Miracle-dz/UniFAR-FTUs huggingface dataset:
On methodologies to select systems for automated personal identification

Systems deployed to automatically identify persons operate in diverse application contexts, ranging from border control policing to on-line banking, attract benefits and risks to stakeholder organisations and to their respective user communities. This thesis explores the efficacy of a systematic methodology to select the optimal system for a given application context. We created a systematic methodology in order to ascertain the extent of a systematic methodology’s efficacy to select the optimal system for a given application context. We also developed criteria in order to assess the efficacy of such selection methodologies. Employing the case study research methodology, we conclude that a systematic methodology is reasonably efficacious for selecting the optimal system when the circumstances surrounding the application context necessitate a comprehensive inquiry. An organisation should conduct a comprehensive inquiry when there is a need to establish objectives and requirements for the system in order to evaluate a range of candidate systems, employ repeatable systematic processes in order to reduce their reliance on the capabilities of discipline experts, and/or produce an audit trail of the programme’s method which may be used as evidence to justify the system selected. We ascertained that the scope of a comprehensive inquiry demands a multi-disciplinary approach to evaluate over 240 factors relating to the selection of the optimal system. An evaluation needs to examine the application context itself in order to determine the stakeholders’ objectives and requirements for a system. Candidate systems may then be appraised on their capabilities to fulfil stakeholders’ requirements. We used our systematic methodology, in a case study involving the enhancement of an enterprise’s user authentication system, to identify contextual exemplars demonstrating when a systematic methodology is efficacious for selecting these systems. Two retrospective case studies served to identify and explain the proficiencies and deficiencies of current approaches pursued by organisations’ programmes.
Study of NoSQL Database Along With Security Comparison

This paper considers the top 5 open source NoSQL Database Redis, MongoDB, CouchDB, Hyper table, and Cassandra. NoSQL Database is leading database in the aspect of the industry and the current requirement of technology. Each database is designed in such a way, in which their performance can be evaluated based on the flexibility, security, durability etc feature provided by them. The database should be selected, with respect to the need of the application. Some database gives worst performance if they are not flexible with the application
Field-aware User Influence Recommendation Model Based on Trust Relationship

In current determining of user influence based on network structure, the overall importance of user node in network usually attracts great attention. However, the significance of the users in specific field has not been fully studied, which result in low accuracy and reliability in user influence measurement. In order to resolve these problems, this research proposes a field-aware user influence model, which constructs user influence for specific fields, and analyses the global influence of users in the whole network on a network structure basis. The model, assisted by historical behaviour data, not only considers the spread of user influence, but also takes the relationship between user influence and fields into account. Referring to the global influence, the model is able to optimize the composition of user influence, and further improve the accuracy of recommendation. The experiments based on real data sets conducted in this research unexceptionally proved that the field-aware user influence model proposed in this paper, compared with other methods, can effectively improve the recommendation accuracy.
Vascular Degeneration in Human Parkinson Disease: Novel Insights in the Pathologies, Functions and Mechanisms

• Any use you make of these documents or images must be for research or private study purposes only, and you may not make them available to any other person. • Authors control the copyright of their thesis. You will recognize the author's right to be identified as the author of this thesis, and due acknowledgement will be made to the author where appropriate. • You will obtain the author's permission before publishing any material from their thesis.
Refining Process Descriptions from Execution Data in Hybrid Planning Domain Models

The creation and maintenance of a domain model is a well recognised bottleneck in the use of automated planning; indeed, ensuring a planning engine is fed with an accurate model of an application is essential in order that generated plans are effective. Engineering domain models using a hybrid representation is particularly challenging as it requires accurately describing continuous processes, which can have complex numeric effects. In this work we consider the problem of the refinement of an engineered hybrid domain model, to more accurately capture the effect of the underlying processes. Our approach exploits the information content of the original model, utilising machine learning techniques to identify important situation and temporal features that indicate a variation in the original effect. We use the problem of modelling traffic flows in an Urban Traffic Management setting as a case study and demonstrate in our evaluation that the refined domain models provide more accurate simulation, which can lead to higher quality plans. The contribution of this work is a general approach to the automated refinement of hybrid planning domain models that reduces the knowledge engineering effort in producing a detailed process model. The approach can be used for refining the domain model during the initial stages of development, or for re-configuring the domain model when used in the same problem area but with a different scenario. We test out the approach within a real world case study.
QoS-aware Scientific Application Scheduling Algorithm in Cloud Environment

