Reviewerly/exHarmony huggingface dataset:
Global Face Pose Detection Based on an Improved PSO-SVM Method

Face detection is widely used in various image and video processing technologies. The detection of face direction is also one of the functions of face detection, but it is not possible to obtain an effective face direction under poor data labeling conditions. This paper proposes an improved global optimal particle swarm optimization method, in which PSO completes the global optimization of SVM parameters during the training phase. The improved PSO-SVM is mainly adjusted for the regularization parameters and kernel parameters of the SVM, and the goal is the global optimal parameter combination. The experimental results show that the improved PSO-SVM has a good effect on positive sample judgment and 45-degree face judgment.
WMPEClus: Clustering via Weighted Meta-Path Embedding for Heterogeneous Information Networks

A low-dimensional embedding of multiple nodes is great convenient for clustering, which is one of the most fundamental tasks for heterogeneous information networks (HINs). In the meantime, the random walk-based network embedding is proved to be equivalent to the method of matrix factorization whose computational cost is very expensive. Moreover, mapping different types of nodes into one metric space may result in incompatibility. To cope with the two challenges above, a weighted meta-path embedding based clustering method (called WMPEClus) is proposed in this paper. On the one hand, in order to solve the incompatibility problem, the original network is transformed into several subnetworks with independent semantics specified by meta-paths which are automatically generated by our method. On the other hand, an approximate commute embedding approach, avoiding eigen-decomposition to reduce computational cost, is leveraged to the representation learning of the nodes in each subnetwork. At last, a unified probabilistic generation model is designed to aggregate the vectorized representations learned in different metric spaces for clustering. Experiment results show that WMPEClus is effective in HIN clustering and outperforms the state-of-the-art baselines on two real-world datasets.
Medical Image Retrieval Using Empirical Mode Decomposition with Deep Convolutional Neural Network

Content-based medical image retrieval (CBMIR) systems attempt to search medical image database to narrow the semantic gap in medical image analysis. The efficacy of high-level medical information representation using features is a major challenge in CBMIR systems. Features play a vital role in the accuracy and speed of the search process. In this paper, we propose a deep convolutional neural network- (CNN-) based framework to learn concise feature vector for medical image retrieval. The medical images are decomposed into five components using empirical mode decomposition (EMD). The deep CNN is trained in a supervised way with multicomponent input, and the learned features are used to retrieve medical images. The IRMA dataset, containing 11,000 X-ray images, 116 classes, is used to validate the proposed method. We achieve a total IRMA error of 43.21 and a mean average precision of 0.86 for retrieval task and IRMA error of 68.48 and F1 measure of 0.66 on classification task, which is the best result compared with existing literature for this dataset.
On Strongly Solving Chinese Checkers

Chinese Checkers is a game for 2–6 players that has been used as a testbed for game AI in the past. The game is easily scalable to different size boards, different numbers of players, and different numbers of pieces for each player. In this paper we provide an overview of what is required to strongly solve versions of the game, including a complete set of rules needed to solve the game. We provide results on smaller boards with result showing that these games are all a first-player win.
A Sense of Quality for Augmented Reality Assisted Process Guidance

The ongoing automation of modern production processes requires novel human-computer interaction concepts that support employees in dealing with the unstoppable increase in time pressure, cognitive load, and the required fine-grained and process-specific knowledge. Augmented Reality (AR) systems support employees by guiding and teaching work processes. Such systems still lack a precise process quality analysis (monitoring), which is, however, crucial to close gaps in the quality assurance of industrial processes.We combine inertial sensors, mounted on work tools, with AR headsets to enrich modern assistance systems with a sense of process quality. For this purpose, we develop a Machine Learning (ML) classifier that predicts quality metrics from a 9-degrees of freedom inertial measurement unit, while we simultaneously guide and track the work processes with a HoloLens AR system. In our user study, 6 test subjects perform typical assembly tasks with our system. We evaluate the tracking accuracy of the system based on a precise optical reference system and evaluate the classification of each work step quality based on the collected ground truth data. Our evaluation shows a tracking accuracy of fast dynamic movements of 4.92mm and our classifier predicts the actions carried out with mean F1 value of 93.8% on average.
Predicting the Change in State of the Human Heart Based on Synthetic Heart Chamber Volume Data

