Miracle-dz/UniFAR-FTUs huggingface dataset:
SQL The Complete Reference, 3rd Edition

The Definitive Guide to SQL Get comprehensive coverage of every aspect of SQL from three leading industry experts. Revised with coverage of the latest RDBMS software versions, this one-stop guide explains how to build, populate, and administer high-performance databases and develop robust SQL-based applications. SQL: The Complete Reference, Third Edition shows you how to work with SQL commands and statements, set up relational databases, load and modify database objects, perform powerful queries, tune performance, and implement reliable security policies. Learn how to employ DDL statements and APIs, integrate XML and Java scripts, use SQL objects, build web servers, handle remote access, and perform distributed transactions. Techniques for managing in-memory, stream, and embedded databases that run on today's mobile, handheld, and wireless devices are included in this in-depth volume. Build SQL-based relational databases and applications Create, load, and modify database objects using SQL Construct and execute simple, multitable, and summary queries Implement security measures with authentication, privileges, roles, and views Handle database optimization, backup, recovery, and replication Work with stored procedures, functions, extensions, triggers, and objects Extend functionality using APIs, dynamic SQL, and embedded SQL Explore advanced topics such as DBMS transactions, locking mechanisms, materialized views, and two-phase commit protocol Understand the latest market trends and the future of SQL Table of contents Part I: An Overview of SQL Chapter 1. Introduction Chapter 2. A Quick Tour of SQL Chapter 3. SQL in Perspective Chapter 4. Relational Databases Part II: Retrieving Data Chapter 5. SQL Basics Chapter 6. Simple Queries Chapter 7. Multitable Queries (Joins) Chapter 8. Summary Queries Chapter 9. Subqueries and Query Expressions Part III: Updating Data Chapter 10. Database Updates Chapter 11. Data Integrity Chapter 12. Transaction Processing Part IV: Database Structure Chapter 13. Creating a Database Chapter 14. Views Chapter 15. SQL Security Chapter 16. The System Catalog Part V: Programming with SQL Chapter 17. Embedded SQL Chapter 18. Dynamic SQL* Chapter 19. SQL APIs Part VI: SQL Today and Tomorrow Chapter 20. Database Processing and Stored Procedural SQL Chapter 21. SQL and Data Warehousing Chapter 22. SQL and Application Servers Chapter 23. SQL Networking and Distributed Databases Chapter 24. SQL and Objects Chapter 25. SQL and XML Chapter 26. Specialty Databases Chapter 27. The Future of SQL Part VII: Appendixes Appendix A. The Sample Database Appendix B. DBMS Vendor Profiles Appendix C. SQL Syntax Reference Index
Control of thermo-acoustic instabilities using pulse width modulation

An experimental study of a closed-loop control system to suppress thermo-acoustic combustion instabilities is described. The control mechanism relies on out-of-phase heat addition by means of an auxiliary fuel injector. Pulse width modulation is used to precisely control the amplitude and phase of the auxiliary fuel pulses. The controller was successfully implemented in a numerical simulation of a dynamic combustion instability and experimentally.
Unsupervised Pretraining for Neural Machine Translation Using Elastic Weight Consolidation

This work presents our ongoing research of unsupervised pretraining in neural machine translation (NMT). In our method, we initialize the weights of the encoder and decoder with two language models that are trained with monolingual data and then fine-tune the model on parallel data using Elastic Weight Consolidation (EWC) to avoid forgetting of the original language modeling task. We compare the regularization by EWC with the previous work that focuses on regularization by language modeling objectives. The positive result is that using EWC with the decoder achieves BLEU scores similar to the previous work. However, the model converges 2-3 times faster and does not require the original unlabeled training data during the fine-tuning stage. In contrast, the regularization using EWC is less effective if the original and new tasks are not closely related. We show that initializing the bidirectional NMT encoder with a left-to-right language model and forcing the model to remember the original left-to-right language modeling task limits the learning capacity of the encoder for the whole bidirectional context.
Toward a better understanding of deblurring digital images in computer forensics

Captured images are believed to be the degraded versions of the original scene. A variety of degradations may affect recorded images such as noise, imperfect illumination and blur. This paper highlights the issue of image deblurring in computer forensic since the blur hides vital information that is indeed present in the captured image. Furthermore, the types of blur that are constantly being dealt with in forensics are the motion and out-of-focus blur. The motion blur happens mainly due to the motion of the recorded object or the camera motion when capturing any desired scene. The out-of-focus blur occurs mainly due to lens defocusing. Diverse deblurring examples are provided to highlight the significance of deblurring in the forensic field. Moreover, brief explanations about the deblurring methods and the blur types will be deliberated to deliver further deblurring knowledge. Keywords-Image deblurring; Computer forensics; Image degradations, Motion blur, Out-of-focus blur, Deblurring software.
A new approach of ranking prevalent news topics from social media using unsupervised techniques

