Different Pieces Of Data Research Articles

Developing and Analyzing Deep Learning and Natural Language Processing Systems in the Context of Medical Information Processing

This work aims to develop and analyze deep learning and natural language processing systems in the context of medical information processing. The amount of data created about patients in the healthcare system is always increasing. The human review of this enormous volume of data derived from numerous sources is expensive and takes a lot of time. Additionally, during a patient visit, doctors write down the patient’s medical encounter and send it to nurses and other medical departments for processing. Often, the doctor doesn’t have enough time to record every observation made while examining the patient and asking about their medical history which takes time for a medical diagnosis to be made. The manual review of this vast amount of data generated from multiple sources is costly and very time-consuming. It brings huge challenges while attempting to review this data meaningfully. Therefore, the goal of this research is to create a system that will address the aforementioned issues. The suggested method extracts voice data from medical encounters and converts it to text using Deep Learning (DL) and Natural Language Processing (NLP) techniques. More so, the system developed will improve medical intelligence processing by using deep learning to analyze medical datasets and produce results of a diagnosis, assisting medical professionals at various levels in making realistic, intelligent decisions in real-time regarding crucial health issues. The system was designed using the Object-Oriented Analysis and Design Methodology (OOADM), and the user interfaces were put into place utilizing Natural Language Processing techniques, particularly speech recognition and natural language comprehension. Speech recognition allows for the taking of free text notes, which can drastically cut down on the amount of time medical staff spends on labor-in the tensive clinical recording. By extracting different pieces of data for medical diagnosis and producing results in a matter of seconds, a deep learning algorithm demonstrates a significant capacity to construct clinical decision support systems. The system’s results demonstrate that the deep learning algorithm enabled medical intelligence to be 96.7 percent accurate.

Reflections on Required Competencies For Health Systems Monitoring, Evaluation, and Learning In India

The movement towards systems thinking and complexity-informed monitoring and evaluation has been ongoing for some time. Covid-19 has accelerated this shift and increased the salience of contextually aware, adaptive forms of evaluation. Drawing from our experience over three years of providing monitoring, evaluation, research and learning (MERL) to a large international philanthropic organization’s health systems portfolio of work in India, we identify three types of evaluator capacities and associated set of competencies that derive from complexity-driven practice in a developing country.
 The first type of capacity relates to deriving evidence in contexts where there are very few traditional forms of data. The MERL portfolio related to health systems outcomes of access, financial protection, and equity even as the health programs themselves were evolving in a period of rapid transformation and dynamism of policy priorities and implementation mechanisms. This required an integration of traditional performance indicators drawn from government datasets with qualitative ‘signals’ focused on drivers of change within the system. Assessment of signals in turn required synthesizing different sources of information, including informal channels of obtaining evidence such as partner meetings or government-sponsored events. The evaluating team thus consciously empowered different kinds of researchers with differential expertise with the goal of building a much more pixelated picture. The goal was not to identify a single source of truth but rather a patchwork of validated information where the relevance of different pieces of data were dependent on evolving outcomes of interest. 
 The second set of competencies related to the skills required to play a convening role for donors and implementing partners, supporting better understanding of the changing operating context and help inform decision-making by program officers and partners. This involved building and sustaining relationships across different stakeholders at different stages of the project – from proposal development to review. Competencies relating to effective dialogue and developing an understanding of the core interests of international, national and sub-national partners as well as international donors and experts could only be developed iteratively and over time, but this was crucial in a distributed health decision-making ecosystem like India.
 The third and final set of competencies relate to operational adaptiveness, while evaluating an ecosystem with few constants. This can be the hardest competency to acquire because it is the farthest from the traditional notions embedded in the training of evaluation scientists and MERL practitioners. We found that the degree of buy-in and commitment to previously agreed upon frames of reference for evaluation can be shifted by changes in personnel or internal organizational structures. These shifts can lead to chain reactions of mismatched expectations that needed to be understood and managed in real time by MERL partners. The pandemic further created a natural experiment that on the one hand required a reexamination of program priorities and on the other depended on reliability of donor support.
 Each of these three types of capacities – synthesizing nuanced evidence for adaptive action, relationship building and communication, and managing operational discontinuities are in fact inter-dependent. Building evaluator competencies isn’t simply about capacity-building but rather a recognition of the diversity of skills and worldviews that need to be encompassed within our monitoring and evaluation functions for today’s complex, discontinuous health systems.

