Miracle-dz/UniFAR-FTUs huggingface dataset:
Semantic Query for Relational Databases

A vast amount of data in legacy systems is stored in relational databases. For the Semantic Web vision to become a reality, it is necessary to provide ways for representation of these data in the Semantic Web to allow software agents to process it intelligently. This paper presents an approach of semantic query for relational databases by establishing semantic layer. Firstly, this approach starts by extracting schema ontology from relational databases and generating mapping information between them. Secondly, the user issues semantic query against relational databases. Finally the results are wrapped in format of resource description framework. Furthermore, instances conversion is dynamically transformed from relational databases on user’s demand. This approach will enable the proliferation of the Semantic Web applications and simply build an infrastructure for the Semantic Web to benefit from a vast amount of legacy relational databases. This approach has been proven to be effective by case study.
Implementing Augmented Reality in Academic Libraries

Column description. The Technology column examines current and developing technology topics in libraries. The column’s focus is creative uses of technology, introductions to new technologies, and critiques of current technologies, their uses, or their future. Readers interested in contributing ideas or writings to this column may contact column co-editors Suzanne Townsdin and Susan Whitmer. The rising popularity of augmented reality (AR) was punctuated by the Pokémon Go craze of 2016. Individuals of all ages were glued to their phones, attempting to catch creatures that displayed on their phone but were not physically in front of them. There are free and low-cost applications that libraries can use to harness the magic and create their own AR content to enhance library usability and marketing. This column explores the history of AR, explains how it works, and provides some examples of how librarians can use it to market their services and provide additional assistance to library users.
Optimization of Cryptography Algorithms in Cloud Computing

Now a day, Security is becoming a main concern to maintain confidentiality and integrity of the data. For this purpose, cryptography techniques are used. Cryptography is used to encrypt the data into non-readable format and decrypt it again into readable format when needed using specific key. Also for business perspective, cloud computing is very useful. Cloud Computing has become one of the most talked about technologies in recent times and has got lots of attention from media as well as analysts because of the opportunities it is offering. It has different meaning to different uses according to their need. According to a business man, it is used to store and access the data to fulfill the memory requirement and consistency. Also secure data transmission feature of cloud computing plays a very important role in business perspective. For using cloud computing, business man has to pay specific amount of money to the cloud service provider. Cloud service provider guaranty either the confidentiality or integrity of the data. This paper proposes and implements the idea of sending already encrypted file through cloud in spite of the original file using RSA and DES algorithm of cryptography. As encrypted file is transmitted, so original file is not available even at the network. So even if any intermediate user sees the data, he will not be able to understand the data. That’s why confidentiality and integrity is maintained by this. Hence security of cloud data will be increased. This work can be enhanced using hybrid approach by integrating multiple cryptography algorithms.
A Digital Conference Circuit for an Instant Speaker Algorithm

When speech is transmitted over a conventional pulsecode modulation carrier the connection between two subscribers on a digital basis is straightforward. However, techniques for digital conference arrangements are not well known. Implementation of a conference arrangement using traditional analog techniques requires elaborate hardware and use of analog-to-digital converters. This paper, after a brief discussion of existing conference arrangements, describes an instant speaker algorithm and the digital circuitry employed to implement this algorithm. During every time frame an "active speaker" sample is sought. The active speaker is identified by comparing each participant's digital sample during the following time frame. The active speaker sample is transmitted to all participants in the conferencing arrangement. A "last speaker" sample is transmitted to the active speaker. The conference circuit consists of 3 modules: comparison and gating module, active speaker memory module, and last speaker memory module. The implementation of these modules with conventional transistor-transistor logic digital circuits is discussed. Overall arrangement of this conference circuit in a local digital exchange is also explored. Some comments are made concerning "on-off" patterns found in normal conversation and their relationship to the instant speaker algorithm.
MUSE: An Efficient and Accurate Verifiable Privacy-Preserving Multikeyword Text Search over Encrypted Cloud Data

