Reviewerly/exHarmony huggingface dataset:
An Enhanced Machine Learning Model for Adaptive Monte Carlo Yield Analysis

This paper presents a novel methodology for generating machine learning models used by an adaptive Monte Carlo analysis. The advantages of this methodology are that model generation occurs at the beginning of the analysis with no retraining required, it applies to both classification and regression models, and accuracy of the Monte Carlo analysis is not impacted by the accuracy of the model. This paper discusses the details of constructing and enhancing the machine learning model with emphasis on model training. It will then show how the model enables a Monte Carlo analysis that monitors and adapts to model mispredictions.
RETRACTED: Research on multidimensional information collection algorithm of marine buoy wireless communication network

The large-scale multidimensional database and data warehouse have become a common phenomenon in various commercial and scientific applications. These data warehouses, several tens of millions of groups, including the respective tens or hundreds of the classification size. One of these databases' main challenges is to extract the critical data's meaning: structure from them, looking for a pattern, will discover a derived cause-and-effect relationship. Promising technology for the analysis of these multidimensional database is visualized. (A project of the plane) in addition to visualizing multidimensional data. Visual analysis partially assigns separately simply and intuitively estimate the similarities and differences between objects. However, when projecting the multifaceted data on a plane, a disadvantage of this analysis is that some information is inevitably lost. The agreed methods, have a combination of the process of classification and visualization. A marine radio is the fastest solution to resolve the sea of systematically interconnected problems; it is still in the early stages of development. Communicate with the marine environment; marine Internet applications are currently on the market. Development of communication systems and challenges of the ocean, need a discussion on the development of maritime radio communication networks.
An Efficient Task Scheduling Algorithm using Total Resource Execution Time Aware Algorithm in Cloud Computing

Cloud computing is a new computing paradigm that allow users to access services over the internet. Cloud provides scalable, highly available and on-demand resources to its endusers. The cloud charges its customers for only the usage of the services. The cloud platform offers a range of computing capabilities with numerous configurable variations. Hence cloud can be used to execute a large number of tasks with different computing and data requirements. To benefit from the cloud and get the optimum, efficient management of the underlying system is essential. Task scheduling is an open problem that needs to be countered and it is known to be an NP-complete problem. Therefore heuristic techniques can be used to derive a better solution for scheduling tasks. Several heuristics has been proposed for addressing the task scheduling problem in the cloud environment considering many factors in finding a better schedule. None of them has considered the total execution time of the virtual machine as a factor for finding a better schedule. In this paper, we propose, Total Resource Execution Time Aware Algorithm (TRETA). The algorithm considers the total execution time of computing resource in finding an optimal schedule. The algorithm is compared with existing state-of-art heuristics Min-Min, Min-Max, FCFS, and MCT for Makespan, Degree of Imbalance, and System Throughput using real-world workload traces of Nasa Ames iPSC/860. The experimentation is performed for workloads consisting of a bigger set of tasks using CloudSim 5.0 Simulator. The proposed algorithm shows a significant amount of improvement in Makespan, Degree of imbalance, and System Throughput compared to other existing heuristics.
Automatic Speech Recognition and Natural Language Understanding for Emotion Detection in Multi-party Conversations

Conversational emotion and sentiment analysis approaches rely on Natural Language Understanding (NLU) and audio processing components to achieve the goal of detecting emotions and sentiment based on what is being said. While there has been marked progress in pushing the state-of-the-art of theses methods on benchmark multimodal data sets, such as the Multimodal EmotionLines Dataset (MELD), the advances still seem to lag behind what has been achieved in the domain of mainstream Automatic Speech Recognition (ASR) and NLU applications and we were unable to identify any widely used products, services or production-ready systems that would enable the user to reliably detect emotions from audio recordings of multi-party conversations. Published, state-of-the-art scientific studies of multi-view emotion recognition seem to take it for granted that a human-generated or edited transcript is available as input to the NLU modules, providing no information of what happens in a realistic application scenario, where audio only is available and the NLU processing has to rely on text generated by ASR. Motivated by this insight, we present a study designed to evaluate the possibility of applying widely-used state-of-the-art commercial ASR products as the initial audio processing component in an emotion-from-speech detection system. We propose an approach which relies on commercially available products and services, such as Google Speech-to-Text, Mozilla DeepSpeech and the NVIDIA NeMo toolkit to process the audio and applies state-of-the-art NLU approaches for emotion recognition, in order to quickly create a robust, production-ready emotion-from-speech detection system applicable to multi-party conversations.
Multisensor Prognostic of RUL Based on EMD-ESN

