Reviewerly/exHarmony huggingface dataset:
Density Difference Grid Design in a Point-Mass Filter

The paper deals with the Bayesian state estimation of nonlinear stochastic dynamic systems. The stress is laid on the point-mass filter, solving the Bayesian recursive relations for the state estimate conditional density computation using the deterministic grid-based numerical integration method. In particular, the grid design is discussed and the novel density difference grid is proposed. The proposed grid design covers such regions of the state-space where the conditional density is significantly spatially varying, by the dense grid. In other regions, a sparse grid is used to keep the computational complexity low. The proposed grid design is thoroughly discussed, analyzed, and illustrated in a numerical study.
Research Data Management for Master’s Students: From Awareness to Action

This article provides an analysis of how sixteen recently graduated master’s students from the Netherlands perceive research data management. It is important to study the master’s students’ attitudes towards this, as students in this phase prepare themselves for their career. Some of them might become future academics or policymakers, thus, potentially, the future advocates of good data management and reproducible science. In general, students were rather unsure what ‘data management’ meant and would often confuse it with data analysis, study design or methodology, or ethics and privacy. When students defined the concept, they focussed on privacy aspects. Concepts such as open data and the ‘FAIR’ principles were rarely mentioned, even though these are the cornerstones of contemporary data management efforts. In practice, the students managed their own data in an ad hoc way, and only a few of them worked with a clear data management plan. Illustrative of this is that half of the interviewees did not know where to find their data anymore. Furthermore, their study programmes had diverse approaches to data management education. Most of the classes offered were limited in scope. Nevertheless, the students seemed to be aware of the importance of data management and were willing to learn more about good data management practices. This report helps to catch an important first glimpse of how master’s students (from different scientific backgrounds) think about research data management. Only by knowing this, accurate measures can be taken to improve data management awareness and skills. The article also provides some useful recommendations on what such measures might be, and introduces some of the steps already taken by the Delft University of Technology (TU Delft).
Construction of an Individual Educational Trajectory as a Way to Reveal the Personal and Professional Potential of a Future Teacher

The construction of an individual educational trajectory changes due to a change in approaches to the personal and professional potential of the future teacher. Provided that the acquisition of professional skills by a teacher has not required constant training throughout life, then the modern environment requires a systematic updating of skills and advanced training. In fact, approaches to training and the role of the teacher in the educational environment are changing. Based on an integrative literature review, the trends of individual educational tools for the development of the personal and professional potential of a future teacher within EU have been investigated. The academic paper has revealed significant differences regarding the disclosure of personal and professional potential of future teachers within EU. Differences include: teachers’ training, the establishment of additional requirements for teachers in addition to curricula, regulating teachers’ mobility, professional and personal support, continuing professional development. The article has stated that induction and mentoring for new teachers is widespread within EU and legally established in most European countries. The investigation attests to the fact that induction may play a key role in supporting teachers’ professional development. Induction programs combine elements of mentoring, training, peer review, and scheduled meetings with school principals and colleagues to provide personal, social, and professional support. Induction activity is connected with increased self-efficacy and job satisfaction. Mentoring programs are designed taking into consideration the school context. Pupils’ knowledge, classroom pedagogy, assessment of pupils and harmonization of curriculum standards differ significantly between primary and secondary schools. Supporting measures to promote teachers’ participation have been developed in almost all EU countries.
Deep Convolutional Neural Network with TensorFlow and Keras to Classify Skin Cancer Images

Skin cancer is a dangerous disease causing a high proportion of deaths around the world. Any diagnosis of cancer begins with a careful clinical examination, followed by a blood test and medical imaging examinations. Medical imaging is today one of the main tools for diagnosing cancers. It allows us to obtain precise images, internal organs and thus to visualize the possible tumours that they present. These images provide information on the location, size and evolutionary stage of tumour lesions. Automatic classification of skin tumours using images is an important task that can help doctors, laboratory technologists, and researchers to make the best decisions. This work has developed a classification model of skin tumours in images using Deep Learning with a Convolutional Neural Network based on TensorFlow and Keras model. This architecture is tested in the HAM10000 dataset consists of 10,015 dermatoscopic images. The results of the classification of the experiment show that the accuracy was achieved by our model, which is in order of 94.06% in the validation set and 93.93% in the test set.
FPGA implementation of an optimized A5/3 encryption algorithm

