Reviewerly/exHarmony huggingface dataset:
The Value of Consensus Priors: A Response to Simpson

In this response, we first show that Simpson’s proposed analysis answers a different and less interesting question than ours. We then justify the choice of prior for our Bayes factors calculations, but we also demonstrate that the substantive conclusions of our article are not substantially affected by varying this choice.
Design and Implementation of the Image Processing Software Based on the Wavelet Transform

The wavelet transform technology is widely used in many fields, such as the graphics processing, the image processing, the video-voice processing, and the digital signal processing, and has achieved good application results. Starting from the principles and types of the image processing technology based on the wavelet transform, two processing methods of the continuous wavelet transform and the discrete wavelet transform are described in detail, and their specific applications and advantages in the image compression and the image denoising are enumerated.
Summary of Research on Computing and Cloud Computing

Computing is one of the oldest achievements of human civilization. From ancient finger counting and not counting to ancient Chinese calculation and abacus calculation, to modern western Nepal domino calculation and Pascal calculator, to modern computer calculation, the infinite development and tremendous role of calculating methods and calculating tools, computing innovation occupies a very important position in human history. We are now in the “cloud computing” era, where users use the “cloud” to store and calculate. “Cloud” includes hundreds of thousands or even millions of computers one by one.
Modular Synthesis of Verified Verifiers of Computation with STV Algorithms

Single transferable vote (STV) is a family of preferential voting systems, different instances of which are used in binding elections throughout the world. We give a formal specification of this family, from which we derive fully verified tools that verify the computation for various instances of STV vote counting. These tools validate an execution of an STV vote counting algorithm, based on a transcript of the count. Our framework distils the similarities and differences of various instances of STV and gives a uniform and modular way of synthesising verifiers for its various instances, and provides the flexibility and ease for adapting and extending it to a variety of STV schemes. We minimise the trusted base in correctness of the tools produced by using the HOL4 and CakeML as the technical basis. We first formally specify and verify the tools in HOL4 and then obtain the machine executable versions for the tools by relying on the verified proof translator and the compiler of CakeML. Moreover, proofs that we establish in HOL4 and CakeML are almost completely automated so that new verified instances of STV can be created with no (or minimal) extra proof. Finally, our experimental results with executable code demonstrate feasibility of deploying the framework for verifying real size elections having an STV counting algorithm.
A New Deep Hierarchical Neural Network Applied in Human Activity Recognition (HAR) Using Wearable Sensors

Human Activity Recognition (HAR) using wearable sensors is becoming more practical in the field of security and health care monitoring. Deep and Machine learning techniques have been widely used in this area. Since smartphones and their applications are the parts of daily life, they can be very helpful in data gathering and online learning in HAR problems. In this paper, a new hierarchical neural network structure with the hierarchical learning method is presented and applied to the HAR. The proposed model is a deep learner neural network without using heavy computations that CNN-based deep learners usually suffer from. This makes the suggested model suitable for being embedded in agent and multi-agent based solutions and online learning, especially when they are implemented in small devices such as smartphones. In addition the hidden layer of the first section of the proposed model benefits automatic nonlinear feature extraction. The extracted features are proper for classifications. Handling the dimension of data is one of the challenges in HAR problem. In our model, data dimension reduction is automatically performed in the hidden layers of the different network sections. According to the empirical results, our proposed model yields better performance on the Opportunity data sets, compared to the similar ML algorithms.
The Design of Word Cloud Rendering Platform and Its Application on Measuring Systematic Financial Risks

With the development of the Internet, the amount of daily output data is constantly increasing, and the value contained in the data is increasing as well; Meanwhile, the difficulty of data mining and the complexity of data analysis increases sharply. Developing a new data processing system is in urgent need especially in the macroeconomic field. Word cloud is a trendy way to visualize hot spot. At first, the design of a distributed batch-based website word cloud rendering platform will be explained, combining the processing mode of big data and the traditional web crawler design method to collect all the information of a website and present the data using word cloud. Then, this platform will be used for practice and applied to the measurement of systemic financial risks.
Towards Machine Translation of Chinese Complex Structures

The machine parsing and translation of Chinese complex structures need the involvement of syntactic and semantic knowledge. The application of computational linguistics in parsing and translation helps bilingual system construct the special knowledge database used to revise and post-edit the strings. It is concluded that with the help of advanced bilingual dictionaries and domain-specific corpora, system can effectively analyze the lexical information and thematic relationship, parse the syntactic structure and translate the original sentence to the target one.
Guidelines for Architecture Design of Software Product Line