An Advanced Abnormal Behavior Detection Engine Embedding Autoencoders for the Investigation of Financial Transactions

Nowadays, (cyber)criminals demonstrate an ever-increasing resolve to exploit new technologies so as to achieve their unlawful purposes. Therefore, Law Enforcement Agencies (LEAs) should keep one step ahead by engaging tools and technology that address existing challenges and enhance policing and crime prevention practices. The framework presented in this paper combines algorithms and tools that are used to correlate different pieces of data leading to the discovery and recording of forensic evidence. The collected data are, then, combined to handle inconsistencies, whereas machine learning techniques are applied to detect trends and outliers. In this light, the authors of this paper present, in detail, an innovative Abnormal Behavior Detection Engine, which also encompasses a knowledge base visualization functionality focusing on financial transactions investigation.

Exploring future challenges for big data in the humanitarian domain

This paper examines the challenges of leveraging big data in the humanitarian sector in support of UN Sustainable Development Goal 17 “Partnerships for the Goals”. The full promise of Big Data is underpinned by a tacit assumption that the heterogeneous ‘exhaust trail’ of data is contextually relevant and sufficiently granular to be mined for value. This promise, however, relies on relationality – that patterns can be derived from combining different pieces of data that are of corresponding detail or that there are effective mechanisms to resolve differences in detail. Here, we present empirical work integrating eight heterogeneous datasets from the humanitarian domain to provide evidence of the inherent challenge of complexity resulting from differing levels of data granularity. In clarifying this challenge, we explore the reasons why it is manifest, discuss strategies for addressing it and, as our principal contribution, identify five propositions to guide future research.

GIS database of groundwater flow characteristics in carbonate aquifers: Tracer test inventory from Slovenian karst

Karst aquifers that supply manifold ecosystem services, such as freshwater resources, and possess unique underground biodiversity are characterized by peculiar flow processes and are thus particularly susceptible to destruction. Their efficient protection is based on artificial tracing results that are usually scattered among different sources. In the case of Slovenia, the results of 231 available tracer tests, performed between 1905 and 2019, have been assembled, arranged and georeferenced. The inventory of the geospatial database, which is unique on a global scale, contains 36 different pieces of data for each tracing that have been compiled using geographic information system tools. In this study, the basic concepts, the step-by-step workflow and a detailed mapping procedure have been presented. The thorough collection of available data that is freely accessible online includes data on the date, location and details of implementation of the tracer test and on the identified groundwater flow characteristics. Also available are the sources of the published or unpublished reports from which the data were taken. The database includes a comment on the tracer test implementation or results. The inventory is very useful in the process of the integrated management of karst water sources, and especially as an early-warning and monitoring system tool in cases where there is an imminent danger of pollution, which requires a fast and accurate response. The database production is transferable to other karst-rich countries.

Analisa Algoritma Apriori Pada Pola Peminjaman Buku di Perpustakaan ITB Ahmad Dahlan

The library is a source of information and a place of learning. Each book lending information is stored by the library so as to produce large data lending books. Big data if not utilized will create problems in the future. In this study, researchers will utilize a priori algorithms and Tanagra software to group library book borrowing data at Ahmad Dahlan ITB based on trends that occur together in library visit activities. In the process of borrowing books, of course the raw data will be processed by dividing it into different pieces of data. Among the lending data tables processed are general lending tables, 2-itemset candidate tables, lending tabular tables, support value tables, confidence value tables. From the results of this study it can be seen what books are often borrowed together with a minimum support of 5% and 10% confidence one of which is Taxation and Taxation Accounting with a minimum support of 7.30% confidence 62.79%. And can be used as a reference for ITB Ahmad Dahlan in the procurement and placement of library book layout.

Cloud computing: peer to peer keyword search on cloud

Cloud computing is come forth computing paradigm in which resources of the computing infrastructure are provided as services over the Internet. This paradigm also brings forth many new challenges for data security and access control when users outsource nociceptive data for sharing on cloud servers, which are not within the same countenanced domain as data owners. Although in the traditional way, encryption scheme to exploit edit distance to quantify keywords similarity and develop an advanced technique on constructing discrete keyword sets, which greatly reduces the storage and representation overheads. In this paper for the first time we formalize and solve the problem of sensitive data shared and stored by third-party sites on the internet. The attack correlation center, dshield.org, presents aggregated views of attacks on the Internet, but stores intrusion reports individually submitted by users. Given the variety, amount, and importance of information stored at these sites, there is cause for concern that personal data will be compromised. This worry is escalated by the surge in recent attacks and legal pressure faced by such services. In this paper, we introduce new techniques to implement fine grained access control. In our techniques, the data is stored on the server in an encrypted form while different users are still allowed to decrypt different pieces of data per the security policy. This effectively eliminates the need to rely on the storage server for preventing unauthorized data access.

