Redundant Entries Research Articles

Event Security Using Face Identification

Abstract: One of the latest trends in event planning is incorporating facial recognition technology into the registration experience. Event planners and hosts have realized how convenient and efficient this method compares to traditional registration methods. The purpose of this system is to automate the existing manual system with the help of computerized equipment’s fulfilling their requirements, so that their valuable data/information can be stored for a longer period with easy accessing and manipulation of the same. The required software and hardware are easily available and easy to work with. Event management system, can lead to secure and fast management system. It can assist the user to concentrate on their other activities rather to concentrate on the record keeping. The organization can maintain computerized records without redundant entries.

738 Access to Burn Care: A 10 Year Perspective of Burn Centers in the U.S.

Abstract Introduction Only 2% of US hospitals have designated burn centers (BC), of which 57% are verified by the American Burn Association (ABA). Further, of the non-verified burn centers, it is unclear which ones meet requirements set by their state departments of health. This gap in knowledge of BC verification and/or designation status combined with the lack of an accurate, publicly available database to route patients and characterize BC resources creates challenges in burn care, especially in disaster preparedness. The goal of our study was to characterize the net effect of domestic BC openings and closings over the last decade on US burn resources and, thus, access to care. Methods Our group has recently created the National Injury Resource Database (NIRD), a comprehensive list of every US Level I and II trauma center and/or burn center. NIRD was created by culling these facilities from 1) the current ABA burn center directory, 2) the current ABA burn center regional map, 3) the American Hospital Association database, 4) the American College of Surgeons Committee on Trauma database, and 5) data from all 50 State Departments of Health. Redundant entries were omitted, and 100% of the remaining institutions were then personally contacted by one of the study investigators to confirm or clarify the status of each center as well as the resources listed in the various databases. The NIRD database was then compared to the ABA’s 2012 burn center directory to compare the number, identities, and characteristics of those US BCs which had either closed or opened in the intervening ten years. Results In 2012, there were 127 total BC, 63 of which were ABA verified. Currently, NIRD shows that there are 134 total BC recognized by the ABA, 76 of which are verified. Since 2012, 11 BC have been removed from the ABA’s burn center directory after either closing or failing to maintain ABA recognition, while 18 BC have been added. In the same time frame, there have been changes in BC verification status across the US, with 14 BC gaining ABA verification and 1 BC losing verification. In comparing the ABA’s current BC directory to their burn center regional map, we found variability in 18 centers across 15 states (CA, DC, IL, IN, KS, MD, ME, MI, MS, MO, NY, TN, TX, VA, VT), i.e., instances in which 1 or more BC found on the ABA’s BC regional map were not found on the ABA’s BC directory list. Conclusions The last 10 years have shown an upward trend in the number of US BC. Further, we found variability in the ABA’s online resources in 15 states, suggesting that their resources are outdated. NIRD can serve as a resource for the construction of publicly available real-time tools to aid in routing burn patients. Applicability of Research to Practice NIRD can serve as a resource for the construction of publicly available real-time tools to aid in routing burn patients.

Automated Question Generator using NLP

Abstract: The purpose of the Automated Paper Generator is to automatize the winning guide machine with the aid of the help of computerized gadgets and full-fledged laptop code, pleasurable their requirements, simply so their precious facts/data can be maintained for an extended quantity with clean gaining access to and manipulation of the desired software program and hardware are honestly available on the market and easy to discern with. automatic query Paper Generator, as represented above, will result in blunders loss, at ease, reliable, and brief control system. it can help the person to not forget their opportunity sports but rather to concentrate on file preserving. So, it's going to assist an organization in the higher utilization of sources. The enterprise will keep computerized records while not redundant entries. which means that one needn't be distracted by info it is now not applicable while having the ability to acquire the statistics. The goal is to automatize its existing guide gadget by way of the assistance of picked equipment and complete-fledged laptop software, pleasurable their requirements, just so their precious facts/statistics can be preserved for an extended quantity with sincere access to and manipulation of the equal. on the whole, the venture describes the manner to manipulate permanently overall performance and better offerings for the customers. Texts with capacity academic well-worth have ended up on the market through the net. however, the victimization of those new texts in lecture rooms introduces several challenges, one in every of which is that they typically lack compliance with physical activities and assessments. right here, we will be inclined to address part of this task by way of automating the creation of a specific form of assessment object and the use of various questions developing with taxonomies. specially, we concentrate on mechanically generating actual WH queries. Our purpose is to make an automatic gadget if you want to take as enter a text and flip out as output questions for assessing a reader's understanding of the records within the text. The questions would possibly then be supplied to a trainer, who would possibly pick and revise people who she or he judges to be beneficial.

