Reviewerly/exHarmony huggingface dataset:
Designing a High-Performance Deep Learning Theoretical Model for Biomedical Image Segmentation by Using Key Elements of the Latest U-Net-Based Architectures

Deep learning (DL) has experienced an exponential development in recent years, with major impact in many medical fields, especially in the field of medical image and, respectively, as a specific task, in the segmentation of the medical image. We aim to create a computer assisted diagnostic method, optimized by the use of deep learning (DL) and validated by a randomized controlled clinical trial, is a highly automated tool for diagnosing and staging precancerous and cervical cancer and thyroid cancers. We aim to design a high-performance deep learning model, combined from convolutional neural network (U-Net)-based architectures, for segmentation of the medical image that is independent of the type of organs/tissues, dimensions or type of image (2D/3D) and to validate the DL model in a randomized, controlled clinical trial. We used as a methodology primarily the analysis of U-Net-based architectures to identify the key elements that we considered important in the design and optimization of the combined DL model, from the U-Net-based architectures, imagined by us. Secondly, we will validate the performance of the DL model through a randomized controlled clinical trial. The DL model designed by us will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. The combined model we designed takes into account the key features of each of the architectures Overcomplete Convolutional Network Kite-Net (Kite-Net), Attention gate mechanism is an improvement added on convolutional network architecture for fast and precise segmentation of images (Attention U-Net), Harmony Densely Connected Network-Medical image Segmentation (HarDNet-MSEG). In this regard, we will create a comprehensive computer assisted diagnostic methodology validated by a randomized controlled clinical trial. The model will be a highly automated tool for diagnosing and staging precancers and cervical cancer and thyroid cancers. This would help drastically minimize the time and effort that specialists put into analyzing medical images, help to achieve a better therapeutic plan, and can provide a “second opinion” of computer assisted diagnosis.
Probabilistic and Neural Network Based POS Tagging of Ambiguous Nepali text: A Comparative Study

There are various approaches to the problem of assigning each word of a text with a parts-of-speech tag, which is known as Part-Of-Speech (POS) tagging. This article presents a comprehensive study and comparison of two different techniques of Part-of-Speech (POS) Tagging for Nepali text viz. Hidden Markov Model (HMM) and General Regression Neural Network (GRNN) based. The POS taggers resolves the problem of ambiguity in POS tagging of Nepali text through two different approaches. The evaluation of the taggers are done on the corpora developed and provided by TDIL (Technology Development for Indian Languages). Apart from corpora, python and Java programming languages and the NLTK Toolkit library has been used for implementation. Both the tagger achieves accuracy of 100 percent for known words (with no ambiguity), 58.29 percent (HMM) and 60.45 percent (GRNN) for ambiguous words and 85.36 percent (GRNN) for non- ambiguous unknown words.
Visual Focus of Attention Estimation in 3D Scene with an Arbitrary Number of Targets

Visual Focus of Attention (VFOA) estimation in conversation is challenging as it relies on difficult to estimate information (gaze) combined with scene features like target positions and other contextual information (speaking status) allowing to disambiguate situations. Previous VFOA models fusing all these features are usually trained for a specific setup and using a fixed number of interacting people, and should be retrained to be applied to another one, which limits their usability. To address these limitations, we propose a novel deep learning method that encodes all input features as a fixed number of 2D maps, which makes the input more naturally processed by a convolutional neural network, provides scene normalization, and allows to consider an arbitrary number of targets. Experiments performed on two publicly available datasets demonstrate that the proposed method can be trained in a cross-dataset fashion without loss in VFOA accuracy compared to intra-dataset training.
Translation of Gesture-Based Static Sign Language to Text and Speech

Abstract As human beings, most of us convey our thoughts by speech and facial expressions, but according to the latest survey conducted, it was found that roughly 1% of the population in India is deaf and mute. These people communicate with others using hand gestures and facial expressions. However, most people find it difficult to understand gestures. To eliminate this gap, we develop static gesture classification based on sign language standards and then converting to text and speech of a given local dialect.
Review Rating Prediction Based on Text Polarity in the Czech and Slovak Languages