A New Trust Management for Secure Communication using MANET

The current Internet service models is based on a few assumptions such as the existence of an end to end path between a source and destination pair, and low round-trip latency between any node pair. However, these assumptions do not hold in some emerging networks. Like battlefield ad-hocNetworks in which wireless devices carried by soldiers operate in hostile environments where jamming, environmental factors and mobility may cause temporary disconnections. To allow nodes to communicate with each other in these extreme networking environments, we proposed a new architecture called the Disruption Tolerant Network (DTN). In some application scenarios, there are some 'storage nodes' in the network where useful data is stored or replicated so that other regular mobile nodes can access the necessary information quickly and efficiently. A requirement in some security-critical applications is to design an access control system to protect the confidential data stored in the storage nodes or contents of the confidential messages routed through the network. Overcome this problem, in this project we propose a cipher text-policy ABE (CP-ABE) provides a scalable way of encrypting data such that the encryptor defines the attribute set that the decryptor needs to possess in order to decrypt the cipher text. Thus, different users are allowed to decrypt different pieces of data per the security policy. The proposed scheme have attribute revocation enhances backward/forward secrecy of confidential data; the coordination of attributes issued from different authorities and avoid third party compromised with key authorities. The proposed scheme to securely and efficiently manage the confidential data distributed in the disruption- tolerant military network.

Profile of Norbert Perrimon.

As a child, one of Norbert Perrimon’s first introductions to the scientific method came from his father. Perrimon’s father was interested in geology. “Often we would go to various areas in Normandy, where I grew up, and collect different types of soils, rocks, and fossils. I always found it quite interesting to see how from the collection of different bits and pieces, you could draw complex geological maps,” Perrimon says. Many years later, Perrimon would use a similar process to make his mark not in geology, but as a biologist working on the fruit fly, Drosophila . Norbert Perrimon. Photo courtesy of Norbert Perrimon. Signaling pathways control the proliferation of stem cells, shown here intermingled among large enterocytes in the Drosophila midgut epithelium. Photo courtesy of M. Markstein and N. Perrimon. Over a long and distinguished career, Perrimon combined different pieces of data collected from the study of various fruit fly mutants to map complex developmental and signaling pathways. In recognition of his discoveries in the field of Drosophila development and signaling, and for developing important genetic tools for studying fruit flies, Perrimon was elected to the National Academy of Sciences in 2013. Now a professor of developmental biology and genetics at Harvard Medical School, Perrimon first started developing tools to study fruit fly genetics as a graduate student at the University of Paris. It was in the early 1980s that Perrimon first became interested in genetics as a tool to tackle questions in developmental biology and embryology. “I was looking for an organism where I could use genetics to study the complexity of the way animals form,” he says. “I started working in Drosophila because it is a great model system to apply genetics.” In 1981, he started his doctoral research studying fruit fly oogenesis in the laboratory of …

Architecture for management and fusion of context information

Information in a context-aware system has diverse natures. Raw data coming from sensors are aggregated and filtered to create more abstract information, which can be processed by context-aware application components to decide what actions should be performed. This process involves several activities: finding the available sources of information and their types, gathering the data from these sources, facilitating the fusion (aggregation and interpretation) of the different pieces of data, and updating the representation of the context to be used by applications. The reverse path also appears in context-aware systems, from changes in the context representation to trigger actions in certain actuators. FAERIE (Framework for AmI: Extensible Resources for Intelligent Environments) is a framework that facilitates management and fusion of context information at different levels. It is implemented as a distributed blackboard model. Each node of the system has a private blackboard to manage pieces of information that can be accessed by observer components, either locally or remotely (from other nodes) in a transparent way. The use of the framework is illustrated with a case study of an application for guiding people to meetings in a university building.

