Reviewerly/exHarmony huggingface dataset:
A new sudden death testing using repetitive sampling under a neutrosophic statistical interval system

In this study, a new sampling design is proposed to investigate sudden death testing under neutrosophic statistical interval methods (NSIMs) by using a repetitive sampling scheme. The failure time of the product follows the Weibull distribution under neutrosophic fuzzy number approach. The proposed design parameters are determined using a grid search optimization method at some pre-specified values of the producer's and the consumer's risks. The parametric results are presented in the form of extensive tables. A parametric test strengthens the argument in favor of derived results after the grid search method. Moreover, the comparison by single sampling based on sudden death testing acceptance under NSIM Weibull distribution proved to outperform in terms of providing the minimum value of the neutrosophic average sample number (NASN). Its implementation is explained in the example section by using some real-life datasets.
VALIDITY OF ULTRASONOGRAPHIC FINDINGS BASED ON ACR-BIRADS SCORES TO DIAGNOSIS BREAST DISEASES IN SULAIMANI BREAST DISEASE TREATMENT CENTER

Background &#x0D; Breast diseases are common in females all over the world. Breast cancer is the second common causes of concept mortality; early detection of breast cancer promotes outcomes.&#x0D; Objectives&#x0D; To evaluate the validity of ultrasonographic findings based on American College of Radiology Breast-Imaging Reporting and Data System (ACR-BIRADS) in diagnosis of breast diseases, and determine sensitivity and specificity of ultrasonographic finding in breast and axillary lesions in Sulaimani breast disease treatment centre.&#x0D; Materials and Methods&#x0D; This retrospective descriptive study was performed on 427 patients with different breast and axillary lesions at Sulaimani breast disease treatment center, from January 2014 till December of 2018. Ethical consideration by the regulatory bodies was followed. Information was recorded according to inclusion criteria by using a questionnaire paper. Finding of ultrasonography by using ACR-BIRADS system was compared with histopathology reports, The Chi-square test was used and the obtained results were judged statistically significant at P≤0.05, P-value &lt; 0.001 is regarded as highly significant. statistical analysis was performed using statistical package for social science SPSS (version 21).&#x0D; Results&#x0D; Age range was 22-84 years old, average age of participants with the standard deviation was 45.32(±11.763). 128 (30.0%) and 125 (29.3%) of all breast lesions were in the range of BIRADS 5 and 3 respectively. Among 427 cases, most of the breast lesions 222 (52.2%) were malignant on histopathology, while 205 (47.8%) were benign on histopathology. Sensitivity; Specificity of ultrasound for breast was 87.38%, 85.85%, respectively. Also Sensitivity, Specificity of ultrasound for axilla was 78.16%, 10%, respectively. In finding correlation between ACR-BIRADS system and histopathology results using chi square P-value was &lt; 0.001 which is highly significant for both breast and axilla.&#x0D; Conclusions&#x0D; Sensitivity and specificity of using ultrasound based on ACR-BIRADS scores in diagnosis of breast pathology were high; it makes a good way for diagnosis of breast diseases. But using BIRADS ultrasound system as sole investigation in diagnosis of breast pathology is the matter of debate. Using ultrasound based on Sensitivity and specificity for axilla didn’t make it an ideal way for diagnosis of pathological lymph nodes.
We Bring Your Identity: A Secure Online Passenger Identity Protocol (SOPIP) for Indian Railways Using Aadhaar Number

Indian Railways offer instant ticket booking service to the passengers via online reservation portals (ORP). The major constraint to the passenger is to bring an original document as personal identity proof (PIP) at the time of travel to prove their identity. However, many passengers bring copy of the documents in order to avoid loss or theft of their original identity card during travel. But, it is not accepted as per the regulations of railway authority. Also, passengers may forget to bring the original ID card due to last-minute hurry. This leads to an unnecessary dispute between the passenger and the travelling ticket examiner (TTE). To address this issue, a secure online passenger identity protocol (SOPIP) is proposed in this paper. The SOPIP provides secure online identity service to authenticate the passenger to the TTE during travel. The proposed protocol also provides confidentiality, integrity and digital signature services to the personal details of the passenger during the transmission personnel details via the Internet. The protocol assumes that the ticket details are available in the ORP and the passenger's personal details are available in the Aadhaar database.
The Department Chair’s Role in Mathematics Service Courses