Cardiovascular Disease (CVD) is one of the most detrimental health issues the world experiences each year. Approximately 647,000 people die from CVD each year in America according to the Centers for Disease Control and Prevention. In other words, one person every 37 seconds. Globally, 17.9 million people die from CVD each year as reported by the World Health Organization. Financially, America spends approximately $219 billion each year. The American Heart Association estimates the financial cost to reach $1.1 trillion by 2035. These metrics show the need to continue research in aiding individuals affected with CVD. In this paper, we use the theory of our previous work, a wearable ultrasound vest to create a real-time near 3D model of the heart, to create the beginnings of a heart state prediction system. That is, we create a synthetic dataset using the ranges of normal and abnormal heart chamber volumes to calculate external surface areas and generate data via statistical bootstrapping. Then, feed this system into a Machine Learning algorithm called a Constrained State Preserved Extreme Learning Machine (CSPELM). Our results show that we can differentiate between abnormal data (Atrial Fibrillation, Chronic Mitral Regurgitation, and Post-Myocardial Infarction) from normal data, where higher percentages for abnormal and low percentages for normal is the goal, with CSPELM predictions of 60.28-88.33% to 33.61%, respectively.
Formally Verified Trades in Financial Markets

We introduce a formal framework for analyzing trades in financial markets. These days, all big exchanges use computer algorithms to match buy and sell requests and these algorithms must abide by certain regulatory guidelines. For example, market regulators enforce that a matching produced by exchanges should be fair, uniform and individual rational. To verify these properties of trades, we first formally define these notions in a theorem prover and then develop many important results about matching demand and supply. Finally, we use this framework to verify properties of two important classes of double sided auction mechanisms. All the definitions and results presented in this paper are completely formalized in the Coq proof assistant without adding any additional axioms to it.
Integration of Mobile Learning into Complex Problem-Solving Processes during STEM Education

&lt;p class="0abstract"&gt;Over the past few years, the teaching process has transformed radically under significant investments in information and communication technologies. In this context, mobile technologies emerge as an innovative educational tool. Mobile devices are being used by a vast number of so-called "digital generation" representatives in daily life and educational activities. It has been proven that the implementation of mobile technologies in education contributes to the increase of students' motivation, participation in the learning process, and faster acquisition of professional competencies. These technologies take the role of the "driving force" in training. However, their comprehensive understanding is essential to use them effectively. The objective of this study was to analyze the intensity of mobile technologies' use and investigate their evolution in higher education using the example of the I.M. Sechenov First Moscow State Medical University. The research sample was represented by 151 students (data collected for 2015/2016) and 274 students (data for 2019/2020). The average participants' age corresponded to 19.8. The study also involved three experts who were required to evaluate questionnaires completed in Google Forms. The scientific method of the study was based on the organized examination, strict control over the involved respondents, and quantitative research. The research outcomes were analyzed through Chi-Square goodness-of-fit test. The target questions of the survey were rated on a 5-point Likert scale. According to the study results, 95% of the respondents used mobile devices for educational purposes, of which 65% agreed with the convenience of having course materials on a mobile device. In the 2015/2016 academic year, the share of students using smartphones for learning comprised 10.4%, while in 2019/2020, their percentage increased to 61.5%. The study findings will be useful for university teachers and representatives of educational institutions' administration.&lt;/p&gt;
Arithmetic constraints of polynomial maps through discrete logarithms

Let q be a prime power, let Fq be the finite field with q elements and let θ be a generator of the cyclic group Fq⁎. For each a∈Fq⁎, let logθ⁡a be the unique integer i∈{1,…,q−1} such that a=θi. Given polynomials P1,…,Pk∈Fq[x] and divisors 1<d1,…,dk of q−1, we discuss the distribution of the functionsFi:y↦logθ⁡Pi(y)(moddi), over the set Fq∖∪i=1k{y∈Fq|Pi(y)=0}. Our main result entails that, under a natural multiplicative condition on the pairs (di,Pi), the functions Fi are asymptotically independent. We also provide some applications that, in particular, relates to past work.
Estimating Rotation Angle and Transformation Matrix Between Consecutive Ultrasound Images Using Deep Learning

