Reviewerly/exHarmony huggingface dataset:
Robust Semi-Supervised Semantic Segmentation Based on Self-Attention and Spectral Normalization

The application of adversarial learning for semi-supervised semantic image segmentation based on convolutional neural networks can effectively reduce the number of manually generated labels required in the training process. However, the convolution operator of the generator in the generative adversarial network (GAN) has a local receptive field, so that the long-range dependencies between different image regions can only be modeled after passing through multiple convolutional layers. The present work addresses this issue by introducing a self-attention mechanism in the generator of the GAN to effectively account for relationships between widely separated spatial regions of the input image with supervision based on pixel-level ground truth data. In addition, the adjustment of the discriminator has been demonstrated to affect the stability of GAN training performance. This is addressed by applying spectral normalization to the GAN discriminator during the training process. The proposed stable self-attention adversarial learning semi-supervised semantic image segmentation network is demonstrated to provide superior image segmentation performance compared with the results of current semi-supervised and fully-supervised semantic image segmentation techniques.
Implementation of data compression scheme for SAR payload on small satellites

This paper reports on a data compression scheme that can be used to reduce the data rate from Synthetic Aperture Radar (SAR) payload on small satellites. A straight forward recording of the imaging data results in very high data rates of the order 5 Gbps which causes complexities in the payload satellite interface and will consume onboard data storage rapidly, thus limiting the number of imaging operations that can be stored on board. A scheme for data rate reduction, namely Block Adaptive Quantization (BAQ) can be implemented to put the data rates well within bandwidth at the same time meeting image quality requirements of SAR payloads.
Transfer Learning in Smart Home Scenario

With the development in sensor technology ambiance of human beings are becoming intelligent to cater to the needs and enhance their living standards. As human is dynamic in nature; therefore, a solution should be tailored to the needs of an individual. This requires the capability to understand, analyze and learn the behavior of a human being. To learn human behavior, machine learning algorithms require a sufficient amount of training data. Collection of data and labeling data consumes an ample amount of time. Also, it is not possible to collect data in every possible scenario. To deal with the mentioned problem, in the paper the concept of Transfer learning has been leveraged. The foremost requirement is to calculate the similarity and differences between a selected source domain and a target domain. For the calculation of similarities and differences, multiple parameters are defined in this paper. Multiple experiments in different scenarios were carried out to support the proposed approach. Results obtained show the effects of transfer learning in the domain of smart homes.
Development of E-Commerce Approach to Service Oriented Architecture Using SOMA Methodology

This study aims to provide solutions to problems in the e-commerce system at PT. XYZ This is due to the number of bugs in the e-commerce system at PT. XYZ Some products cannot enter the shopping design page; there are no features that should be available in e-commerce systems such as shipping costs. An e-commerce system with a Service Oriented Architecture (SOA) approach is needed to overcome these problems. The results of the study found that several web services can be combined into one part to help the development process become better and easier.
Creation of Image Segmentation Classifiers for Sign Language Processing for Deaf and Dumb

Recognition of sign language is an emerging area of research in the domain of gesture recognition. Research has been carried out around the world on sign language recognition, for many sign languages. The basic phase of sign language recognition systems is accurate hand segmentation. This paper used Otsu's technique of segmentation to create an improved vision-based recognition of sign language. There are about 466 million users suffering from hearing loss globally, of whom no. of kids is 34 million. Deaf people have very little or no capacity to hear. For communication, they use sign language. People in distinct areas of the globe use distinct sign languages which is very small in amount compared to spoken languages. Our goal is to create a static-gesture recognizer, a multi-class classifier that predicts the gestures of static sign language. In the proposed work, we identified the hand in the raw image and provided the static gesture recognizer (the multi-class classifier) with this section of the image. We build multi-class classifiers from the scikit learning library by first building the data set, each image being converted into a feature vector (X) and each has a label that matches the sign language alphabet denoted by (Y). Our predicted classifiers analyzed 65% of the said images with clarity.
Improved Speech Command Classification System for Sinhala Language based on Automatic Speech Recognition

