Reviewerly/exHarmony huggingface dataset:
Recognizing Solo Jazz Dance Moves Using a Single Leg-Attached Inertial Wearable Device

We present here a method for recognising dance moves in sequences using 3D accelerometer and gyroscope signals, acquired by a single wearable device, attached to the dancer’s leg. The recognition entails dance tempo estimation, temporal scaling, a wearable device orientation-invariant coordinate system transformation, and, finally, sliding correlation-based template matching. The recognition is independent of the orientation of the wearable device and the tempo of dancing, which promotes the usability of the method in a wide range of everyday application scenarios. For experimental validation, we considered the versatile repertoire of solo jazz dance moves. We created a database of 15 authentic solo jazz template moves using the performances of a professional dancer dancing at 120 bpm. We analysed 36 new dance sequences, performed by the professional and five recreational dancers, following six dance tempos, ranging from 120 bpm to 220 bpm with 20 bpm increment steps. The recognition F1 scores, obtained cumulatively for all moves for different tempos, ranged from 0.87 to 0.98. The results indicate that the presented method can be used to recognise repeated dance moves and to assess the dancer’s consistency in performance. In addition, the results confirm the potential of using the presented method to recognise imitated dance moves, supporting the learning process.
A Convolution Neural Network-Based Representative Spatio-Temporal Documents Classification for Big Text Data

With the proliferation of mobile devices, the amount of social media users and online news articles are rapidly increasing, and text information online is accumulating as big data. As spatio-temporal information becomes more important, research on extracting spatiotemporal information from online text data and utilizing it for event analysis is being actively conducted. However, if spatiotemporal information that does not describe the core subject of a document is extracted, it is rather difficult to guarantee the accuracy of core event analysis. Therefore, it is important to extract spatiotemporal information that describes the core topic of a document. In this study, spatio-temporal information describing the core topic of a document is defined as ‘representative spatio-temporal information’, and documents containing representative spatiotemporal information are defined as ‘representative spatio-temporal documents’. We proposed a character-level Convolution Neuron Network (CNN)-based document classifier to classify representative spatio-temporal documents. To train the proposed CNN model, 7400 training data were constructed for representative spatio-temporal documents. The experimental results show that the proposed CNN model outperforms traditional machine learning classifiers and existing CNN-based classifiers.
Preference-Driven Classification Measure

Classification is one of the main problems of machine learning, and assessing the quality of classification is one of the most topical tasks, all the more difficult as it depends on many factors. Many different measures have been proposed to assess the quality of the classification, often depending on the application of a specific classifier. However, in most cases, these measures are focused on binary classification, and for the problem of many decision classes, they are significantly simplified. Due to the increasing scope of classification applications, there is a growing need to select a classifier appropriate to the situation, including more complex data sets with multiple decision classes. This paper aims to propose a new measure of classifier quality assessment (called the preference-driven measure, abbreviated p-d), regardless of the number of classes, with the possibility of establishing the relative importance of each class. Furthermore, we propose a solution in which the classifier's assessment can be adapted to the analyzed problem using a vector of preferences. To visualize the operation of the proposed measure, we present it first on an example involving two decision classes and then test its operation on real, multi-class data sets. Additionally, in this case, we demonstrate how to adjust the assessment to the user's preferences. The results obtained allow us to confirm that the use of a preference-driven measure indicates that other classifiers are better to use according to preferences, particularly as opposed to the classical measures of classification quality assessment.
Görüntü Şifreleme Yöntemlerinin Kapsamlı Bir İncelemesi

In today’s technology world, data security has a great importance. Because each data type has its own characteristics, there are various methods of providing this security. The main subject of this study is the security of image data which are more complex structures than text data. Using traditional encryption methods alone to ensure security in image data types can create security weaknesses. For this reason, nowadays, some traditional methods are combined with each other or different methods to encrypt image data. In this study, 131 articles were examined and image encryption methods were classified according to the traditional methods they contain. Studies on both colored and gray level images have been handled together. Finally, the images used in the articles were compared with each other in many ways and the results were presented graphically.
Image Compression Using Deep Learning: Methods and Techniques

In recent years images have been used widely by online social networks providers or numerous organizations such as governments, police departments, colleges, universities, and private companies. It held in vast databases. Thus, efficient storage of such images is advantageous and its compression is an appealing application. Image compression generally represents the significant image information compactly with a smaller size of bytes while insignificant image information (redundancy) already been removed for this reason image compression has an important role in data transfer and storage especially due to the data explosion that is increasing significantly. It is a challenging task since there are highly complex unknown correlations between the pixels. As a result, it is hard to find and recover a well-compressed representation for images, and it also hard to design and test networks that are able to recover images successfully in a lossless or lossy way. Several neural networks and deep learning methods have been used to compress images. This article survey most common techniques and methods of image compression focusing on auto-encoder of deep learning.
Fast Recognition Algorithm for Human Motion Posture Using Multimodal Bioinformation Fusion