Product Line Architecture design is a key activity for developing successful Software Product Line projects. But it is difficult and complex task since architecture of software product line should be considered with variability. In this research, we addressed detail guidelines for identifying the component of architecture from the feature model and defining the variability of component in concerns of feature.
Real Time 3D Pose Estimation of Both Human Hands via RGB-Depth Camera and Deep Convolutional Neural Networks

3D human hand pose estimation (HPE) is an essential methodology for smart human computer interfaces. Especially, 3D hand pose estimation without attached or hand-held sensors provides a more natural and convenient way. In this work, we present a HPE system with a single RGB-Depth camera and deep learning methodologies which recognizes 3D hand poses of both hands in real-time. Our HPE system consists of four steps: hands detection and segmentation, right and left hand classification using a Convolutional Neural Network (CNN) classifier, hand pose estimation using a deep CNN regressor, and 3D hand pose reconstruction. First, both hands are detected and segmented from each RGB and depth images using skin detection and depth cutting algorithms. Second, a CNN classifier is used to distinguish right and left hands. Our CNN classifier consists of three convolutional layers and two fully connected layers, and uses the segmented depth images as input. Third, a trained deep CNN regressor estimates the key sixteen joints of hands in 3D from the segmented left and right depth hands separately. The regressor is hierarchically composed of multiple convolutional layers, pooling layers and dense fully connected layers to estimate the hand joints from the segmented hand depth images. Finally, 3D hand pose of each hand gets reconstructed from the estimated hand joints. The results show that our CNN classifier distinguishes the right and left hands with an accuracy of 96.9%. The 3D human hand poses are estimated with an average distance error of 8.48 mm. The presented HPE system can be used in various application fields including medical VR, AR, and MR applications. Our presented HPE system should allow natural hand gesture interfaces to interact with various medical contents.
Substantive and Procedural Legislation in Estonia to Combat Webcam-Related Child Sexual Abuse

Estonia is a civil law country influenced by the German legal doctrine. The virtual character of Sweetie (a neutral digital avatar of a 10-year-old girl) poses several challenges for Estonian material and procedural criminal law if used in practice. This chapter answers the question whether the use of Sweetie would be permitted under current material and criminal procedural law of Estonia. Under material criminal law, the act of requesting access to a webcam session with Sweetie is not criminalized, as Sweetie would not be considered ‘child pornography’. However, in the context of sexual enticement, law enforcement bodies have used police agents depicted as children and courts have declared such acts punishable as impossible attempts. Theoretically, enticing a digital avatar could be punishable under the same logic. In the case of making arrangements for meeting a child for sexual purposes, the law implies that the victim must be a ‘person’. Considering that this criminal act is a delict of abstract danger and that Sweetie is an ultra-real digital persona, one could argue that trying to meet with Sweetie for sexual purposes and making a preparatory act for this purpose also could be punishable as an impossible attempt under Estonian law. Although material criminal law provides room for interpretation, criminal procedure law sets limits to the use of a digital avatar for the apprehension of digital offenders. For Sweetie to be a tool for catching sex offenders online, the Estonian Code of Criminal Procedure would need to explicitly permit the use of digital avatars as their use could be categorized as a surveillance activity and would need a clear legal base. This is a debate Estonia is still to have.
Behavior Similarity Awared Abnormal Service Identification Mechanism

In order to maintain network security, it is very important to identify services with abnormal behavior and take targeted measures to prevent abnormal behaviors. We propose abnormal service identification mechanism based on behavior similarity. This method proposes a formula for service behavior similarity calculation of flow ports for services with correlation. And then k-similarity clustering algorithm is proposed to find abnormal service behaviors. Meanwhile, we analyse outliers to improve the accuracy of clustering results. At last, the experimental results show that k-similarity clustering algorithm can differentiate abnormal services accurately.
A Fuzzy-Based Study for Biomedical Imaging Applications

Recent advancement in biomedical image processing area has been augmented by big data research and machine learning techniques. It is observed that image segmentation is contributing significantly in this domain. In this study, an efficient method for segmenting magnetic resonance (MR) images is proposed. The strategy for the method developed here is as follows. First, the MR images are preprocessed through a vector median filter to mitigate the noise inherent in images. Next, Otsu thresholding is implemented for initial image segmentation which detects the homogenous regions of the MR image. Finally, a modified suppression factor based suppressed fuzzy c-means is implemented for segmentation. For computational evaluation, the metrics such as signal-to-noise ratio (SNR), mean square error (MSE), and the peak signal-to-noise ratio (PSNR) are considered in this study. The proposed method shows better results over other algorithms by considering the above metrics.
An Integrated Multi-scale Model for Breast Cancer Histopathological Image Classification Using CNN-Pooling and Color-Texture Features