A Mathematics Department Chair plays many roles within the department and within the university as a whole. Facilitating the development and teaching of mathematics services courses requires working with mathematics faculty as well as members of other departments on campus. In this paper, we discuss many issues related to the Department Chair’s role in mathematics service courses such as determining course content needs, placing students in appropriate courses, scheduling classes, assessing service courses, and looking to the future of mathematics services courses. Central to all of these issues is ongoing communication with colleagues in and out of the department.
How does adiabatic quantum computation fit into quantum automata theory?

Quantum computation has emerged as a powerful computational medium of our time, having demonstrated the remarkable efficiency in factoring a positive integer and searching databases faster than any currently known classical computing algorithm. Adiabatic evolution of quantum systems has been studied as a potential means that physically realizes quantum computation. Up to now, all the research on adiabatic quantum systems has dealt with polynomial time-bounded computation and little attention has been paid to, for instance, adiabatic quantum systems consuming only constant memory space. Such quantum systems can be modeled in a form similar to quantum finite automata. This exposition dares to ask a bold question of how to make adiabatic quantum computation fit into the rapidly progressing framework of quantum automata theory. As our answer to this eminent but profound question, we first lay out a fundamental platform to carry out adiabatic evolutionary quantum systems (AEQSs) with limited computational resources (in size, energy, spectral gap, etc.) and then demonstrate how to construct such AEQSs by operating suitable families of quantum finite automata. We further explore fundamental structural properties of decision problems (as well as promise problems) solved quickly by the appropriately constructed AEQSs.
Inferred Social Networks: A Case Study

The behavior, environment, and characteristics of clients form a crucial source of information for various businesses. There exists a number of supervised as well as unsupervised data mining or other approaches that allow analyzing the respective data. In our ongoing project, focusing primarily on the financial sector, we suggest an innovative strategy that will overcome persisting shortcomings of the state-of-the-art methods using an analysis of a social network of clients. In addition, we do not assume the existence of such a network, but from a given set of client financial activities, we are able to infer a social network representing their relationships and behavior. Using real-world data and selected use cases from our domain, we show (a part of) the process of construction of an inferred social network, i.e., what kind of "hidden" information can, for example, be found and exploited.
On EMG Silent-speech Recognition Data Collection

Silent-speech interfaces and communications are expected and desired technologies, that currently providing only the command-set options. Transferring recognition technology from predefined set to extensible one took 40 years[1]-[2] and thousand-fold growth of paper number according to IEEE library in case of audible speech. This paper proposes the approach for collection of time marked silent-speech recognition data(mostly EMG) using audible speech recognition engines and predefined texts, without using predefined time-related markdown.
Behavior Acquisition on a Mobile Robot Using Reinforcement Learning With Continuous State Space

In the application of Reinforcement Learning to real tasks, the construction of state space is a significant problem. In order to use in the real-world environment, we need to deal with the problem of continuous information. Therefore, we proposed a method of the construction of state space using Growing Neural Gas. In our method, the agent constructs a state space model from its own experience autonomously. Furthermore, it can reconstruct the suitable state space model to adapt the complication of the environment. Through the experiments, we showed that Reinforcement Learning could be performed efficiently by successively updating the state space model according to the environment.
Preliminary Experiment on the Effect of a Magic Circle on Behavior Change

In this paper, we investigated the effect of a magic circle projected on the ground as a shikake. The experiment was conducted for nine days under three conditions; nothing projected, projecting a circular light, and projecting a magic circle. We observed the behavior of pedestrians and found that the magic circle had no effect on changing the direction of movement, but it did have an effect on path; pedestrians avoided the projected circle, though not the circular light. Using the idea of semiotics, we concluded that the behavioral changes could have been caused by pedestrians' interpretations of the message of the magic circle.
Describing Approach for Model-Driven Collaborative Application Development

