Miracle-dz/UniFAR-FTUs huggingface dataset:
Handbook of Research on Organizational Transformations through Big Data Analytics

Big data analytics utilizes a wide range of software and analytical tools to provide immediate, relevant information for efficient decision-making. Companies are recognizing the immense potential of BDA, but ensuring the data is appropriate and error-free is the largest hurdle in implementing BDA applications. The Handbook of Research on Organizational Transformations through Big Data Analytics not only catalogues the existing platforms and technologies, it explores new trends within the field of big data analytics (BDA). Containing new and existing research materials and insights on the various approaches to BDA; this publication is intended for researchers, IT professionals, and CIOs interested in the best ways to implement BDA applications and technologies. Market: This premier publication is essential for all academic and research library reference collections. It is a crucial tool for academicians, researchers, and practitioners. Ideal for classroom use.
Alignment of Standards using WordNet for Assessing K-12 Engineering Practices in a Participatory Learning Environment

Student assessment based on advanced computational methods will play a critical role in 21 century educational practices. In this paper we describe the challenge of automating the instructional assessment of student discourse based on national standards, in the context of an experimental participatory learning platform. Latent Semantic Analysis, machine learning, data mining and natural language processing techniques were used in conjunction with WordNet to create a classification scheme for engineering standards based on the Science and Engineering Practices in the U.S. Next Generation Science Standards. The scheme was applied to interactive student presentations, to assess and report on students’ engineering and domain learning.
Data Mining: Theory, Methodology, Techniques, and Applications (Lecture Notes in Computer Science)

Eventually, you will totally discover a new experience and skill by spending more cash. yet when? reach you undertake that you require to get those every needs subsequently having significantly cash? Why don't you attempt to acquire something basic in the beginning? That's something that will lead you to comprehend even more just about the globe, experience, some places, when history, amusement, and a lot more?
Race Condition and Its Analysis Approach of Real-time Embedded Systems

In real-time embedded systems, due to race conditions, synchronization order of operations to the shared variables or shared resource during the multiple tasks may be different from one execution to another. This may cause abnormal behaviors of systems. In order to detect the possible race conditions and analyze the impacts of race conditions effectively in real-time embedded systems, a formal model of execution sequences and operation events are presented according to the timing behaviors and execution characteristics of real-time embedded systems. Their characteristics are also discussed. Based on the execution sequence model, race conditions including message races and semaphore races in real-time embedded systems are described formally and precisely. And then, a new race set is presented to describe and store race conditions in systems. It includes the information of happened-before relations and race synchronization relations among the operation events which have races. With the race set generated, a race condition graph is constructed to visualize the race conditions. It is also used to predict the potential race synchronization relations of systems. The case study shows that the approach proposed can be used to analyze and predict efficiently the potential race synchronization relations as well as the different execution situations and results of real-time embedded systems.
PERFORMANCE AUGEMENTATION of USER SEARCH HISTORIES

As the size and richness of information on the Web grows, so does the variety and the complexity of tasks that users try to accomplish online. Users now pursue much broader informational and task-oriented goals such as arranging for future travel, managing their finances, or planning their purchase decisions. They usually break down the tasks into a few co-dependent steps and issue multiple queries around these steps repeatedly over long periods of time. To better support users, search engines keep track of their queries and clicks of their long-term information quests on the Web while searching online. In this paper, we study the problem of performance augmentation of user's historical queries into groups in a dynamic and automated fashion. By this, identifying query groups is helpful for a number of different search engine components and applications, such as query suggestions, result ranking, query alterations, sessionization, and collaborative search. In our approach, we go beyond approaches that rely on textual similarity or time thresholds, and we propose a more robust approach that leverages search query logs. We experimentally study the performance of different techniques, and show their potential, especially when combined together. Keywords—user history, search history, query clustering, query reformulation, click graph, task identification
Vehicle tracking research based on computer vision

Vision-based object segment and tracking is broadly applied for video surveillance.In this paper,an object segment and shadow elimination algorithm with background image updating is proposed to eliminate shadow in HSV color space.And then a vehicle tracking method based on Kalman filtering and motion estimating is presented and the vehicle tracking effect is improved.Finally,some experiment results about vehicle segment and tracking are given.
Asynchronous Parallel Coordinate Update Methods for Large Scale Problems