Visualizing Benefits of Case Management Software Using Utility Effect Chains

Labor shortages lead to crucial investment decisions, such as selecting software supporting work processes. The healthcare sector stands out because of additional restructuring due to demographic changes. This is particularly true for the care sector; hence, customized case management software (CMSW) solutions for healthcare professionals are being developed. In an increasingly profit-oriented healthcare system, sustainability, cost-effectiveness and quantification of benefits of investments play a major role. We analyzed research dealing with the benefits of case and care management software and, additionally, interviewed case managers who use recently developed CMSW within a case study. We used utility effect chains to visualize and quantify the gathered benefits of an information system (IS) investment along with the healthcare system in Germany. The findings show that benefits from care management software need to be seen more holistically. Utility effect chains can serve as a helpful instrument for the visualization of indirect benefits in healthcare. The most significant benefits of CMSW were found to be various cost savings for each of the participating stakeholders, a reduction in redundant entries of patient data and the prevention of cost-intensive revolving door cases. Additionally, the insight into patient records reduces time-consuming communication among health experts and family caregivers.

Creative Scrapyard Web Application

The Creative Scrapyard is an e-commerce website that aims to be a platform where people can sell their old and scrap project. From our website, an artist can buy those scrap item and customize that scrap items to make useful and decorative items. These creative items can be sold on the website again itself and other people can buy these items. It helps people in to earn by selling their scrap, it help the artists to buy stuff on the cheap rate and earn money by showing their creativity on it and make the world a better place by recycling old items. It can assist the user to concentrate on their other activities rather to concentrate on the record keeping. Thus, it will help organization in better utilization of resources. The organization can maintain computerized records without redundant entries. That means that one need not be distracted by information that is not relevant, while being able to reach the information. The aim is to automate its existing manual system by the help of computerized equipment’s and full- fledged computer software, fulfilling their requirements, so that their valuable data/information can be stored for a longer period with easy accessing and manipulation of the same. Basically the project describes how to manage for good performance and better services for the clients..

A Graph-Based Blocking Approach for Entity Matching Using Contrastively Learned Embeddings

Data integration is considered a crucial task in the entity matching process. In this process, redundant and cunning entries must be identified and eliminated to improve the data quality. To archive this, a comparison between all entities is performed. However, this has quadratic computational complexity. To avoid this, `blocking' limits comparisons to probable matches. This paper presents a k-nearest neighbor graph-based blocking approach utilizing state-of-the-art context-aware sentence embeddings from pre-trained transformers. Our approach maps each database tuple to a node and generates a graph where nodes are connected by edges if they are related. We then invoke unsupervised community detection techniques over this graph and treat blocking as a graph clustering problem. Our work is motivated by the scarcity of training data for entity matching in real-world scenarios and the limited scalability of blocking schemes in the presence of proliferating data. Additionally, we investigate the impact of contrastively trained embeddings on the above system and test its capabilities on four data sets exhibiting more than 6 million comparisons. We show that our block processing times on the target benchmarks vary owing to the efficient data structure of the k-nearest neighbor graph. Our results also show that our method achieves better performance in terms of F1 score when compared to current deep learning-based blocking solutions.