Many complex scientific applications are modeled in the form of workflows to carry out large-scale experiments. Because of complexity of scientific processes, scientific workflows need intensive computation and data requirements. Clouds make opportunity for scientific that need high performance computing infrastructure. So scientific can run their application on cloud by their desired QoS. We propose an algorithm that able scientific to select execute plan based on their preference QoS, like time and cost. Proposed algorithm ranks the tasks in workflow and then use UPFF function for select accurate resource, based on user’s QoS. We compared our proposed algorithm with the same work by several scenarios and results show proposed algorithm has better efficiency. Keywords Scientific application, Workflow scheduling, Cloud computing
What's really wrong: diagnosis for learning transfer system change

This chapter presents a framework for diagnosing learning transfer systems that consists of three components: (1) a set of factors that influence transfer; (2) a diagnostic instrument, in this instance the Learning Transfer System Inventory (LTSI); and (3) a change process model. The LTSI is described and the administration process is explained. The LTSI change process is also described and a case study of its use is offered. The author concludes that this diagnostic instrument offers an easy, economical and sound means of identifying targets for learning transfer intervention.
An Overview of Web Intelligence

: This paper gives an overview of Web intelligence which will enable the current Web to reach the Wisdom level by containing Distributed, Integrated, and Active knowledge. In this case it will be capable of performing tasks like problem solving and question-answering. In addition, it will be capable of processing and understanding natural languages. Web intelligence draws results from a number of disciplines like: Artificial intelligence, Information technology. Mathematics and Physics, Psychology and Linguistics. The paper covers the following topics: Web evolution and architecture-Topics related to Web intelligence-The Deep Web-Semantic computing and the Semantic Web-The Wisdom Web- Precisiated Natural Language.
Multimodal data fusion algorithm applied to robots

In recent years, the use of multimodal human-computer interaction technology to achieve the enhancement of human intelligence has become a new topic in human-computer interaction research. When the robot can’t react correctly in a single mode, it is necessary to realize multimodal fusion. To this end, this paper proposes a multimodal fusion algorithm that applies the data obtained by the CNN feature layer to the decision-level. The speech recognition text is semantically matched with the text in the text library, and the similar probability vector is returned. At the same time, the similarity probability vector of the gesture recognition is obtained, and the data is filtered by the threshold, and the set of high probability data codes is assigned to the two modes. The intersection operation, and the final instruction is sent to the robot. The experimental results show that the influence of environmental factors on the single channel result is reduced, and the single mode ambiguity problem is eliminated. The multi-channel fusion algorithm with additional weight is more accurate than the common multi-channel fusion algorithm. At the same time, it has also been well received by many test users.
USAGE OF BIOINFORMATIC DATA FOR REMOTE AUTHENTICATION IN WIRELESS NETWORKS

Authentication is the step to approve the correctness of an attribute of a individual or entity group. Sensitive information might help in making the authentication. Regularly this encrypted information is processed via wireless network and which need remote authentication for information access process. In the proposed work, a robust authentication technique is performed, which is based on segmentation, symmetric encryption and data hiding. If a user wants to be remotely authenticated, initially user has to select a video. The user’s biometric signal is encrypted using a symmetric encryption method. After encrypted information is vectorized the information hiding process is accomplish using Qualified Significant Wavelet Trees (QSWTs). QSWT is effectively achieve the invisibility and resistance during attacks and stability in data hidden process. Also, the Inverse Discrete Wavelet Transform (IDWT) is applied to extract the hiding data from the stego-object subsequently an appropriate decryption process to recover the biometric image. Experimental results are stated that the proposed method would turnout security virtue and robustness. Triple DES technique is used in the proposed work. This is the technique that is used to encrypt the biometric data into a scrambled format which is difficult to understand by the attackers. It is a very useful and efficient method of encryption because of its tendency to use less data for performing its services.
Energy-Efficient Capacitance-to-Digital Converters for Smart Sensor Applications