The recent advancements in conversational Artificial Intelligence (AI) are fastly getting integrated with every realm of human lives. Conversational agents who can learn, understand human languages and mimic the human thinking process have already created a revolution in human lifestyle. Understanding the intention of a speaker from his natural speech is a significant step in conversational AI. A major challenge that hinders the efficacy of this process is the lack of language resources. In this research, we address this issue and develop a domain-specific speech command classification system for the Sinhala language, one of the low-resourced languages. An effective speech command classification system can be utilized in several value added applications such as speech dialog systems. Our speech command classification system is developed by integrating Automatic Speech Recognition (ASR) and Natural Language Understanding (NLU). The ASR engine is implemented using Gaussian Mixture Model-Hidden Markov Model (GMM-HMM) and it converts a Sinhala speech command into a corresponding text representation. The text classifier, which is implemented as an ensemble unit of several classifiers, predicts the intent of the speaker when provided with the above text output. In this paper, we discuss and evaluate various algorithms and techniques that can be utilized to optimize the performance of both the ASR and text classifier. As well, we present our novel Sinhala speech data corpus of 4.15[Formula: see text]h which is based on the banking domain. As the final outcome, our system reports its Sinhala speech command classification accuracy as 91.03%. It shows that our system outperforms the state-of-the-art speech-to-intent mapping systems developed for the Sinhala language. The individual evaluation on the ASR system reports a 9.91% Word Error Rate and a 19.95% Sentence Error Rate, suggesting the applicability of advanced speech recognition techniques despite the limited language resources. Finally, our findings deliver useful insights on further research in speech command classification in the low-resourced context.
Research of the Context Recommendation Algorithm Based on the Tripartite Graph Model in Complex Systems

With the rapid development of information technology, the information overload has become a very serious problem in web information environment. The personalized recommendation came into being. Current recommending algorithms, however, are facing a series of challenges. To solve the problem of the complex context, a new context recommendation algorithm based on the tripartite graph model is proposed for the three-dimensional model in complex systems. Improving the accuracy of the recommendation by the material diffusion, through the heat conduction to improve the diversity of the recommended objects, and balancing the accuracy and diversity through the integration of resources thus realize the personalized recommendation. The experimental results show that the proposed context recommendation algorithm based on the tripartite graph model is superior to other traditional recommendation algorithms in recommendation performance.
Implementation of Cryptographic Approaches in Proposed Secure Framework in Cloud Environment

Through Internet, cloud computing shows extraordinary potential to give on-demand services to its clients with higher suppleness in an economical manner. Security is the primary need for this leading approach of computing ability. This paper presents the analysis and implementation of some symmetric cryptographic algorithms. It finds the best symmetric method and combines it with asymmetric approach and shows the importance of hybrid encryption model for cloud security. Moreover, this paper presents a framework by integrating CAPTCHA, Two Factor Authentication (TFA), hybrid encryption approach, and separate server concepts like authentication server with cryptographic server. It also suggests the use of optimization for attaining availability and security strength.
Exploring Deep Convolutional Neural Networks as Feature Extractors for Cell Detection

Among different biological studies, the analysis of leukocyte recruitment is fundamental for the comprehension of immunological diseases. The task of detecting and counting cells in these studies is, however, commonly performed by visual analysis. Although many machine learning techniques have been successfully applied to cell detection, they still rely on domain knowledge, demanding high expertise to create handcrafted features capable of describing the object of interest. In this study, we explored the idea of transfer learning by using pre-trained deep convolutional neural networks (DCNN) as feature extractors for leukocytes detection. We tested several DCNN models trained on the ImageNet dataset in six different videos of mice organs from intravital video microscopy. To evaluate our extracted image features, we used the multiple template matching technique in various scenarios. Our results showed an average increase of 5.5% in the \(\text {F}_{1}\)-score values when compared with the traditional application of template matching using only the original image information. Code is available at: https://github.com/brunoggregorio/DCNN-feature-extraction.
Toxicity in Texts and Images on the Internet

In this paper we studied the most typical characteristics of toxic images on the web. To get a set of toxic images we collected a set of 8800 images from 4chan.org. Then we trained a BERT-based classifier to find toxic texts with accompanying images. We manually labelled approximately 2000 images accompanying these texts. This revealed that toxic content in images does not correlate with toxic content in texts. On top of manually annotated images there was trained a neural network that inferred labels for unannotated pictures. Neural network layer activations for these images were clustered and manually classified to find the most typical ways of expressing aggression in images. We find that racial stereotypes are the main cause of toxicity in images (https://github.com/denis-gordeev/specom20).
Hybrid Pooling Networks for Few-shot Learning