AGFT: Adaptive entries aggregation scheme to prevent overflow in multiple flow table environment

AbstractThe revolutionary architecture termed Software‐Defined Network provides flexible network management by detaching the control logic from the underlying data plane. The flow table resides in Ternary Content Addressable Memory imparts rules for the incoming flows with a limitation of high cost, limited storage, and consumes high power. In data center networks, when the traffic rate is high the overflow occurs due to storage limitation with high packet drop, frequent rule miss, and severe controller overhead. To overcome these challenges and to provide Quality of Service to the current network design sketch‐based entry reduction scheme is proposed. It incorporates three concurrent modules integrated to function sequentially where (1) Periodical analysis in Multiple Flow Tables is performed to ensure the availability of redundant entries using the robust data mining algorithm called Term Frequency. (2) Recurrent entries are further classified and clustered using the Boyer–Moore pattern matching algorithm to facilitate the forthcoming aggregation process. (3) A compact flow table is achieved with a customized multibit trie using Huffman coding compression technique. The experimental outcomes prove that this work prevents overflow by 99.98% with 98.99% enhanced flow table space and provides a significant reduction of controller overhead than the existing schemes.

Accelerating vasculature imaging in tumor using mesoscopic fluorescence molecular tomography via a hybrid reconstruction strategy

Mesoscopic fluorescent molecular tomography (MFMT) enables to image fluorescent molecular probes beyond the typical depth limits of microscopic imaging and with enhanced resolution compared to macroscopic imaging. However, MFMT is a scattering-based inverse problem that is an ill-posed inverse problem and hence, requires relative complex iterative solvers coupled with regularization strategies. Inspired by the potential of deep learning in performing image formation tasks from raw measurements, this work proposes a hybrid approach to solve the MFMT inverse problem. This methodology combines a convolutional symmetric network and a conventional iterative algorithm to accelerate the reconstruction procedure. By the proposed deep neural network, the principal components of the sensitivity matrix are extracted and the accompanying noise in measurements is suppressed, which helps to accelerate the reconstruction and improve the accuracy of results. We apply the proposed method to reconstruct in silico and vascular tree models. The results demonstrate that reconstruction accuracy and speed are highly improved due to the reduction of redundant entries of the sensitivity matrix and noise suppression.

Life’s Changing With E-Learning Education System

The purpose of E-Learning education system is to automate the existing manual system by the help of computerized equipment’s and full ledged computer software, fulfilling their requirements’, so that their valuable data/information can be stored for a longer period with easy accessing and manipulation of the same. The required software and hardware are easily available and easy to work with. E-learning Education System, as described above, can lead to error free, secure, reliable and fast education system, as described above, can lead to error free, secure, reliable and fast education system. It can assist the user to concentrate on their other activities rather to concentrate on the record keeping. Thus it will help organization kin better utilization of resources. The organization can maintain computerized records without redundant entries. That means that one need not be distracted by information that is not relevant. While being able to reach the information.

Redundancy-weighting the PDB for detailed secondary structure prediction using deep-learning models

The Protein Data Bank (PDB), the ultimate source for data in structural biology, is inherently imbalanced. To alleviate biases, virtually all structural biology studies use nonredundant (NR) subsets of the PDB, which include only a fraction of the available data. An alternative approach, dubbed redundancy-weighting (RW), down-weights redundant entries rather than discarding them. This approach may be particularly helpful for machine-learning (ML) methods that use the PDB as their source for data. Methods for secondary structure prediction (SSP) have greatly improved over the years with recent studies achieving above 70% accuracy for eight-class (DSSP) prediction. As these methods typically incorporate ML techniques, training on RW datasets might improve accuracy, as well as pave the way toward larger and more informative secondary structure classes. This study compares the SSP performances of deep-learning models trained on either RW or NR datasets. We show that training on RW sets consistently results in better prediction of 3- (HCE), 8- (DSSP) and 13-class (STR2) secondary structures. The ML models, the datasets used for their derivation and testing, and a stand-alone SSP program for DSSP and STR2 predictions, are freely available under LGPL license in http://meshi1.cs.bgu.ac.il/rw. Supplementary data are available at Bioinformatics online.