Online product reviews represent a valuable source of information about customer preferences. To process such reviews automatically, sentiment analysis is usually employed. Our primary goal in this paper is to determine the utility of an emotional lexicon capturing affective and attitudinal values of individual words for sentiment analysis of product reviews written in the Czech and Slovak languages. Both languages are highly inflectional and synthetic, and morphologically more complex than English. The specific challenge that we address is the prediction of the overall review rating from its text. (The rating is expressed on the scale of one to five stars.) We analyse the contribution of the “emotional” features derived from the emotional lexicon to the classification accuracy in the context of product reviews that are already pre-structured into positive and negative text segments by the reviewers. Contrary to our initial expectations, our investigation showed that the independent contribution of the “emotional” features towards classification accuracy was virtually nil in this context, and rather modest (about 7%) otherwise. These results imply that providing customers with an option to structure their product reviews into positive and negative segments might simplify their subsequent analysis significantly.
Imperialist Competitive Algorithm Optimised Adaptive Neuro Fuzzy Controller for Hybrid Force Position Control of an Industrial Robot Manipulator: A Comparative Study

Due to the nonlinear nature of the dynamics of a robot manipulator, controlling the robot meticulously is a challenging issue for control engineers. The key purpose of this paper is to provide an accurate intelligent method for refining the functionality of orthodox PID controller in the problem of force/position control of a robot manipulator with unspecified robot dynamics during external disturbances. A grouping of imperialist competitive algorithm (ICA) and adaptive neuro fuzzy logic is applied for the tuning of PID parameters. This, therefore, forms an intelligent structure, adaptive neuro fuzzy inference system with proportional derivative plus integral (ANFISPD + I) controller, which is more precise in definite and indefinite circumstances. To show the efficiency of the proposed method, this algorithm is applied to solve constrained dynamic force/position control problem of PUMA robot manipulator. The simulated results are compared to those achieved from other evolutionary techniques such as Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO). The simulation results exhibit that ICA-based ANFISPD + I outperforms the other evolutionary techniques.Highlights An ICA-ANFISPD + I-based hybrid force/position controller has been proposed.Easy to implement.Works well in the case of disturbances.Actuator Dynamics has been considered.External disturbances have been considered.Robot dynamics are unknown.
The Hybrid Methods of Object Detection and Localization in Images

The paper considers the task of watermark detection and localization in the images. Several heuristic methods for processing the characteristic images obtained from artificial neural network output for the task of identification are considered. The post-processing of characteristic images generated by the artificial neural network is used to improve the accuracy of detecting the presence of a watermark (semitransparent logo or inscription) in an image. A description of two approaches to image post-processing (the method of clustering areas and the method of selecting areas based on a template) and the results of experiments on a test sample of 500 images, artificially generated based on a set of HD resolution photos and a database of logos "UMD Logo Database for Research" are shown. The selection criteria for clusters potentially containing regions of interest can be the number of pixels in the cluster, the size of the cluster (determined based on dispersion or by using the algorithm for constructing the minimal convex hull). The use of such conditions for the selection of clusters gives a satisfactory result in the absence of noise in the form of compact artefacts.
What Do You Say? Comparison of Metrics for Post-editing Effort

The improvement in machine translation quality is creating a constantly increasing number of post-editing jobs. As a result, research geared toward ensuring an efficient translation process for post-editors has become more important than ever. To this end, being able to measure and predict the effort involved during the post-editing activity is essential. This work aims to assess whether simple post-editing effort metrics associated with the three effort dimensions (temporal, cognitive, and technical) correlate among themselves. Also, it seeks to examine whether these simple metrics are able to capture the variation in effort involved in addressing different error types. To address these objectives, we asked professional translators to post-edit a test suite of sentences that include one pre-selected error each and used a set of simple metrics to measure the post-editing effort. Results seem to indicate that the correlation between the metrics is rather low, which suggests that the use of a single metric to measure the effort might produce biased measurements. We also observe that, overall, metrics report very similar effort values for the different error types but some distinctions are noticeable, which allow us to rank error types per difficulty.
HyGraph: Accelerating Graph Processing with Hybrid Memory-centric Computing

Graph applications are challenging to run efficiently on conventional systems because of their large and irregular data. Several works have exploited near-data processing (NDP) based on emerging 3D-stacked memory to accelerate graph processing applications by offloading computations to massively parallel cores in the memory chip. Even though NDP can efficiently support parallel operations in a memory scalable way, it still requires data movement between memory and near-memory cores. Such data movement introduces large overhead because of the random data pattern in graph workloads. Furthermore, the parallelism provided by NDP systems is still insufficient for graph applications because of the limited number of processing cores. In this work, we tackle these challenges by integrating processing in-memory (PIM) technology in the NDP-based accelerator. We propose HyGraph, a software-hardware co-design for graph acceleration that exploits hybrid memory-centric computing technologies, including NDP and PIM. The design of HyGraph includes an optimization algorithm for hybrid memory layout, a run-time system combining both NDP and PIM processing flows, and customized hardware for efficiently enabling PIM functionality in NDP systems. Our experimental results show that HyGraph is up to 1.9× faster and 2.4× more energy-efficient than state-of-the-art memory-centric graph accelerators on several widely used graph algorithms with various real-world graphs.
Web Analytics for User Experience: A Systematic Literature Review