With the development of cloud computing, services outsourcing in clouds has become a popular business model. However, due to the fact that data storage and computing are completely outsourced to the cloud service provider, sensitive data of data owners is exposed, which could bring serious privacy disclosure. In addition, some unexpected events, such as software bugs and hardware failure, could cause incomplete or incorrect results returned from clouds. In this paper, we propose an efficient and accurate verifiable privacy-preserving multikeyword text search over encrypted cloud data based on hierarchical agglomerative clustering, which is named MUSE. In order to improve the efficiency of text searching, we proposed a novel index structure, HAC-tree, which is based on a hierarchical agglomerative clustering method and tends to gather the high-relevance documents in clusters. Based on the HAC-tree, a noncandidate pruning depth-first search algorithm is proposed, which can filter the unqualified subtrees and thus accelerate the search process. The secure inner product algorithm is used to encrypted the HAC-tree index and the query vector. Meanwhile, a completeness verification algorithm is given to verify search results. Experiment results demonstrate that the proposed method outperforms the existing works, DMRS and MRSE-HCI, in efficiency and accuracy, respectively.
Steady State Filters Bank Based Interacting Multiple-Model-Extended Viterbi Algorithm for Maneuvering Target Tracking

The performance of Interacting Multiple-Model (IMM) algorithm depends on model set and filters bank. Most of the improved IMM algorithms originate from designing more reasonable and effective model set structures. One example in case is the Interacting Multiple-Model-Extended Viterbi (IMMEV) algorithm, which can not only inherit the effective cooperation strategies of the IMM algorithm, but also adapt to the outside world like variable structure IMM algorithm by eliminating those mode transition probabilities that are harmful. However, this design approach is difficult to achieve significant tracking performance and substantial complexity reduction, simultaneously. In this paper, the steady state filters bank based Interacting Multiple-Model-Extended Viterbi algorithm for maneuvering target tracking is presented. The main idea of the algorithm is to further improve the IMM-EV algorithm performance and reduce its complexity by designing a new filters bank. The new approach is based on the steady state filters (ab andabg filters) bank using IMM-EV algorithm design instead of a Kalman filters (second and third order filters) bank. Real maneuvering target tracking application experiments demonstrate that the SS-IMM–EV algorithm with some suitable choice of ”m” is superior to the IMM, Fast-IMM and IMM-EV algorithms, which can obtain a considerable tracking performance and reduce complexity markedly.
A power scaleable and low power pipeline ADC using power resettable opamps

A 10-bit pipeline Analog-to-Digital Converter (ADC) is designed such that its average power is scaleable with sampling rate over a large variation of sampling rates. Fabricated in CMOS 0. 18μm technology, while having an area of 1.21mm, the ADC uses a novel fast Power Resettable Opamp (PROamp), to achieve power scalability between sampling rates as high as 50Msps (35mW), and as low as 1ksps (15μW), while having 54-56dB of SNDR (at Nyquist) for all sampling rates. A current modulation technique is used to avoid weakly inverted transistors for low bias currents, thus avoiding less accurate simulation, poorer matching, and increased bias sensitivity. The PROamp due to its short power on/off time also affords reduced power consumption in high speed pipeline ADCs, where opamps can be completely powered off when not required. Measured results show an ADC using PROamps has 2030% less power than an ADC which does not use PROamps.
Inception of Medi Java: An Open Source Library for Medical Science

Background and objectives: Medical Software development process needs valuable time and efforts of the developers and experts of the respective field. As the software developers need to write the code from scratch. So there has to be some medical science library functions in the all frequently used programming languages to facilitate faster and efficient software development process. So an attempt has been made to investigate the existence of any such library functions in the existing programming language and to propose the possibility of such library in Java as Java API. Medi Java (Name proposed by the first author) is expected to cover all important areas of medical science. Medi Java is intended to be an open source project. Methods: As described in method of the library creation starts from generating the UML class diagram where the different specialists of Medical Science and their equations are classified into an hierarchy of classes and interfaces. For library designing, classic life cycle model of SDLC is followed. To design the class diagram of the open source library we used StarUML software. It shows the classes of a system, their interrelationship, the operations and attributes of the classes.JDK 1.7. 1 is a program development environment which is used to code, compile, debug, and test the library written in the java language. The JDK tools used in library are java is the loader, javac is the compiler, and jar is the archiver.The NetBeansIDE is open source and is written in the programming. It provides the services common to creating desktop applications. Results: In Medical Sciences there is no such Open Source Libraries. Medical Software Development can be made faster and cheaper if an Open Source Library can be designed for various fields of medical sciences. So we have studied a total 31 specialties of Medical Science and 400 hundred Medical Equations has been categorized based on the specialty so an attempt has been made to create an Open Source library for medical science. Initially four specializations have been considered i.e. Cardiology, Surgery, Anesthesia and Pulmonology. The main emphasis has been given in Medical Equations and Clinical Criteria of these fields. Then a Unified Modeling Language (UML) class diagram has been designed. Then creating the Interface, Class, Package and jar file. The Open Source Library is made available to the developers by jar file creation. The developers can copied the path of jar file and paste it to the ext folder of jre jdk1.7. 1 of java folder present in the program files. The library can be useful for the design of Softwares in the field of medical science which can be useful for Clinicians, Educators, and Students of Medical Science.
(C, K)m-Anonymity: A Model to Resist Sub-trajectory Linkage Attacks