This thesis focuses on coordinate update methods (CU), which are useful for solving problems involving large or high-dimensional datasets. Coordinate update methods decompose a problem into simple subproblems, where each updates one, or a small block of, variables while fixing others. These methods can deal with linear and nonlinear mappings, smooth and nonsmooth functions, as well as convex and nonconvex problems. In addition, they are easy to parallelize.The great performance of coordinate update methods depends on solving sim- ple subproblems. To derive simple subproblems for several new classes of appli- cations, this thesis systematically studies coordinate friendly (CF) operators that perform low-cost coordinate updates. Based on the discovered coordinate friendly operators, as well as operator splitting techniques, I obtained new coordinate up- date algorithms for a variety of problems in machine learning, image processing, as well as sub-areas of optimization. Several problems are treated with coordinate update for the first time in history.CU can be further scaled up to solve larger problems through asynchronous parallel (async-parallel) techniques. Asynchrony is crucial to parallel computing since it reduces synchronization wait, relaxes communication bottleneck, and thus speeds up computing significantly. I proposed ARock, an algorithmic framework in which multiple agents (machines, processors, or cores) update coordinates in an asynchronous parallel fashion. At each step of ARock, an agent updates a randomly selected coordinate based on possibly out-of-date information. Conver- gence results are established for solving fixed-point problems. Numerical tests on regression and equations show that ARock significantly outperforms its syn- chronous counterpart.The other novel contribution of this thesis is the introduction of an abstract framework for implementing asynchronous parallel algorithms on shared memory platforms. The programming model adopts the thread library from C++ at its lowest level. At the highest level, the goal is to enable fast prototyping of async- parallel algorithms. I developed a multilevel approach to reduce the gap between expert to low-level programming and novice-level programming. A spectrum of applications have been implemented under this framework.
A Performance Analysis of Large ABox Reasoning in OWL-DL Reasoners

Reasoners using typical Tableaux algorithm such as RacerPro, Pellet have a problem in Tableaux algorithm large ABox reasoning. Researches to solve these Problems are dealt with Instance Store of University of Manchester which uses Tableaux algorithm based reasoner and DBMS and KAON2 of University of Karlsruhe using Disjunctive Datalog approach. An evaluation experiment for present reasoners is the experiment of TBox reasoning in most of Tableaux algorithm based one. The most of benchmarking tests in reasoning systems haven't done with ABox reasoning based Tableaux Algorithm but done with TBox reasoning based Tableaux Algorithm. Especially, rarely reported benchmarking tests in reasoners have been issued nowadays. Therefore, this thesis evaluates systems with theory of each reasoners for large ABox reasoning that becomes issues recently with typical reasoners. The large AoBx reasoning engine will be analyzed using Instance Store and KAON2 of Manchester University for large ABox processing. At the analysing method, LUBM(Lehigh University BenchMark), benchmarking test method, and it's test system will be introduced. In conclusion, I recommend appropriate reasoner in various environment with experiment result and characteristic of algorithm used for each reasoner.
RSBHCWT: Re-Sampling Binary Hex Code Windowing Technique to Enhance Target Detection

Objectives: To Design Doppler tolerant radar codes to enhance multiple moving targets. Methods/Statistical Analysis: In this paper we present a novel mathematical approach to improve the target detection in Doppler tolerant radar. The presented approach in this paper is called Re-Sampling Binary Hex Code Windowing Technique (RSBHCWT). This approach not only increases the amplitude of main lobe, but also decreases the amplitude of side lobs to create multiple windows at desired Doppler. We use mat lab to validate our results through simulation. Findings: We developed a windowing approach that can detect multiple moving targets. Application/Improvements: The presented model is useful in military applications. Our approach is on top of oversampled PTM code by creating number of windows with respect to desired Doppler’s.
The Values of Taksu in Pendet Dance at Saraswati Balinese Dance Studio Yogyakarta

This research aims to know the learning process and examination of Pendet dance with taksu values in Saraswati Balinese dance studio in Yoguakarta. This research used qualitative research with ethnographic approach by Spradley. The primary data source is the studio's activity and the results of interview. The secondary data source is previous research with the same theme which refers to Pendet dance and taksu. The data was collected by applying participative observation techniques, structured interviews, and documentation. Data validity evaluation was done with triangulation of sources and triangulation of techniques. The data was analyzed descriptively including data reduction, data presentation and conclusion. The analysis of motions and dance supportive elements use performance studies approach. The result of the research is that Pendet dance learning process in Saraswati Balinese dance studio was done directly or by imitating teachers’ movements. The teachers then performed the counting through movements to ease the students. Taksu values concept in Pendet dance are sabda or affective is body attitude in dancing or the structure of daily routine. Bayu or psychomotor is movements balance and the dance power. Idep or cognitive is how students memorize the
Privacy Preserving Protocol Using K-nearest Neighbor Algorithm for Cloud Based E-healthcare Systems

