scholarly journals Ordering of Huge Biometric Information in Database System

2021 ◽  
Vol 2 (2) ◽  
pp. 1-19
Author(s):  
Varisha Alam ◽  
Keyword(s):  
Information Retrieval ◽  
Database System ◽  
Error Rates ◽  
Behavioral Characteristics ◽  
Access Management ◽  
Retrieval Time ◽  
Biological Features ◽  
Reduced Rate ◽  
Improved Accuracy ◽  
Biometric Information

The word biometrics is derived from the Greek words 'bios' and 'metric' which means living and calculation appropriately. Biometrics is the electronic identification of individuals based on their physiological and biological features. Biometric attributes are data take out from biometric test which can be used for contrast with a biometric testimonial. Biometrics composed methods for incomparable concede humans based upon one or more inherent material or behavioral characteristics. In Computer Science, bio-metrics is employed as a kind of recognition access management and access command. Biometrics has quickly seemed like an auspicious technology for attestation and has already found a place in the most sophisticated security areas. A systematic clustering technique has been there for partitioning huge biometric databases throughout recognition. As we tend to are still obtaining the higher bin-miss rate, so this work is predicated on conceiving an ordering strategy for recognition of huge biometric database and with larger precision. This technique is based on the modified B+ tree that decreases the disk accesses. It reduced the information retrieval time and feasible error rates. The ordering technique is employed to proclaims a person’s identity with a reduced rate of differentiation instead of searching the whole database. The response time degenerates, further-more because the accuracy of the system deteriorates as the size of the database increases. Hence, for vast applications, the requirement to reduce the database to a little fragment seems to attain higher speeds and improved accuracy.

scholarly journals Efficient End-to-End Sentence-Level Lipreading with Temporal Convolutional Networks

2021 ◽  
Vol 11 (15) ◽  
pp. 6975
Author(s):  
Tao Zhang ◽  
Lun He ◽  
Xudong Li ◽  
Guoqing Feng
Keyword(s):  
Performance Improvement ◽  
State Of The Art ◽  
Error Rates ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Sentence Level ◽  
End To End ◽  
High Level ◽  
Improved Accuracy ◽  
Talking Face

Lipreading aims to recognize sentences being spoken by a talking face. In recent years, the lipreading method has achieved a high level of accuracy on large datasets and made breakthrough progress. However, lipreading is still far from being solved, and existing methods tend to have high error rates on the wild data and have the defects of disappearing training gradient and slow convergence. To overcome these problems, we proposed an efficient end-to-end sentence-level lipreading model, using an encoder based on a 3D convolutional network, ResNet50, Temporal Convolutional Network (TCN), and a CTC objective function as the decoder. More importantly, the proposed architecture incorporates TCN as a feature learner to decode feature. It can partly eliminate the defects of RNN (LSTM, GRU) gradient disappearance and insufficient performance, and this yields notable performance improvement as well as faster convergence. Experiments show that the training and convergence speed are 50% faster than the state-of-the-art method, and improved accuracy by 2.4% on the GRID dataset.

Aspects of Intelligence in an "SP" Database System

2009 ◽  
pp. 725-754
Author(s):  
J. Gerard Wolff
Keyword(s):  
Pattern Recognition ◽  
Information Retrieval ◽  
Natural Language ◽  
Unsupervised Learning ◽  
Probabilistic Reasoning ◽  
Database System ◽  
Theory Of Computing ◽  
New Knowledge ◽  
Database Models

This chapter describes some of the kinds of “intelligence” that may be exhibited by an intelligent database system based on the SP theory of computing and cognition. The chapter complements an earlier paper on the SP theory as the basis for an intelligent database system (Wolff, forthcoming b) but it does not depend on a reading of that earlier paper. The chapter introduces the SP theory and its main attractions as the basis for an intelligent database system: that it uses a simple but versatile format for diverse kinds of knowledge, that it integrates and simplifies a range of AI functions, and that it supports established database models when that is required. Then with examples and discussion, the chapter illustrates aspects of “intelligence” in the system: pattern recognition and information retrieval, several forms of probabilistic reasoning, the analysis and production of natural language, and the unsupervised learning of new knowledge.

