Reviewerly/exHarmony huggingface dataset:
Computerized segmentation of liver tumor using integrated fuzzy level set method

CT abdominal image requires the automated diagnosis of part of the liver and lesions. It is challenging to segment the liver and the tumor due to the high strength resemblance between liver and other organs nearby. In this paper, an automatic method of segmenting liver from CT image using fuzzy level set algorithm is proposed. It can evolve immediately through spatial fuzzy clustering from preliminary segmentation. Reasonable initialization and effective specification of controlling parameters requiring significant manual intervention are subject to the efficiency of the level set segmentation. In the following ways, the algorithm is considerably improved. First during adaptive optimization, fuzzy clustering integrates spatial information, which removes intermediate morphological operations. Secondly, the level set segmentation controlling parameters are now extracted directly from the performance of fuzzy clustering. Thirdly, a approach is suggested to regularize the evolution of the level collection, which is distinct from other approaches, driven by fuzzy clustering. Finally, the fuzzy level set algorithm on CT liver was tested. Performance analysis of this algorithm was carried out in various modalities on medical images. The results supported its suitability for segmentation of liver tumor.
Software Quality Analysis for Halodoc Application using ISO 25010:2011

The rapid spread of the Covid-19 disease and the ongoing social distancing have disrupted community activities.This consequently makes people use information and communication technology, especially in the fields of health, education, and business.The use of information and communication technology in the health sector plays an important role in preventing the spread of the Covid-19 disease.One of its uses is Telemedicine.One of the developing telemedicine in Indonesia is Halodoc.This study will test the quality of the Halodoc application to determine the quality of the Halodoc application using the model of the International Organization for Standardization (ISO), namely ISO 25010:2011.Software quality assurance can use this model as the basis for testing.Therefore the test will include 8 characteristics and 29 sub-characteristics.The assessment in this tests will use a weight where this weight is determined using Analytical Hierarchy Process (AHP) method.The testing method is carried out using Black-Box testing, stress testing, and distributing questionnaires to 100 respondents for Usability testing.Functional Suitability, Compatibility, Reliability, and Maintainability got a maximum score of 5. Performance Efficiency get a score of 4,886, Usability gets a score of 4, Security gets a score of 3,549, Portability gets a score of 3,718.The total results of the Halodoc application assessment get a score of 4,515 out of a maximum score of 5.
End-to-End AUV Motion Planning Method Based on Soft Actor-Critic

This study aims to solve the problems of poor exploration ability, single strategy, and high training cost in autonomous underwater vehicle (AUV) motion planning tasks and to overcome certain difficulties, such as multiple constraints and a sparse reward environment. In this research, an end-to-end motion planning system based on deep reinforcement learning is proposed to solve the motion planning problem of an underactuated AUV. The system directly maps the state information of the AUV and the environment into the control instructions of the AUV. The system is based on the soft actor–critic (SAC) algorithm, which enhances the exploration ability and robustness to the AUV environment. We also use the method of generative adversarial imitation learning (GAIL) to assist its training to overcome the problem that learning a policy for the first time is difficult and time-consuming in reinforcement learning. A comprehensive external reward function is then designed to help the AUV smoothly reach the target point, and the distance and time are optimized as much as possible. Finally, the end-to-end motion planning algorithm proposed in this research is tested and compared on the basis of the Unity simulation platform. Results show that the algorithm has an optimal decision-making ability during navigation, a shorter route, less time consumption, and a smoother trajectory. Moreover, GAIL can speed up the AUV training speed and minimize the training time without affecting the planning effect of the SAC algorithm.
Toward an intellectual capital cyber security theory: insights from Lebanon

Purpose Intellectual capital (IC) cyber security is a priority in all organizations. Because of the dearth in IC cyber security (ICCS) research theories and the constant call to theory building, this study proposes a theory of ICCS drawing upon tested empirical data of information systems security (ISS) theory in Lebanon. Design/methodology/approach After a pilot test, the authors tested the newly developed ISS theory using a field study consisting of 187 respondents, representing many industries, thus contributing to generalizability. ISS theory is used as a proxy for the development of ICCS theory. Findings Based on a review of the literature from the past three decades in the information systems (IS) discipline and a discovery of the partial yet significant relevance of ISS literature to ICCS, this study succinctly summarized the antecedents and independent variables impacting security compliance behavior, putting the variables into one comprehensive yet parsimonious theoretical model. This study shows the theoretical and practical relevancy of ISS theory to ICCS theory building. Practical implications This paper highlights the importance of ISS compliance in the context of ICCS, especially in the area of spoken knowledge in environments containing Internet-based security devices. Originality/value This research article is original, as it presents the theory of ICCS, which was developed by drawing upon a comprehensive literature review of the IS discipline and finding the bridges between the security of both IS and IC.
Robustness of neural network calibration model for accurate spatial positioning

