Reviewerly/exHarmony huggingface dataset:
IDNet-A: Variant of DenseNet with Inception-Family

A lot of interest in deep learning and advances in computer hardware (especially GPU) has recently led to many studies on network architecture in various fields. With these studies, the technology using machine learning in various fields shows good performance. Especially in the field of computer vision, solutions using convolutional neural networks (CNNs) are becoming overwhelming, with much better performance than solutions using image processing algorithms. In image recognition of computer vision, various network architectures using CNNs have been introduced and developed. In reference to previous studies, we introduce IDNet-A in this paper, which combines two impressive and powerful networks (DenseNet and Inceptionfamily). We studied how to increase the size of network to get good performance with increased representational power. We applied the Inception Module concept of Inception-family and Dense Connectivity of DenseNet to IDNet-A to efficiently increase the size of the network at a reasonable cost. As a result, we can train well in deep network architecture. We constructed several models with different hyperparameters and experimented with CIFAR datasets. Finally, IDNet-A, which we introduce in this paper, increases the size of the network by increasing the depth and width appropriately and achieves good performance with fewer parameters compared to other networks.
Analysis and Testing of Dual Axis Solar Tracker for a Standalone PV System

To sustain life in earth we need considerable amount of energy. For our body it is given by food we eat but we also need energy for machines and equipment for them to work. Since non-renewable source of energy is exhaustible in nature so nowadays we move towards renewable source of energy. One of the major sources of renewable energy is sun. Today we are using only a small amount of solar energy by using solar cells. These solar cells are placed at fixed position so amount is limited. To increase the amount of energy we receive we use solar tracker. Solar tracker is the device used to follow the sun. In this paper we deal with dual axis solar tracker with real time tracking technology. By this method we can increase the solar energy production up to 45%.
Emotional Response Generation using Conditional Variational Autoencoder

Neural response generation is to generate human-like response given human utterance by using a deep learning. In the previous studies, expressing emotion in response generation improve user performance, user engagement, and user satisfaction. Also, the conversational agents can communicate with users at the human level. However, the previous emotional response generation model cannot interpret why the model generates such response with emotions. We propose an interpretable emotional response generation model which generates emotional responses by using a latent space. The extraction part is to extract the emotion of input utterance as a vector form by using the Bidirectional GRU based classification model. The generation part is to generate an emotional response to the input utterance by exploiting emotion vector and latent space. All of these parts are jointly optimized at the training process. We will evaluate our model on the emotion-labeled dialogue dataset: DailyDialog.
Empowering Caregivers to Customizing the Assistive Computing Support of Older Adults - an End-User Domain-Specific Approach

Smart homes are a promising infrastructure to support assistive services that can prolong independent living of older adults. However, smart homes are mostly developed using a technology-centered approach, raising challenges for users to comprehend the purpose of smart home services and anticipate their behavior. These challenges are more acute when services are to be deployed in homes of older adults, preventing the expert knowledge of their caregivers to be leveraged. This paper presents a user study that assesses the comprehensibility of an end-user language, named Maloya, dedicated to developing assistive services. Participants (9 professional caregivers) are presented with services written in Maloya and must determine whether they detect scenarios of daily living. Our results show that Maloya is well-understood by participants; they successfully determine whether a service detects a scenario (success rate of 94%) and substantiate their answers with consistent explanations. As such, Maloya should be effective in empowering caregivers to select appropriate services for their care-receivers and to accurately anticipate the service behavior.
Non-Audible Speech Classification Using Deep Learning Approaches

Research advancement of human-computer interaction (HCI) has recently been made to help post-stroke victims dealing with physiological problems such as speech impediments due to aphasia. This paper investigates different deep learning approaches used for non-audible speech recognition using electromyography (EMG) signals with a novel approach employing continuous wavelet transforms (CWT) and convolutional neural networks (CNNs). To compare its performance with other popular deep learning approaches, we collected facial surface EMG bio-signals from subjects with binary and multi-class labels, trained and tested four models, including a long-short term memory(LSTM) model, a bi-directional LSTM model, a 1-D CNN model, and our proposed CWT-CNN model. Experimental results show that our proposed approach performs better than the LSTM models, but is less efficient than the 1-D CNN model on our collected data set. In comparison with previous research, we gained insights on how to improve the performance of the model for binary and multi-class silent speech recognition.
On the zero entries in a unitary matrix