User experience is an essential software quality concept for companies nowadays. Development teams attempt to guarantee that their digital products provide a satisfying experience. Likewise, several evaluation methods have emerged to determine if the software product's interfaces are understandable, intuitive, and attractive to users. However, few of these methods suggest the use of data analytics for user experience evaluation. Many companies use tools that allow them to obtain multiple data about using their leading websites but do not use this information to generate relevant results that can be used to improve the design of their graphical interfaces. In this study, we present the results of conducting a systematic literature review to identify case studies that report web analytics usage to evaluate websites' user experience. The search retrieved a total of 315 papers. The databases used for this review were: Scopus, Web of Science, IEEExplore, and ACM Digital Library. After applying the inclusion and exclusion criteria, 18 relevant articles were obtained, which have allowed answering the research questions. The results show a tendency to incorporate data analysis to explain specific design problems or usability issues. Additionally, we have identified some methodological proposals that have been established on how to use data analytics for the user experience evaluation process and some metrics that could offer relevant information about the degree of ease of use of a website.
EFCam: Configuration-Adaptive Fog-Assisted Wireless Cameras with Reinforcement Learning

Visual sensing has been increasingly employed in industrial processes. This paper presents the design and implementation of an industrial wireless camera system, namely, EFCam, which uses low-power wireless communications and edge-fog computing to achieve cordless and energy-efficient visual sensing. The camera performs image pre-processing (i.e., compression or feature extraction) and transmits the data to a resourceful fog node for advanced processing using deep models. EFCam admits dynamic configurations of several parameters that form a configuration space. It aims to adapt the configuration to maintain desired visual sensing performance of the deep model at the fog node with minimum energy consumption of the camera in image capture, pre-processing, and data communications, under dynamic variations of application requirement and wireless channel conditions. However, the adaptation is challenging due primarily to the complex relationships among the involved factors. To address the complexity, we apply deep reinforcement learning to learn the optimal adaptation policy. Extensive evaluation based on trace-driven simulations and experiments show that EFCam complies with the accuracy and latency requirements with lower energy consumption for a real industrial product object tracking application, compared with four baseline approaches incorporating hysteresis-based adaptation.
Performance Measurement of Federated Learning on Imbalanced Data

AI often suffers from getting imbalanced data distribution as unequal samples in classes which will increase the bias of machine learning algorithms. This research aimed to study effects of skew data distribution towards development of data rebalancing on Federated learning (FL) in the future. This research sets left skewed distribution, right skewed distribution and symmetric distribution on Modified National Institute of Standards and Technology database (MNIST) to operate on Convolutional neural network (CNN) in FL mechanism. Then, FL's performance for working on these imbalanced distributions was tested. Results showed that in overview, the symmetric, left skewed, and right skewed distribution were not different in accuracy but theses imbalanced distributions were different in accuracy from the balanced distribution which has equal samples in all classes at significant level of.05. Standard deviation (SD) of data distribution was directly correlated with FL's accuracy in high level.
Joint nonlinear feature selection and continuous values regression network

The curse of dimensionality is a long-standing intractable issue in many machine learning and computer vision tasks. Feature selection, a data preprocessing technique, aims to select most discriminative feature subsets for improving performance of downstream machine learning tasks. However, most existing feature selection methods concentrate upon learning a linear relationship between data points and their labels, which causes that they are incapable of handling nonlinear complex data in real-world applications. In this paper, we first propose a novel end-to-end Nonlinear Feature Selective Networks (NFSN) that be able to select discriminative feature subsets while preserving their nonlinear structure by embedding a ℓ2,p-norm regularized hidden layer into designed continuous values regression networks. In addition, we propose an efficient optimization algorithm that joins back propagation algorithm and re-weighted optimization strategy to acquire derivative of all weights accurately. Experimental results on the nonlinear analog pulse signal and real-world datasets demonstrate the superiority of proposed method compared to some related methods on feature selection. Our source code available on: https://github.com/StevenWangNPU/NFSN.
Boosting the multiple aircraft online tracking performance via enriching the associated data with fused targets features