We propose Hybrid Pooling Networks (HPN) for few-shot learning, where a classifier must learn to recognize new classes not seen in the training set, given only few examples from each new class. HPN learns deep similarity metrics between labeled and unlabeled instances, consisting of two independent networks named Bilinear Pooling Network (BPN) and Element-wise Interaction Network (EIN). By using bilinear pooling, BPN produces a combination of the inputs containing comprehensive information about pair-wise similarities. In EIN, we apply "alignment" on features, making feature-wise interactions on input images. This operation contributes to capturing finer similarities. Both of BPN and EIN bring significant improvements on miniImageNet and Omniglot few-shot datasets. We incorporate the two networks to form HPN by simply averaging their outputs, showing much better performance. Besides, all of our models are easily extended to zero-shot learning. Experiments on CUB and AWA2 demonstrate that our approaches can achieve state-of-the-art performance on zero-shot tasks.
Controllable Question Generation via Sequence-to-Sequence Neural Model with Auxiliary Information

Automatic question generation (QG) has found applications in the education sector and to enhance human-machine interactions in chatbots. Existing neural QG models can be categorized into answer-unaware and answer-aware models. One of the main challenges faced by existing neural QG models is the degradation in performance due to the issue of one-to-many mapping, where, given a passage, both answer (query interest/question intent) and auxiliary information (context information present in the question) can result in different questions being generated. We propose a controllable question generation model (CQG) that employs an attentive sequence-to-sequence (seq2seq) based generative model with copying mechanism. The proposed CQG also incorporates query interest and auxiliary information as controllers to address the one-to-many mapping problem in QG. Two variants of embedding strategies are designed for CQG to achieve good performance. To verify its performance, an automatic labeling scheme for harvesting auxiliary information is first developed. A QG dataset is also annotated with auxiliary information from a reading comprehension dataset. Performance evaluation shows that the proposed model not only outperforms existing QG models, it also has the potential to generate multiple questions that are relevant given a single passage.
A Topic Modeling Approach To Evaluate The Comments Consistency To Source Code

A significant amount of source code in software systems is made up of comments, parts of the code that are ignored by the compiler. Comments in the code are a primary source for system documentation. These are crucial for the work of software maintainers, as a basis for code traceability, for maintenance activities, but also for the use of the code itself as a library or framework in other projects. Although many software developers consider comments important, existing approaches to software quality analysis mainly disregard code comments and focus only on source code. This paper presents an approach, based on topic modeling, for analyzing the comments consistency to the source code. A model was provided to analyze the quality of comments in terms of consistency since comments should be consistent with the source code they refer to. The results show a similarity in the trend of topic distribution and it emerges that almost all classes are associated with no more than 3 topics.
Requirements Re-usability in Global Software Development: A Systematic Mapping Study

In global software development, requirements re-usability is a common practice which ultimately helps to maintain project quality and reduce both development time and cost. However, when a large-scale project is distributed, there are some critical factors needed to be maintained and managed for reusing requirements and it is considered a challenging job to interrelate the requirements between two identical projects. In this study, we have pointed out 48 challenges faced and 43 mitigation techniques used when implementing requirements re-usability in global software development projects among distributed teams. The challenges distributed teams frequently encounter can be divided into three considering issues as Communication, Coordination and Control of distributed teams in global software development. The results from this study can be used to plan development strategies while reusing requirements in distributed manners.
Sparse Attributed Network Embedding via Adaptively Aggregating Neighborhood Information

Network representation learning (NRL), which aims to map nodes in a network into low-dimensional vectors, has attracted wide attention due to its potential on various net-work applications. Recently, attributed network embedding that incorporates both network structure and node attributes has shown a good performance in NRL. However, most existing methods cannot achieve promising results in sparse attributed networks because of the lack of structural information. In order to further alleviate the negative effect of sparseness, we propose a novel model named AANI (Adaptively Aggregating Neighborhood Information). Specifically, AANI exploits the information of current node and its neighborhood in a smoothing way to obtain more informative node representations. At the same time, AANI introduces attention mechanism to adaptively aggregate the smoothed information according to their respective importance. We conduct extensive experiments on three real-world datasets which demonstrate that AANI outperforms the state-of-the-art embedding methods in sparse attributed networks.
Sensor Data for Human Activity Recognition: Feature Representation and Benchmarking

The field of Human Activity Recognition (HAR) focuses on obtaining and analysing data captured from monitoring devices (e.g. sensors). There is a wide range of applications within the field; for instance, assisted living, security surveillance, and intelligent transportation. In HAR, the development of Activity Recognition models is dependent upon the data captured by these devices and the methods used to analyse them, which directly affect performance metrics. In this work, we address the issue of accurately recognising human activities using different Machine Learning (ML) techniques. We propose a new feature representation based on consecutive occurring observations and compare it against previously used feature representations using a wide range of classification methods. Experimental results demonstrate that techniques based on the proposed representation outperform the baselines and a better accuracy was achieved for both highly and less frequent actions. We also investigate how the addition of further features and their pre-processing techniques affect performance results leading to state-of-the-art accuracy on a Human Activity Recognition dataset.
Sentiment Analysis for Smartphone Operating System: Privacy and Security on Twitter Data