We investigate the number of zero entries in a unitary matrix. We show that the sets of numbers of zero entries for n×n unitary and orthogonal matrices are the same. They are both the set {0,1,…,n2−n−4,n2−n−2,n2−n} for n>4. We explicitly construct examples of orthogonal matrices with the numbers in the set. We apply our results to construct a necessary condition by which a multipartite unitary operation is a product operation. The latter is a fundamental problem in quantum information. We also construct an n×n orthogonal matrix of Schmidt rank n2−1 with many zero entries, and it solves an open problem in Muller-Hermes and Nechita [Operator Schmidt ranks of bipartite unitary matrices. Linear Algebra Appl. 2018;557:174—187].
RDF graph mining for cluster-based theme identification

Purpose The purpose of this paper is to present a generic pipeline for Resource Description Framework (RDF) graph mining to provide a comprehensive review of each step in the knowledge discovery from data process. The authors also investigate different approaches and combinations to extract feature vectors from RDF graphs to apply the clustering and theme identification tasks. Design/methodology/approach The proposed methodology comprises four steps. First, the authors generate several graph substructures (Walks, Set of Walks, Walks with backward and Set of Walks with backward). Second, the authors build neural language models to extract numerical vectors of the generated sequences by using word embedding techniques (Word2Vec and Doc2Vec) combined with term frequency-inverse document frequency (TF-IDF). Third, the authors use the well-known K-means algorithm to cluster the RDF graph. Finally, the authors extract the most relevant rdf:type from the grouped vertices to describe the semantics of each theme by generating the labels. Findings The experimental evaluation on the state of the art data sets (AIFB, BGS and Conference) shows that the combination of Set of Walks-with-backward with TF-IDF and Doc2vec techniques give excellent results. In fact, the clustering results reach more than 97% and 90% in terms of purity and F -measure, respectively. Concerning the theme identification, the results show that by using the same combination, the purity and F -measure criteria reach more than 90% for all the considered data sets. Originality/value The originality of this paper lies in two aspects: first, a new machine learning pipeline for RDF data is presented; second, an efficient process to identify and extract relevant graph substructures from an RDF graph is proposed. The proposed techniques were combined with different neural language models to improve the accuracy and relevance of the obtained feature vectors that will be fed to the clustering mechanism.
Effectiveness of self-instructional module on awareness on screening regarding breast cancer with Intelligent Breast Examination among primary schoolteachers in Karad

As Breast cancer is most common in developed and developing world. Despite of seriousness & importance of breast cancer the awareness is low. This Study highlights the primary screening of breast cancer with the device Intelligent Breast Examination.The objectives of this study are to assess the knowledge regarding awareness on screening of breast cancer with Intelligent Breast Examination (IBE), to develop a self-instructional module (SIM) on awareness on screening regarding breast cancer with IBE, and to assess its effectiveness on awareness about screening breast cancer with IBE.Evaluative research approach was used. The study used preexperimental research design. The samples (60 primary schoolteachers) were selected by the use of two sampling techniques, i.e., random sampling (for schools) and purposive sampling (study participants). Data were collected in two phases (pre and post) using structured questionnaire followed by the administration of SIM.The results revealed a significant increase in mean knowledge scores (mean pre 11.16 standard deviation [SD] = 2.631 to mean post 21.2 SD = 1.842) regarding awareness on screening of breast cancer with IBE. The calculated paired t value (23.902) is greater than the table value ( t = 1.67) at 0.05 level of significance.The study concluded that the SIM prepared proved to be effective in increasing the awareness regarding screening with IBE in breast cancer.
A MV-based Steganographic Algorithm for H.264/AVC without Distortion

H.264/AVC video is one of the most popular multimedia and has been widely used as the carriers of video steganography. In this paper, a novel motion vector (MV) based steganographic algorithm is proposed for the H.264/AVC compressed video without distortion. Four modules are introduced to eliminate the distortion caused by the modifications of motion vectors and guarantee the security of the algorithm. In the embedding block, the motion vector space encoding is used to embed a (<i>2n</i>+1)-ary notational number into an <i>n</i>-dimension vector composed of motion vectors generated from the selection block. Scrambling is adopted to disturb the order of steganographic carriers to improve the randomness of the carrier before the operation of embedding. The re-motion compensation (re-MC) block will re-construct the macroblock (MB) whose motion vectors have been modified by embedding block. System block plays the role of the generator for chaotic sequences and encryptor for secret data. Experimental results demonstrate that our proposed algorithm can achieve high embedding capacity without stego video visual quality distortion, it also presents good undetectability for existing MV-based steganalysis feature. Performance comparisons with other existing algorithms are provided to demonstrate the superiority of the proposed algorithm.
Sistem Pakar dalam Mengidentifikasi Penyakit Kandungan Menggunakan Metode Forward Chaining Berbasis Android

