of business processes resulting from security controls to the broader scope that compliance provides as part of corporate governance. By handling obstructions, security in business processes is supposed to be improved. For this purpose, an indicator-based view of security that extends the classic IT security controls will be introduced.
On Learning the Transformer Kernel

In this work we introduce KERNELIZED TRANSFORMER, a generic, scalable, data driven framework for learning the kernel function in Transformers. Our framework approximates the Transformer kernel as a dot product between spectral feature maps and learns the kernel by learning the spectral distribution. This not only helps in learning a generic kernel end-to-end, but also reduces the time and space complexity of Transformers from quadratic to linear. We show that KERNELIZED TRANSFORMERS achieve performance comparable to existing efficient Transformer architectures, both in terms of accuracy as well as computational efficiency. Our study also demonstrates that the choice of the kernel has a substantial impact on performance, and kernel learning variants are competitive alternatives to fixed kernel Transformers, both in long as well as short sequence tasks.
Motion Detection of Human on Video: State of the Art

In current scenario, almost everyone is using camera in smart phones; in all buildings, CCTV is fixed for surveillance. Despite of all these, crime rate is increasing more and more every second. Human motion detection is one of the recent topics in computer vision. Human motion detection in video is used in many fields like medicine, military, cybercrime, road traffic control system, etc. The videos which are recorded in CCTV may or may not be clear due to various reasons. Hence, human motion detection is very much needed to identify the motion of human in the video which is recorded by CCTV or any other sources. This human motion detection will help to detect the crime happening in the world. There are many algorithms and techniques introduced by various authors. In this paper, I am going to analyse the various techniques introduced by various authors.
Walking Assistant for Vision Impaired by Using Deep Learning

In this paper, we address the people in the globe living with a visual impairment or living in isolation. Although it is already very difficult to live with sight loss, still we try to provide some support for them to access the environment freely. The lack of limited accessibility to activities and information, the societal stigma and the deficiency of joblessness are all factors frequently leading blind or low-vision individuals in isolation. We have used CNN technique and developed a method for processing an image and tells about the obstacle and depth of potholes to make the walking easy for visually impaired person. CNN has almost dominated computer vision and shows powerful performance on object classification. We create our own network on the basis of RCNN algorithm in which we consider 128 filters having size of (2 × 2). This network consists one input layer, nine middle layers, and five final layers and train the same network with CINIC-10 dataset having initial learn rate is 0.001, Learn rate drop factor is 0.1; learn rate drop period is 8, and maximum epochs are 40. After validation of our trained network, it gives the accuracy of 64.7%, and it wonderfully makes out results.
Results of multi-agent system and ontology to manage ideas and represent knowledge in a challenge of creativity

This article is about an intelligent system to support ideas management as a result of a multi-agent system used in a distributed system with heterogeneous information as ideas and knowledge, after the results about an ontology to describe the meaning of these ideas. The intelligent system assists participants of the creativity workshop to manage their ideas and consequently proposing an ontology dedicated to ideas. During the creative workshop many creative activities and collaborative creative methods are used by roles immersed in this creativity workshop event where they share knowledge. The collaboration of these roles is physically distant, their interactions might be synchrony or asynchrony, and the information of the ideas are heterogeneous, so we can say that the process is distributed. Those ideas are writing in natural language by participants which have a role and the ideas are heterogeneous since some of them are described by schema, text or scenario of use. This paper presents first, our MAS and second our Ontology design.