Energy-Efficient Capacitance-to-Digital Converters for Smart Sensor Applications Abdulaziz Mohammad Alhoshany One of the key requirements in the design of wireless sensor nodes and miniature biomedical devices is energy efficiency. For a sensor node, which is a sensor and readout circuit, to survive on limited energy sources such as a battery or harvested energy, its energy consumption should be minimized. Capacitive sensors are candidates for use in energy-constrained applications, as they do not consume static power and can be used in a wide range of applications to measure different physical, chemical or biological quantities. However, the energy consumption is dominated by the capacitive interface circuit, i.e. the capacitance-to-digital converter (CDC). Several energy-efficient CDC architectures are introduced in this dissertation to meet the demand for high resolution and energy efficiency in smart capacitive sensors. First, we propose an energy-efficient CDC based on a differential successive-approximation data converter. The proposed differential CDC employs an energy-efficient operational transconductance amplifier (OTA) based on an inverter. A wide capacitance range with fine absolute resolution is implemented in the proposed coarse-fine DAC architecture which saves 89% of silicon area. The proposed CDC achieves an energy efficiency figureof-merit (FOM) of 45.8fJ/step, which is the best reported energy efficiency to date. Second, we propose an energy efficient CDC for high-precision capacitive resolution by using oversampling and noise shaping. The proposed CDC achieves 150 aF absolute resolution 5 and an energy efficiency FOM of 187fJ/conversion-step which outperforms state of the art high-precision differential CDCs. In the third and last part, we propose an in-vitro cancer diagnostic biosensor-CMOS platform for low-power, rapid detection, and low cost. The introduced platform is the first to demonstrate the ability to screen and quantify the spermidine/spermine N1 acetyltransferase (SSAT) enzyme which reveals the presence of early-stage cancer, on the surface of a capacitive biosensor. This platform, which is a biosensor combined with a highly energy-efficient digital CDC, is implemented and fabricated in a CMOS technology and can sense and convert the capacitance value from the biosensor to a digital word in an energy efficient way. The platform achieves an ultralow power consumption: four orders of magnitude less than the state-of-the-art biosensorCMOS platforms.
A Sustainability Oriented and Mentored Approach to Implementing a Fall Prevention Guideline in Acute Care Over 2 Years

The purpose of this study was to assess the impact of a mentored guideline implementation (Registered Nurses’ Association of Ontario Prevention of Falls and Falls Injuries in the Older Adult Best Practice Guideline) focused on enhancing sustainability in reducing fall rates and number of serious falls and the experience of staff in three acute care hospitals. The National Health Service (NHS) Sustainability Model was used to guide the study. Interviews and focus groups were held with 82 point-of-care professional staff, support staff, volunteers, project leaders, clinical leaders, and senior leaders. Study results supported the importance of the factors in the NHS model for sustainability of the guideline in these practice settings. There were no statistically significant decreases in the overall fall rate and number of serious falls. The results supported strategies of participating hospitals to become senior friendly organizations and provided opportunities to enhance staff collaboration with patients and families.
Whole-genome alignments and polytopes for comparative genomics

Whole-genome sequencing of many species has presented us with the opportunity to deduce the evolutionary relationships between each and every nucleotide. The problem of determining all such relationships is that of multiple whole-genome alignment. Most previous work on whole-genome alignment has focused on the pairwise case and on the string pattern-matching aspect of the problem. However, to completely describe and determine the evolution of nucleotides in multiple genomes, refined definitions as well as algorithms that go beyond pattern snatching are required. This thesis addresses these issues by introducing new evolutionary terms and describing novel methods for alignment at both the whole-genome and nucleotide levels. Precise definitions for the evolutionary relationships between nucleotides, presented at the beginning of this work, provide the framework within which our methods for genome alignment are described. The sensitivity of alignments to parameter values can be ascertained through the use of alignment polytopes, which are explained. For the problem of aligning multiple whole genomes, this work presents a method that constructs orthology maps, which are high-level mappings between genomes that can be used to guide nucleotide-level alignments. Combining our methods for orthology mapping and alignment polytope determination, we construct a parametric alignment of two whole fruit fly genomes, which describes the alignment of the two genomes for all possible parameter values. The usefulness of whole-genome and parametric alignments in comparative genomics is shown through studies of cis-regulatory element evolution and phylogenetic tree reconstruction.
IMPLEMENTATION OF DONOR HUMAN MILK IN A NEWBORN NURSERY: A QUALITY IMPROVEMENT INITIATIVE TO INCREASE EXCLUSIVE BREAST MILK FEEDING