Maternal Mortality Rate (MMR) in Indonesia is very high, so that maternal health problems are a national problem. This problem needs to get top priority. The health of pregnant women is crucial for the growth of the fetus they contain. Pregnancy can cause a decrease in maternal resistance. This decrease will trigger the arrival of various diseases. For that we need a system that can identify uterine diseases quickly and accurately. This study aims to identify uterine diseases in pregnant women based on symptoms experienced. This identification is the initial information that is useful to support the decision to take preventative action. Data processed in this study were 20 patients. This data is sourced from the Sungai Melati City Clinic which goes to an obstetrician, Dr. Yandi Zulkarnaen, SpOG. The method used in processing data is Android-based Forward Chaining. The results of this study include the name of the disease, description of the disease, and treatment solutions. After testing and calculating the level of system accuracy, a good degree of accuracy is obtained from the system calculation results with an expert decision of 90% of the 20 test data. Based on the level of accuracy, the expert system is very precise in identifying uterine diseases quickly and accurately.
Photo and Video Technologies Target New Frontiers: Innovative Imaging Research Enabled by Signal Processing Is Making Cameras More Powerful and Versatile [Special Reports]

Researchers worldwide are continuing to advance photo and video technologies, developing faster high-resolution cameras and taking advantage of artificial intelligence (AI), machine learning (ML), and other cutting-edge tools. As a result, novel applications are appearing in an almost endless number of fields, including scientific research, sports, public safety, and personal security.
Natural evolution strategies and quantum approximate optimization.

A notion of quantum natural evolution strategies is introduced, which provides a geometric synthesis of a number of known quantum/classical algorithms for performing classical black-box optimization. Recent work of Gomes et al. [2019] on combinatorial optimization using neural quantum states is pedagogically reviewed in this context, emphasizing the connection with natural evolution strategies. The algorithmic framework is illustrated for approximate combinatorial optimization problems, and a systematic strategy is found for improving the approximation ratios. In particular it is found that natural evolution strategies can achieve state-of-art approximation ratios for Max-Cut, at the expense of increased computation time.
Balancing Personal Privacy and Public Safety in COVID-19: Case of Korea and France

There has been vigorous debate on how different countries responded to the COVID-19. To secure public safety, Korea has actively used personal information at the risk of personal privacy whereas France encourages voluntary cooperation at the risk of public safety. In this article, we examine the pros and cons of these two different approaches in epidemiological investigations. In addition, we present technological options of de-identification of personal information in the course of managing this pandemic under control. We discuss lessons for future research and policy implications.
Analysis of stereo camera parameters effect on stereo matching performed by remote operator

To move toward productive remote work, the burden and annoyance associated with remote operation using remote controlled robots must be reduced so that people with severe disabilities may participate without going out. Our proposed interface directly uses the parallax of a stereo image captured by a stereo camera and the stereo matching by the operators to reduce the burden in recognizing the remote depth position error. In this study, we analyzed the effect of stereo camera parameters on the displacement of the corresponding spatial feature points in each image and then verified with the machine to determine the most advantageous interface design. We found that if the parameter of the baseline length was large, several properties, which may assist in reducing the burden of remote operation, varies in accordance with the camera posture.
JOKOWI VS PRABOWO: THE POLITENESS AND ITS VIOLATION IN POLITICAL COMMUNICATION OF INDONESIAN PRESIDENT CANDIDATES

Purpose of the study: The study aims are to investigate levels of language politeness and its violations in the political communication of Jokowi and Prabowo and to describe the types of politeness and its violations in political communication of Jokowi and Prabowo as Indonesian president candidates in 2019.&#x0D; Methodology: This research was used a qualitative approach with the descriptive method by paying attention to the Interactive Model theory to describe the object in analysis data through a pragmatic approach to identify the politeness principles and its violation following Leech’s (1983) theory. The subject of data on this research has conducted the utterances of Indonesian president candidates 2019 in the second debate session.&#x0D; Main Findings: The result of the study can be concluded that five principles of politeness seen in the utterances of the presidential candidate. They are tact, approbation, modesty, agreement, and sympathy maxim. Furthermore, in this research, Prabowo was more polite than Jokowi where he has produced utterances of approbation, agreement, and less violation of modesty, while Jokowi more violated the modesty maxim.&#x0D; Applications of this study: The study has an impact on political behavior. Other areas of study include social and political science and communication&#x0D; Novelty/Originality of this study: This research is the new way in the context of language politeness study where combined the language politeness principles with socio-political science especially political communication.
Probabilistic, Multi-Scale Fractional Tonal Correction Bilateral Filter-Based Hazy Image Enhancement