In order to resolve the problems of model-driven collaborative application development, a new formal description framework was proposed for the description of software architecture model, in which category theory, algebraic specifications and process algebra were combined together. Software components and the relationship between them were described respectively by category objects and morphisms. The different types of morphisms depicted the semantics contained by component relationships. Thus typed category diagram fully represented software architecture model, as well as functors of category theory described the mapping relationships between architecture models. The formal framework was available to determine whether a transformation to meet certain characteristics or constraints, and a collaborative authoring system was used as an example to illustrate the application of this method. The application research shows that, the approach enhances semantic description capabilities of architecture model, and can be used as an effective supplement for existing software modeling method.
An Interaction Method for Transferring Electronic Information by Tearing a Sheet of Paper

With the spread of electronic devices such as smartphones and tablets, the transfer of electronic information such as images and videos has become commonplace. To exchange electronic information using e-mail or social network services a sender needs to know the recipient's contact information. However, if the recipient is a newly-encountered person or an ad hoc partner, a significant number of them are reluctant to exchange contact details. To address this issue, we propose an interaction method for transferring electronic information by tearing a sheet of paper. This is an approach that utilizes the fact that when a piece of paper is torn into two pieces, the features of the torn edges of the two pieces match.
Age Label Distribution Estimation Algorithm Based on Kernel Extreme Learning Machine

Age label distribution learning can effectively describe the age label by adding information of adjacent ages. Based on this, this paper proposes an age label distribution estimation algorithm based on kernel extreme learning machine (ALDEA-KELM). Firstly, the features in the training and testing images are extracted, transform the age labels through the probability density function, convert age label to age label distribution. Secondly, establish an ALDEA-KELM regression model with features corresponding to the age label distribution. Finally, calculate the maximum probabilistic age and expected age of the predicted sample through the model. The maximum probability age and the expected age are combined for joint prediction. The comparison between other algorithms shows that method of this paper can effectively reduce the age prediction error. In terms of age prediction, the results come closer to the true values.
Local, Mid-Level and Convolutional Features Fusion Using Multiple Kernel Learning for Image Classification

Feature representation and feature fusion are important factors in image classification problem. In this paper, the local features, mid-level features and convolutional features are combined using the multiple kernel learning method. Experimental results show that the local features, mid-level features and convolutional features can be fused effectively to improve the classification performance about 4%-6% on several popular benchmarks.
Research on Earthquake Simulation Shaking Table Test of 110 kV Transformers

As an important part of an ultra-high voltage (UHV) substation, the 110 kV station transformer is mainly used for providing the substation with the power for station equipment operation control, lighting, maintenance power, living of personnel and so on. The failure of this station transformer under the seismic action will affect the overall operation of substation.
Analysis And Synthesis of Image Using Wavelet Transform and Compression Methods

In this paper, deal with image for analyze and compress without loss of brightness by wavelet transformation with various compression methods. Generally, image or signal can be analysed either frequency or time domain. In wavelet transformation the image can be considered in frequency and time domain. In this technique image can be compressed or reduced about 70% of original image without loss of brightness. The wavelet transformation is performed for small and large data. It can be chunked signal from large signal and examined for small signal without maximum loss of threshold.
Primitives Generation Policy Learning without Catastrophic Forgetting for Robotic Manipulation

Catastrophic forgetting is a tough challenge when agent attempts to address different tasks sequentially without storing previous information, which gradually hinders the development of continual learning. Except for image classification tasks in continual learning, however, there are little reviews related to robotic manipulation. In this paper, we present a novel hierarchical architecture called Primitives Generation Policy Learning to enable continual learning. More specifically, a generative method by Variational Autoencoder is employed to generate state primitives from task space, then separate policy learning component is designed to learn torque control commands for different tasks sequentially. Furthermore, different task policies could be identified automatically by comparing reconstruction loss in the autoencoder. Experiment on robotic manipulation task shows that the proposed method exhibits substantially improved performance over some other continual learning methods.
Information sources in Romanian on medicines

The Internet has become a huge source of information, in­clu­ding medical information. Doctors are required to dis­cri­mi­nate between different types of online information sources. The official source of information on medicines in Romania is the Nomenclature of drugs pu­blished online by the National Agency for Medicines and Medical Devices, but there are also websites and commercial ap­pli­ca­tions that assume the role of informing doctors or pa­tients. The accuracy of the in­for­ma­tion, the frequency of up­dates, the degree of ob­jec­ti­vi­ty and independence from the pharmaceutical in­dus­try of these media are not well determined.
REVIEW OF THE METHODS TO DETECT FORGERY IN DIGITAL IMAGES FOR INTELLECTUAL DECISION SUPPORT SYSTEMS BASE ON NOISE ANALYSIS

