Reviewerly/exHarmony huggingface dataset:
One Network for Multi-Domains: Domain Adaptive Hashing with Intersectant Generative Adversarial Networks

With the recent explosive increase of digital data, image recognition and retrieval become a critical practical application. Hashing is an effective solution to this problem, due to its low storage requirement and high query speed. However, most of past works focus on hashing in a single (source) domain. Thus, the learned hash function may not adapt well in a new (target) domain that has a large distributional difference with the source domain. In this paper, we explore an end-to-end domain adaptive learning framework that simultaneously and precisely generates discriminative hash codes and classifies target domain images. Our method encodes two domains images into a semantic common space, followed by two independent generative adversarial networks arming at crosswise reconstructing two domains’ images, reducing domain disparity and improving alignment in the shared space. We evaluate our framework on four public benchmark datasets, all of which show that our method is superior to the other state-of-the-art methods on the tasks of object recognition and image retrieval.
Peningkatan Kemampuan Berbicara Menggunakan Metode Kooperatif Tipe NHT pada Mahasiswa Tingkat I-A PBSI Tahun Akademik 2018/2019

The purpose of this research is to improve: (1) the quality of the learning process by using the cooperative type NHT method on PBSI study program level I-A IKIP PGRI Bojonegoro and (2) speaking skills by applying the cooperative type NHT method on PBSI I-A IKIP PGRI Bojonegoro study program students. This research is a classroom action research. The subjects of this study were I-A level students with 32 students consisting of 24 female students and 8 male students and speaking skills lecturers. The object of this research is learning to speak. Data collection techniques used were observation, in-depth interviews, and document analysis. The data validity technique uses data source triangulation techniques and method triangulation. The data analysis technique used is a comparative description analysis technique and interactive analysis. This research was conducted in two cycles which included four stages, namely: planning, implementation, observation, and reflection. The results showed an increase in learning speaking skills, marked by an increase in the number of students who reached the completeness limit, namely in the first cycle there were 5 students (16%) and the second cycle 28 students (87.5%). The average value of students has increased, namely 64.1 in the first cycle and 75.3 in the second cycle. This proves that the application of the NHT type cooperative method can increase student activeness, process and learning outcomes of student speaking skills.
Two Party Distribution Testing: Communication and Security

We study the problem of discrete distribution testing in the two-party setting. For example, in the standard closeness testing problem, Alice and Bob each have $t$ samples from, respectively, distributions $a$ and $b$ over $[n]$, and they need to test whether $a=b$ or $a,b$ are $\epsilon$-far for some fixed $\epsilon>0$. This is in contrast to the well-studied one-party case, where the tester has unrestricted access to samples of both distributions, for which optimal bounds are known for a number of variations. Despite being a natural constraint in applications, the two-party setting has evaded attention so far. We address two fundamental aspects: 1) what is the communication complexity, and 2) can it be accomplished securely, without Alice and Bob learning extra information about each other's input. Besides closeness testing, we also study the independence testing problem, where Alice and Bob have $t$ samples from distributions $a$ and $b$ respectively, which may be correlated; the question is whether $a,b$ are independent of $\epsilon$-far from being independent. Our contribution is three-fold: 1) Communication: we show how to gain communication efficiency with more samples, beyond the information-theoretic bound on $t$. The gain is polynomially better than what one obtains by adapting standard algorithms. 2) Lower bounds: we prove tightness of our protocols for the closeness testing, and for the independence testing when the number of samples is unbounded. These lower bounds are of independent interest as these are the first 2-party communication lower bounds for testing problems. 3) Security: we define secure distribution testing and argue that it must leak at least some minimal information. We then provide secure versions of the above protocols with an overhead that is only polynomial in the security parameter.
A HoloLens Based Augmented Reality Navigation System for Minimally Invasive Total Knee Arthroplasty

In minimally invasive total knee arthroplasty (MIS-TKA), it is difficult to obtain a global vision of the surgical site in the course of surgery without using X-ray imaging, which brings radiation hazard. To provide the surgeon with accurate image guidance, a Microsoft HoloLens Head-Mounted Display (HMD) based augmented reality approach is proposed. This approach mixes the patient's virtual models with the reality by obtaining the spatial transformations via HoloLens calibration and image registration. It employs the HoloLens and a custom-made binocular camera to overlay the patient's preoperative CT image onto the physician's field of view by combining a hand-eye calibration method and a Sample Consensus Initial Alignment (SAC-IA) based Iterative Closest Point (ICP) registration algorithm. Using the methods above, the preoperative anatomical model of the patient's knee together with the virtual surgical tool's path can be displayed at correct location in the view of HoloLens. Experiments show that the final overlay error was evaluated to be within 3 mm. The results have shown the potential use of the HoloLens to provide minimally invasive total knee arthroplasties with real-time intuitive surgical visualization without introducing any X-ray radiation.
Genetic Programming based Transfer Learning for Document Classification with Self-taught and Ensemble Learning