To address the problems of low feature extraction accuracy, large bias of human motion pose recognition and posture recognition error, poor recognition effect, and low recognition rate of traditional human motion posture fast recognition algorithm, we propose a human motion posture fast recognition algorithm using multimodal bioinformation fusion. First, wavelet packet decomposition with sample entropy is used to extract the human motion posture hand features such as kurtosis, time domain feature skewness, and frequency domain feature electromyogram (EMG) integral value and time domain features such as mean, standard deviation, and interquartile distance of leg motion amplitude. Second, after normalizing the two features, the human hand and leg motion feature set is obtained, and finally the feature set is used to construct a human motion posture fast recognition model based on multimodal bioinformation fusion, and the feature set is input into the recognition model, which completes the fusion of human motion posture information by improving the typical correlation analysis method, and the fusion result is used as the input of the minimum distance classifier to achieve human motion posture fast recognition. The results show that the proposed algorithm has high accuracy of feature extraction, small bias of human motion posture recognition, the posture recognition error is -0.21∼0.02, the recognition rate is always above 95%, and the practical application effect is good.
Object-Level Visual-Text Correlation Graph Hashing for Unsupervised Cross-Modal Retrieval

The core of cross-modal hashing methods is to map high dimensional features into binary hash codes, which can then efficiently utilize the Hamming distance metric to enhance retrieval efficiency. Recent development emphasizes the advantages of the unsupervised cross-modal hashing technique, since it only relies on relevant information of the paired data, making it more applicable to real-world applications. However, two problems, that is intro-modality correlation and inter-modality correlation, still have not been fully considered. Intra-modality correlation describes the complex overall concept of a single modality and provides semantic relevance for retrieval tasks, while inter-modality correction refers to the relationship between different modalities. From our observation and hypothesis, the dependency relationship within the modality and between different modalities can be constructed at the object level, which can further improve cross-modal hashing retrieval accuracy. To this end, we propose a Visual-textful Correlation Graph Hashing (OVCGH) approach to mine the fine-grained object-level similarity in cross-modal data while suppressing noise interference. Specifically, a novel intra-modality correlation graph is designed to learn graph-level representations of different modalities, obtaining the dependency relationship of the image region to image region and the tag to tag in an unsupervised manner. Then, we design a visual-text dependency building module that can capture correlation semantic information between different modalities by modeling the dependency relationship between image object region and text tag. Extensive experiments on two widely used datasets verify the effectiveness of our proposed approach.
Intelligent Radio Frequency Identification for URLLC in Industrial IoT Networks

In the era of Industry 4.0, there are many emerging industrial Internet of Things (IIoT) applications that require ultrareliable and low-latency communications (URLLCs), such as time-sensitive networking (TSN), status reporting, and remote control. With the deployment of massive intelligent devices in IIoT networks, providing security for device physical-layer access is a challenging issue, especially for URLLC applications with strict latency and reliability constraints. We thus developed an intelligent radio frequency identification (RFI) framework to provide a lightweight and energy-efficient physical-layer access scheme for URLLCs via leveraging unique hardware-level imperfections of transmitter. We propose a novel semisupervised-learning (SSL) algorithm to realize intelligent RFI in URLLCs scenarios. One-dimensional network construction is also exploited to improve the accuracy of the proposed SSL algorithm. On the basis of the proposed RFI framework, we analyze the overall uplink transmission error probability and network availability of URLLCs with massive MIMO, which can achieve comparable symmetry performance with that of downlink, and experimental evaluation is also provided to gain comprehensive insight on RFI. Numerical and experimental results demonstrate the effectiveness of our proposed RFI framework and the impact of channel correlation, and provide design guidelines for supporting the radio frequency identification of URLLC applications in IIoT systems.
LINGUISTIC TENDENCIES OF ANOREXIA NERVOSA ON SOCIAL MEDIA USERS FACEBOOK (PRAGMATIC STUDY)