Placemento – An Android based Project for the Automation of Placement and Training Department

Training and Placement department is one of the important area to any educational Institute, even in this era, we are doing most of the work by using human interventions. The main aim of this paper is to automate the Training and Placement cell of Reva University. The main feature of this project is the generation, verification, authentication and easy analysis with maintenance of relevant data. This is achieved by means of modern Technology like Android and database servers. This will provide the facility to maintaining student data along with placement records of the college. This will serve as a medium of free communication and feedback between the students and the placement department. The Planner in the application will help all the users to select what they want to study and, plan their day accordingly. All the syllabus and faculties will be one touch away from the users. Users can post their queries and can be in direct touch with the Training and placement cell. The project aims to provide maximum optimization and security along with minimal manual work. This will be helpful in efficient and better management of all placement and Training activities on campus. With the development of this project, the University can maintain computerized records without redundant entries.

A method for harmonization of clinical abbreviation and acronym sense inventories.

Previous research has developed methods to construct acronym sense inventories from a single institutional corpus. Although beneficial, a sense inventory constructed from a single institutional corpus is not generalizable, because acronyms from different geographic regions and medical specialties vary greatly. Develop an automated method to harmonize sense inventories from different regions and specialties towards the development of a comprehensive inventory. The method involves integrating multiple source sense inventories into one centralized inventory and cross-mapping redundant entries to establish synonymy. To evaluate our method, we integrated 8 well-known source inventories into one comprehensive inventory (or metathesaurus). For both the metathesaurus and its sources, we evaluated the coverage of acronyms and their senses on a corpus of 1 million clinical notes. The corpus came from a different institution, region, and specialty than the source inventories. In the evaluation using clinical notes, the metathesaurus demonstrated an acronym (short form) micro-coverage of 94.3%, representing a substantial increase over the two next largest source inventories, the UMLS LRABR (74.8%) and ADAM (68.0%). The metathesaurus demonstrated a sense (long form) micro-coverage of 99.6%, again a substantial increase compared to the UMLS LRABR (82.5%) and ADAM (55.4%). Given the high coverage, harmonizing acronym sense inventories is a promising methodology to improve their comprehensiveness. Our method is automated, leverages the extensive resources already devoted to developing institution-specific inventories in the United States, and may help generalize sense inventories to institutions who lack the resources to develop them. Future work should address quality issues in source inventories and explore additional approaches to establishing synonymy.

Accelerating incremental attribute reduction algorithm by compacting a decision table

The evolution of object sets over time is ubiquitous in dynamic data. To acquire reducts for this type of data, researchers have proposed many incremental attribute reduction algorithms based on discernibility matrices. Although all reducts of an updated decision table can be obtained using these algorithms, their high computation time is a critical issue. To address this issue, we first construct three new types of discernibility matrices by compacting a decision table to eliminate redundant entries in the discernibility matrices of the original decision table. We then demonstrate that the set of reducts obtained from the compacted decision table are the same as those acquired from the original decision table. Extensive experiments have demonstrated that an incremental attribute reduction algorithm based on a compacted decision table can significantly accelerate attribute reduction for dynamic data with changing object sets while the acquired reducts are identical to those obtained using existing algorithms.

MS_HistoneDB, a manually curated resource for proteomic analysis of human and mouse histones

BackgroundHistones and histone variants are essential components of the nuclear chromatin. While mass spectrometry has opened a large window to their characterization and functional studies, their identification from proteomic data remains challenging. Indeed, the current interpretation of mass spectrometry data relies on public databases which are either not exhaustive (Swiss-Prot) or contain many redundant entries (UniProtKB or NCBI). Currently, no protein database is ideally suited for the analysis of histones and the complex array of mammalian histone variants.ResultsWe propose two proteomics-oriented manually curated databases for mouse and human histone variants. We manually curated >1700 gene, transcript and protein entries to produce a non-redundant list of 83 mouse and 85 human histones. These entries were annotated in accordance with the current nomenclature and unified with the “HistoneDB2.0 with Variants” database. This resource is provided in a format that can be directly read by programs used for mass spectrometry data interpretation. In addition, it was used to interpret mass spectrometry data acquired on histones extracted from mouse testis. Several histone variants, which had so far only been inferred by homology or detected at the RNA level, were detected by mass spectrometry, confirming the existence of their protein form.ConclusionsMouse and human histone entries were collected from different databases and subsequently curated to produce a non-redundant protein-centric resource, MS_HistoneDB. It is dedicated to the proteomic study of histones in mouse and human and will hopefully facilitate the identification and functional study of histone variants.