Background: At the site of this quality improvement project, exclusive breast milk feeding rates before discharge from a newborn nursery were behind benchmark goals. The aim of this project was to improve exclusive breast milk feeding rates by implementing a Pasteurized Donor Human Milk (PDHM) Program for breastfeeding newborns requiring medically indicated supplementation. The study site was a Level I newborn nursery of a 20-bed, labor, delivery, recovery, and postpartum unit within a 100-bed community hospital (i.e., the agency). Methods: This retrospective pre-post cohort study used the Plan, Do, Study, Act (PDSA) framework for rapid cycle change to assess the impact of the program on breast milk feeding exclusivity on discharge. The primary outcomes of interest were on the change in exclusivity among the full-term population according to the Joint Commission’s Perinatal Care Core Measures PC-05 and PC-05a (retired). Variables of interest included the hour of newborn age at first supplementation, indications for supplementation, and formula use in the breastfeeding population within the first two days of life. Data were collected by abstraction and medical record review for 3 months preand 3 months postPDHM Program implementation. Interventions: A multi-disciplinary team was developed to plan and implement the PDHM Program. The process began by developing necessary documents and workflows and ended with staff training and safe provision of PDHM. Results: Three months after implementation, exclusive breast milk feeding rates increased 3% (pre68%, post71%) in the full-term population and 11% (pre77%, post88%) within the full-term, breastfeeding population. Conclusions: The implementation of a PDHM Program was successful in improving exclusive breast milk feeding rates. Exclusive breast milk feeding rates exceeded the intended 10% increase based on the PC-05a metric. While many successes were realized in this quality improvement project, further work is needed to better grasp the impact of a PDHM Program at the agency and the implications of using PDHM in the fullterm population.
The pitfalls and virtues of population genetic summary statistics: Detecting selective sweeps in recent divergences

During evolution, genomes are shaped by a plethora of forces that can leave characteristic signatures. A common goal when studying diverging populations is to detect the signatures of selective sweeps, which can be rather difficult in complex demographic scenarios, such as under secondary contact. Moreover, the detection of selective sweeps, especially in whole‐genome data, often relies heavily on a narrow set of summary statistics that are affected by a multitude of factors, frequently leading to false positives and false negatives. Simulating genomic regions makes it possible to control these demographic and population genetic factors. We used simulations of large genomic regions to determine how different secondary contact and sympatric speciation scenarios affect the footprint of hard and soft selective sweeps in the presence of varying degrees of gene flow and recombination. We explored the ability of an array of population genetic summary statistics to detect the footprints of these selective sweeps under specific demographies. We focussed on metrics that do not require phased data or ancestral sequences and therefore have wide applicability. We found that a newly developed beta diversity measure, B¯GD utperformed all other metrics in detecting selective sweeps and that FST also performed well. High accuracy was also found in Δπ and genotype distance‐derived metrics. The performance of most metrics strongly depended on factors such as the presence of an allopatric phase, migration rates, recombination, population growth, and whether the sweep was hard or soft. We provide suggestions for locating and analysing the response to selective sweeps in whole‐genome data.
UPDATE ON MILK FAT: IDENTIFICATION OF RUMEN BIOHYDROGENATION INTERMEDIATES THAT INHIBIT SYNTHESIS

Fat is the most variable component in milk and is markedly affected by diet. Nutrition affects both the quantity and composition of milk fat and a striking example is milk fat depression (MFD). Diet-induced MFD represents a challenging problem that involves the interrelationship between bacterial fermentation processes in the rumen and the synthesis of milk fat by the mammary glands. The cause of MFD has perplexed scientists for over a century and various theories have been advanced and subsequently found inadequate (Bauman and Griinari, 2003). Of special importance in elucidating the basis for MFD was the observation by Davis and Brown (1970) that increases in the milk fat content of trans 18:1 fatty acids (TFA) coincided with MFD. As analytical techniques improved it became apparent that MFD was correlated with changes in the pattern of TFA and conjugated linoleic acid (CLA) isomers in milk fat.
Surgical and Rehabilitative Treatment of Misdiagnosed Posterior Dislocation of the Shoulder: Case Series

Background: Posterior gleno-humeral joint (GHJ) dislocation is a rare injury accounting for 2.7–3% of all shoulder dislocations. Early detection is the key for an effective treatment. Poor clinical outcomes or prosthetic solutions depend on the severity of the lesions at the time of trauma and worsening of the McLaughlin lesion during the months when the dislocation remains unrecognized or untreated. The aim of the study is to report our experience in treating, and clinically and radiographically evaluate the outcomes of, four neglected posterior GHJ dislocations. Methods: Four patients with inveterate posterior dislocation of the shoulder were evaluated. In three cases, the technique of McLaughlin was performed, and in one case, plication of the subscapularis tendon was performed. During the follow up, patients were clinically evaluated using the Constant shoulder score; an imaging investigation, using X-ray integrated with computed tomography(CT) and magnetic resonance imaging (MRI), was assessed. Results: Following the Constant criteria, treated shoulders reported an excellent outcome with an average score of 91. The mean score of untreated shoulders was 92.5. The humeral appearance that resulted was radiographically well-structured. Surgical treatment with McLaughlin lesion or transposition of the subscapularis were satisfactory from a subjective and objective point of view. To perform surgery soon as possible after the traumatic episode is considered a positive prognostic factor.