Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Capabilities Theory: Pinning Down a Shifting Concept

The theory of dynamic capabilities (DC) has been an important topic for research in the field of strategic business studies over the last two decades or so. Despite this, there is still considerable debate among researchers about the basic concepts underpinning this theory that can be used to create the theoretical basis for empirical research in this area. Consequently, this paper aims to answer the following question: Is there a commonly agreed-upon empirically based definition of DC and what are the most influential conceptual definitions of DC that have affected previous empirical DC research over the period 1997 to 2015? Our study used a systematic review method to retrieve 53 empirical studies based on DC theory from relevant available data sources (Ebsco, ProQuest, Scopus and Wiley Online Library) from 1997 to 2015. Using formal concept analysis (FCA), those 53 studies were assessed along the dimension the source of the definition of DC which they used to build their theoretical foundation and then tested empirically based on their hypotheses and objectives. Our systematic review of the 53 selected studies presented in this research shows that no consensus has been reached about a commonly agreed-upon empirically based definition of DC. Instead, the studies under consideration can be considered as dividing into two schools. Our paper concludes that there is still a wide disparity among scholars and researchers in their definitions of DC. Finally, the present paper concludes that the Teece and colleagues’ theoretical school can be considered the most influential school in shaping the theoretical foundations of the empirical studies reviewed and analysed in our research.
An Interactive Model for the Voice Source

A parametric model for the voice source is described which includes the acoustic interaction between the glottal source and the subglottal and supraglottal acoustic systems. The acoustic theory of speech production, as first proposed, and as generally now implemented in formant-based speech synthesis, models the speech production mechanism during vocalic sounds with three relatively independent subsystems. These subsystems, shown diagrammatically in Fig. I-A-1, are (1) the respiratory system, which produces a slowly varying tracheal air pressure, (2) a time-varying glottal flow resistance (more properly, a complex impedance) whose valving action creates quasi-periodic air pulses, and (3) a supraglottal vocal tract that shapes the spectrum of the glottal flow pulses. Though each of these systems interacts with the other two systems to some degree, order-of-magnitude calculations, model studies and early measurements have indicated that for many applications it is sufficient to consider these three subsystems as operating independently, at least during voiced sounds with no strong supraglottal oral constriction (Fant, 1960; Flanagan, 1972). However, as we look for more precise models of the voice source, whether this be for higher quality synthesis of speech or singing, or for the study of unusual or pathological voice qualities, it is necessary to return to an interactive model. Detailed physicalacoustic models of the subglottal systems have been proposed that can generate patterns of pressure and air flow that seem quite realistic (Flanagan & Landgraf, 1968; Mrayati & Guerin, 1976; Titze & Talkin, 1979). However, such detailed models often do not make clear which aspects of the interaction between the glottal source and vocal tract are most active in determining the quality of the voice. In order to understand the way in which voice quality is affected by the source-tract interaction it is desirable to formulate a model or models that break down this interaction into its more important and less important components, just as in acoustic phonetics the supraglottal vocal tract in non-nasalized vocalic speech is modeled by a number of resonances with varying degrees of importance (the "formants") and, in physiological phonetics, by a small number of minimally
Comparison of SVM and CNN classification methods for infrared target recognition

Recognizing targets from infrared images is a very important task for defense system. Recently, deep learning becomes an important solution of the classification problems which can be used for target recognition. In this study, a machine learning approach SVM and a deep learning approach CNN are compared for target recognition on infrared images. This paper applies SVM to measure the linear separability of the classes and obtain the baseline performance for the classes. Then, the constructed CNN model is applied to the dataset. The experimental results show that CNN model increases the overall performance around % 7.7 than SVM on prepared infrared image datasets.
Lessons From a One-to-One Laptop Pilot

ABSTRACT The purpose of this study was to document the process one district used to design, develop, and implement a one-to-one pilot at its high school as part of its broader commitment to contemporary learning. Specifically, this qualitative case study (a) outlines the process of the laptop pilot, (b) describes the pilot implementation experience for both teachers and students, and (c) shares the results of this pilot study that informed the district's next steps in its one-to-one initiative. Recommendations are also offered for other schools to consider when implementing a one-to-one pilot or program.
Information Systems Technologies: A Public-Private Sector Comparison

This study examines the relative importance public and private sector high-ranking executives place on different information systems technologies (i.e., computer hardware and software, storage technology, and communications technology). Chief information officers or equivalent executives from both the U.S. Federal Government and Fortune 1000 corporations were asked to rank order these technologies. Unexpectedly, this research found no statistically significant difference between these sectors' rankings, contrary to extant literature. An exploration of these findings and opportunities for future study conclude this paper.
Free-space gesture mappings for music and sound