Metrics for the Human Proteome Project 2016: Progress on Identifying and Characterizing the Human Proteome, Including Post-Translational Modifications.

The HUPO Human Proteome Project (HPP) has two overall goals: (1) stepwise completion of the protein parts list-the draft human proteome including confidently identifying and characterizing at least one protein product from each protein-coding gene, with increasing emphasis on sequence variants, post-translational modifications (PTMs), and splice isoforms of those proteins; and (2) making proteomics an integrated counterpart to genomics throughout the biomedical and life sciences community. PeptideAtlas and GPMDB reanalyze all major human mass spectrometry data sets available through ProteomeXchange with standardized protocols and stringent quality filters; neXtProt curates and integrates mass spectrometry and other findings to present the most up to date authorative compendium of the human proteome. The HPP Guidelines for Mass Spectrometry Data Interpretation version 2.1 were applied to manuscripts submitted for this 2016 C-HPP-led special issue [ www.thehpp.org/guidelines ]. The Human Proteome presented as neXtProt version 2016-02 has 16,518 confident protein identifications (Protein Existence [PE] Level 1), up from 13,664 at 2012-12, 15,646 at 2013-09, and 16,491 at 2014-10. There are 485 proteins that would have been PE1 under the Guidelines v1.0 from 2012 but now have insufficient evidence due to the agreed-upon more stringent Guidelines v2.0 to reduce false positives. neXtProt and PeptideAtlas now both require two non-nested, uniquely mapping (proteotypic) peptides of at least 9 aa in length. There are 2,949 missing proteins (PE2+3+4) as the baseline for submissions for this fourth annual C-HPP special issue of Journal of Proteome Research. PeptideAtlas has 14,629 canonical (plus 1187 uncertain and 1755 redundant) entries. GPMDB has 16,190 EC4 entries, and the Human Protein Atlas has 10,475 entries with supportive evidence. neXtProt, PeptideAtlas, and GPMDB are rich resources of information about post-translational modifications (PTMs), single amino acid variants (SAAVSs), and splice isoforms. Meanwhile, the Biology- and Disease-driven (B/D)-HPP has created comprehensive SRM resources, generated popular protein lists to guide targeted proteomics assays for specific diseases, and launched an Early Career Researchers initiative.

Global Optimization for Multi-Channel Wireless Data Broadcast with AH-Tree Indexing Scheme

Multi-channel wireless data broadcast is an appropriate approach to disseminate data to a mass number of mobile clients. In this paper, we present a global optimization for multi-channel wireless data broadcast with Alphabetic Huffman Tree (AH-Tree) Indexing scheme, which can deal with skewed access frequencies well. We present three novel designs to reduce the access latency and tuning time of the broadcast system. Firstly, we design a dynamic programming for AH-Tree construction with low time complexity. Compared with traditional Hu-Tucker algorithm in $O(n^k)$ , our algorithm can build a $k$ -ary AH-Tree index in $O(n^2)$ . Next, we depict a new control table design, which eliminates up to 50 percent redundant entries while keeps the searching efficiency. We also theoretically prove that an optimal alphabetic tree has the minimum average tuning time among all tree-based index structures for skewed data broadcast. Thirdly, we design the new index and data allocation algorithms to further reduce the tuning time and access latency. The simulation results validate the effectiveness of our algorithms. In all, our global optimization mechanism can greatly improve the system performance and time efficiency for wireless data broadcast applications.

Mobile Agent Initiated Energy Efficient Data Aggregation in WSNs: MAEDA

A sensor node in WSNs has limited resources (e.g., memory capacity, battery power etc.) and among all, energy is the most crucial factor because refilling or recharging of power is not possible here. There are many possible ways to conserve energy in WSNs; data aggregation is one of them. Applications of WSNs cross a broad scale including environment monitoring, Habitat monitoring, monitoring Water and Air quality, Gas emission, etc. The above mentioned applications and many other needs Data Aggregation because here sensors frequently reports sensed values to the Processing Elements without removing redundant and disused entries. This paper proposes Mobile Agent initiated Energy efficient Data Aggregation (MAEDA) approach to prolong network lifetime. This method skillfully integrates Mobile Agent (MA) with wireless sensor networks. The proposed method is emulated by MATLAB platform; our simulation results are very impressive and prove that MAEDA is able to save more energy in data aggregation as well as prolonged network lifetime than other existing one.