The radio link connecting users to network services is one of the most sensitive parts of mobile networks. This wireless channel is not protected physically to prevent unauthorized access to the carried information. Therefore, network providers use a security mechanism mainly based on cryptographic algorithms. For example, data protection (confidentiality) in the second and third generations of mobile networks is ensured using the A5/3 encryption algorithm (f8 function) standardized by the Third Generation Partnership Project (3GPP). In this work, we defined two main objectives for obtaining an optimized architecture of the A5/3 algorithm. The first one focuses on the optimization of the algorithm’s kernel (the KASUMI block cipher) by simplifying its internal architecture. The second one aims at the optimization of the A5/3 algorithm using a single block of the simplified KASUMI, unlike the standard A5/3 algorithm based on five blocks of the basic KASUMI. As a result, good performance has been achieved by considering the tradeoff between high throughput and required hardware logic resources compared to previous works. The proposed architecture was implemented on several Xilinx Virtex Field Programmable Gate Arrays (FPGA) technology devices. The synthesis results obtained after place and route have demonstrated the feasibility and efficiency of our solution. This promising technique can be applied to provide real-time data protection on embedded applications of mobile networks.
Data Mining Application on Weather Prediction Using Classification Tree, Naïve Bayes and K-Nearest Neighbor Algorithm With Model Testing of Supervised Learning Probabilistic Brier Score, Confusion Matrix and ROC

&lt;p class="AbstractL-MAG"&gt;—&lt;em&gt; One of data mining techniques is Classification, used to predict relationships between data on a dataset. The prediction performed by classifying data into several different classes considering certain factor. Classification is a performance of Supervised Learning application where the training data already has a label when entered as input data. Classification is an approach of empirical techniques that can be utilized for short-term weather prediction. The most widely used algorithms in Classification Techniques are Classification Tree, Naïve Bayes and K-Nearest Neighbors. In this study, the author used these three algorithms to predict rain with validation parameters of Brier Score, Confusion Matrix and ROC curves. The input data is synoptic data of Kemayoran Meteorological Station, Jakarta (96745) for 10 years (2006 - 2015) consists of 3528 datasets and 8 attributes. Based on a series of data processing, selection and model testing shows that the Naïve Bayes Algorithm has the best accuracy rate of 77.1% with the category of fair classification so it is quite potential to be used in the operational. The dominant weather attributes in rain formation are moisture (RHavg), minimum temperature (Tmin), maximum temperature (Tmax), average temperature (Tavg) and wind direction (ddd).&lt;/em&gt;&lt;/p&gt;
SDFS: A Standardization Technique for Nonparametric Analysis

Due to availability of computational tools for data acquisition, it is very easy to collect many dimensions from an object. Nevertheless, data acquisition from an object in an experiment may have a low number of dimensions. The analysis of low dimensional data has break-through role. But raw and sparse nature of dataset imposes new challenges and requirements for data analysis due to their special and unique characteristics. In the process of overall characterization of low-dimensional data, the data pre-processing plays crucial role. One of the first processes is normalization and standardization process. Therefore, in this paper, I would like to propose novel standardization technique called SDFS (Standardization for Distribution Free Statistics) for nonparametric data analysis. This technique is robust for small sample size with missing values of data points, which commonly exist in real time experiments lead to sparse low-dimensional data. The comprehensive experimental evaluation shows that SDFS standardization is significantly outperforms on existing standardization methods.
Dual-View Normalization for Face Recognition

Face normalization refers to a family of approaches that rotate a non-frontal face to the frontal pose for better handling of face recognition. While a great majority of face normalization methods focus on frontal pose only, we proposed a framework for dual-view normalization that generates a frontal pose and an additional yaw-45° pose to an input face of an arbitrary pose. The proposed Dual-View Normalization (DVN) framework is designed to learn the transformation from a source set to two normal sets. The source set contains faces collected in the wild and covers a wide scope of variables. One normal set contains face images taken under controlled conditions and all faces are in frontal pose and balanced in illumination. The other normal set contains faces also taken under controlled conditions and balanced in illumination, but in 45° pose. The DVN framework is composed of one face encoder, two layers of generators, and two sets of discriminators. The encoder is made of a state-of-the-art face recognition network, which is not updated during training, and it acts as a facial feature extractor. The Layer-1 generators are trained on both the source and normal sets, aiming at learning the transformation from the source set to both normal sets. The trained generators can transform an arbitrary face into a pair of normalized faces, one in frontal pose and the other in 45° pose. The Layer-2 generators are trained to enhance the identity preservation of the faces made by the Layer-1 generators by minimizing the cross-pose identity loss. The discriminators are trained to ensure the photo-realistic quality of the dual-view normalized face images generated by the generators. The loss functions employed in the generators and the discriminators are designed to achieve satisfactory dual-view normalization outcomes and identity preservation. We verify the DVN framework on benchmark databases and compare with other state-of-the-art approaches for tackling face recognition.
Visualizing probabilistic models in Minkowski space with intensive symmetrized Kullback-Leibler embedding