This paper presents a prognostic method for RUL (remaining useful life) prediction based on EMD (empirical mode decomposition)-ESN (echo state network). The combination method adopts EMD to decompose the multisensor time series into a bunch of IMFs (intrinsic mode functions), which are then predicted by ESNs, and the outputs of each ESN are summarized to obtain the final prediction value. The EMD can decompose the original data into simpler portions and during the decomposition process, much noise is filtered out and the subsequent prediction is much easier. The ESN is a relatively new type of RNN (recurrent neural network), which substitutes the hidden layers with a reservoir remaining unchanged during the training phase. The characteristic makes the training time of ESN is much shorter than traditional RNN. The proposed method is applied to the turbofan engine datasets and is compared with LSTM (Long Short-Term Memory) and ESN. Extensive experimental results show that the prediction performance and efficiency are much improved by the proposed method.
Graph-based Dynamic Analysis of Elastic Systems

The emerging paradigm shift for analysis of elastic systems is caused by demands of reorganization behavior of systems at run-time. For solution this problem a mathematical framework for graph-based dynamic analysis is introduced. The essential of the framework is a definition of the generalized graph-based model of the elastic system. Bond graphs, Fractal Petri nets, fractal graphs, and their matrix presentation are included in the framework and inherit technique of generalized model.
Edge IoT-cloud Framework based on Blockchain

The Internet of things (IoT) can be used to exchange information and data, but there are many concerns of users in terms of privacy and theft of confidential information to the patient or change the data only seen by the doctor. Through blockchain technology, many hospitals have been able to store data and provide the concept of trust. This project aims to improve data exchange through the use of blockchain techniques and data operations to keep them from changing. IoT can be used to monitor the patient's condition remotely and follow up and send his information to the health authority, thus warning them of possible dangerous situations. In order to exchange reliable and reliable information between the caregivers and the patient, the data is taken from the patient and processed in operations and then stored.
Bio-WTiP: Biology lesson in handheld augmented reality application using tangible interaction

Abstract Augmented Reality (AR) is a combination of objects of both real and virtual that partially merged into a single environment. In the education field, the process of teaching and learning can be more interesting with the engagement of AR as aided learning tools during classes. However, the remaining problems are, Biology was one of the subjects that hard for students to easily understand such as learning water transport in the plant. The use of touchscreen in handheld interaction is limited due to screen size for user to use in handheld AR interaction for 3D object manipulation. Therefore, this project aims to develop a Biology lesson handheld AR application using tangible interaction (Bio-WTiP AR). There are three phases that have been carried out, which are AR feature-based tracking, integrate Biology application with tangible interaction in handheld AR and evaluation. The testing and evaluation have been performed based on usability and user acceptance. The results show the application has been accepted by the users as the users found it is interesting to learn the process of water transport in the plant on hand. Based on the results, this project has successfully produced a Bio-WTiP application that can enhance the users learning process at once gave users experience in AR.
Identifying human contact points on environmental surfaces using heat traces to support disinfect activities

The disinfection of environmental surfaces is an effective countermeasure for COVID-19. In this paper, we use a thermographic camera and lightweight background image processing and propose a method that detects and visualizes the places touched by a person. Our method will support effective disinfection activities.
Spatially Variant Convolutional Autoencoder Based on Patch Division for Pill Defect Detection

Detecting pill defection remains challenging, despite recent extensive studies, because of the lack of defective data. In this paper, we propose a pipeline composed of a pill detection module and an autoencoder-based defect detection module to detect defective pills in pill packages. Furthermore, we created a new dataset to test our model. The pill detection module segments pills in an aluminum-plastic package into individual pills. To segment pills, we used a shallow segmentation network that is then divided into individual pills using the watershed algorithm. The defect detection module identifies defects in individual pills. It is trained only on the normal data. Thus, it is expected that the module will be unable to reconstruct defective data correctly. However, in reality, the conventional autoencoder reconstructs defective data better than expected, even if the network is trained only on normal data. Hence, we introduce a patch division method to prevent this problem. The patch division involves dividing the output of the convolutional encoder network into patch-wise features, and then applying patch-wise encoder layer. In this process, each latent patch has its independent weight and bias. This can be interpreted as reconstructing the input image using multiple local autoencoders. The patch division makes the network concentrate only on reconstructing local regions, thereby reducing the overall capacity. This prohibits the proposed network reconstructing unseen data well. Experiments show that the proposed patch division technique indeed improves the defect detection performance and outperforms existing deep learning based anomaly detection methods. The ablation study shows the efficacy of patch division and compression following the concatenation of patch-wise features.
Pruning Convolutional Filters Using Batch Bridgeout