SDI: a multi-domain SDN mechanism for fine-grained inter-domain routing

Software-defined networking (SDN) scheme decouples network control plane and data plane, which can improve the flexibility of traffic management in networks. OpenFlow is a promising implementation instance of SDN scheme and has been applied to enterprise networks and data center networks in practice. However, it has less effort to spread SDN control scheme over the Internet to conquer the ossification of inter-domain routing. In this paper, we further innovate to the SDN inter-domain routing inspired by the OpenFlow protocol. We apply SDN flow-based routing control to inter-domain routing and propose a fine-granularity inter-domain routing mechanism, named SDI (Software Defined Inter-domain routing). It enables inter-domain routing to support the flexible routing policy by matching multiple fields of IP packet header. We also propose a method to reduce redundant flow entries for inter-domain settings. And, we implement a prototype and deploy it on a multi-domain testbed.

Name Recognition to Identify Patients of South Asian Ethnicity within the Cancer Registry

Objective:The goal of this project was to develop a list of forenames and surnames of South Asian (SA) women that could be used to identify SA breast cancer patients within the cancer registry. This list was compiled, evaluated, and validated to ensure comprehensiveness, accuracy, and applicability of SA names.Methods:This project was conducted by Canadian researchers who are immersed in conducting behavioral studies with SA women diagnosed with cancer in the province of British Columbia. Recruiting SA cancer patients for research can be a difficult task due to social and cultural factors. Methods used by other researchers to identify ethnicity related unique names were employed to filter surnames and forenames that were not common to this ethnic group. Co-author (Gurpreet Oshan) of SA ethnicity rigorously identified and deleted multiple lists and redundant entries along with common English forenames which resulted in a list of 16,888 SA forenames. All co-authors of Indian ethnicity (Gurpreet Oshan, Savitri Singh-Carlson, Harajit Lail) were involved in critiquing and manually reviewing the names list throughout this process. Comprehensive lists of SA surnames and women's forenames were reviewed to identify those that were unique to SA ethnicity. Accuracy was ensured by constantly filtering the redundancy by using an Excel program which helped to illustrate the number of times each name was spelled in different ways.Results:The final lists included 9112 surnames and 16,888 forenames of SA ethnicity. On the basis of the surname linkage only, the sensitivity of the list was 76.6%, specificity was 62.9%, and the positive predictive value was 58.5%. On the basis of both the surname and forename linkage, the specificity of the list was 88.6%. These lists include variations in spelling forenames and surnames as well.Conclusions:The list of surnames and forenames can be useful tools to identify SA ethnic groups from large population database in healthcare-related research. Ethnicity-specific population research is important in order to help identify how cancer care should be delivered for the SA population, as well as for planning and provision of other related health services. We are willing to share this list upon request to the authors.

An Efficient Adaptive Window Size Selection Method for Improving Spectrogram Visualization

Short Time Fourier Transform (STFT) is an important technique for the time-frequency analysis of a time varying signal. The basic approach behind it involves the application of a Fast Fourier Transform (FFT) to a signal multiplied with an appropriate window function with fixed resolution. The selection of an appropriate window size is difficult when no background information about the input signal is known. In this paper, a novel empirical model is proposed that adaptively adjusts the window size for a narrow band-signal using spectrum sensing technique. For wide-band signals, where a fixed time-frequency resolution is undesirable, the approach adapts the constant Q transform (CQT). Unlike the STFT, the CQT provides a varying time-frequency resolution. This results in a high spectral resolution at low frequencies and high temporal resolution at high frequencies. In this paper, a simple but effective switching framework is provided between both STFT and CQT. The proposed method also allows for the dynamic construction of a filter bank according to user-defined parameters. This helps in reducing redundant entries in the filter bank. Results obtained from the proposed method not only improve the spectrogram visualization but also reduce the computation cost and achieves 87.71% of the appropriate window length selection.