The authors show that for any N-parameter statistical model, the manifold of probability distributions can be visualized in an (N+N) dimensional Minkowski space, preserving the natural distances between model predictions.
Employing artificial intelligence techniques for student performance evaluation and teaching strategy enrichment: An innovative approach

An intelligent tutoring system is an excellent Artificial Intelligence (AI) alternative for the haunting problems of the teaching and evaluation system in university education.It evinces a paradigm shift in the current system by employing AI techniques to evaluate students' performance and enrich the myriad teaching strategies.Unlike in regular classes where a teacher has to control 30 to 50 students, a teacher has to monitor hundreds of students, which is quite difficult and mentally exhausting.In such circumstances, mentors or teachers alone are not enough for monitoring the students and offering each student's optimum attention and care.A new and original approach is needed to facilitate reliable and flexible methods of university student monitoring systems.The system should be able to evaluate the performance of many students, predict the final grade, and formulate intelligent decisions in real-time.Several computer-based models of AI are progressively performing an important role in teaching and performance evaluation of students.This paper proposes a new strategy to illustrate the advantages of applying AI techniques to predict the final grade of students.The validation process was carried out with the real-time 1000 students' dataset of 12 core and 18 elective courses in Bachelor of Computer Science during the academic year 2018-2019.In this paper, hybrid SVM with a Fuzzy Expert System is proposed to show the techniques proficiency for teaching and students' final grade prediction and the possibility of future work.
Promising Blockchain Technology Applications and Use Case Designs for the Identification of Multinational Victims of Mass Disasters

In mass disasters with multinational victims, it is critical to identify the deceased for judicial, ethical, religious and human rights reasons, as well as to allow the next of kin to complete the grieving process. Disaster Victim Identification (DVI) process is a complex procedure where Post-mortem (PM) identifying data, essentially fingerprints, DNA and dental, is collected in order to be compared with equivalent Antemortem (AM) data related to the missing persons list. Although there are solutions used in the field of human identification, they all fall short of equipping them with the tools needed for achieving human identification in a timely manner. Initially, it is significantly challenging to manage missing person lists containing years, and sometimes decades, of family AM data resources’ updates. Furthermore, there is currently no record of any holistic technical solutions for managing both AM and PM for human identification to support collaborative multinational and interjurisdictional processes. Blockchain technology provides the tools to facilitate building trustworthy, secure and holistic ecosystems, and it can disseminate siloed AM and PM data across systems, protecting data breaches, redundancies, inconsistencies, and errors. As such, blockchain technology can revolutionize the human identification process worldwide in terms of managing missing person lists, AM data repositories for living people, PM data repositories of recovered unidentified victims, and contribute to the comparison of compatible biological profiles for definitive identification. Using real-world scenarios, the authors propose a number of promising use cases to attain a holistic understanding of the challenges, and present how blockchain technology meets such challenges and facilitates multi-jurisdictional data information-sharing in conjunction with the forthcoming circulation of patients’ electronic medical and dental records.
A Multifrequency Heterodyne Phase Error Compensation Method for 3D Reconstruction

In view of the problem of “jumping points” in the phase unwrapping of the multifrequency heterodyne principle, this paper proposes a novel method to improve the multifrequency heterodyne. By solving the root-mean-square error of the original frequency function, it includes the relationship between the error and the adjacent phase in the condition of constrained phase unwrapping, and it compensates phase <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mo>±</mml:mo><mml:mn>2</mml:mn><mml:mi>π</mml:mi></mml:math> of the skip point. To ensure the accuracy of phase unwrapping, the function with a “jump point” after each phase unwrapping and the absolute phase curve of the principal value function are used to establish the threshold judgment model of the least square method, and the initial phase unwrapping of the principal value function with different frequencies is carried out continuously. The simulation analysis of phase compensation with the four-step phase-shifting method shows that the error is reduced to 36% under the set environment. The experimental result of 3D reconstruction by measuring the flatness of the plate shows that the error decreases by 41% after phase compensation compared with before phase compensation. The three-dimensional reconstruction experiment of pitch measurement with a nut shows that the nut after phase compensation is smooth without noise, and the pitch error is 0.033 mm, which verified the method is workable and effective.
Clustering for Item Delivery Using Rule-K-Means