This research is motivated by the large number of facebook users who express their discomfort with body shape and after further review, the discomfort expressed is included in the characteristics of people who have a tendency to anorexia nervosa. This study was conducted to describe the linguistic tendency of anorexia nervosa on facebook social media users, especially on the "MinuteVideos Indonesia" account based on the characteristics of anorexia nervosa tendencies seen from the form and meaning of the language conveyed. This type of research is a qualitative research using descriptive method. The data of this study is the linguistic expressiveness of facebook users who have anorexia nervosa tendencies in comments on the "MinuteVideos Indonesia" account. Data were collected using the free-of-charge listening technique (SBLC) and note-taking techniques. The data were analyzed using the matching method and the agih method. Based on the results of the study found thirteen types of expressive speech acts used by Facebook users who have anorexia nervosa tendencies in comments on the "MinuteVideos Indonesia" account and each of these utterances has a referential meaning. The tendency of anorexia nervosa is dominantly experienced by women. However, from the data obtained there are also men who experience anorexia nervosa tendencies.
Digi-HTA, assessment framework for digital healthcare services: information security and data protection in health technology – initial experiences

It is well-known that security issues in medical devices, services and applications have potentially catastrophic consequences. To avoid compromising patient data or information systems, it is essential that healthcare services and products meet the relevant information security and data protection requirements. For these reasons, the Digi-HTA assessment includes information security and data protection assessment domains. The outcome of the Digi-HTA process is a recommendation that decision-makers can use during the procurement process. We present results and experiences from the first assessments made in the Digi-HTA process. We have assessed six products so far and multiple assessments are in progress. The results indicate that healthcare product manufacturers have found the process useful, and usually, the manufacturers have had to improve the security of their product during the Digi-HTA process to get a favourable recommendation for their product. The assessment processes have taken longer than expected due to shortcomings and ambiguities in the provided self-assessment forms, and due to feedback cycles and meetings prompted by assessment findings. Of the six assessed products, four received a green light in information security and data protection, whereas two have received a yellow light due to issues that were not fixed during the process. In addition to shortcomings in adhering to best practices, we have also found exploitable security issues.
Kernel Analyses of Volcanic Vent Distribution: How Accurate and Complete are the Objective Bandwidth Selectors?

Kernel Density Estimation is a powerful tool that can be used to extract information about the underlying plumbing system in zones of distributed volcanism. Different approaches concerning the form in which this tool should be applied, however, exist on the literature. One of those approaches sustains that an unbiased selection of a parameter known as the bandwidth is preferable to other alternatives because it reduces biases on the analysis. Nevertheless, there are more than 30 different forms in which a bandwidth can be “objectively” selected, therefore questioning the meaning of “objectivity” on the selection of a method used for its calculation. Furthermore, as shown in this work, the range of allowed “objective” choices of the bandwidth is not much different from a typical range that could be selected subjectively. Consequently, instead of focusing on the question of “what is the best method?” it is shown here that a more informative approach is to focus on the questions of “what are the special values of different methods, and what are their several advantageous applicabilities?”. The benefits of this shift in approach are illustrated with application to three locations of volcanic interest that have a previously well-constrained volcanic structure.
Red Seaweed (Gracilaria verrucosa Greville) Based Polyurethane as Adsorptive Membrane for Ammonia Removal in Water

Polyurethane membranes are widely developed polymers by researchers because they can be made from synthetic materials or natural materials. Red seaweed (Gracilaria verrucosa Greville) is a natural material that can be developed as a raw material for polyurethane membranes. This study used red seaweed biomass (RSB) as a raw material to manufacture polyurethane as an adsorptive membrane for removing ammonia in water. The membrane composition was determined using the Box-Behnken design from Response Surface Methodology with three factors and three levels. In the ammonia adsorption process, the adsorption isotherm was determined by varying the concentration, while the adsorption kinetics was determined by varying the contact time. Red seaweed biomass-based polyurethane membrane (PUM-RSB) can adsorb ammonia in water with an adsorption capacity of 0.233 mg/g and an adsorption efficiency of 16.2%. The adsorption efficiency followed the quadratic model in the Box-Behnken design, which resulted in the optimal composition of RSB 0.15 g, TDI 3.0 g, and glycerin 0.4 g with predicted and actual adsorption capacities of 0.224 mg/g and 0.226 mg/g. The ammonia adsorption isotherm using PUM-RSB follows the Freundlich isotherm, with a high correlation coefficient (R2) of 0.977, while the Langmuir isotherm has a low R2 value of 0.926. The Freundlich isotherm indicates that ammonia is adsorbed on the surface of the adsorbent as multilayer adsorption. In addition, based on the analysis of adsorption kinetics, the adsorption phenomenon follows pseudo-order II with a chemisorption mechanism, and it is assumed that the bond that occurs is between the anion -SO42- with the NH4+ cation to form ammonium sulfate (NH4)2SO4 and between isocyanates (NCO) with NH4+ cations to form substituted urea.
A Comparison of Dense and Sparse Optical Flow Techniques for Low-Resolution Aerial Thermal Imagery

