Reviewerly/exHarmony huggingface dataset:
Automatic Classification of Diagnosis-Related Groups Using ANN and XGBoost Models

The optimization of the resources used in hospitals is a key problem in hospital management. In this sense, the development of tools that can help health care providers to ensure that inpatients can be discharged at the times indicated by international standards according to their pathological condition is of great interest for the optimization of resources, especially in developing countries. There are different standards for grouping patients according to their diagnoses and procedures information, this work focuses on the Diagnosis-Related Groups (DRG) patient classification system. Typically, DRG are obtained after hospital discharge, only for billing and payment purposes, which reduce the ability of health providers to take corrective actions when the health care attention deviates from the standard attention of specific patient conditions. This work focuses on the use of ML techniques as an alternative to DRG regular classification methods. The main aim is to evaluate whether ML methods can classify patients according to the DRG standard, using the information available at the hospital discharge. This result would be the base line for further analysis focused on the prediction of DRG in early stages of the inpatient. The results shown that DRG classification using ANN and Ensemble methods can achieve up to 96% of accuracy in a real database of more than 57.032 health records.
Using Negative Affect to Reinforce Moral Norms in Casual Speech

The words and phrases we utilize in our daily lives reflect our social context, namely its hierarchies and inequalities (e.g., racial, gender). Furthermore, the usage of specific forms of expression can be harmful to vulnerable populations. Here, we propose the development of a robotic agent that will help users seeking to change their speaking habits (i.e., using words that could be harmful to vulnerable populations) fulfill their goal. We provide an overview of our project as well as its tentative design and potential features to be included, such as voice recognition to identify when the user uses a specific word, and the incorporation of a dictionary to inform users of the historical background of certain terms.
Secured Healthcare Monitoring System

The healthcare IOT (Internet of Things) measures the patient’s medical details continuously and stores it in the database, so that the medical details can be accessed by the users at any time. As the medical details of the patient is sensitive, it must be secured. So, the secured healthcare monitoring system is needed to encrypt the medical files and it can be securely accessed by the authorized users. The medical details of the patients are collected from healthcare IOT and these details can be combined into medical files. These files are stored in the database and then shared to the authorized users securely. To prevent patient’s privacy, the medical files of the patients are encrypted before storing the files in the database. The authorized users only can access the medical details of the patient. The Lightweight Break-Glass Access Control (LiBAC) system supports two different ways for retrieving encrypted medical files which includes attribute-based access and break-glass access. In normal circumstances, the attribute-based access mechanism allows the data user to decrypt the medical details of the patient only if his attribute set satisfies with the access policy of the medical file. In emergency circumstances, the break-glass access mechanism ignores the normal access policy of the medical file but allows timely access to the medical file by Emergency Contact Person (ECP) in order to save patient’s life. The LiBAC system is lightweight, since the processing speed of storing and retrieving the medical files is very fast.
Multi-Person Pose Estimation Based on Hierarchical Residual-Like Connections

Recent methods of multi-person pose estimation focus on different aspects to increase the accuracy of keypoints localization. Although fusing the multi-scale feature maps to improve the recognition accuracy has achieved great results, there still have some space to promote. In this paper, we present two novel modules to enhance the multi-scale feature and increase the range of receptive fields by constructing hierarchical residual-like connections. First, the channel shuffle unit and Res2 block are combined to fuse the different level of features in pyramid feature maps, which prompts feature information communication. Second, a new residual block is built to fuse both spatial and channel-wise information within local receptive fields at each layer, and the residual block used in original basic network structure is replaced. The experiment have been evaluated on the COCO keypoint benchmark, which shows that our approach achieves better results than the other state-of-the-arts.
Learning material management system based on database

Abstract In recent years, with the rapid development of Internet technology, more and more online learning methods have emerged, and the main body introduced in this article is the integration of the original offline data and the status quo of online learning and database development. Under the premise of, under the review requirements of undergraduates at the end of the direction, the design of learning materials management system.
A Semantic Layer Querying Tool

Web archiving is the process of gathering data from the Web, storing it and ensuring the data is preserved in an archive for future explorations. Despite the increasing number of web archives, the absence of meaningful exploration methods remains a major hurdle in the way of turning them into a useful information source. With the creation of profiles describing metadata information about the archived documents it is possible to offer a more exploitable environment that goes beyond the simple keyword-based search. By exploring the expressive power of SPARQL language and providing a user friendly web-based search interface, users can run sophisticated queries searching for documents that meet their information needs.
Finding the Optimal Network Depth in Classification Tasks