The precious data from online origin has developed into a extended research. The mass media and news media provides the daily events to the common people. Huge amount of information is been achieved by an online social media suchlike Twitter, which contains more information about news-associated content. It is necessary to find a way to filter noise, for these resources to be useful and grab the content that is depend on the similarity to news media. Despite after the noise is eliminated the excessive data still remain in the data so it is essential to prioritize it for utilization. We are introducing three factors for prioritization. The unsupervised technique finds the news topics that are common in the pair of social media and news media, and then ranks them by the applicability factors such as MF, UA and UI. Initially the temporal prevalence of the appropriate topic in news media focus (MF). Secondary the temporal prevalence of the appropriate topic in social media illustrates the user attention (UA). Finally the interconnection among the social media users who specify this topic demonstrates the power of the society who is discussing; it is termed as the user interaction (UI).
A Novel Detection and Prevention (DAP) Framework for Abuse of cloud service threat

Cloud security mitigation techniques has not reached the level of transparency and hence, the journey of understanding and accepting cloud computing services still remains uncharted for many organizations or individuals. While mitigation techniques for different security issues are actively being researched, however, there is still insignificancy in research on Abuse of Cloud Services threat, in particular. The threat has been identified as one of the top amongst nine by Cloud Security Alliance (CSA). The study proposes a Detection and Prevention (DAP) framework with fourteen major security measures for significant areas of concern in cloud business, operational and organizational. The key benefits of the framework are: it has improved the process of registration, resource allocation and asset inventory system. It has modeled an improvised human resource system for managing information processing facilities more efficiently, along with employee inventory system and malicious insider activity index. A universal cloud APIs process has been proposed to act as an integrator and decode service providers APIs to run business seamlessly. Furthermore, strategic plan for business continuity, disaster recovery and risk assessment processes have been proposed to define policies and procedures for running business during adverse circumstances. The security measures will be validated and evaluated with existing security infrastructure in cloud. The DAP framework is fully capable of providing better security preservation to the identified threat, which could be utilized for further development in cloud security infrastructure.
Adaptive Robust Control for Trajectory Tracking of Autonomous underwater Vehicles on Horizontal Plane

This manuscript addresses trajectory tracking problem of autonomous underwater vehicles (AUVs) on the horizontal plane. Adaptive sliding mode control is employed in order to achieve a robust behavior against some uncertainty and ocean current disturbances, assuming that disturbance and its derivative are bounded by unknown boundary levels. The proposed approach is based on a dual layer adaptive law, which is independent upon the knowledge of disturbance boundary limit and its derivative. The approach tends to play a significant role to reduce the chattering effect which is prevalent in conventional sliding mode controllers. To guarantee the stability of the proposed control technique, the Lyapunov theory is used. Simulation results illustrate the validity of the proposed control scheme compared to the finite-time tracking control method.
UTILIZING UNIVERSITY AND STUDENT DIGITAL DEVICES FOR TEACHING AND LEARNING

This study is two-fold. First, students constantly use digital devices during class. Learning how to coordinate university assignments with digital devices used by students is essential. Second, what do professors need to change in teaching methodology to better engage the students with meaningful experiences while using digital devices brought to class? The idea was to use other methods of teaching/facilitating where students could utilize the digital devices in class. Digital device usage in the classroom is a method tried for two freshmen level courses.
EXtensible Animator for Mobile Simulations: EXAMS

One of the most widely used simulation environments for mobile wireless networks is the Network Simulator 2 (NS-2). However NS-2 stores its outcome in a text file, so there is a need for a visualization tool to animate the simulation of the wireless network. The purpose of this tool is to help the researcher examine in detail how the wireless protocol works both on a network and a node basis. It is clear that much of this information is protocol dependent and cannot be depicted properly by a general purpose animation process. Existing animation tools do not provide this level of information neither permit the specific protocol to control the animation at all. EXAMS is an NS-2 visualization tool for mobile simulations which makes possible the portrayal of NS-2 internal information like transmission properties and node data structures. This is mainly possible due to EXAMS extensible architecture which separates the animation process into a general and a protocol specific part. The latter can be developed independently by the protocol designer and loaded on demand. These and other useful characteristics of the EXAMS tool can be an invaluable help for a researcher in order to investigate and debug a mobile networking protocol.
Optimizing OpenCL Local Work Group Size With Machine Learning