This article provides a review of the up-to-date methods on noise analysis to detect forgery in digital images, which are used as a source of data in decision support systems.
Deterministic Controlled Remote State Preparation of Arbitrary Real-parameter Multi-qubit States via Three-qubit Entangled States

This paper offers a theoretical protocol for deterministic controlled remote state preparation of arbitrary real-parameter multi-qubit states, in which the maximally three-qubit entangled states are used as quantum channel. We elaborate the processes of implementing remote state preparation of the real-parameter two- and three-qubit states, and extend the protocol to realize the real-parameter n-qubit states. By performing the appropriate multi-qubit projective measurement basis and multi-qubit unitary operations, the successful probability of our scheme can reach 100%.
Learning From Weights

In sponsored search, we need to retrieve the relevant top few ads from millions of ad copies within milliseconds. Running deep learning models or interaction models online becomes computationally expensive and hence not feasible for retrieval. There is not much discussion on improving cost-effective online retrieval models which work on representation based learning. In this paper we discuss one such improvement by incorporating cost-sensitive training. Online retrieval models which are trained on click-through data treats each clicked query-document pair as equivalent. While training on click-through data is reasonable, this paper argues that it is sub-optimal because of its noisy and long-tail nature (especially for sponsored search). In this paper, we discuss the impact of incorporating or disregarding the long tail pairs in the training set. Also, we propose a weighing based strategy using which we can learn semantic representations for tail pairs without compromising the quality of retrieval.
Analisis Sistem Informasi Akuntansi Penggajian Pada CV ABC di Jawa Tengah

This research was conducted at CV ABC which is engaged in the furniture industry. In managing the payroll system this company is still less effective. It can be seen from several salary payment activities to employees who still often make mistakes in the process of calculating salaries and employee attendance records. In addition, in the process of paying salaries to employees, they often experience late payments. The purpose of this study is to analyze the payroll accounting information system in the company which is expected to help resolve the problems faced by the company. The research method used in this study is a qualitative descriptive research method. The thing that will be done by the researcher is to describe the use of payroll accounting system on CV ABC. The conclusion of this study is that the payroll system on CV ABC is already good because it has used a computerized system. The internal control in the payroll process is still weak because there are still division of tasks and responsibilities of each part that is unclear, namely there is a double duty and interdependence in working tasks, besides that supervision is still lacking for activities that should be routinely checked or corrected.
Identifying Concepts and Relations in a Transition-Based AMR Parser

AMR is a graph-based semantic representation for natural language. It relies on concepts and their relations to transcend words and distill the meaning of English sentences. In this work, we propose a solution for identifying both the concepts and their associated relations as a post-processing step of a transition-based parsing system. We incorporate these contributions within an existing system. Furthermore, we enhance the LSTM transition learning component by optimizing the input features and extending the predicted output.
Head-Gesture-Based Human–Computer Interface for Disabled People

Human–computer interface is the communication between humans and computer, and to what degree computers are designed for effective communication with human beings. People with Cerebral Palsy, motor impairments, amyotrophic lateral sclerosis, multiple sclerosis, and various others can use this system to control computer mouse. The system uses a webcam to capture for image acquisition. Further with the different classifiers, it extracts the features from the images, i.e., position of eyes and mouth. The framework is tried for a few times on various users and result to be a dependable solution for disabled people.
The Implementation of the Chinese Language and Character Recognition System Based on the Deep Learning

The Chinese language is an important symbol of the Chinese traditional cultures, and also an important manifestation of the integration of the information technology and the traditional cultures. How to use the intelligent technology to promote the Chinese language and writing has also become an important trend of the development of the current era. In the process of the Chinese character recognition, the feature learning method and the DLQDF classifier proposed in this paper can obtain the performance of the deep convolution neural network (deep CNN). Therefore, the computational cost of the recognition is lower than that of the deep convolution neural network.