It is necessary to establish the relative performance of established optical flow approaches in airborne scenarios with thermal cameras. This study investigated the performance of a dense optical flow algorithm on 14 bit radiometric images of the ground. While sparse techniques that rely on feature matching techniques perform very well with airborne thermal data in high-contrast thermal conditions, these techniques suffer in low-contrast scenes, where there are fewer detectable and distinct features in the image. On the other hand, some dense optical flow algorithms are highly amenable to parallel processing approaches compared to those that rely on tracking and feature detection. A Long-Wave Infrared (LWIR) micro-sensor and a PX4Flow optical sensor were mounted looking downwards on a drone. We compared the optical flow signals of a representative dense optical flow technique, the Image Interpolation Algorithm (I2A), to the Lucas-Kanade (LK) algorithm in OpenCV and the visible light optical flow results from the PX4Flow in both X and Y displacements. The I2A to LK was found to be generally comparable in performance and better in cold-soaked environments while suffering from the aperture problem in some scenes.
Decoding Human Behavior with Big Data? Critical, Constructive Input from the Decision Sciences

Big data analytics employs algorithms to uncover people's preferences and values, and support their decision making. A central assumption of big data analytics is that it can explain and predict human behavior. We investigate this assumption, aiming to enhance the knowledge basis for developing algorithmic standards in big data analytics. First, we argue that big data analytics is by design atheoretical and does not provide process-based explanations of human behavior; thus, it is unfit to support deliberation that is transparent and explainable. Second, we review evidence from interdisciplinary decision science, showing that the accuracy of complex algorithms used in big data analytics for predicting human behavior is not consistently higher than that of simple rules of thumb. Rather, it is lower in situations such as predicting election outcomes, criminal profiling, and granting bail. Big data algorithms can be considered as candidate models for explaining, predicting, and supporting human decision making when they match, in transparency and accuracy, simple, process-based, domain-grounded theories of human behavior. Big data analytics can be inspired by behavioral and cognitive theory.
Can Question Rewriting Help Conversational Question Answering?

Question rewriting (QR) is a subtask of conversational question answering (CQA) aiming to ease the challenges of understanding dependencies among dialogue history by reformulating questions in a self-contained form.Despite seeming plausible, little evidence is available to justify QR as a mitigation method for CQA.To verify the effectiveness of QR in CQA, we investigate a reinforcement learning approach that integrates QR and CQA tasks and does not require corresponding QR datasets for targeted CQA.We find, however, that the RL method is on par with the end-to-end baseline.We provide an analysis of the failure and describe the difficulty of exploiting QR for CQA.
Short-Term Forecasting of Energy Production for a Photovoltaic System Using a NARX-CVM Hybrid Model

In this paper, a methodology for short-term forecasting of power generated by a photovoltaic module is reported. The method incorporates a nonlinear autoregressive with exogenous inputs (NARX) fed by the solar radiation and temperature times series, as well as an estimation of power time series obtained by implementing an ideal single diode model. This synthetic time series was validated against an actual photovoltaic module. The NARX model has been implemented in conjunction with the corrective vector multiplier (CVM) technique, which uses solar radiation under clear sky conditions to adjust the forecasting results. In addition, collinearity and the Granger causality tests were used to choose the input variables. The forecasting horizon was 24-h-ahead. The hybrid NARX-CVM model was compared to a nonlinear autoregressive neural network and persistence model using the typic forecasting error measures such as the mean bias error, mean squared error, root mean squared error and forecast skill. The results showed that the forecasting skills of the hybrid model are about 34% against the NAR model and about 42% against the Persistence model. The model was validated by blind forecasting. The results demonstrated evidence of the quality of the conformed forecasting model and the convenience of its implementation and building.
Advanced Features Generation Algorithm for MPSK and MQAM Classification in Flat Fading Channel

The Automatic Modulation Classification (AMC) performance depends on the selected features.Conventionally, Higher-Order Cumulants (HOCs) are the wellknown features due to their discrimination ability under different channel conditions.HOCs have good performance under the Additive white Gaussian noise (AWGN) channel, but their performance degrades under fading channel.This paper proposes an Advanced Features Generation Algorithm (AFGA) that generates mathematical forms of new features based on the maximum discrimination between the digital modulation types to overcome this performance limitation.These features have similar complexity to HOCs but better performance accuracy.The simulation results show that the proposed AFGA improves the performance accuracy up to 4.5% for a Signal-to-noise ratio (SNR) value of 10 dB under fading channel conditions with respect to conventional methods.