Document classification is a common but challenging task in text mining, since the feature set used is often high-dimensional and sparse. Transfer learning has been applied to improve the classification performance of a (target) domain by transferring knowledge from a previously learnt (source) domain. When there are no labels provided for documents in target domains, it is challenging to effectively transfer knowledge from source domains to target domains. In this paper, we develop a new Genetic Programming (GP) based transfer learning method for document classification, which utilises the evolved GP programs from the source domain to learn a set of weak GP classification models on the target domain with unlabelled documents, which is called self-taught learning. These weak classifiers are combined with the GP programs transferred from the source domain to predict the labels of test documents in the target domain. The experimental results show that the GP programs from source domains with their weak classifiers can effectively classify documents in the target domain.
Testing the Number and the Nature of the Components in a Mixture Distribution

In this paper, we propose a test procedure for the number of components of mixture distributions in a parametric setting. The test statistic is based on divergence estimators derived through the dual form of the divergence in parametric models. We provide a standard limit distribution for the test statistic under the null hypothesis that holds for mixtures of any number of components $$k\ge 2$$ .
Virtual-Gym<sup>VR</sup>: A Virtual Reality Platform for Personalized Exergames

Virtual-Gym <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VR</sup> is a platform for serious exergames in virtual reality. Its purpose is to provide older adults with a fun experience, while, at the same time, encouraging them to complete their personalized exercise sessions. The platform takes as input a description of a prescribed exercise, in terms of a posture-transition grammar, and constructs personalized versions of its games by accordingly configuring the behavior of the interactive objects in these games. The game-configuration process essentially controls the placement and the interaction behavior of the games' objects so that they induce the user to adopt the proper postures, as described by the input exercise specification. At run time, the sequence of game events stimulate the user to move to the prescribed exercise postures and, thus, accomplish their own personalized exercise goals. Given the intended user population of older adults, we have designed three different Virtual-Gym <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VR</sup> games with metaphors appropriate for three different types of exercises. Our initial experimentation with the Virtual-Gym <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">VR</sup> games indicates that the approach is promising.
A construction of bent functions with optimal algebraic degree and large symmetric group

As maximal, nonlinear Boolean functions, bent functions have many theoretical and practical applications in combinatorics, coding theory, and cryptography. In this paper, we present a construction of bent function $ f_{a,S} $ with $ n = 2m $ variables for any nonzero vector $ a\in \mathbb{F}_{2}^{m} $ and subset $ S $ of $ \mathbb{F}_{2}^{m} $ satisfying $ a+S = S $. We give a simple expression of the dual bent function of $ f_{a,S} $ and prove that $ f_{a,S} $ has optimal algebraic degree $ m $ if and only if $ |S|\equiv 2 (\bmod 4) &nbsp;$. This construction provides a series of bent functions with optimal algebraic degree and large symmetric group if $ a $ and $ S $ are chosen properly. We also give some examples of those bent functions $ f_{a,S} $ and their dual bent functions.
Knowledge Transfer in a Pair of Uniformly Modelled Bayesian Filters

The paper presents an optimal Bayesian transfer learning technique applied to a pair of linear state-space processes driven by uniform state and observation noise processes. Contrary to conventional geometric approaches to boundedness in filtering problems, a fully Bayesian solution is adopted. This provides an approximate uniform filtering distribution and associated data predictor by processing the involved bounds via a local uniform approximation. This Bayesian handling of boundedness provides the opportunity to achieve optimal Bayesian knowledge transfer between bounded-error filtering nodes. The paper reports excellent rejection of knowledge below threshold, and positive transfer above threshold. In particular, an informal variant achieves strong transfer in this latter regime, and the paper discusses the factors which may influence the strength of this transfer.
ORCID Pop-ups: Starting Small to Disseminate at Large