Image registration plays a crucial role in biomedical imaging, especially in image-guided surgery. Obtaining real-time images with an Ultrasound Imaging System (US) makes it possible to register them with magnetic resonance (MR) or computed tomography (CT) images and increase the accuracy of imageguided surgery. Differences in the resolution and intensity of these images motivated us to register ultrasound images with each other. Ultrasound images suffer from low contrast and resolution in comparison to other image modalities such as MR. By acknowledging the fact that the transformation matrix is the building block of the registration concept. Also, given the success of deep learning in classification, we choose to apply it to identify the angle difference and rotation matrix of three consecutive ultrasound images. This paper attempts to find the Euler angles and rotation matrix of three consecutive ultrasound images by applying a deep learning method. At the end of the study, we attain promising results when our learning rate is 0.00002 and the scaling factor is 64× 32. Furthermore, the comparison of positive and negative angles demonstrates that the overall network performs better in predicting positive angles.
Popularity and correlation aware data replication strategy based on half‐life concept and clustering in cloud system

Summary In the new millennium, a myriad of large‐scale applications (e.g., social networks, ecommerce, internet of things, and scientific experiments) often generate large volumes of data. Given their volumes, their heterogeneous and distributed nature, the management of such data constitutes a challenge for distributed systems, particularly cloud computing. In this regard, data replication is a well‐known and effective data management technique that consists in creating multiple copies of the same data in different storage resources. In this article, we propose a popularity and correlation based data replication strategy, called PCDR. The main idea of our strategy is to replicate a set of the most popular correlated file groups based on files access history analysis. In this respect, the popularity of the files in each group is determined while taking into consideration temporal locality. Moreover, a clustering technique is used to find groups of correlated files. Using the CloudSim simulator, extensive experimentations show that our proposed strategy outperforms other strategies for several evaluation metrics.
Handling of Mathematical Expression on Latin-to-Balinese Script Transliteration Method on Mobile Computing

This study is aimed at analyzing the handling of mathematical expression on the Latin-to-Balinese Script transliteration method since there has not been studied yet. It is one of the ways to preserve the endangered local culture knowledge through the collaboration between Computer Science and Language discipline. Moreover, this study was conducted on mobile computing that supports ubiquitous learning. There are three aspects related, i.e.; 1) Balinese Language uses verbal mathematical expression rather than mathematical expression using the notation in its Balinese writing; 2) In transliteration case, the Latin text of mathematical expression using notation should be preserved to avoid complexity related to various verbal mathematical expressions (which they have the same meaning); and 3) The second aspect was limitedly handled by the supporting computer font, which in this case is Bali Simbar Dwijendra (SD) font, and special algorithm needed to be applied on them. This research added a certain perspective and strengthened the transliteration knowledge, as part of Balinese Language ubiquitous learning that supports Balinese Language education, which is a mandatory local subject from basic to high school in Bali Province. This analysis was conducted on pioneering Aksara Bali SD mobile application that receives Latin text input and outputs Balinese Script based on Bali SD font. Through the experiment, it's handling of mathematical expression gave good transliteration results since a special rule-based algorithm was applied.
To be an Artist: Automatic Generation on Food Image Aesthetic Captioning

Image aesthetic captioning is a multi-modal task that is to generate aesthetic critiques for images. In contrast to common image captioning tasks, where different captions aimed at providing factual descriptions of a same image are always similar, captions with respect to different aesthetic attributes of the same image can be totally different in an aesthetic captioning task. Such inter-aspect differences are always overlooked, which leads to the lack of diversity and coherence of the captions generated by most of the existing image aesthetic captioning systems. In this paper, we propose a novel model to generate aesthetic captions for food images. Our model redefines food image aesthetic captioning as a compositional task that consists of two separated modules, i.e., a single-aspect captioning and an unsupervised text compression. The first module is guaranteed to generate the captions and learn feature representations of each aesthetic attribute. Then, the second module is supposed to study the associations among all feature representations and automatically aggregate captions of all aesthetic attributes to a final sentence. We also collect a dataset which contains pair-wise image-comment data related to six aesthetic attributes. Two new evaluation criteria are introduced to comprehensively assess the quality of the generated captions. Experiments on the dataset demonstrate the effectiveness of the proposed model.
Text Emotion Recognition Using GRU Neural Network with Attention Mechanism and Emoticon Emotions