Background/Objectives: To develop a kNN-privacy preserving model for preserving the privacy of the patients in a cloud assisted e-healthcare system as the sensitive information is needed to be maintained confidential and should not be revealed to public users other than the physicians. Methods/Statistical analysis: PPDM uses a privacy-preserving fully Homomorphic data aggregation as the basic scheme. The outsourcing of disease modeling and the early intervention is performed by developing privacypreserving function correlation matching PPDM1 from dynamic medical text mining and also a privacy-preserving medical image feature extraction PPDM2. Both PPDM1 and PPDM2 provides higher security level with reduced cipher text attach possibility and minimal overhead. Though the computational and communication overhead are reduced in PPDM, the use of correlation function threshold in PPDM1 can further be improved by utilizing an efficient machine learning algorithm. Hence, the simplest and efficient machine learning algorithm, k-nearest neighbor is utilized to develop kNN-PPP model. Findings: In kNN-PPP model, instead of using correlation function threshold based matching, secure squared Euclidean distance of encrypted personal data and encrypted physician template is determined and then matched with better probability. Secure squared Euclidean distance protocol and secure multiplication protocols are the most prominent protocols among those utilized in kNN-PPP model. Improvements/Applications: Using kNN-PPP protocols, the computation and communication overheads are also reduced considerably than the PPDM model for the better health status determination of the patients. Experimental results also show that the kNN-PPP model has minimized overheads and higher matching probability.
A conceptual framework for valuing on-farm genetic resources

iv Acknowledgrnents v Table vi Figures vi lntroduction 1 Farmer Management of Diversity 2 Components of Farmer Management of Diversity 2 Farmers' Varieties as Bundles of Characteristies . 4 Economic Framework for Analyzing Farmers' lncentives to Maintain Diversity 4 Strategie Decisions in Collection and Conservation 8 Choosing Populations to Conserve 8 Measuring the Usefulness of Varieties 9 Measuring a Variety's Contribution to Genetic Diversity in a Reference Area 10 Farmers' Responses to Economic Change 12 Further Issues 14 Conclusions 15 References 17
Some preliminary observations about Baṅgāṇī pronominal and nominal declension

Baṅgāṇī, an Indo-Aryan language spoken in the so-called Baṅgāṇ area, a land enclosed by the Pabar and the Tons rivers (Uttarkāśī district, Uttarākhaṇḍ state), shows in its grammar and lexicon some peculiar features still rather controversial. The debate is still in course, due to the lack of enough documentation available, as the majority of scholars complains about. Moreover Baṅgāṇī, among the Western Pahāṛī languages of New Indo-Ayan, is now esteemed as a critically endangered language by the UNESCO Atlas of the World’s Languages in Danger. The paper presents the preliminary results of a fieldwork research with Baṅgāṇī mother-tongue speakers and the unique elicited text available. In particular a description of Baṅgāṇī pronominal and nominal declension, focusing on the case marking and agreement system of the Subject-like and Object-like arguments of intransitive and transitive clauses (in perfective and non-perfective tenses), is offered. The comparison between the data that I collected enabled me to offer a good amount of Baṅgāṇī sentences exemplifying the function of the different forms, and thus to understand their use in depth, that is to shed light on the peculiarities of Baṅgāṇī case marking system.
Using Computer Behavior Profiles to Differentiate between Users in a Digital Investigation

Most digital crimes involve ﬁnding evidence on the computer and then linking it to a suspect using login information, such as a username and a password. However, login information is often shared or compromised. In such a situation, there needs to be a way to identify the user without relying exclusively on login credentials. This paper introduces the concept that users may show behavioral traits which might provide more information about the user on the computer. This hypothesis was tested by conducting an experiment in which subjects were required to perform common tasks on a computer, over multiple sessions. The choices they made to complete each task was recorded. These were converted to a ’behavior proﬁle,’ corresponding to each login session. Cluster Analysis of all the proﬁles assigned identiﬁers to each proﬁle such that 98% of proﬁles were attributed correctly. Also, similarity scores were generated for each session-pair to test whether the similarity analysis attributed proﬁles to the same user or to two diﬀerent users. Using similarity scores, the user sessions were correctly attributed 93.2% of the time. Sessions were incorrectly attributed to the same user 3.1% of the time and incorrectly attributed to diﬀerent users 3.7% of the time. At a conﬁdence level of 95%, the average correct attributions for the population was calculated to be between 92.98% and 93.42%. This shows that users show uniqueness and consistency in the choices they make as they complete everyday tasks on a system, and this can be useful to diﬀerentiate between them.
Quasi-simultaneous multiplane calcium imaging of neuronal circuits