In this lightning talk for the North Carolina Serials Conference, Lynnee Argabright describes the efforts of the ORCID team at the University of North Carolina at Chapel Hill. In addition to discussing the team’s pop-up outreach planning and documentation, Argabright shares advice from the team’s experience on how to run an effective ORCID pop-up event to facilitate faculty ORCID registration.
Onboard Generation of Optimal Flight Trajectory for Delivery of Fragile Packages

Real-time onboard flight trajectory generation is of great importance for all kinds of flying vehicles. This paper proposes a method to generate the trajectory which minimizes the damage due to flight motion to a fragile package. The proposed methodology has several potential applications including drone organ delivery. A similar procedure can also be used in applications where the objective is to maximize passenger comfort during flight. An analytical solution of the optimal trajectory generation problem is derived under arbitrary two-point boundary value constraints. An approach to solve for the optimal flight time is also proposed, which can be easily implemented on common embedded processors. The algorithm is extended to guarantee that a peak velocity constraint is verified. The effects of a parameter called the cost index on the optimal solution are also discussed. Examples show how the procedure can be used in a specific application.
Applications of Automated Model’s Extraction in Enterprise Systems

As enterprise software systems become more and more complex, the need of automated approaches for their understanding and quality assessment increases. Usually the automated approaches make use of a meta-model according to the information is mainly extracted from the source code but when considering enterprise systems, the meta-model should contain information from two different paradigms (e.g., object-oriented and relational) which are not enough to be loaded only from the source code. In this paper, based on a specific meta-model for enterprise systems, we present a set of applications (approaches) which help us to understand and assess the quality of the design, as part of the maintenance process.
A note on the fast algebraic immunity and its consequences on modified majority functions

Boolean functions used as nonlinear filters and/or combiners in LFSR-based stream ciphers should satisfy several desired cryptographic properties simultaneously, to withstand all known cryptographic attacks. In the past decade, the algebraic and fast algebraic immunities are the most infusive criteria on the design of cryptographic Boolean functions, due to the high efficiency of the algebraic and fast algebraic attacks on stream ciphers. Up to now, Boolean functions with optimal algebraic immunity have been built in several ways, but there are not many known results on their fast algebraic immunities. In this paper, we first derive a relation on the fast algebraic immunity between a Boolean functionfand it’s modificationsf+s, which shows that iffhas low fast algebraic immunity and s has low algebraic immunity thenf+smay also have low fast algebraic immunity in general. Thanks to this relation, we obtain some upper bounds on the fast algebraic immunity of several known classes of modified majority functions.
Effect of Neighbourhood Size Selection in SOM-Based Image Feature Extraction

Feature extraction is one of the processes in pattern recognition systems.It is the procedure of selecting the best possible subset of input features from a high dimensional problem space and transforming them into a lower dimensional space.Mathematically, feature extraction can be perceived as dimensionality reduction problem.Kohonen's self-organizing map (SOM) is an unsupervised learning approach in which a group of neurons regularly adjusts their basic structure as a function of its experience and environment.Preliminary research reported that neighbourhood functions is one of the necessary parameters that influence the results.The neighbourhood size refers to the region covered by the activated neighbouring neurons in relation to the winner neuron in each iteration of learning.This study aims to evaluate the implementation of SOM in image feature extraction.Different neighbourhood size value was tested in the SOM learning process to observe the quality of the extracted feature.Gaussian neighbourhood function was applied to the SOM-based feature extraction in current study.This study also compared the feature extraction performance of different neighbourhood functions.Experimental results show that the resulting feature loses its details as the neighbourhood size increases.Gaussian neighbourhood function gives more significant extracted feature.
An Iterative Image Inpainting Method Based on Similarity of Pixels Values

Image inpainting is a process of completion of missing places by using other undamaged sections of the image or removal of unwanted objects of the image. In this study, we propose a novel image inpainting method. This method constitutes an essential place in image processing. This proposed method fills the corrupted area by using similarity of the boundary pixels values around that corrupted regions in every iteration step. Afterwards, to evaluate image inpainting quality of the proposed method, we use Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics and present some visual results. The acquired results show that our proposed inpainting method gives an outstanding performance to fill the corrupted areas and to remove objects. We also discuss the need for further research.
Bioinspired Direct Visual Estimation of Attitude Rates with Very Low Resolution Images using Deep Networks