State-of-the-art computer vision models are rapidly increasing in capacity, where the number of parameters far exceeds the number required to fit the training set. This results in better optimization and generalization performance. However, the huge size of contemporary models results in large inference costs and limits their use on resource-limited devices. In order to reduce inference costs, convolutional filters in trained neural networks could be pruned to reduce the run-time memory and computational requirements during inference. However, severe post-training pruning results in degraded performance if the training algorithm results in dense weight vectors. We propose the use of Batch Bridgeout, a sparsity inducing stochastic regularization scheme, to train neural networks so that they could be pruned efficiently with minimal degradation in performance. We evaluate the proposed method on common computer vision models VGGNet, ResNet and Wide-ResNet on the CIFAR10 and CIFAR100 image classification tasks. For all the networks, experimental results show that Batch Bridgeout trained networks achieve higher accuracy across a wide range of pruning intensities compared to Dropout and weight decay regularization.
Requirements Traceability for Detecting Defects in Agile Software Development

The process of developing applications with agile methods especially extreme programming (XP) is very popular these days. The short iteration process makes software development faster. However, this method has weaknesses on the documentation side that makes it difficult to see the defect related to the requirements level, coupled with changes that can occur quickly and continuously. In this study, the author will look at the extent of software quality management at each stage of this method and build traceability using the Requirement Traceability Matrix (RTM). After the matrix is formed, each document contained in it can be taken as information that affects the occurrence of bugs during the development and defect at the end of the product when released. In the requirements document, it can be seen how many times there have been changes or additions. At each stage of the test the number of tests performed at each stage can be seen, and the time it takes for the application to pass each test. All of these variables will be seen as correlated to the number of bugs and defects. Seen the number of tests and the number of additional requirements has a strong correlation to the number of bugs appearing throughout the development process and defects at the end of the product release. It can be concluded that tracing the relationship between requirements, testing, and implementation at the code level is very important to identify the quality of the software.
Playable Cartography

A In a push away from the functional and utilitarian intentions behind the ‘smart city’ creative movements such as ‘playable cities’ have emerged often using contemporary ubiquitous locative mobile technologies as a means for more meaningfully connecting people and public places. Drawing on recent studies that explore three key concepts: ‘Game cartography’, ‘emotional cartography’ and ‘performative cartography’, this paper describes three projects by the author that advocate for the capacity of cartographic interfaces in location based mobile media experiences to share human based, ‘experiential data’ relating to physical spaces through playful, insightful and creative interventions with public spaces. Presenting the term ‘playable cartography’ as a means of describing emerging creative practices regarding uses of interactive cartographic interfaces in contemporary locative mobile media-based experiences.
A Myhill-Nerode Theorem for Register Automata and Symbolic Trace Languages

We propose a new symbolic trace semantics for register automata (extended finite state machines) which records both the sequence of input symbols that occur during a run as well as the constraints on input parameters that are imposed by this run. Our main result is a generalization of the classical Myhill-Nerode theorem to this symbolic setting. Our generalization requires the use of three relations to capture the additional structure of register automata. Location equivalence $$\equiv _l$$ captures that symbolic traces end in the same location, transition equivalence $$\equiv _t$$ captures that they share the same final transition, and a partial equivalence relation $$\equiv _r$$ captures that symbolic values v and $$v'$$ are stored in the same register after symbolic traces w and $$w'$$ , respectively. A symbolic language is defined to be regular if relations $$\equiv _l$$ , $$\equiv _t$$ and $$\equiv _r$$ exist that satisfy certain conditions, in particular, they all have finite index. We show that the symbolic language associated to a register automaton is regular, and we construct, for each regular symbolic language, a register automaton that accepts this language. Our result provides a foundation for grey-box learning algorithms in settings where the constraints on data parameters can be extracted from code using e.g. tools for symbolic/concolic execution or tainting. We believe that moving to a grey-box setting is essential to overcome the scalability problems of state-of-the-art black-box learning algorithms.
A Blockchain based solution for Managing Transplant Waiting Lists and Medical Records