GPU architectures are becoming increasingly important due to their high number of processors. The single input multiple data architecture has proven to work not just for the graphics domain, but also for many other disciplines. This is due to the potential performance that can be achieved by a consumer-level GPU being significantly higher than the CPU counterpart. High level abstractions, such as OpenCL and CUDA, have been introduced to separate the hardware from the software, allowing programmers to develop for a variety of GPUs. However, the programmer needs highly-specialized knowledge of the underlying architecture in order to get the optimal performance of a system. An important part is splitting the workload up into manageable chunks, known as the work group size in OpenCL, that can be distributed across the cores of a GPU. However, due to memory access time and computational complexity of a GPU program, this task is often optimized on a trial-and-error basis. State of the art analytical models and tools can be used to choose the optimal work group size parameter with accuracy less than 30%. We propose a solution using predictive modelling from the domain of machine learning that can choose the optimal parameter of splitting the work between GPU cores a maximum of 86% of the time. OpenCL was the GPGPU framework that was used.
Healthchain: A Blockchain-Based Privacy Preserving Scheme for Large-Scale Health Data

With the dramatically increasing deployment of the Internet of Things (IoT), remote monitoring of health data to achieve intelligent healthcare has received great attention recently. However, due to the limited computing power and storage capacity of IoT devices, users’ health data are generally stored in a centralized third party, such as the hospital database or cloud, and make users lose control of their health data, which can easily result in privacy leakage and single-point bottleneck. In this paper, we propose Healthchain, a large-scale health data privacy preserving scheme based on blockchain technology, where health data are encrypted to conduct fine-grained access control. Specifically, users can effectively revoke or add authorized doctors by leveraging user transactions for key management. Furthermore, by introducing Healthchain, both IoT data and doctor diagnosis cannot be deleted or tampered with so as to avoid medical disputes. Security analysis and experimental results show that the proposed Healthchain is applicable for smart healthcare system.
Complex and Adaptive Dynamical Systems: A Primer

We are living in an ever more complex world, an epoch where human actions can accordingly acquire far-reaching potentialities. Complex and adaptive dynamical systems are ubiquitous in the world surrounding us and require us to adapt to new realities and the way of dealing with them. This primer has been developed with the aim of conveying a wide range of "commons-sense" knowledge in the field of quantitative complex system science at an introductory level, providing an entry point to this both fascinating and vitally important subject. The approach is modular and phenomenology driven. Examples of emerging phenomena of generic importance treated in this book are: -The small world phenomenon in social and scale-free networks; -Phase transitions and self-organized criticality in adaptive systems; -Life at the edge of chaos and coevolutionary avalanches resulting from the unfolding of all living; -The concept of living dynamical systems and emotional diffusive control within cognitive system theory. Technical course prerequisites are a basic knowledge of ordinary and partial differential equations and of statistics. Each chapter comes with exercises and suggestions for further reading - solutions to the exercises are also provided.
Temporally enhanced erasure codes for real-time communication protocols

A new technique, named as temporally enhanced erasure codes (TEEC), is developed as an enhancement to the existing forward error correction (FEC) technique. In TEEC the scopes of encoding/decoding are expanded beyond block boundaries, and they may overlap with scopes of neighboring blocks. To show its advantages, a real-time protocol was implemented using the TEEC technique. In terms of bandwidth requirements and end-to-end delays for packet delivery, it is shown that the protocol employing TEEC outperforms block-by-block-based protocols.
Educational bandwidth traffic prediction using non-linear autoregressive neural networks

Time series network traffic analysis and forecasting are important for fundamental to many decision-making processes, also to understand network performance, reliability and security, as well as to identify potential problems. This paper provides the latest work at London South Bank University (LSBU) network data traffic analysis by adapting nonlinear autoregressive exogenous model (NARX) based on the Levenberg-Marquardt backpropagation algorithm. This technique can analyze and predict data usage in its current and future states, as well as visualise the hourly, daily, weekly, monthly, and quarterly activities with less computation requirement. Results and analysis proved the accuracy of the prediction techniques.
A Review on Recent Statistical Models for RNA-Seq Data

The next generation sequencing technology, RNA-sequencing (RNA-seq), has got growing acceptance in transcriptome analyses. Statistical methods used for gene expression analyses with RNAseq provide meaningful inferences of gene expression using counts of reads. There are various statistical models with its pros and cons available for RNA-seq data analysis. There is a need for consistent statistical methods to explore the information from the developing sequencing technologies. The current article gives a review of the statistical methods with their limitations that can be useful for the RNA-seq analysis. The main emphasis is given to the parametric, nonparametric and hybrid models for identifying the genes with differential expression.
Social Recommendation with Optimal Limited Attention