(k, l)m-anonymity is an effective model to preserve privacy for trajectory data publishing. However, the model cannot prevent sensitive location disclosure on the condition that l is large. To solve the problem, this paper proposes a (c, k)m-anonymity model, which can prevent the disclosure of identity and sensitive location information. We also propose an algorithm to realize (c, k)m-anonymity. Experiments show that the proposed algorithm can generate anonymous trajectories with high data utility.
Isabelle tutorial and user’s manual

This manual describes how to use the theorem prover Isabelle. For beginners, it explains how to perform simple single-step proofs in the built-in logics. These include first-order logic, a classical sequent calculus, zf set theory, Constructive Type Theory, and higher-order logic. Each of these logics is described. The manual then explains how to develop advanced tactics and tacticals and how to derive rules. Finally, it describes how to define new logics within Isabelle. Acknowledgements. Isabelle uses Dave Matthews’s Standard ml compiler, Poly/ml. Philippe de Groote wrote the first version of the logic lk. Funding and equipment were provided by SERC/Alvey grant GR/E0355.7 and ESPRIT BRA grant 3245. Thanks also to Philippe Noel, Brian Monahan, Martin Coen, and Annette Schumann.
Speaker Verification from Short Utterance Perspective: A Review

ABSTRACT Speaker recognition has emerged as an important field over the past several decades and has evolved over time. This work is attempted to investigate speaker verification (SV), particularly focusing on short utterances for the purpose of person authentication. The significance of short utterance-based SV is highlighted from its potential for practical deployment in person authentication-based application. Although the general view of short utterances is towards the text-dependent SV, this work focuses on the scope of text-independent SV using short utterances in relation to develop a robust SV framework in application-oriented scenario. First, it includes brief review of different features, modelling techniques, and classifiers that are widely used in the area of text-independent SV. The work then focuses on highlighting probable directions suitable from the outlook of short utterance-based SV. Three different directions are categorized based on their similar aspects that can be seen as, use of different/complementary features, utilization of acoustic-phonetic information and patten recognition and normalization techniques useful for handling short utterances. We bring the end to this work by bringing out a framework involving the directions discussed into a common platform for having a deployable system using short utterance-based SV.
Acceleration of profile creation for three-dimensional vector video with GPGPU

In the report the optimization of image similarity metric computation method for three dimensional vector video with general-purpose computations on graphical processor unit (GPGPU) is discussed. The use of stream processors in graphics accelerators and Compute Unified Device Architecture (CUDA) platform allows significant performance gain in comparison to calculations on general-purpose processors, while solving problems of computer vision and image similarity determination. The performance of the GPGPU metric value computation is measured and researched.
Evolving communication through the inference of meaning

In this thesis, I address the problem of how successful communication systems can emerge between agents who do not have innate or explicitly transferable meanings, cannot read the minds of their interlocutors, and are not provided with any feedback about the communication process. I develop a solution by focusing on the role of meanings within the framework of language evolution, and on communication through the repeated inference of meaning. Much recent work on the evolution of language has concentrated on the emergence of compositional syntax as the crucial event which marked the genesis of language; all the experimental models which purport to demonstrate the emergence of syntax, however, rely on models of communication in which the signals are redundant and which contain pre-defined, structured meaning systems which provide an explicit blueprint against which the syntactic structure is built. Moreover, the vast majority of such meaning systems are truly semantic in name only, lacking even the basic semantic characteristics of sense and reference, and the agents must rely on mind-reading or feedback (or both) in order to learn how to communicate. By contrast, at the heart of this thesis is a solution to the signal redundancy paradox based on the inference of meaning and the disambiguation of potential referents through exposure in multiple contexts. I describe computational models of meaning creation in which agents independently develop individual conceptual structures based on their own experiences of the environment, and show through experimental simulations that the agents can use their own individual meanings to communicate with each other about items in their environment. I demonstrate that the development of successful communication depends to a large extent on the synchronisation of the agents’ conceptual structures, and that such synchronisation is significantly more likely to occur when the agents use an intelligent meaning creation strategy which can exploit the structure in the information in the environment.
An Object-Oriented Approach to Counter-Model Constructions in A Fragment of Natural Language