In this work we present a bioinspired visual system sensor to estimate angular rates in unmanned aerial vehicles (UAV) using Neural Networks. We have conceived a hardware setup to emulate Drosophila’s ocellar system, three simple eyes related to stabilization. This device is composed of three low resolution cameras with a similar spatial configuration as the ocelli. There have been previous approaches based on this ocellar system, most of them considering assumptions such as known light source direction or a punctual light source. In contrast, here we present a learning approach using Artificial Neural Networks in order to recover the system’s angular rates indoors and outdoors without previous knowledge. A classical computer vision based method is also derived to be used as a benchmark for the learning approach. The method is validated with a large dataset of images (more than half a million samples) including synthetic and real data. The source code of the algorithms and the datasets used in this paper have been released in an open repository.
Mathematical model for identifying the leading geopolitical actor by the principal component analysis

Abstract In this paper, an approach is developed that allows one to solve an applied problem of identifying hidden factors of geopolitical influence. It uses the principal component analysis. The solution of the problem is based on the principal components and the method of informative selection of the components of the response of the linear regression model. A numerical example is given on the basis of data on the cost of armament of a number of leading countries.
Dynamic Shannon Performance in a Multiobjective Particle Swarm Optimization

Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress is usually measured by the fitness of the best particle and the average fitness of the particles. When several objectives are considered, the PSO may incorporate distinct strategies to preserve nondominated solutions along the iterations. The performance of the multiobjective PSO (MOPSO) is usually evaluated by considering the resulting swarm at the end of the algorithm. In this paper, two indices based on the Shannon entropy are presented, to study the swarm dynamic evolution during the MOPSO execution. The results show that both indices are useful for analyzing the diversity and convergence of multiobjective algorithms.
Economic mechanisms for improving the efficiency of the territorial distribution of production points and temporary storage facilities

Abstract This article discusses the algorithms for finding the optimal solution of problems related to the location of temporary storage of goods, warehouses, firms for processing raw materials and shops selling the final product in the transport network. An algorithm is also proposed for finding a compromise solution to the problem of maximizing profits for each agent.
Automated Machine Learning for Studying the Trade-Off Between Predictive Accuracy and Interpretability

Automated Machine Learning (Auto-ML) methods search for the best classification algorithm and its best hyper-parameter settings for each input dataset. Auto-ML methods normally maximize only predictive accuracy, ignoring the classification model's interpretability – an important criterion in many applications. Hence, we propose a novel approach, based on Auto-ML, to investigate the trade-off between the predictive accuracy and the interpretability of classification-model representations. The experiments used the Auto-WEKA tool to investigate this trade-off. We distinguish between white box (interpretable) model representations and two other types of model representations: black box (non-interpretable) and grey box (partly interpretable). We consider as white box the models based on the following 6 interpretable knowledge representations: decision trees, If-Then classification rules, decision tables, Bayesian network classifiers, nearest neighbours and logistic regression. The experiments used 16 datasets and two runtime limits per Auto-WEKA run: 5 h and 20 h. Overall, the best white box model was more accurate than the best non-white box model in 4 of the 16 datasets in the 5-hour runs, and in 7 of the 16 datasets in the 20-hour runs. However, the predictive accuracy differences between the best white box and best non-white box models were often very small. If we accept a predictive accuracy loss of 1% in order to benefit from the interpretability of a white box model representation, we would prefer the best white box model in 8 of the 16 datasets in the 5-hour runs, and in 10 of the 16 datasets in the 20-hour runs.
Genes Expression Classification Through Histone Modification Using Temporal Neural Network

Background: Genes expression is high dimensional data, so it is very difficult to classify high dimensional data through traditional machine learning approaches. In this work we have proposed a model based on combined approach of Convolutional Neural Network and Recurrent Neural Network, both belong to deep learning model. The prediction has shown improved result than other machine learning algorithms. Expressions are generated through histone modification. Methods: To improve the accuracy deep learning model is proposed i.e. based on Convolutional and Recurrent neural network. This proposed model uses filter, causal convolutional layers and Residual Block for predictions. Results: In this work we have implemented the machine learning algorithms and deep learning algorithms like Logistic Regression, SVM, CNN, Deep Chrome and the proposed Temporal Neural Network. The performance is measured on the basis of parameters like accuracy, precision and AUC on the training and testing set. Conclusion: The proposed Temporal Neural Network model has shown better performance than other machine learning and deep learning algorithms. Due to this proposed deep learning algorithm can be successfully applied on the genes expression dataset.