We develop a fast end-to-end method for training lightweight neural networks using multiple classifier heads. By allowing the model to determine the importance of each head and rewarding the choice of a single shallow classifier, we are able to detect and remove unneeded components of the network. This operation, which can be seen as finding the optimal depth of the model, significantly reduces the number of parameters and accelerates inference across different processing units, which is not the case for many standard pruning methods. We show the performance of our method on multiple network architectures and datasets, analyze its optimization properties, and conduct ablation studies.
Velocity distribution of 43Ca+ion cloud in the low temperature limit in a quadrupole Penning Trap

Penning trap has electric field created by DC voltage applied between ring and end cap electrodes and magnetic field is applied along symmetry axis, as the electric field confines ions in the axial direction through an electric potential minimum and the magnetic field confines the ions in the radial direction. The trapping potential created by the DC voltage applied between the end cap and ring electrodes in the low temperature limit is cancelled by Coulomb interaction of ions and the total energy is mainly kinetic energy of ions. The velocity distribution of 43Ca+ ions along axial direction, in radial plane and total velocity distribution due to resulting motion of both axial and radial motion of ions in low temperature limit in a Quadrupole Penning trap are presented here. These results reveal the properties of 43Ca+ ion cloud and are useful to study confining techniques for different types of ions in low temperature limit and a qubit can be encoded in the hyperfine ground states of 43Ca+ isotope for ion trap quantum computation.
Text Infilling Method based on Key Semantic Information Selection Mechanism

Aiming at the problem that the lack of key semantic information in text infilling leads to weak semantic coherence of the filled text, this paper designs a text infilling method based on a Bi-directional long-short term memory network with a key semantic information selection mechanism. First, this paper uses the Bi-directionallong-short term memory network to capture the characteristics of potential long-distance dependencies and obtain context hiding features; then, we design an information selection mechanism to obtain context-critical semantic features by calculating the semantic distribution weight of words in the text; and finally, through the multi-head attention mechanism the key information of the local context and the global semantic information will be captured to fill the missing parts one by one, thereby improving the logic of the semantics and making the generated text more coherent. The experimental results show that testing on the Yelp dataset, Grimm dataset and Chinese poetry dataset, the value of the semantic fluency evaluation index perplexity has all decreased, indicating that the method proposed in this paper significantly improves the semantic coherence of the text, which significantly outperform the state-of-the-art text infilling model.
The design and implementation of data system based on Polymorphic Graphics and text

Abstract This paper introduces a set of professional data management system based on polymorphic graphics, which is established according to the requirements of software engineering management. It introduces the key technologies of the system, such as the main function module design, three-tier architecture quick design tool, online visio file and pdf file browsing. The system has strong practicability and good user interface. It can effectively improve the current professional data management mode.
DECH: A Novel Attack Pattern of Cloud Environment and Its Countermeasures

Cloud computing has become more and more vital in digital world as a basic technique of many services. As the foundation of cloud computing, the cloud platform hosts various cloud services running on it and providing interfaces to cloud users. Thus, it becomes a high-value target in attacker's eyes. This paper proposes a novel attack pattern named DECH for intruding and controlling a cloud platform standing on the side of attackers. From the defenders' perspective, this paper points out the key measures to enforce cloud service security are to restrict the Trust Computing Base (TCB) size and strengthen the isolation between cloud components. Then we provide countermeasures against “Detect Escape Control and Harvest” (DECH) based on the combination of Software Guard Extensions (SGX) and Trust Domain Extensions (TDE). This research provisions references for building secure cloud services.
Knowledge Awareness and Perception on the Addiction to Movies among Urban Teenagers: A Survey

Recent years, a variety of scientific studies have provided data on the prevalence of movies and theater addiction among different age groups. Addiction is once”s psychological and physiological inability to stop consuming a chemical, drug or activity. Some teenagers are addicted to watching movies, video games, Internet etc. Addiction to internet has been associated with indicators of social isolation and remarkably a measure of depression. In day to day life, the internet has become a most important part of daily life for most teenagers. They mainly need it for education, information searching, entertainment, mail and social interaction purposes. But moreover, extensive use of internet can lead to addiction. Addiction to internet has been consistently reported to result in dangerous outcomes including internet gambling, misuse of social media, and a host of other internet-related problems that can often result in depression, suicides, and death. A questionnaire was designed online in a Google docs with questions related to the awareness on movie addiction. The study population included was the dental students. Survey results were analysed statistically. The survey response collected and the data was analysed. From the survey, it is very much evident that, urban students have adequate awareness on movie addiction and its side effects. This survey concluded that teenagers are aware of movie addiction.
Implementation of Reconfigurable Data Encryption Technique using different AES versions