In this study, we propose an emotion identify model called SEER, in this model, we first combined a Bi-directional Gate Recurrent Unit (Bi-GRU) network and attention mechanism to capture the emotion vectors for the aspect of input words, and second, we statistically analyze the emoticon that appears in our data set to obtain the emoticon distribution, then, use the emoticon distribution to enhance the emotion vectors. The experiment proved that combine with Attention Mechanism and Emoticon Distribution is an effective way to improve the accuracy of emotion recognition. Compared with other deep learning methods, machine learning methods, and other methods, the experimental results show that the method we posed in this paper has achieved the highest accuracy in emotion recognition.
A comparative analysis of bio-inspired optimization algorithms for automated test pattern generation in sequential circuits

In recent years, bio-inspired optimization algorithms have demonstrated the ability to produce optimal solutions to numerous complex computational problems in science and engineering. In this work, a comparative analysis of bio-inspired algorithms is presented to understand and quantify the performance of algorithms in guiding the search process toward better solutions over all feasible solutions. Three evolutionary algorithms, namely, the Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Differential Evolution (DE), are implemented to generate an optimized test sequence set for digital sequential circuits to investigate how they explore and the exploitation realized in their search spaces. The merit of using a comparative method to analyze and optimize search spaces is to reach a reliable and quantifiable conclusion about the relative performance of each algorithm. An improvement in the quality of solutions was achieved, particularly in terms of testing time, number of test vectors, and fault coverage of tested sequential circuits in comparison with other algorithmic test generators that have been presented in the literature. The study shows how to effectively reduce the search space without negatively affecting the results and guides the search process over a large space. In addition, the experiments highlight the limitations of each optimization algorithm and offer some constructive methods of improvement. Moreover, several recommendations and guidelines regarding the use of optimization algorithms as test pattern generators to improve their performance and increase their efficiency are presented. Finally, we emphasize the relevance of bio-inspired algorithms in solving complex computational problems, data manipulation, and optimization objectives.
Bayesian Network Structure Learning Using Case-Injected Genetic Algorithms

In this paper, we propose a new hybrid structure learning method that incorporates case-injected genetic algorithms as a score-and-search method for determining Bayesian network structure from data. In our approach, we first find the probabilistic dependencies among variables to constrain the search space and then employ case-injected genetic algorithms in the score-and-search phase to find a quality structure from the reduced search space. The novelty of our work lies with the introduction of combining case-based reasoning with genetic algorithms to evolve a near-optimal Bayesian network in fewer generations compared to a randomly initialized genetic algorithm. Our case-injected genetic algorithms enhance Bayesian network structure learning performance over a sequence of similar problems by extracting and storing knowledge from previously solved problems and utilizing the accumulated knowledge to solve subsequent similar problems. To evaluate the viability of our proposed approach, we conducted a series of experiments by generating a sequence of similar problems based on using data sets obtained randomly from three well-known benchmark Bayesian networks. We also compared the performance of our proposed approach with the state-of-the-art algorithm. Our preliminary results show that case-injected genetic algorithms provide better performance in learning Bayesian network structure compared to GA and the state-of-the-art algorithm. Our proposed approach has applications in real-world domains such as e-commerce system and health care.
Aligning document layouts extracted with different OCR engines with clustering approach

Layout analysis is essential step in information extraction from scanned document images. In this paper we propose an algorithm for aligning layouts generated with different OCR engines. The main requirement is to always generate the same layout for the given document image regardless of OCR engine used for image processing. In that way information extraction from scanned documents, that is heavily dependent on fields positions in the document, does not depend on specific OCR engine. In other words, it is sufficient to maintain universal extractor knowledge and not necessary to train extractor explicitly with samples processed on specific OCR engine. The proposed algorithm can handle administrative documents with complex layout.
Coverless Text Steganography Method Based on Characteristics of Word Association

The search-based coverless steganography methods can effectively resist the potential steganalysis attacks, but there are still some limitations such as low hiding capacity and unstable success rate of hiding. To address those problems, this paper proposed a coverless steganography method based on characteristics of word association. In this method, both parties of communication construct the word node tree using the characteristics of word association, encode the transfer path of the word node tree to embed the secret information, and establish the mapping relation between the transfer path and texts by introducing isomorphic text set mechanism. Experimental results show that the proposed method is ideal in security and resistance to detection, and greatly improves the success rate of hiding and hiding capacity.