EGFR-Targeting as a Biological Therapy: Understanding Nimotuzumab’s Clinical Effects

Current clinical trials of epidermal growth factor receptor (EGFR)-targeted therapies are mostly guided by a classical approach coming from the cytotoxic paradigm. The predominant view is that the efficacy of EGFR antagonists correlates with skin rash toxicity and induction of objective clinical response. Clinical benefit from EGFR-targeted therapies is well documented; however, chronic use in advanced cancer patients has been limited due to cumulative and chemotherapy-enhanced toxicity. Here we analyze different pieces of data from mechanistic and clinical studies with the anti-EGFR monoclonal antibody Nimotuzumab, which provides several clues to understand how this antibody may induce a biological control of tumor growth while keeping a low toxicity profile. Based on these results and the current state of the art on EGFR-targeted therapies, we discuss the need to evaluate new therapeutic approaches using anti-EGFR agents, which would have the potential of transforming advanced cancer into a long-term controlled chronic disease.

Comments on “Reanalyses Suitable for Characterizing Long-Term Trends”

recent article by Thorne and Vose (2010, hereafter TV) concerns the use of reanalysis data for characterizing long-term climate trends. The article raises legitimate questions about the ability to extract accurate climate information from time-varying observational datasets by means of model-based data assimilation techniques. TV predict that current approaches adopted by producers of reanalysis data are unlikely to result in climate-quality datasets. They make several recommendations for improving reanalysis methodology, and also propose a definition of “climate-quality” that is based on robust accuracy requirements. The purpose of this note is to explain our views on these issues and to address some of TV’s specific recommendations. To derive accurate and complete representations of climate variability and trends from observations is an ambitious goal. The difficulties involved, of course, are not specific to reanalysis. Fundamentally, the climate system is incompletely and inaccurately observed; data coverage, measurement techniques, and associated uncertainties are continually changing. Any such change can generate or modulate systematic errors in estimates of climate parameters. The idea behind reanalysis is to try to combine the observations by making optimal use of all available information. This includes metadata pertaining to data quality, as well as information about the physics of the climate system that can help us interpret and compare different pieces of data. In this way it becomes possible to expose the underlying uncertainties and to reduce their impact on the representation of climate parameters.

Uncertainty in the analysis and interpretation of research information

Uncertainty in the analysis and interpretation of research information

'Gene shaving' as a method for identifying distinct sets of genes with similar expression patterns.

Large gene expression studies, such as those conducted using DNA arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful groupings. We illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of survival. The gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.

SIMD processors-expanding its horizons by emulating others

Parallel processing has established itself as the key enabling technology for the future of high performance computing. A parallel machine can be abstracted as a collection of processing elements (PEs) connected using some form of communication network. Parallel machines have been built along two major architectural formats: multiple-instruction streams multiple-data streams (MIMD) machines and single-instruction stream multiple-data streams (SIMD) machines. The hardware differences and general programming models for SIMD and MIMD processors have led many to believe each can solve only limited types of problems. SIMD machines, for instance, are often used in applications such as image processing. In that application, the same set of operations are performed on many different pieces of data. MIMD machines are commonly used when the control flow needs vary in correspondence with differences in data, such as parallel simulation.

Correlation between MZ, and mt bounds (I). Neutral current data

We discuss present neutral current data constraints (combined with M W and M Z measurements) on m t (and m H) in the minimal gauge extension of the standard model. The extra gauge boson mass M Z, is the only new parameter (for fixed new gauge couplings λ and θ 1). Fits performed with M Z, and m t free, translate into allowed regions in the M Z,− m t plane where the M Z, lower bound is correlated with the m t upper bound. As a consequence, the m t upper bound in extended electroweak models is stronger (lower) than the one obtained in the minimal standard model, and coincides with it in the limits M Z, → ∞. The influence of the different pieces of data on the fits is also discussed. We briefly comment on other constraints, such as those from charged current data and direct production at large hadron colliders.

A Walsh-Hadamard Based Distributed Storage Device for the Associative Search of Information

A computer model for a distributed associative memory has been developed based on Walsh-Hadamard functions. In this memory device, the information storage is distributed over the entire memory medium and thereby lends itself to parallel comparison of the input with stored data. These inherent economic storage and parallel processing capabilities may be found effective especially in real-time processing of large amount of information. However, overlaying different pieces of data in the same memory medium creates the problem of interference or crosstalk between stored data and may lead to recognition errors. In this paper, a crosstalk reduction technique utilizing the gradient descent procedure is developed first. This minimizes the memory processing error and enhances memory saving. Second, for an efficient implementation of the memory structure, these associative memories are configured in a hierarchical structure which not only expands storage capacity but also utilizes the speed of tree search. Finally, a self-correcting technique is developed which achieves error-free recognition of near neighbors for any training pattern even among the presence of crosstalk.