This thesis describes a set of software applications for real-time gesturally controlled interactions with music and sound. The applications for each system are varied but related, addressing unsolved problems in the field of audio and music technology. The three systems presented in this work capture 3D human motion with spatial sensors and map position data from the sensors onto sonic parameters. Two different spatial sensors are used interchangeably to perform motion capture: the radiodrum and the Xbox Kinect. The first two systems are aimed at creating immersive virtually-augmented environments. The first application uses human gesture to move sounds spatially in a 3D surround sound by physically modelling the movement of sound in a space. The second application is a gesturally controlled selforganized music browser in which songs are clustered based on auditory similarity. The third application is specifically aimed at extending musical performance through the development of a digitally augmented vibraphone. Each of these applications is presented with related work, theoretical and technical details for implementation, and discussions of future work.
Toward a Smart Cloud: A Review of Fault-Tolerance Methods in Cloud Systems

This paper presents a comprehensive survey of the state-of-the-art work on fault tolerance methods proposed for cloud computing. The survey classifies fault-tolerance methods into three categories: 1) ReActive Methods (RAMs); 2) PRoactive Methods (PRMs); and 3) ReSilient Methods (RSMs). RAMs allow the system to enter into a fault status and then try to recover the system. PRMs tend to prevent the system from entering a fault status by implementing mechanisms that enable them to avoid errors before they affect the system. On the other hand, recently emerging RSMs aim to minimize the amount of time it takes for a system to recover from a fault. Machine Learning and Artificial Intelligence have played an active role in RSM domain in such a way that the recovery time is mapped to a function to be optimized (i.e., by converging the recovery time to a fraction of milliseconds). As the system learns to deal with new faults, the recovery time will become shorter. In addition, current issues and challenges in cloud fault tolerance are also discussed to identify promising areas for future research.
Managing Web Service Interface Complexity via an OO Metric-based Early Approach

Web Services have been steadily gaining maturity as their adoption in the software industry grew. Accordingly, metric suites for assessing different quality attributes of Web Service artifacts have been proposed recently – e.g., for services interfaces in WSDL (Web Service Description Language). Like any other software artifact, WSDL documents have several inherent attributes (e.g., size or complexity) that can be measured. We present an approach to prevent a high complexity on services interfaces (WSDLs), to ease consumers to reason about services’ offered functionality. Mostly, WSDLs are automatically derived from object-oriented (OO) source code, with a likely impact on complexity. Thereby, we study the statistical relationships between a recent metric suite of service interface complexity (proposed by Baski & Misra) and the well-known Chidamber & Kemerer’s OO metric suite (applied to service implementations), on a data-set of 154 real-world services. First, a theoretical validation of Baski & Misra’s suite (using Weyuker’s properties) is presented, to prove the ability to measure complexity in WSDL documents. Then, after finding high correlation between both metric suites, we have conducted a series of experiments to analyze how certain refactorings on services’ source codes prior to derive WSDLs might reduce complexity. In this way, our approach exploits OO metrics as development-time indicators, to guide software developers towards obtaining less complex service interfaces.
Perceptually driven stereoscopic camera control in 3D virtual environments

PERCEPTUALLY DRIVEN STEREOSCOPIC CAMERA CONTROL IN 3D VIRTUAL ENVIRONMENTS Elif Bengü Kevinç M.S. in Computer Engineering Supervisor: Asst. Prof. Dr. Tolga Çapın August, 2013 Depth notion and how to perceive depth have long been studied in the field of psychology, physiology, and even art. Human visual perception enables to perceive spatial layout of the outside world by using visual depth cues. Binocular disparity among these depth cues, is based on the separation between two different views that are observed by two eyes. Disparity concept constitutes the base of the construction of the stereoscopic vision. Emerging technologies try to replicate binocular disparity principles in order to provide 3D illusion and stereoscopic vision. However, the complexity of applying the underlying principles of 3D perception, confronted researchers the problem of wrongly produced stereoscopic contents. It is still a great challenge to give realistic but also comfortable 3D experience. In this work, we present a camera control mechanism: a novel approach for disparity control and a model for path generation. We try to address the challenges of stereoscopic 3D production by presenting comfortable viewing experience to users. Therefore, our disparity system approaches the accommodation/convergence conflict problem, which is the most known issue that causes visual fatigue in stereo systems, by taking objects’ importance into consideration. Stereo camera parameters are calculated automatically with an optimization process. In the second part of our control mechanism, the camera path is constructed for a given 3D environment and scene elements. Moving around important regions of objects is a desired scene exploration task. In this respect, object saliencies are used for viewpoint selection around scene elements. Path structure is generated by using linked Bézier curves which assures to pass through pre-determined viewpoints. Though there is considerable amount of research found in the field of stereo creation, we believe that approaching this problem from scene content aspect iii
Self-assessment as ‘assessment as learning’ in translator and interpreter education: validity and washback