Nowadays, the medical domain is suffering from a lack of transparency, security, and immutability. Developing a decentralized application based on blockchain technology can be helpful in achieving the requirements that will solve the identified problems. This paper proposes a blockchain-based solution for managing the waiting list for a transplant as well as a solution for sharing medical records between parties. Sharing medical records between patients and doctors represents a sensitive process since it must be secured, and it addresses a challenging requirement in terms of storage scalability which is approached by using a distributed file system. Results show that our solution allows the waiting list to be more trustful and increases the patients' chances for receiving the transplant and at the same time assures the owner of the medical records, reducing the chances of attacks for stealing medical data.
Variance reduced optimization with implicit gradient transport

The question of how to readily reduce variance in stochastic optimization methods is challenging. The most widely used approach of reducing variance in stochastic optimization is to employ past gradients. Although utilizing second order information in stochastic optimization has recently been proved to be an effective way to track gradients, research on taking second order information to track gradients is quite limited due to a high computational burden. In this paper, we introduce the modified form of the implicit gradient transport (MIGT) technique into the classical stochastic variance reduced methods, i.e., SVRG, which leads to a new method called SVRG–MIGT. The SVRG–MIGT method enjoys second order information to provide faster variance reduction, but without computing the Hessian explicitly. We analyze the theoretical convergence properties of the SVRG–MIGT method for strongly convex objective functions and prove that it is comparable to state-of-the-art methods. To further confirm the effectiveness of MIGT, we propose Acc-Prox-SVRG–MIGT, a novel variant of the accelerated mini-batch Prox-SVRG (Acc-Prox-SVRG) method, by introducing it into Acc-Prox-SVRG. In addition, to greatly reduce the computational complexity, we propose L-SVRG–MIGT, by introducing MIGT to loopless SVRG (L-SVRG), which uses the full gradient with a small probability. Moreover, we establish a convergence analysis for L-SVRG–MIGT. Experimental results on a wide range of data sets demonstrate the efficacy and robustness of our proposed methods.
WITHDRAWN: Software bug prediction and classification by global pooling of different activation of convolution layers

To enhance the reliability of the software, the prediction of software defects is being used to identify software bugs and prioritize testing efforts. Some authors have successfully adopted deep learning models, like the convolutional neural network (CNN), and Deep Belief Network (DBN), and used automated features extracted from deep learning models and abstract syntax trees (ASTs) to boost efficiency in the prediction of defects. However, due to its insufficient repeated experiments, limited dataset size, and obsolete baseline choice, investigation mostly on the CNN model failed to present conclusive results. To fix these issues, we built the PROMISE Source Code (PSC) dataset to broaden the original data set throughout the CNN studies, that designers labelled the Simplified PROMISE Source Code (SPSC) dataset. All the variations have been used in the dataset. The proposed methodology uses CNN hybridization with distinct activation function with global pooling and then learning by additional tree classification.
Recognition of Turkish Sign Language (TID) Using sEMG Sensor

Using computer-based recognition of hand gestures can enhance interaction between humans and computers. In this paper, the recognition using surface electromyography (sEMG) of stationary hand signs of Turkish Sign Language (TID) is evaluated. We used an armband to obtain sEMG signals and a sliding window-based method to split the data for extracting features. We obtained sEMG and inertial measurement unit (IMU) data from ten subjects (five male and five female) for a selected subset of stationary TID signs. We pre-processed the signals, extracted time-domain and time-frequency-domain features from the sEMG signals. The signs are analyzed for their classification performance with sEMG signals. A random forest classifier for five TID signs is trained on the dataset and achieved 78% leave-one-subject-out cross-validation accuracy for the male subjects. The difference in sEMG signals of males and females is analyzed. The performance of time-domain and time-frequency domain features of sEMG, and the inertial measurement unit (IMU) on the hand gesture recognition are evaluated.
Real TimeDetection and Tracking Method of Pilot’sHeadPositionBased on MTCNN-DeepSORT

Abstract In view of the problems of the traditional head detection and tracking technology in pilot cabin simulation training, such as low precision, poor real-time performance and easy to be interfered by the outside world, we propose a head detection and tracking method based on MTCNN-DeepSORT combined with deep learning method. Firstly, the continuous video images in the cockpit are preprocessed by computer vision, and then the processed images are extracted by convolution neural network to detect the face. At the same time, the facial feature points are marked. Through the spatial coordinate calculation of the obtained facial feature points, the pilot’s head posture angle is obtained, and then combined with DeepSORT tracking algorithm can track the pilot’s head continuously and in real time, and finally complete the detection and tracking of pilot’s head position. The experimental results show that the method based on MTCNN-DeepSORT has higher detection accuracy, better real-time performance and stronger robustness than the traditional methods.