The present study devotes to a systematical exploration for the robustness of neural network-based camera calibration method in the circumstance of three-dimensional (3D) spatial positioning via machine vision technique. By analyzing the error propagation route in the calibration-reconstruction process, a dimensionless error attenuation coefficient is proposed to measure the robustness of a calibration model with respect to input calibration error. Using this metric, the robustness of the neural network (NN) model under different optical configurations, i.e., input noise level, optical distortion and camera viewing angle, are analyzed in detail via synthetic simulation. Due to its generalized fitting capacity, the NN model is found to be superior to conventional pinhole model and polynomial model in terms of model robustness. To take full advantage of this feature, the NN model is further deployed to the scenarios of asymmetric camera layout and multiple camera joint calibration. Both synthetic simulation and experiment test demonstrate that the NN model can significantly improve the robustness and the accuracy of 3D spatial positioning in these non-normal scenarios.
Gaze Focalization System for Driving Applications Using OpenFace 2.0 Toolkit with NARMAX Algorithm in Accidental Scenarios

Monitoring driver attention using the gaze estimation is a typical approach used on road scenes. This indicator is of great importance for safe driving, specially on Level 3 and Level 4 automation systems, where the take over request control strategy could be based on the driver’s gaze estimation. Nowadays, gaze estimation techniques used in the state-of-the-art are intrusive and costly, and these two aspects are limiting the usage of these techniques on real vehicles. To test this kind of application, there are some databases focused on critical situations in simulation, but they do not show real accidents because of the complexity and the danger to record them. Within this context, this paper presents a low-cost and non-intrusive camera-based gaze mapping system integrating the open-source state-of-the-art OpenFace 2.0 Toolkit to visualize the driver focalization on a database composed of recorded real traffic scenes through a heat map using NARMAX (Nonlinear AutoRegressive Moving Average model with eXogenous inputs) to establish the correspondence between the OpenFace 2.0 parameters and the screen region the user is looking at. This proposal is an improvement of our previous work, which was based on a linear approximation using a projection matrix. The proposal has been validated using the recent and challenging public database DADA2000, which has 2000 video sequences with annotated driving scenarios based on real accidents. We compare our proposal with our previous one and with an expensive desktop-mounted eye-tracker, obtaining on par results. We proved that this method can be used to record driver attention databases.
Cognitive Computing and its Relationship to Computing Methods and Advanced Computing from a Human-Centric Functional Modeling Perspective

Recent advances in modeling human cognition have resulted in what is suggested to be the first model of Artificial General Intelligence (AGI) with the potential capacity for human-like general problem-solving ability, as well as a model for a General Collective Intelligence or GCI, which has been described as software that organizes a group into a single collective intelligence with the potential for vastly greater general problem-solving ability than any individual in the group. Both this model for GCI and this model for AGI require functional modeling of concepts that is complete in terms of meaning being self-contained in the model and not requiring interpretation based on information outside the model. The combination of a model of cognition to define an interpretation of meaning, and this functional modeling technique to represent information that way together results in fully self-contained definitions of meaning that are suggested to be the first complete implementation of semantic modeling. With this semantic modeling, and with these models for AGI and GCI, cognitive computing and its capacity for general problem-solving ability become far better defined. However, semantic representation of problems and of the details of solutions, as well general problem-solving ability in navigating those problems and solutions is not required in all cases. This paper attempts to explore the cases in which it is, and how the various computing methods and advanced computing paradigms are best utilized in each case from the perspective of cognitive computing.
Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation

This paper presents a systematic approach for accurate short-time cloud coverage prediction based on a machine learning (ML) approach. Based on a newly built omnidirectional ground-based sky camera system, local training and evaluation data sets were created. These were used to train several state-of-the-art deep neural networks for object detection and segmentation. For this purpose, the camera-generated a full hemispherical image every 30 min over two months in daylight conditions with a fish-eye lens. From this data set, a subset of images was selected for training and evaluation according to various criteria. Deep neural networks, based on the two-stage R-CNN architecture, were trained and compared with a U-net segmentation approach implemented by CloudSegNet. All chosen deep networks were then evaluated and compared according to the local situation.
Bridging Few-Shot Learning and Adaptation: New Challenges of Support-Query Shift