The Advanced Encryption Standard is widely used for encrypting data today. Unauthorized individuals will not be able to access data encrypted using AES. This paper presents the implementation of three different variants of AES in FPGA using Reconfiguration. The hardware utilization is a major factor considered to reduce the area used on a chip and thus the system cost. Using the reconfiguration feature of the FPGA, the different AES versions implemented can be loaded as and when required with reduced hardware utilization.
Blockchain Technology

Blockchain technology is one of the latest innovations in computer science that has world-wide scale and reach. A blockchain is a mathematical structure for storing digital transactions in a distributed and decentralized digital ledger consisting of multiple blocks that are linked using cryptographic signatures and spread across multiple computers. Each block records digital transactions data securely and has a hash link pointing to the previous block in the network in an efficient manner. New blocks can be added and removed through an agreed consensus of all nodes that each share the distributed ledger without the need for a centralized controlling authority. This chapter aims to introduce the blockchain technology, also known as distributed ledger technology (DLT), and discuss its applications in various sectors of the society including banking, healthcare, transportation, manufacturing, supply chain, and education. Extension of the internet of things vision using the DLT is also briefly discussed.
Visualizing UML’s Sequence and Class Diagrams Using Graph-Based Clusters

Abstract The paper discusses the creation of UML diagram based recommendation system using java and class files as the input. The existing systems do not make use of techniques available in text-mining for creating UML diagrams. The overall methodology makes use of keyphrase extraction, contextual similarity calculation, and graph-based clusters in creating UML diagrams. The existing systems survey of state-of-art UML diagram generation techniques and keyphrase extraction survey is also provided. A comparative analysis of the existing tools for generating UML-diagrams is also provided. The recommendation system generated is useful to maintenance engineers and software developers.
Comparison of machine learning algorithm for Santander dataset

Abstract The dataset for Santander banks is released on kaggle.com to decide whether the customer makes a transaction or not. The classes in this dataset are 2 with 200,000 entries in records. Earlier experiments using the regression algorithm led to a precision rate of 89%. In this analysis, the best accuracy value from the algorithm was obtained by using 6 different algorithms, namely Support for the Vector Machine (SVM), Neural Network (NN), Naive Bayes (NB), Decision Tree (DT). Before performing the data mining with the algorithm, preprocessing is carried out using a normalizing technique using the range transformation method with values 0 and 1. From the study, the best results were obtained in a Decision Tree 96.03% accurate algorithm, 95.82%, and 95.71%, 95.38%, 90.42%, 90.42%, and Naive Bayes 14.69%. The algorithms of the Decision Tree are 95.03%, 95.71% and 92%. Except for the Naïve Bayes algorithm, the precise value is better than previous study.
Security of Finger Prints with Video Watermarking Techniques Based On DWT and SVD

Abstract Content producers and service providers face highly important challenges in securing digital video content. As watermarking is an important copyright protection technology, this literature includes several methods of combining data into a multimedia component. Due to its increasing application, in recent watermark schemes, the Discrete Wavelet Transformation (DWT) is used. In this text we suggest a DWT and the SVDbased hybrid system. After the image is separated into four strips, we add SVD to each strip and adjust the specific values into the same detail. Modification at all frequencies allows a framework to be established that is consistent with a broad range of threats.
Kakkot List- An Improved Variant of Skip List

Kakkot list is a new data structure used for quick searching in a well ordered sequence of list like Skip list. This ordered sequence of list is created using linked list data structure and the maximum number of levels here will be limited to log n in all input behavioral cases. The maximum number of items in each level is halved to that of previous levels and thus guarantees a fast searching in a list. The basic difference between Kakkot list and Skip list lies in the creation of levels and decision of when an item has to be included in the higher levels. In skip list the levels are created and items are added to each level during the insertion of an item where as in Kakkot list this will be done at the time of searching an item. This modification have made drastic impact in searching time complexity in the Kakkot list. Another issue in Skip list is that it is not cache friendly and does not optimize locality of reference wherein this problem is also addressed in Kakkot List.
Model Bridging: Connection Between Simulation Model and Neural Network