Primate Antisaccades. I. Behavioral Characteristics

1998 ◽  
Vol 80 (4) ◽  
pp. 1775-1786 ◽  
Author(s):  
Nelly Amador ◽  
Madeleine Schlag-Rey ◽  
John Schlag
Keyword(s):  
Human Performance ◽  
Error Rates ◽  
Behavioral Characteristics ◽  
Peripheral Stimulus ◽  
Primate Model ◽  
The Past ◽  
Frontal Lobe Lesions ◽  
Long Latency ◽  
Saccade Task ◽  
Central Fixation

Amador, Nelly, Madeleine Schlag-Rey, and John Schlag. Primate antisaccades. I. Behavioral characteristics. J. Neurophysiol. 80: 1775–1786, 1998. The antisaccade task requires a subject to make a saccade to an unmarked location opposite to a flashed stimulus. This task was originally designed to study saccades made to a goal specified by instructions. Interest for this paradigm surged after the discovery that frontal lobe lesions specifically and severely affect human performance of antisaccades while prosaccades (i.e., saccades directed to the visual stimulus) are facilitated. Training monkeys to perform antisaccades was rarely attempted in the past, and this study is the first one that describes in detail the properties of such antisaccades compared with randomly intermingled prosaccades of varying amplitude in all directions. Such randomization was found essential to force the monkeys to use the instruction cue (pro- or anti-) and the location cue (peripheral stimulus) provided within a trial rather than to direct their saccades to the location of past rewards. Each trial began with the onset of a central fixation target that conveyed by its shape the instruction to make a pro- or an antisaccade to a subsequent peripheral stimulus. In one version of the task, the monkey was allowed to make an immediate saccade to the goal; in a second version, the saccade had to wait for a go signal. Analyses of the accuracy, velocity, and latency of antisaccades compared with prosaccades were performed on a sample of 7,430 pro-/antisaccades in the “immediate saccade” task (delayed saccades suffering from known distortions). Error rates fluctuated ∼25%. Results were the same for the two monkeys with respect to accuracy and velocity, but they differed in terms of reaction time. Both monkeys generated antisaccades to stimuli in all directions, at various eccentricities, but antisaccades were significantly less accurate and slower than prosaccades elicited by the same stimuli. Interestingly, saccades to the stimulus could be followed by appropriate antisaccades with no intersaccadic interval. Such instances are here referred to as “turnaround saccades.” Because they occurred sometimes with a long latency, turnaround saccades did not simply reflect the cancellation of an early foveation reflex. Consistent with human data, latencies of one monkey were longer for antisaccades than for prosaccades, but the reverse was true for the other monkey who was trained differently. In summary, this study demonstrates the feasibility of providing a subhuman primate model of antisaccade performance, but at the same time it suggests some irreducible differences between human and monkey performance.

Aspects of Intelligence in an "SP" Database System

2011 ◽  
pp. 197-237
Author(s):  
J. Gerard Wolff
Keyword(s):  
Pattern Recognition ◽  
Information Retrieval ◽  
Natural Language ◽  
Unsupervised Learning ◽  
Probabilistic Reasoning ◽  
Database System ◽  
Theory Of Computing ◽  
New Knowledge ◽  
Database Models

This chapter describes some of the kinds of “intelligence” that may be exhibited by an intelligent database system based on the SP theory of computing and cognition. The chapter complements an earlier paper on the SP theory as the basis for an intelligent database system (Wolff, forthcoming b) but it does not depend on a reading of that earlier paper. The chapter introduces the SP theory and its main attractions as the basis for an intelligent database system: that it uses a simple but versatile format for diverse kinds of knowledge, that it integrates and simplifies a range of AI functions, and that it supports established database models when that is required. Then with examples and discussion, the chapter illustrates aspects of “intelligence” in the system: pattern recognition and information retrieval, several forms of probabilistic reasoning, the analysis and production of natural language, and the unsupervised learning of new knowledge.