This article presents a technical construction of reasonings and counter-models for some sen-tenteces called fragments as in [10] in English. We bring speaking English and logical inferences together in computer based approach to natural language. We give not only the infrences in the language [7] but also counter-model constructions in case of no inference from input sentences. Approach of this construction considers usage of minimal set elements.
The Method of a Two-level Text-meaning Similarity Approximation of the Customers' Opinions

Summary: The method of two-level text-meaning similarity approximation, consisting in the implementation of the classification of the stages of text opinions of customers and identifying their rank quality level was developed. Proposed and proved the significance of major hypotheses, put as the basis of the developed methodology, notably about the significance of suggestions about the existence of analogies between mathematical bases of the theory of Latent Semantic Analysis, based on the analysis of semantic relationship between the variables and degree of participation of the document or term in the corresponding concept of the document data, and instruments of the theory of Social Network Analysis, directed at revealing the features of objects on the basis of information about structure and strength of their interaction. The Contextual Cluster Structure, as well as Quantitative Ranking evaluation for interpreting the quality level of estimated customers’ opinion has formed.
A supply-noise-insensitive CMOS PLL with a voltage regulator using DC-DC capacitive converter

A 500-MHz supply-noise-insensitive CMOS phase-locked loop (PLL) with a voltage regulator using a capacitive dc-dc converter (VRCC) achieves a jitter level of 30-ps RMS for quiet supply, and 42-ps RMS for 600-mV supply noise, with a locking range of 110 to 850 MHz. The worst-case power supply noise rejection (PSNR) using the VRCC shows -45 dB in the mid-frequency band. The circuit is fabricated in a 0.35-/spl mu/m 3.3-V standard digital CMOS process and occupies 2.3 mm/sup 2/. The power consumption at 3.3 V including buffer is 42 mW at 500 MHz.
Underwater detection by using ultrasonic sensor

This paper described the low cost implementation of hardware and software in developing the system of ultrasonic which can visualize the feedback of sound in the form of measured distance through mobile phone and monitoring the frequency of detection by using real time graph of Java application. A single waterproof transducer of JSN-SR04T had been used to determine the distance of an object based on operation of the classic pulse echo detection method underwater. In this experiment, the system was tested by placing the housing which consisted of Arduino UNO, Bluetooth module of HC-06, ultrasonic sensor and LEDs at the top of the box and the transducer was immersed in the water. The system which had been tested for detection in vertical form was found to be capable of reporting through the use of colored LEDs as indicator to the relative proximity of object distance underwater form the sensor. As a conclusion, the system can detect the presence of an object underwater within the range of ultrasonic sensor and display the measured distance onto the mobile phone and the real time graph had been successfully generated.This paper described the low cost implementation of hardware and software in developing the system of ultrasonic which can visualize the feedback of sound in the form of measured distance through mobile phone and monitoring the frequency of detection by using real time graph of Java application. A single waterproof transducer of JSN-SR04T had been used to determine the distance of an object based on operation of the classic pulse echo detection method underwater. In this experiment, the system was tested by placing the housing which consisted of Arduino UNO, Bluetooth module of HC-06, ultrasonic sensor and LEDs at the top of the box and the transducer was immersed in the water. The system which had been tested for detection in vertical form was found to be capable of reporting through the use of colored LEDs as indicator to the relative proximity of object distance underwater form the sensor. As a conclusion, the system can detect the presence of an object underwater within the range of ultrasonic sensor .
Prediction of Stock Market Price using Hybrid of Wavelet Transform and Artificial Neural Network

Background/Objectives: Accurate prediction of stock market is highly challenging. This paper presents a forecasting model based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) for predicting financial time series. Methods/Statistical analysis: The idea of forecasting stock market prices with discrete wavelet transform is the central element of this paper. The proposed forecasting model uses the Discrete Wavelet Transform to decompose the financial time series data. The obtained approximation and detail coefficients after decomposition of the original time series data are used as input variables of back propagation neural network to forecast future stock prices. Approximation coefficients can characterize the coarse structure of the data and detail coefficients capture ruptures, discontinuities and singularities in the original data, to recognize the long-term trends in the original data. Findings: The proposed model was applied to five datasets. For all of the datasets, accuracy measures showed that the presented model outperforms a conventional model. It also proved that the hybrid forecasting technique has achieved better results compared with the approach which is not using the wavelet transform. Applications/Improvements: The accuracy of the proposed hybrid method can also be improved by developing a model using artificial neural network with Adaptive Neuro Fuzzy Interference System.