Multi aircraft tracking from an aerial view is a backbone for several military and civilian applications. Recent tracking by detection approaches was utilized to accomplish such multiple target tracking (MTT) tasks as Simple Online and Real-time Tracking (SORT). SORT is a strong and fast MTT, that employs a Kalman filter for the target motion parameters and the Hungarian method for the data association. But it discards the target appearance for resolving the association problem to preserve the real-time execution which results in increasing the number of (IDS) Identity Switches and decreasing the tracking accuracy. In this work, the target appearance information is incorporated alongside its geometry to leverage the tracking accuracy and reduce the tracklet fragments due to the high number of IDS. The target shape and contextually based feature of Histogram orientation of Gradient (HOG) are combined with its color histogram to enrich the Hungarian association with the appearance information. A recent-released multi-aircraft data set is utilized to examine the proposed improvement through a comparative experiment that reveals the MTT performance-boosting while keeping the real-time execution. The proposed method reduces the tracked targets IDsw by 60.97% that improves the tracker overall accuracy by 8.6% compared to the SORT tracker.
Virtually Interactive User Manual for Command and Control Systems Using Rule-Based Chatbot

Command and Control (C2) Systems are complex information systems consisting of humans, integrated hardware, and course of action. Traditionally, a documented user manual is provided to the operators to facilitate them with a strong understanding of the system. However, with increasing number of complex functionalities, it becomes difficult and time-consuming for operators to comprehend the document. This study proposes a conceptual framework of Interactive User Manual (C2IUM), which uses a rule-based chatbot and provides clear instructions in a chat-like interface using Natural Language Processing. The paper outlines three-layered methodology including (i) Dataset creation, (ii) Model building, and (iii) Integration. To verify the applicability of the tool, an experiment has been performed on C2 surveillance system and an accuracy of 82% is obtained. The proposed tool introduces automation and enables better customer support in terms of 24 * 7 accessibility, swift answers, and well-structured responses. The tool is robust and scalable.
Extending the Participant’s Voice to Guide Artificial Intelligence Transformation Using Futures Methodology and Layered User Story Analysis

Artificial intelligence (AI) is a vital driver of the next wave of automatisation of Industry 4.0. It impacts product-based and service-based organisations and is becoming an investment stream in organisational transformation strategies. The transformation teams that deploy AI use agile incremental methodologies that ideally match the learning and adaptation requirements for the machine, as well as the human user who is the source of data and requires a service response. This research outlines a layered analysis process of 65 user stories (a common agile method of obtaining user requirements) generated via a participatory process, involving 110 participants in three workshop settings, in what they determined AI would not do. The results outline the workshop approach undertaken to generate user stories and the analysis of user stories via persona and futures methodology causal layered analysis (Inayatullah S. 1998. Causal layered analysis. Futures 30(8): 815-829). The final component of the analysis generated a futures focussed set of guiding principles that can be used as a lens to broaden the transformation teams perspective in AI deployment. Concepts also consider the importance of futures literacy as a key competency of AI creation teams.
Human Computer Interaction Opportunities in Hand Tracking and Finger Recognition in Ship Engine Room VR Training

The research conducted for this paper is an extension of the continuous efforts at the Turku University of applied Sciences to optimize the Maritime Immersive Ocean Technology (MarsISOT) by integrating advanced immersive technologies. This paper reports the integration of hand tracking and finger recognition in the ShipSEVR learning episode. ShipSEVR, part of MarISOT, is a VR Training next generation learning environment focused specifically on ship engines and engine rooms safety procedures. The technology has been designed and developed at Turku University of Applied Sciences for Wärtsilä Land and Sea Academy utilizing latest VR technologies. The delivered learning episode consists of a 3D ship engine room space where trainees are expected to find certain devices and equipment by utilizing the available technical drawings. This enhanced human computer interaction environment reflects to industry requirements the derived after the first version of the technology and its test with industry experts.
Machine learning based on wave and diffusion physical systems

Recently, the application of physics to machine learning and the interdisciplinary convergence of the two have attracted wide attention. This paper focuses on exploring the internal relationship between physical systems and machine learning, and also on promoting machine learning algorithm and physical implementation. We summarize the researches of machine learning in wave systems and diffusion systems, and introduce some of the latest research results. We first discuss the realization of supervised learning for wave systems, including the wave optics realization of neural networks, the wave realization of quantum search, the recurrent neural networks based on wave systems, and the nonlinear wave computation of neural morphology. Then, we discuss the machine learning algorithms inspired by diffusion systems, such as the classification algorithm based on diffusion dynamics, data mining and information filtering based on thermal diffusion, searching for optimization based on population diffusion, etc. The physical mechanism of diffusion system can inspire the construction of efficient machine learning algorithms for the classification and optimization of complex systems and physics research, which may create a new vision for the development of physics inspired algorithms and hardware implementation, and even the integration of software and hardware.