Breast cancer is one of the most common human neoplasms in women, commonly diagnosed through histopathological microscopy imaging. The automated classification of histopathology images can relieve some workload of pathologists by triaging the cases. Knowing that histopathological images show a high degree of variability, useful information is often obtained at different optical magnification levels in order to make the correct diagnosis. For automated scoring, if there are differences in the patient’s score at each considered magnification, the decision may not be reliable if only one magnification level is taken into consideration. This study proposes an integrated model in which scores across magnifications are combined by weights estimated from the least square methods. Moreover, unlike the existing methods, we consider a novel heterogeneous committee which includes deep and traditional members, to design a system for each magnification. As few studies have shown, such in an ensemble, often only a subset of members is sufficient to provide enough discriminative information. Hence, we use an information theoretic measure (ITS) to select optimal members for each magnification. We use publicly available BreaKHis dataset for the experimentation, and demonstrate that the proposed approach yield comparable or better performance when compared with most CNN based frameworks.
An Efficient Multi Lingual Optical Character Recognition System for Indian Languages Through Use of Bharati Script

Optical character recognition performs a critical part in interpreting videos and documents. Document specific issues like low image quality, distortions, composite background, noise etc. and language specific issues like cursive connectivity among the characters etc. makes OCR challenging and erroneous for Indian languages. The language specific challenges can be overcome by computing the script-based features and can achieve better accuracy. Computing the script based invariant features and patterns is computationally complex and error prone. In this background, we put forward Bharathi script ( www.bharatiscript.com ) based OCR system in which the inherent drawbacks of Indian scripts i.e. Hindi, Tamil, Telugu etc. are eliminated. The proposed OCR model has been tested on a synthetic dataset of documents of Bharathi script (in which Hindi scripts are converted to Bharathi script). Thorough experimental analysis with varied levels of noise confirms the promising results of character recognition accuracy of the proposed OCR model which out-performs the state-of-the-art OCR systems for Indian scripts. The proposed model achieves 76.70% with test documents consists of 50% noise and 99.98% with test documents of 0% noise.
Utilization of Complex Techniques for the Extraction of Human Body Parameters from Uncalibrated Image

The principal goal of this paper is to propose a technique that will allow extraction of human body parameters from uncalibrated image, having some known reference height. Analysis focuses on the algorithms for detection of objects from the image. The designed approaches were experimentally tested in a form of a software solution that utilizes vanishing line height extraction approach. The system allows to measure all human body skeleton model parameters from the image with known height of reference object. System utilizes recently proposed Human Body Description Format (HBDF) to store the measured results.
Risk Mapping in the Selected Town

This article describes the risk analysis and method of risk mapping in the town of Uherský Brod invented by the Fire Rescue Service of the Moravian-Silesian Region on the basis of the methodology recommended by the European Union. QGIS software tool was used for risk mapping in order to visualize the maps. The risk analysis was performed on the basis of identification of types of hazards that are very likely to occur in Uherský Brod. Subsequently, the results of the risk analysis were processed by means of the software QGIS. This software enables the creation of the risk map once the individual maps have been compiled. Evaluating the resulting risk map, the individual areas of the town were assessed according to the discovered level of risk. Risk maps can consequently serve.
Reflective Methodical Platforms Consult-Study of Business Organizations

The article proposes an express study of the criteria for the transition of business organizations into a digital transformative format, as well as the assessment of risk situations in the managerial relations of the heads of business organizations in the initial stage of DH. The methodical basis of research and development are reflexive blank platforms, the content of which is the awareness, understanding and rethinking of this complex process. As a research tool, the article uses two refl exive platforms, presented as methodological research consultant-development. The relevance of the problem lies in the need for such social research and consulting development, which would help to visualize diagnostic methods that reveal the degree of preparation of managers to enter the digital process of transformation of managerial relations. In modern conditions of a gradual transition to digital technologies, it is important to develop such platforms refl exively and methodically ensuring this process, which could replace cumbersome questionnaires in the personnel management service. The scientifi c novelty of the proposed article is that the author uses express methods, i.e. new research tools that combine at the same time the speed of obtaining results and refl exive training of managers on the results obtained, i.e. intensify self-knowledge, self-esteem and self-correction of their own “I” of the leader, changing his behavior in managerial relations with the staff . Thus, there is a process of autonomous assimilation by managers of rethought management values. Practical value. Using this methodology, the personnel management service thus gets the opportunity to quickly obtain the results of self-interview managers and develop on this basis, more qualitative teaching methods. This refl exive-methodical platform of digital transformation makes it possible to make the transition to new technologies, thereby self-supporting further advancement to such developments that directly and organically stimulate in a phase, that is, in a step-by-step organization of the transition to new development conditions, reducing stress situations and thereby minimizing the costs of innovation. It activates development of intellectual resources in personnel management.
Wavelet Kernels for Support Matrix Machines