Few-Shot Learning (FSL) algorithms have made substantial progress in learning novel concepts with just a handful of labelled data. To classify query instances from novel classes encountered at test-time, they only require a support set composed of a few labelled samples. FSL benchmarks commonly assume that those queries come from the same distribution as instances in the support set. However, in a realistic setting, data distribution is plausibly subject to change, a situation referred to as Distribution Shift (DS). The present work addresses the new and challenging problem of Few-Shot Learning under Support/ Query Shift (FSQS) i.e., when support and query instances are sampled from related but different distributions. Our contributions are the following. First, we release a testbed for FSQS, including datasets, relevant baselines and a protocol for a rigorous and reproducible evaluation. Second, we observe that well-established FSL algorithms unsurprisingly suffer from a considerable drop in accuracy when facing FSQS, stressing the significance of our study. Finally, we show that transductive algorithms can limit the inopportune effect of DS. In particular, we study both the role of Batch-Normalization and Optimal Transport (OT) in aligning distributions, bridging Unsupervised Domain Adaptation with FSL. This results in a new method that efficiently combines OT with the celebrated Prototypical Networks. We bring compelling experiments demonstrating the advantage of our method. Our work opens an exciting line of research by providing a testbed and strong baselines. Our code is available at https://github.com/ebennequin/meta-domain-shift.
Face Recognition based on Convolutional Neural Network

Recognition holds great significance to give biometric authentications that are utilized in various applications particularly in attendance and security. A gathered database of the subjects is converted applying image processing methods to make this task. This paper suggests a cascade object detector based face detection and convolutional neural network alexnet based face recognition that can recognize the faces. The techniques used for face recognition are machine learning-based methods because of their great precision as associated with different methods. Face detection is the initial level before face recognition that is done utilizing a cascade object detector classifier. Face recognition is performed utilizing Deep Learning's sub-field that is Convolutional Neural Network (CNN). It is a multi-layer network which is used to train the network, to perform a particular task using classification. Check learning of a trained CNN model that is AlexNet is used for face recognition.
Prerequisite Relationships of the OntoMathEdu Educational Mathematical Ontology

We present the 2nd release of the OntoMathEdu educational mathematical ontology. In this new release, the logical relationships between concepts have been complemented with prerequisite ones. The concept A is called a prerequisite for the concept B, if a learner must study the concept A before approaching the concept B. We adopted two approaches for defining prerequisite relationships: directly by establishing a relationship between two concepts and indirectly by arrangement the concepts by educational levels. Arrangement of concepts by educational levels, in turn, allows to extract an educational projection, i.e. projection of the ontology to an education system. Prerequisite relationships and educational projections will be used in developing of the digital mathematical educational platform of Kazan Federal University.
Synthesizing contracts correct modulo a test generator

We present an approach to learn contracts for object-oriented programs where guarantees of correctness of the contracts are made with respect to a test generator. Our contract synthesis approach is based on a novel notion of tight contracts and an online learning algorithm that works in tandem with a test generator to synthesize tight contracts. We implement our approach in a tool called Precis and evaluate it on a suite of programs written in C#, studying the safety and strength of the synthesized contracts, and compare them to those synthesized by Daikon.
A Comparative Analysis of Standard Classifiers with CHDTC to Detect Credit Card Fraudulent Transactions

Nowadays, credit card frauds grow rapidly due to the technological advancements. Fraudsters are well trained with the technology factors, so it is quite tough enough to avoid such frauds in real-life scenario. Even though many fraud detection systems are introduced to reduce the fraud transactions, still some loop holes will be the cause for such frequent fraud transactions. It is necessary to build a model to detect fraud transactions at an earlier stage to prevent the further loss. We proposed a chained hashing decision tree classifier to detect the fraud transactions at a faster rate and also to reduce false positives. We did data normalization first and then applied feature correlation algorithm to select the appropriate attributes for training and testing purpose. After our algorithm is trained and tested with different subdatasets to improve accuracy, it is also compared with other existing models such as NB and RF to prove its high performance through various parameters such as false alarm rate and sensitivity in association of confusion matrix. Our CHDTC algorithm is divided into two phases like pattern analysis and pattern detection phase; during the pattern analysis phase, the rules are stringent to identify the behavioral pattern, and during the pattern detection phase, pattern is detected based on the decision like either fraud or legitimate. With the use of chained hashing along with decision tree classifier, it is fast enough to predict the class either fraud or legitimate for any input transactions.
autoAssembler: Automatic Reconstruction of Laser-Cut 3D Models