This paper describes an algorithm utilizing a modified multi-scale fractional order-based operator combined with a probabilistic tonal operator, adaptive color enhancement and bilateral filtering to process hazy and underwater images. The multi-scale algorithm complements the tonal operator by enhancing edges, preventing overexposure of bright image regions, while enhancing details in the dark areas. The addition of a previously developed global enhancement operator removes color cast and improves global contrast in underwater images. The color enhancement function augments the color results of the dehazing algorithm without distorting image intensity. Furthermore, the bilateral filter suppresses noise while preserving enhanced details/edges due to the multi-scale algorithm. Experimental results indicate that the proposed system yields comparable or better results than other algorithms from the literature.
Computer modeling of irregularly spaced signals. Statistical properties of the wavelet approximation using a compact weight function

The algorithm of modified wavelet analysis is discussed. It is based on the weighted least squares approximation. Contrary to the Gaussian as a weight function, we propose to use a compact weight function. The accuracy estimates using the statistically correct expressions for the least squares approximations with an additional weight function are compared with that obtained using the bootstrap method.
Effect of denoising on the localization of microcalcification clusters in digital mammography

Noise negatively impacts the detection and characterization of lesions in mammography. While denoising filters may be used to suppress noise, they might also negatively affect the conspicuity of small lesions due to signal blurring and smearing. In previous works, we designed and validated a denoising pipeline, dedicated to mammography, capable of suppressing noise and avoiding excessive blur and smear. This is achieved by a fine-tuned noisy-denoised image blending step, which leverages a Poisson-Gaussian noise model. In the current work, we investigate the impact of the denoising pipeline on the localization of low contrast microcalcification clusters. To this end, a human observers study was conducted with a team of five medical physicists with experience in breast imaging. First, in the pilot study, we defined the limit of contrast for the localization task with and without the application of the denoising pipeline. Next, we investigated the effect of the denoising on the localization of microcalcification clusters. Clinical patient cases with dense breasts and simulated microcalcification clusters were used throughout this study to emulate challenging cases and to guarantee fine control over the lesion’s contrast. The results from six readers show that the limit of localization occurred at the contrasts 0.090 and 0.079 without and with denoising, respectively. The average correct localization rate was 77% and 81% without and with denoising, respectively. Thus, the results show that the readers were able to correctly locate significantly less conspicuous lesions (p&lt;0.05), and also performed significantly better localizing microcalcification clusters (p&lt;0.05) when the denoising pipeline was applied.
An AI-based method to retrieve hematoxylin and eosin breast histology images using mammograms

Early diagnosis of breast cancer can increase survival rate. The assessment process for breast screening follows a triple assessment model: appropriate imaging, clinical assessment and biopsy. Retrieving prior cases with similar cancer symptoms could be used to circumvent incompatibilities in breast cancer grading. Abnormal mass lesions in breast are often co-located with normal tissue, which makes it difficult to describe the whole image with a single binary code. Therefore, we propose an AI-based method to describe mass lesions in semantic abstracts/codes. These codes are used in a searching based method to retrieve similar cases in the archive. This simple and effective network is used for unifying classification and retrieval in a single learning process, while enforcing similar lesion types to have similar semantic codes in a compact form. An advantage of this approach is its scalability to large-scale image retrievals.
Pedagogical techniques of Earth remote sensing data application into modern school practice

The article dwells upon the Earth remote sensing data as one of the basic directions of Geo-Information Science, a unique source of information on processes and phenomena occurring in almost all spheres of the Earth geographic shell (atmosphere, hydrosphere, lithosphere, etc.). The authors argue that the use of aerospace images by means of the information and communication technologies involvement in the learning process allows not only to increase the information context value of learning, but also contributes to the formation of students’ cognitive interest in such disciplines as geography, biology, history, physics, computer science, etc. It has been grounded that remote sensing data form students’ spatial, temporal and qualitative concepts, sensory support for the perception, knowledge and explanation of the specifics of objects and phenomena of geographical reality, which, in its turn, provides an increase in the level of educational achievements. The techniques of aerospace images application into the modern school practice have been analyzed and illustrated in the examples: from using them as visual aids, to realization of practical and research orientation of training on the basis of remote sensing data. Particular attention is paid to the practical component of the Earth remote sensing implementation into the modern school practice with the help of information and communication technologies.