The aim of the study was to investigate the privacy and security of the user data on Twitter. For gathering the essential information, more than two million relevant tweets through the span of two years were used to conduct the study. In addition, we are classifying sentiment of Twitter data by exhibiting results of a machine learning by using the Naive Bayes algorithm. Although this algorithm is time consuming compared to the listing method yet can lead to effective estimation relatively. The tweets are extracted and pre-processed and then categorized them in neutral, negative and positive sentiments. By applying the chosen methodology, the study would end up in identifying the most effective mobile operating systems according to the sentiments of social media users. Additionally, the application of the algorithm needs to meet the privacy and security needs of Twitter users in order to optimize the use of social media intelligence. The approach will help in assessing the competitive intelligence of the Twitter data and the challenges in the form of privacy and- security of the user content and their contextual information simultaneously. The findings of the empirical research show that users are more concerned about the privacy and security of iOS compared to Android and Windows phone.
Joint Pedestrian Detection and Risk-level Prediction with Motion-Representation-by-Detection

The paper presents a pedestrian near-miss detector with temporal analysis that provides both pedestrian detection and risk-level predictions which are demonstrated on a self-collected database. Our work makes three primary contributions: (i) The framework of pedestrian near-miss detection is proposed by providing both a pedestrian detection and risk-level assignment. Specifically, we have created a Pedestrian Near-Miss (PNM) dataset that categorizes traffic near-miss incidents based on their risk levels (high-, low-, and no-risk). Unlike existing databases, our dataset also includes manually localized pedestrian labels as well as a large number of incident-related videos. (ii) Single-Shot MultiBox Detector with Motion Representation (SSD-MR) is implemented to effectively extract motion-based features in a detected pedestrian. (iii) Using the self-collected PNM dataset and SSD-MR, our proposed method achieved +19.38% (on risk-level prediction) and +13.00% (on joint pedestrian detection and risk-level prediction) higher scores than that of the baseline SSD and LSTM. Additionally, the running time of our system is over 50 fps on a graphics processing unit (GPU).
A Filtering Process to Enhance Topic Detection and Labelling

In the digitization air, it is very important to detect and analyze the related topics to some discussions, occurred in social media or to label some visited web pages or documents. This information could be very helpful to the process of personalization as well as user satisfaction. There are various and different methods that study and deal with a huge data to provide insights into user behaviors. In this paper, we propose a filtering process that enhances topic detection and labelling. The latter aims to compact the result delivered by inferential algorithms such as Latent Dirichlet Allocation and Dirichlet Mixture Model. Our filtering process relies on words dependency on each contextual use for delivering high correlated label. Indeed, we use Word2vec as well as N-grams to eliminate non-significant words in each topic. We also use Hellinger distance to aggregate redundant words to the appropriate topic. Besides, we eliminate the non-reliable topics according to some metric. We associate this proposal to different topic-modeling algorithms. Experiments demonstrate the effectiveness of the made association between inferential model and our filtering process compared to the state of the art. We also use different textual data to validate our proposal.
Automation of the Reliability Modelling Using GSDFD Framework

The article presents the test results of a program code generator for streaming data processing in tasks of automation of technological processes. The framework automatically generates a program according to a visual model compiled by the user, which ensures simplicity and affordability of independent development of unique automation tools. As a cognitive model for representing the engineering problem, a data flow diagram was chosen, which is widely known, easy to understand, and is in good agreement with the architecture Pipe-and-Filter. This allowed us to develop a web application for the automatic generation of program code with implementation according to the Decorator template. The results of the trial operation of the cross-platform software Generator Source Data Flow Diagram Framework are analyzed in detail on the example of calculating the reliability of a computer system using the RBD method. The metamodel for calculating reliability, taking into account the accepted limitations for the computing system, was transformed into a data flow model with internal storage. Based on the auto-generated code, we implemented the program and measured software metric. A simple implementation of the same task in monolith architecture was used as a reference. The studies confirmed the flexibility of the developed framework for automating tasks with non-linear solutions and revealed ways to further modernize the framework to reduce manual coding by including a description of the input parameters in the input pipe.