In this paper, we introduce an alternative approach as model for cluster analysis. The data were analyzed by rule-k-means algorithm. It's combine between k-means algorithm and rules. As an application, we use the simulate of item delivery data to classify items based on destination addresses. The goal is to map the item based on type of delivery vehicle. The clustering can be used as a recommendation to the item delivery service company.
IMPLEMENTASI AUGMENTED REALITY PADA MEDIA PROMOSI PENJUALAN RUMAH

The development of the industry in Indonesia has been very broad until now, it can be seen from the varied types of products produced by the company. The level of competition in product sales at companies is increasing. Increased competition in product sales can be seen from the number of exhibition events that aim to increase product sales of each company. However, the sales technique is still classified as traditional and has shortcomings in marketing, especially for marketing housing promotions that exist today. Therefore, with the rapid development of technology at this time, the real form of the house promoted by the company can be helped by using Augmented Reality technology, which will help the buyer to get a clear and clear picture of the shape and condition of the house in 3D. The development of this application uses the waterfall method with stages, (1) Requirement Analysis, (2) System Design, (3) Implementation, (4) Integration and testing and (5) Operation &amp; Maintenance. This research resulted in an application that uses Augmented Reality technology aimed at companies in order to facilitate housing marketing where buyers can clearly see the original picture of the house being promoted in 3D
Robust Deep Radio Frequency Spectrum Learning for Future Wireless Communications Systems

Intelligent capabilities are of utmost importance in future wireless communication systems.For optimum resource utilization, wireless communication systems require knowledge of the prevalent situation in a frequency band through learning.To learn appropriately, it is imperative for practitioners to select the right parameters for building robust data-driven learning models as well as use the appropriate algorithms and performance evaluation methods.In this paper, we evaluate the performance of deep learning models against the performance of other machine learning methods for wireless communication systems.We explore the different wireless communication scenarios in which deep learning can be used given Radio Frequency (RF) data, and evaluate its performance in various scenarios.Furthermore, we express it as a distribution alignment problem in which deep learning models do not perform well when learning from RF data of a particular distribution and evaluating on RF data from a different distribution.We also discuss our results in the light of how signal quality affects deep learning model leveraging on the knowledge from computer vision domain.The effect of Signal-to-Noise Ratio (SNR) selection for training on the model performance as it relates to practical implementation of deep learning in communications systems is also discussed.From our analysis, we conclude that the design and use of RF spectrum learning must be tailored to each specific scenario being considered in practice.
A Robust Improved Network for Facial Expression Recognition

With the development of deep learning, even important progress has been made in the field of image classification and recognition.But facial expression recognition still faces many problems.This article is an experiment on the FER2013 dataset, the purpose is to get the facial expression attributes from the facial image.Because the pictures in this dataset have low resolution, and some pictures have no faces at all.This reduces the accuracy of facial expression recognition.In this paper, we propose a robust improved model.In this model, we introduce attention mechanism and separable convolution to improve the extraction of image features, and use data argumentation techniques to enhance the generalization ability of the model.The model obtained 65.2% test set accuracy on the FER2013 dataset.
Self-supervised video representation learning by maximizing mutual information

We address the problem of learning representations from the videos without manual annotation. Different video clips sampled from the same video usually have a similar background and consistent motion. A novel self-supervised task is designed to learn such temporal coherence, which is measured by the mutual information in our work. First, we maximize the mutual information between features extracted from the clips which are sampled from the same video. This encourages the network to learn the shared content by these clips. As a result, the network may focus on the background and ignore the motion in videos due to that different clips from the same video normally have the same background. Second, to address this issue, we simultaneously maximize the mutual information between the feature of the video clip and the local regions where salient motion exists. Our approach, which is referred to as Deep Video Infomax (DVIM), strikes a balance between the background and the motion when learning the temporal coherence. We conduct extensive experiments to test the performance of the proposed DVIM on various tasks. Experimental results of fine-tuning for the high-level action recognition problems validate the effectiveness of the learned representations. Additional experiments for the task of action similarity labeling also demonstrate the generalization of the learned representations of the DVIM.