Recent research showed how to import laser cut 3D models encoded in the form of 2D cutting plans into a 3D editor (assembler3 [28]), which allows users to perform parametric manipulations on such models. In contrast to assembler3 , which requires users to perform this process manually, we present autoAssembler, which performs this process automatically. AutoAssembler uses a beam search algorithm to search possible ways of assembling plates. It uses joints on these plates to combine them into assembly candidates. It thereby preferably pursues candidates (1) that have no intersecting plates, (2) that fit into a small bounding box, (3) that use plates whose joints fit together well, (4) that do not add many unpaired joints, (5) that make use of constraints posed by other plates, and (6) that conform to symmetry axes of the plates. This works for models that have at least one edge joint (finger or t-joint). In our technical evaluation, we imported 66 models using autoAssembler. AutoAssembler assembled 79% of those models fully automatically; another 18% of models required on average 2.7 clicks of post-processing, for an overall success rate of 97%.
FedMe: Federated Learning via Model Exchange

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)FedMe: Federated Learning via Model ExchangeKoji Matsuda, Yuya Sasaki, Chuan Xiao, and Makoto OnizukaKoji Matsuda, Yuya Sasaki, Chuan Xiao, and Makoto Onizukapp.459 - 467Chapter DOI:https://doi.org/10.1137/1.9781611977172.52PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Federated learning is a distributed machine learning method in which a single server and multiple clients collaboratively build machine learning models without sharing datasets on clients. Numerous methods have been proposed to cope with the data heterogeneity issue in federated learning. Existing solutions require a model architecture tuned by the central server, yet a major technical challenge is that it is difficult to tune the model architecture due to the absence of local data on the central server. In this paper, we propose Federated learning via Model exchange (FedMe), which personalizes models with automatic model architecture tuning during the learning process. The novelty of FedMe lies in its learning process: clients exchange their models for model architecture tuning and model training. First, to optimize the model architectures for local data, clients tune their own personalized models by comparing to exchanged models and picking the one that yields the best performance. Second, clients train both personalized models and exchanged models by using deep mutual learning, in spite of different model architectures across the clients. We perform experiments on three real datasets and show that FedMe outperforms state-of-the-art federated learning methods while tuning model architectures automatically. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
The Effect of BERT, ELECTRA and ALBERT Language Models on Sentiment Analysis for Turkish Product Reviews

Nowadays, shopping is done more comfortably and without time constraints with the throwing of e-commerce platforms. These platforms allow consumers to examine reviews before purchasing products. Thus, consumers can decide whether to buy a product with positive or negative comments about the products. In this paper, Turkish sentiment analysis was carried out on the product comments at the Hepsiburada platform. For sentiment analysis, firstly, the success of Random Forest, Naive Bayes and Logistic Regression machine learning methods was measured. Then, the effect of BERT, ELECTRA and ALBERT language models on sentiment analysis was analyzed and the success of language models was compared with machine learning methods. While Naive Bayes achieved the highest accuracy with 89.95% among machine learning methods, ELECTRA was the most successful with 92.54% among language models. As a result of the study, it has been shown that the ELECTRA and ALBERT language models are more successful than machine learning methods.
Evaluation of Hyperparameter-Optimization Approaches in an Industrial Federated Learning System

Federated Learning (FL) decouples model training from the need for direct access to the data and allows organizations to collaborate with industry partners to reach a satisfying level of performance without sharing vulnerable business information. The performance of a machine learning algorithm is highly sensitive to the choice of its hyperparameters. In an FL setting, hyperparameter optimization poses new challenges. In this work, we investigated the impact of different hyperparameter optimization approaches in an FL system. In an effort to reduce communication costs, a critical bottleneck in FL, we investigated a local hyperparameter optimization approach that – in contrast to a global hyperparameter optimization approach – allows every client to have its own hyperparameter configuration. We implemented these approaches based on grid search and Bayesian optimization and evaluated the algorithms on the MNIST data set using an i.i.d. partition and on an Internet of Things (IoT) sensor based industrial data set using a non-i.i.d. partition.
An active contour model based on local pre-piecewise fitting image

Active contour model (ACM) has been widely used in the field of image segmentation and successfully solved numerous problems. Segmentation of intensity inhomogeneity images is a major focus of current research. To seek a better solution, this paper proposes an active contour model based on local pre-piecewise fitting image. First of all, the piecewise function is pre-fitted by the dilation and erosion operator in gray morphology. Second, a novel energy function is drawn out with a rewritten image fitting function. Third, an optimized gradient descent flow formula is proposed to further improve the robustness of this model. Fourth, an improved regularization term is proposed to optimize the level set function. Experiments have proved that the model can effectively segment images with uneven grayscale. Compared with other models, the proposed model has obvious advantages in robustness to initialization and segmentation efficiency. In addition, the model can also choose various targets by selecting different initial contours.