The interpretability of machine learning, particularly for deep neural networks, is crucial for decision making in real-world applications. One approach is replacing the un-interpretable machine learning model with a surrogate model, which has a simple structure for interpretation. Another approach is understanding the target system by using a simulation modeled by human knowledge with interpretable simulation parameters. Recently, simulator calibration has been developed based on kernel mean embedding to estimate the simulation parameters as posterior distributions. Our idea is to use a simulation model as an interpretable surrogate model. However, the computational cost of simulator calibration is high owing to the complexity of the simulation model. Thus, we propose a "model-bridging" framework to bridge machine learning models with simulation models by a series of kernel mean embeddings to address these difficulties. The proposed framework enables us to obtain predictions and interpretable simulation parameters simultaneously without the computationally expensive calculations of the simulations. In this study, we apply the proposed framework to essential simulations in the manufacturing industry, such as production simulation and fluid dynamics simulation.
Web Impact Factor of Knowledge Resources Heritage of India

Web Impact Factor (WIF) is one of the extensively used indicators in webometric studies. This paper examines Web Impact Factor of 16 websites of Knowledge Resources Heritage of India under Ministry of Culture, Government of India. It attempts to calculate the simple web impact factor, self-link web impact factor, external web impact factor and revised web impact factor through Google search engine. It is found that Anthropological Survey of India has highest SWIF of 5,437.88, Asiatic Society has highest SLWIF of 0.964, while Thanjavur Maharaja Serfoji’s Sarasvati Mahal Library has scored high RWIF of 0.568.
An Adaptive Bitrate Switching Algorithm for Speech Applications in Context of WebRTC

Web Real-Time Communication (WebRTC) combines a set of standards and technologies to enable high-quality audio, video, and auxiliary data exchange in web browsers and mobile applications. It enables peer-to-peer multimedia sessions over IP networks without the need for additional plugins. The Opus codec, which is deployed as the default audio codec for speech and music streaming in WebRTC, supports a wide range of bitrates. This range of bitrates covers narrowband, wideband, and super-wideband up to fullband bandwidths. Users of IP-based telephony always demand high-quality audio. In addition to users’ expectation, their emotional state, content type, and many other psychological factors; network quality of service; and distortions introduced at the end terminals could determine their quality of experience. To measure the quality experienced by the end user for voice transmission service, the E-model standardized in the ITU-T Rec. G.107 (a narrowband version), ITU-T Rec. G.107.1 (a wideband version), and the most recent ITU-T Rec. G.107.2 extension for the super-wideband E-model can be used. In this work, we present a quality of experience model built on the E-model to measure the impact of coding and packet loss to assess the quality perceived by the end user in WebRTC speech applications. Based on the computed Mean Opinion Score, a real-time adaptive codec parameter switching mechanism is used to switch to the most optimum codec bitrate under the present network conditions. We present the evaluation results to show the effectiveness of the proposed approach when compared with the default codec configuration in WebRTC.
A Novel Approach for Sentiment Analysis of Hinglish Text

Hinglish is an informal language, which is written entirely in Latin script but contains informal words, phrases or even slang in a piece of writing, written both in Hindi and English. The field of sentiment analysis is vast, and there have been a lot of work done in the past couple of decades, yet sentiment analysis of Hinglish texts remains unexplored. Therefore, this paper presents a new approach for sentiment analysis of Hinglish text. The proposed approach uses algorithms such as Stemming, Levenshtein distance and Soundex index for preprocessing the data. Thereafter, it applies various classification models to identify the polarity of Hinglish text. The results of experiment show that the proposed approach is effective for sentiment analysis of Hinglish text.
Concept Bank Driven Semantic Video Retrieval Model

Semantic description of scenes is a challenging problem in video retrieval. Early works enlarged the semantic gap owing to the retrieval mechanism based on text matching between textual metadata provided by the up loader and text-based query. The need for bridging the semantic gap between user’s query content and retrieved videos prompted the research in content-based semantic video retrieval. This chapter proposes a semantic video retrieval model for human habitual scene search without textual metadata annotation. The proposed semantic model relies on understanding of the query content in the video to retrieve user’s query over a highly challenging video database. This work encompasses automatic concept/object detection in frames using concept bank, relates the detected concepts using concept occurrence vector to retrieve the relevant videos for the user’s query. The proposed semantic model relies on pedagogical understanding of the query content in the video using the prototype to retrieve users query over a highly challenging video database by poisson binomial radius measure. To evaluate the proposed approach, we validated the concept bank driven semantic retrieval model on user-created database for the user’s query by example. The experimental observation shows the astounding performance of the proposed concept knowledge-based semantic retrieval model.
A primality test based on modular hyperbolic curves

Prime numbers are indispensable basic parameters in public-key cryptography which is used to network data exchange or storage, so determining whether a large integer is a prime or not is a fundamental subject. In this paper, we build a primality test on anew group structure. For example, the test for Mersenne numbers can reduce the number of iterations by nearly half compared to the Lucas-Lehmer test.