The Comparative Benefits and Hazards of EFBs and Paper Documents in the Cockpit

2017 ◽  
Vol 61 (1) ◽  
pp. 99-100
Author(s):  
John Sweet ◽  
Kim-Phuong L. Vu ◽  
Vernol Battiste ◽  
Thomas Z. Strybel
Keyword(s):  
Information Retrieval ◽  
Human Factors ◽  
Error Rates ◽  
Categorical Variables ◽  
Simulation Studies ◽  
Access To Information ◽  
Flight Planning ◽  
Flight Operations ◽  
Outcome Function

Electronic Flight Bags (EFBs) are replacing the traditional paper documents used by pilots for pre-flight planning and in-flight operations. Simulation studies comparing information retrieval times and error rates for EFBs and paper documents have found that pilots are faster and perform better with EFBs, but it is unclear whether this is true for all EFB systems. This study compared reports from the ASRS across categorical variables such as Human Factors Issue, Outcome, Function in Use, Operating Regulations and Phase of Flight. The most significant human factors issues relating to EFBs were lack of training, distractions/workload, and inhibited access to information. Crewmembers consistently ran into difficulty with the zooming/panning feature of EFBs, especially on multi-touch touchscreen displays.

scholarly journals Multibiometrics System Design Based on Feature Level Fusion

2019 ◽  
Vol 9 (1) ◽  
pp. 4127-4132
Keyword(s):  
Hamming Distance ◽  
Research Work ◽  
Recognition Rate ◽  
Error Rates ◽  
Discrete Wavelet ◽  
Biometric Data ◽  
Multimodal System ◽  
Extraction Algorithm ◽  
Authentication System ◽  
Biometric Information

An efficient multimodal biometric system which combines biometric data originated from face, iris and signature biometrics has been presented. Proposed feature extraction algorithm for unimodal and multimodal system has been based on discrete wavelet transform. Among the various biometrics face and iris based human authentication system are proved reliable and efficient. Signature as a behavioral biometrics is very important in financial transaction. Signature has highest variability among all biometrics. This research work proposes an approach to combine signature biometrics with face and iris biometric. Proposed method fuses biometric information originated from face, iris and signature at feature level. Hamming distance based classifier has been used for classifying feature vector as a genuine or imposter. Proposed multibiometrics system has been evaluated on chimeric databases. It has been shown by the reported results that proposed multimodal system outperforms unimodal system performance. Proposed system has been analyzed for recognition rates and error rates. Performance of proposed multimodal system shows improvement in recognition rate and reduction in error

Vividness of Image and Retrieval Time

1991 ◽  
Vol 73 (1) ◽  
pp. 115-123 ◽  
Author(s):  
Shinsuke Hishitani
Keyword(s):  
Information Retrieval ◽  
The Other ◽  
Construction Time ◽  
Retrieval Time ◽  
Image Construction ◽  
Verbal Rehearsal ◽  
Presentation Position ◽  
Image Set ◽  
The Relationship ◽  
Rehearsal Condition

The relationship between vividness of image and information-retrieval time was examined. In an integrated-image condition the retrieval time for a vivid-image set was shorter than the retrieval time for a dim-image set; on the other hand, there was no difference in retrieval time between vivid- and dim-image sets in a verbal-rehearsal condition. Since the retrieval time difference between the two sets in the integrated-image group was not an artifact of image-construction time or presentation position, it was concluded that vividness of image affected information-retrieval time in the integrated-image group. The results were explained in terms of a picture-metaphor hypothesis of imagery.

scholarly journals Classification of Large Biometric Data in Database System

2021 ◽  
Vol 10 (10) ◽  
pp. 1-8
Author(s):  
Varisha Alam* ◽  
◽  
Dr. Mohammad Arif ◽  
Keyword(s):  
Recovery Time ◽  
Database System ◽  
Error Rates ◽  
Small Data ◽  
Biometric Data ◽  
Data Set ◽  
Main Motivation ◽  
Information Recovery ◽  
Life Estimation

"Biometrics" is got from the Greek word 'life' and 'measure' which implies living and evaluation take apart. It simply converts into "life estimation". Biometrics uses computerized acknowledgment of people, dependent on their social and natural attributes. Biometric character are data separated from biometric tests, which can use for examination with a biometric orientation. Biometrics involves techniques to unusually recognize people dependent on at least one inherent physical or behavior attribute. In software engineering, specifically, biometric is used as a form of character retrieve the Committee and retrieve command. Biometric identically utilized to recognize people in bunches that are in observation. Biometric has quickly risen like a auspicious innovation for validation and has effectively discovered a spot in most of the scientific safety regions. An effective bunching method suggest for dividing enormous biometrics data set through recognizable proof. This method depends on the changed B+ tree is decreasing the discs get to. It diminishes the information recovery time and also possible error rates. Hence, for bigger applications, the need to reduce the data set to a more adequate portion emerges to accomplish both higher paces and further developed precision. The main motivation behind ordering is to recover a small data set for looking through the inquiry

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