Social recommendation has been playing an important role in suggesting items to users through utilizing information from social connections. However, most existing approaches do not consider the attention factor causing the constraint that people can only accept a limited amount of information due to the limited strength of mind, which has been discovered as an intrinsic physiological property of human by social science. We address this issue by resorting to the concept of limited attention in social science and combining it with machine learning techniques in an elegant way. When introducing the idea of limited attention into social recommendation, two challenges that fail to be solved by existing methods appear: i) how to develop a mathematical model which can optimally choose a subset of friends for each user such that these friends' preferences can best influence the target user, and ii) how can the model learn an optimal attention for each of these selected friends. To tackle these challenges, we first propose to formulate the problem of optimal limited attention in social recommendation. We then develop a novel algorithm through employing an EM-style strategy to jointly optimize users' latent preferences, optimal number of their best influential friends and the corresponding attentions. We also give a rigorous proof to guarantee the algorithm's optimality. The proposed model is capable of efficiently finding an optimal number of friends whose preferences have the best impact on target user as well as adaptively learning an optimal personalized attention towards every selected friend w.r. t. the best recommendation accuracy. Extensive experiments on real-world datasets demonstrate the superiority of our proposed model over several state-of-the-art algorithms.
The Language of Financial Reports and News

Nowadays there is a huge volume of financial information available on the web. There is also considerable debate about the impact, if any, that this information has on share prices and overall market sentiment. Fundamental and technical analysts believe that there is some value in financial information and that it can be exploited for predictive purposes. This article provides a commentary on various studies that involved the analysis of financial content for various goals using various methods. This article includes a discussion of their main findings and recurrent themes and concludes with some possible future directions for research.
PERILAKU PETANI PADI ORGANIK TERHADAP RISIKO DI KECAMATAN SUSUKAN KABUPATEN SEMARANG

Desa ketapang terletak di Kecamatan Susukan Kabupaten Semarang. Petani di desa ini sebagian besar tergabung dalam Paguyuban Petani Al- Barokah yang menerapkan sistem pertanian padi organik. Usahatani padi organik di desa Ketapang, Kabupaten Semarang telah memberikan kesadaran akan pentingnyapertanian organik. Situasi ketadakpastian selalu terjadi dalam bidang pertanian, sehingga berakibat pada hasil yang tidak pasti pula. Fluktuasi hasil pertanian (produksi) dan fluktuasi harga yang menjadi sumber ketidakpastian di sektor pertanian. (Ningsih, 2013). Penelitian ini bertujuan untuk menganalisis perilakupetani terhadap risiko usahatani padi organik dan mengetahui faktor-faktor yang mempengaruhi perilaku petani terhadap risiko di Kecamatan Susukan Kabupaten Semarang. Jumlah responden yang digunakan dalam penelitian ini 76 petani padi organik. Penelitian ini menggunakan metode survei. Pemilihan lokasi penelitian secara purposive sampling dan pengambilan sampel dilakukan melalui simple random sampling. Metode analisis perilaku petani terhadap risiko menggunakan pendekatan fungsi produksi cobb Doughlas kemudian dianalisis dengan regresi linear berganda dan untuk mengukur perilaku petani terhadap risiko menggunakan metode Moscardy dan de Janvry. Analisis linear berganda digunakan untuk mengatahui faktor –faktor yang mempengaruhi perilaku petani terhadap risiko. Penelitian ini penting dilakukan agar petani melakukan pertimbangan dalam pengelolaan dan perencanan usahatani padi organik dimasa datang serta bagi instansi sebagai masukan dalam rangka kebijakan peningkatan produksi padi organik dan mengurangi risiko usahatani padi organik. Hasil penelitian menunjukan bahwa petani padi organik di Desa Ketapang Kecamatan susukan mayoritas 76 petani (100%) menghindari risiko atau menolak risiko (risk averter. Luas lahan dan pendapatan petani signifikan mempengaruhi perilaku petani terhadap risiko.
Performance evaluation of Max-Min Ant System Algorithm for Robot Path Planning in Grid Environment

Path planning is an essential task for the robot to navigate and control its motion in any environment. The optimal path needs to be rerouted each time a new obstacle appears in front of the robot in the dynamic environment. This research focuses on the MAX-MIN Ant System Algorithm(MMAS) which is an Ant Colony Algorithm derived from Ant System(AS) and is different from it in terms of the pheromone deposition. The effectiveness of this algorithm to obtain a near optimal solution is illustrated by the means of experimental study. Using a greedier search than the Ant System algorithm is one of the specific characteristics of the MMAS, which will be studied in the research. The robot environment model is represented by a grid which has obstacles whose positions change in each map that is used. Local search routines and diversification mechanisms introduced by the previous researchers are used to enhance the performance of the MMAS algorithm. To implement the MMAS algorithm used in our research, the experiments are performed in MATLAB development environment where a simulation program is designed, and the algorithm is implemented in grid maps of sizes starting from the smallest grid 10x10 to the grid of size 400x400. We implemented and analyzed the performance of the algorithm in larger grid environments to understand how it would perform when the search space is too huge; which would enable researchers to use the MMAS algorithm in experiments involving real life environments. In our experiments a new obstacle is added after every iteration of the algorithm which makes it challenging for the robots to find the near optimal path. The performance evaluation of the MMAS algorithm is studied and is also compared to that of the ACO algorithm when implemented in differently sized grid maps.