ABSTRACT T&I programmes aim to produce self-reliant graduates capable of judging their own work against agreed criteria and increasing their levels of competence over the course of their careers. An essential element to achieve this goal is student-centred assessment, i.e. ‘assessment as learning’ (AaL) (also known as ‘sustainable assessment’), which requires students to critically and actively analyse and reflect on their learning progress and thus to guide new learning. The purpose of this study is to examine the validity and washback of self-assessment (SA), a particular form of AaL. The participants were 54 undergraduate translation majors enrolled in a sight translation course and their instructor. The students conducted four SAs of their translation performance which were also assessed by the teacher using the same criteria. The results show: the students’ SA correlates positively with teacher assessment; their SA accuracy improved over time with regular repetition; and SA promotes positive learning attitudes among learners. The findings provide further evidence on the validity of SA as a learning technique and suggest positive washback associated with learning and learners. It is hoped that this study will increase awareness of the validity and positive washback of the use of SA in T&I classrooms.
Variable elimination for reliable parallel robot calibration

We study overconstrained polynomial systems from an algebraic and numerical perspective in order to improve the accuracy and reliability of Gough platform calibration. By elimination theory, we may limit ourselves to measuring only the position information by external sensors, besides internal sensor measurements. Measuring orientation, which is intricate and error-prone, becomes unnecessary. A planar analogue is used to formally develop the technique and analyze observability by means of the Jacobian. Several experiments are used to compare the existing alternatives with partial information to our approach, by numerical simulation. It is shown that our method significantly improves robustness without compromising accuracy, especially when the measured configurations lie at the workspace's boundary.
Optimization of river network representation data models for web‐based systems

Web‐based systems allow users to filter data and resources using river network structure on interactive map environments that use server side processing. With the increasing resolution of river networks, optimized methods for storage of river network representation in databases and efficient queries on the river network structure become critical. This paper presents a detailed study of widely used methods for representing generic networks in relational databases and benchmarking common queries on river network data using these methods. The analysis has been applied to a data set consisting of the river network of Iowa, with over 620,000 individual subwatersheds/nodes in the network. For typical river network queries (path to the outlet; contributing watersheds), two of considered data models (Adjacency List and Nested Set) guarantee response times below 1 s. This will allow users to carry out large‐scale analysis and visualizations tasks on the web for hydrological data sets. A new data model, Stream Network, is proposed based on Path Enumeration, stores directly additional hydrological information, enabling, for example, retrieval of the main stream.
Depression Tendency Screening Use Text Based Emotional Analysis Technique

This paper proposes a text recognition model for semantic analysis of the interview records related to depression, which can effectively identify whether the interviewee is a patient with depression tendency. It mainly consists of two components: 1) The framework of Support Vector Machine (SVM) for Classification of depression related questions; 2) the framework of Doc2vec and Text Convolutional Neural Network (TextCNN) for classification of whether the interviewee has a tendency to depression. Finally, the results obtained by the two classification methods are combined to establish a text classification model that is easy to analyze the tendency of depression.
A Multiuser Model of Privacy-Preserving Auditing for Storing Data Security in Cloud Computing

: In cloud computing frequently referred to as basically, the concept of data security for accessing the data to control effectively. These systems give a secure cloud storage environment for privacy preserving mechanism. The data integrity accessing method is a very effective for the information which is stored in the cloud storage system, so data outsourcing mechanism to allocate in cloud server or each unauthorized user. Typically, cloud computing described as just “the cloud computing” as the conveyance of request processing of the information. yet, for security auditing process of this shared information in cloud, to save the identity information of the users remaining part is the big challenge. This paper presents the best method of this system for privacy preserving so as to authorize the reviewing data of the users in cloud storage. That’s why, we easily access the shared information in the cloud. This system truly focuses on the verification of data is necessary for reviewing process to check the integrity of shared data. During this system, the signature identity method is processed for every chunk is stored as securely as a TPA.The final output observed in this system confirms the integrity of data on that system though reviewing the common data which are present in cloud.
From Printed Materials to Electronic Demonstrative Dictionary – the Story of the National Photocorpus of Polish and its Korean and Vietnamese Descendants