Classification of matrix variate data is a major challenge in statistics and many statistical studies like gray digital images and electroencephalography (EEG), for example. Popular methods such as support vector machines have limitations when second-order tensor or matrix is considered as input data. Using a matrix directly instead of its vector representation is efficient when matrix is the input and helps retain the data topology more efficiently. Also, for high dimensional data, matrix representation reduces significantly the number of parameters and helps dealing with overfitting issues. Wavelet techniques are very powerful approximations for non-stationary signals. Wavelet kernels were proposed recently and had emerged as valid kernels for support vector machine. In this paper, we, first, extend support vector machines to the matrix-based method support matrix machines (SMM), which accepts matrix as input, and then propose new wavelet kernels for support matrix machine. We show that these kernels are admissible kernels.
Development of Adaptive Arithmetic Coding Method to the Sequence of Bits

To reduce the volume of transmitted information within the boundaries of the concept of the Internet, adaptive arithmetic coding was developed. This is a method which is based on the analysis of subsequent bits group with the ability to encode a different number of bits per rank. The effectiveness of the method increases with the correct choice the sequence with the highest probability.
Interpretation of first-order recurrent neural networks by means of fuzzy rules

First-order recurrent neural networks can be trained to recognize strings of a regular language. Finite state automata can be extracted from these neural networks. Normally, a search process in the output domain of the neurons is necessary for carrying out this extraction procedure. On the other ha nd, studies about fuzzy rules extraction from feedforward multilayered neural networks can be considered to define new techniques that transform first-order recurrent neural networks into finite state automata. With these new techniques, a fuzzy description of the action of each neuron can be obtained. From these descriptions, the transition function of the automaton can be directly found and, in this way, the search process is not necessary. A technique with this approach is presented in this paper. Besides, the used method to extract fuzzy rules from a neuron has the advantage that the inputs of the fuzzy system coincide with the inputs of the neuron. Thus, the fuzzy system is more intuitive. Once the transition function is obtained, the automaton structure can be found with the analysis of the transitions for every state and input from the initial state. Finally, several examples are presented to illustrate the method.
Pancreas Segmentation in Medical Images Based on Gaussian Mixture Model in Bandelet Domain

The pancreas is one of important organs in human body. The pancreas segmentation from medical images is a difficult task. Because adjacent organs have the similar intensity values. In this paper, we proposed a new method for pancreas segmentation in bandelet domain. Our method includes two stages. Firstly, applying the Gaussian mixture model for foreground and background is estimated using marked pixels by the user in bandelet domain. Finally, that is pancreas segmentation in medical images in the first stage by objcut. Our dataset is pancreas images that have been collected in many hospitals. We undervested the proposed method by calculating the Jaccard index values. The results of the proposed method are better than the other recent methods.
Communication Without a Shared Reference Frame

This chapter generalizes a quantum communication protocol introduced by Bartlett et al. (New J Phys 11:063013, 2009) in which two parties communicating do not share a classical reference frame, to the case where changes of their reference frames form a one-dimensional noncompact Lie group. Alice sends to Bob the state ρ R ⊗ ρ S, where ρ S is the state of the system Alice wishes to communicate and ρ R is the state of an ancillary system serving as a token of her reference frame. Because Bob is ignorant of the relationship between his reference frame and Alice’s, he will describe the state ρ R ⊗ ρ S as an average over all possible reference frames. Bob measures the reference token and applies a correction to the system Alice wished to communicate conditioned on the outcome of the measurement. The recovered state $$\rho _S^{\prime }$$ is decohered with respect to ρ S, the amount of decoherence depending on the properties of the reference token ρ R. An example of this protocol is presented in which Alice and Bob do not share a reference frame associated with the one-dimensional translation group and the fidelity between ρ S and $$\rho _S^{\prime }$$ is used to quantify the success of the recovery operation.