Two-photon excitation fluorescence microscopy is widely used to study the activity of neuronal circuits. However, the fast imaging is typically constrained to a single lateral plane for a standard microscope design. Given that cortical neuronal networks in a mouse brain are complex three-dimensional structures organised in six histologically defined layers which extend over many hundreds of micrometres, there is a strong demand for microscope systems that can record neuronal signalling in volumes. Henceforth, we developed a quasi-simultaneous multiplane imaging technique combining an acousto-optic deflector and static remote focusing to provide fast imaging of neurons from different axial positions inside the cortical layers without the need for mechanical disturbance of either the objective lens or the specimen. The hardware and the software are easily adaptable to existing two-photon microscopes. Here, we demonstrated that our imaging method can record, at high speed and high image contrast, the calcium dynamics of neurons in two different imaging planes separated axially with the in-focus and the refocused planes 120 µm and 250 µm below the brain surface respectively.
Visual‐based and object‐conscious image retrieval by block reallocation into object region

‘Visual‐based’ image retrieval based on the visual similarities over the entire image is one of the powerful and useful ways when targeting large volume content with inadequate annotation. Generally, conventional methods divide a query image and target images in the database into grid‐shaped blocks and calculate the similarity based on image features by comparing each corresponding block straightforwardly. However, the method sometimes fails in terms of object‐conscious retrieval when their backgrounds are almost the same but the only the object is different or the object's size and/or position is different. To solve the problem, we propose a new method featuring the reallocation of some blocks to the object region (OB‐blocks) and a new similarity score by placing a weight on the OB‐blocks, which are derived from the visual saliency map. Our proposed method could realize ‘visual‐based and object‐conscious’ image retrieval. We verify the effectiveness of this method through comparison experiments. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
Dantzig-Wolfe Decomposition and Lagrangean Relaxation-Based Heuristics for an Integrated Production and Maintenance Planning with Time Windows

In this chapter, two approaches are developed to solve the integrated production planning and maintenance problem. Moreover, Some Propositions and mathematical properties were suggested and applied in the proposed heuristics to solve the problem. The first heuristic developed is based on Dantzig-Wolfe decomposition. The Dantzig-Wolfe Decomposition principle reformulates the original model and Column generation is then used to deal with the huge number of variables of the reformulated model. A simple rounding heuristic and a smoothing procedure are finally carried out in order to obtain integer solutions. The second heuristic is based on Lagrangean relaxation of the capacity constraints and sub-gradient optimization. At every step of sub-gradient method, feasibility and improvement procedures are applied to the solution of the Lagrangean problem. Computational experiments are carried out to show the results obtained by our approaches and compared to those of commercial solver. Dantzig-Wolfe Decomposition and Lagrangean RelaxationBased Heuristics for an Integrated Production and Maintenance Planning with Time Windows
Compressed digital holography: from micro towards macro

signal processing methods from software-driven computer engineering and applied mathematics. The compressed sensing theory in particular established a practical framework for reconstructing the scene content using few linear combinations of complex measurements and a sparse prior for regularizing the solution. Compressed sensing found direct applications in digital holography for microscopy. Indeed, the wave propagation phenomenon in free space mixes in a natural way the spatial distribution of point sources from the 3-dimensional scene. As the 3-dimensional scene is mapped to a 2-dimensional hologram, the hologram samples form a compressed representation of the scene as well. This overview paper discusses contributions in the field of compressed digital holography at the micro scale. Then, an outreach on future extensions towards the real-size macro scale is discussed. Thanks to advances in sensor technologies, increasing computing power and the recent improvements in sparse digital signal processing, holographic modalities are on the verge of practical high-quality visualization at a macroscopic scale where much higher resolution holograms must be acquired and processed on the computer.
A Detection Method and System Implementation for Android Malware

An Android malware detection system is designed and implemented to focus on the problem that malware on Android becomes widespread.The system combines static and dynamic analysis technologies.The APK features such as permission,API call sequences,component, resource and structure are extracted to form a feature vector in static analysis,and a similarity-based method is proposed to detect known malware samples using these features.Android source code is then updated to generate new kernel images in dynamic analysis.The new kernel images can monitor the Android program' s behaviors such as file reading and writing,network connection,SMS sending and telephone calling,etc.Thus,unknown malware samples can be successfully identified through analyzing these behaviors.Experimental results show that the proposed system is efficient and performs well on detecting Android malware.The proposed system has been released online and free use of the system is available on the Internet.
ChoCD: Usable and Secure Graphical Password Authentication Scheme

Since designing effective graphical password authentication schemes is of vital important, this paper attempts to address the need by providing a new way for designing and developing hybrid graphical scheme named ChoCD. ChoCD combines the method of "Click-based”, “Choice-based”, and “Draw-based”. By combining these, it is anticipated that it will offer better usability and security. An evaluation towards ChoCD was conducted to measure its viability and practicality as the alternative user authentication. From these conduct of evaluations, it was found that ChoCD is easy to use and provides more security than other existing schemes and thus potentially be used for user authentication.