The most popular form of lexicographic exemplification is plain-text transcript. Apart from the doubtless advantages of such a quotation method, it may be perceived as a kind of trade-off when considering readability, accessibility, simplicity, accuracy, and even the logistics of a documentation project. Another approach is to gather and present excerpts in the form in which they were originally published, that is, as the clippings from publications (this is referred to as photodocumentation). The photodocumentary technique is a distinctive feature of both the National Photocorpus of Polish and its Korean and Vietnamese descendants. The main goal of the first of the above-mentioned projects was to describe around 250,000 lexical units, which would be enough to outperform all of the 20th-century dictionaries of Polish. Even more momentously, the process was entirely corpus-driven – that is, all of the principial lexicographic works preceding the project were intentionally ignored. As a result, the material contains largely the words of which linguists were unaware of or which were perceived as later neologisms under leading derivative models of Polish. This article describes the projects from their early stages, namely the acquisition of printed materials, to the final level of development where an electronic lexicographic tool is made available to both amateur and professional users. Also described is the struggle to avoid unthinking imitation of p-lexicographic techniques. The methodology had to be adapted to meet modern web usability standards.
Improving Bayesian Optimization for Machine Learning using Expert Priors

Improving Bayesian Optimization for Machine Learning using Expert Priors Kevin Swersky Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2017 Deep neural networks have recently become astonishingly successful at many machine learning problems such as object recognition and speech recognition, and they are now also being used in many new and creative ways. However, their performance critically relies on the proper setting of numerous hyperparameters. Manual tuning by an expert researcher has been a traditionally effective approach, however it is becoming increasingly infeasible as models become more complex and machine learning systems become further embedded within larger automated systems. Bayesian optimization has recently been proposed as a strategy for intelligently optimizing the hyperparameters of deep neural networks and other machine learning systems; it has been shown in many cases to outperform experts, and provides a promising way to reduce both the computational and human time required. Regardless, expert researchers can still be quite effective at hyperparameter tuning due to their ability to incorporate contextual knowledge and intuition into their search, while traditional Bayesian optimization treats each problem as a black box and therefore cannot take advantage of this knowledge. In this thesis, we draw inspiration from these abilities and incorporate them into the Bayesian optimization framework as additional prior information. These extensions include the ability to transfer knowledge between problems, the ability to transform the problem domain into one that is easier to optimize, and the ability to terminate experiments when they are no longer deemed to be promising, without requiring their training to converge. We demonstrate in experiments across a range of machine learning models that these extensions
Learning driving behaviour using holistic image descriptors

This study evaluated whether holistic image descriptors carry enough information to classify context and predict actions in complex driving situations. The results showed very good performance at both tasks, demonstrating the power of such pre-attentive percepts. Even some very local events, are generally associated with enough contextual information for allowing robust prediction. A future system could use the areas of high activation provided by the classifier as an attention mechanism to improve the performance. Classification (GentleBoost)
Design and Simulation of Image Preprocessor Based on FPGA

With the continuous development of image processing technology, and with the development and application of ASIC, DSP and FPGA devices, image processing has acquired a number of new technology, and FPGA based image processing technology has become the development trend in the field of image processing. This paper gives a brief introduction of the digital image, and gives the FPGA design scheme of the algorithm of median filter, edge sharpening and gray level histogram, and also gives the design principle diagram and simulation results. The experiment results showed that in the image processing system, it is feasible to realize the fast algorithm through the hardware of the software, which can well achieve the established objectives and meet the requirements of real-time.
Competitive convergence in retailing

Abstract This article’s objective is to study the influence of a new hybrid format in the retail distribution sector and this emerging market context is defined as competitive convergence. To attain this objective, 1150 surveys of retail distribution professionals were conducted. These surveys aid in distinguishing retail formats and indicate their competitive position allowing us to generate positioning maps of the Spanish retailing. In addition, Cramer’s coefficient V was used as an association measure between qualitative variables and latent class analysis (LCA) modelling was used to build a segmentation analysis of the competing offer. This analysis shows how retail formats evolve and adapt their competitive variables, even adopting characteristics from different formats (competitive convergence). Supermarkets dominate the Spanish retailing and a hybrid retail format’s better competitive position in aspects commonly associated with other formats supports. Spanish retailing provides an example of the non-static nature of retail formats and business models.