Miracle-dz/UniFAR-FTUs huggingface dataset:
An analysis of optical flow on real and simulated data with degradations

Estimating the motion of moving targets from a moving platform is an extremely challenging problem in un-manned systems research. One common and often successful approach is to use optical flow for motion estimation to account for ego-motion of the platform and to then track the motion of surrounding objects. However, in the presence of video degradation such as noise, compression artifacts, and reduced frame rates, the performance of state-of-the-art optical flow algorithms greatly diminishes. We consider the effects of video degradation on two well-known optical flow datasets as well as on a real-world video data. To highlight the need for robust optical flow algorithms in the presence of real-world conditions, we present both qualitative and quantitative results on these datasets.
Women Managers and Entrepreneurs and Digitalization: On the Verge of a New Era or a Nervous Breakdown?

Chris McPhee A Management Innovation Approach to Project Planning Haven Allahar Women Managers and Entrepreneurs and Digitalization: On the Verge of a New Era or a Nervous Breakdown? Mervi Rajahonka and Kaija Villman The Strategies of Technology Startups Within and Between Business Ecosystems Taina Tukiainen, Thommie Burström, and Martin Lindell The Status and Future of Action Research: An Interview with Professor David Coghlan David Coghlan and Erik Lindhult Author Guidelines June 2019 Volume 9 Issue 6 http://doi.org/10.22215/timreview/1243 Technology Innovation Management Review
Visual Programming meets Tangible Interfaces - Generating city simulations for decision support in early design stages

The utilization of visual programming languages (VPL) as tools for generating complex simulations has seen a constant increase in application in architect planning phases. The major advantage of such languages is, that they enable the user to create programs without needing traditional software development skills. In the last few years the CDP // Collaborative Design Platform was developed that seamlessly connects physical models with analyses and simulations in real-time. To facilitate an easier creation, modification and user interaction with the individual simulations, a VPL and an accompanying IDE were conceptualized and developed. In the context of this paper the core requirements, the concept and prototypical implementation of these new components are described in detail.
IT outsourcing issues in Singapore : the vendor's perspective

Most studies undertaken in the area of IT outsourcing seem to focus only on the customer’s perspective leaving a gap in knowledge about the vendor’s perspective. This study aims to correct this deficiency by investigating the views of four organisations providing IT services in Singapore, and comparing this to the current literature base of customer views. The findings showed that the vendor’s views about security, contract management and flexibility differed from the customer’s point of view. However, two issues, partnerships and vendor inexperience, seem to match the vendor issues found in this study. Additionally, two approaches were found to be used by vendors in the IT outsourcing activities, multiple-team approach and single-team approach. A multiple–team approach is likely to be used by vendors having a contract-based relationship and single-team approach is likely to be used by vendors having a partnership-based relationship.
A POD-Based Reduced-Order Stabilized Crank–Nicolson MFE Formulation for the Non-Stationary Parabolized Navier–Stokes Equations

AbstractWe firstly employ a proper orthogonal decomposition (POD) method, Crank–Nicolson (CN) technique, and two local Gaussian integrals to establish a POD-based reduced-order stabilized CN mixed finite element (SCNMFE) formulation with very few degrees of freedom for non-stationary parabolized Navier–Stokes equations. Then, the error estimates of the reduced-order SCNMFE solutions, which are acted as a suggestion for choosing number of POD basis and a criterion for updating POD basis, and the algorithm implementation for the POD-based reduced-order SCN-MFE formulation are provided, respectively. Finally, some numerical experiments are presented to illustrate that the numerical results are consistent with theoretical conclusions. Moreover, it is shown that the reduced-order SCNMFE formulation is feasible and efficient for finding numerical solutions of the non-stationary parabolized Navier–Stokes equations.
Getting the Balance Right? Party Competition on Immigration and Conflicting Ideological ‘Pulls’

Will a plurality of cleavages ‘pull’ parties in different directions? Are these strains particularly troublesome when competing on issues that lack an obvious dimensional fit? Are some parties more likely than others to experience these tensions? And does it matter? While the essence of the party-political space has received substantial coverage, less attention is paid to the effects that multidimensionality may have on issue competition. Comparing British and Swedish parties, this article analyses how any contradictory positions have been negotiated, and when such tensions are likely to emerge.
Content Based Temporal Segmentation for Video Analysis

This temporal video segmentation aims at getting video segments so that the object’s action should be enveloped by one segment. It is more challenging when each frame in the video is having multiple objects. Hence, it provides overlapped segments as a solution based on object-level information of the video rather than abrupt and gradual transitions. Temporal coherence between the content of frames is found to represent object-level information followed by a histogram intersection method to find a level of coincidence between the frames. The outcome of this work is suitable for many further video content analysis tasks such as understanding multimedia databases, tracking an object motion completely, efficient video content indexing, and so on. The segmentation method was tested on the sports genre which is one of the challenging domains because these video genres are composed of diverse scenes with multiple objects.
Rule Based Systems in a Distributed Environment: Survey

Over the past years, the internet has become faster, computer storage has become larger and the data from internet users and sensors is piling up to larger amount and is also spread around the globe. This requires more time and space for a single computer to cope with them. Ecosystems like Hadoop helps to store, process and retrieve back the data efficiently in a distributed fashion. Data mining finds substantial improvements over such distributed frameworks to process a large volume of data in a lesser time. Currently, there are many approaches to do data mining tasks such as classification and clustering in a distributed setup using Hadoop MapReduce, Spark, and other Cloud platforms. Actionable pattern mining is a rule based data mining approach for discovering knowledge from information systems in a form of Action Rules. An emphasis of traditional classification rules from a supervised Machine Learning is to predict class label of a data object. Whereas Action Rules produce actionable knowledge in the form of suggestions on how an object can change from one class value to another more desirable class value. This paper gives a brief survey of previous works on association and classification rule mining algorithms in a distributed environment, as well as action rule mining algorithms, and discusses Action Rule Mining in a distributed environment.
PENGARUH PERSEPSI ATAS REWARD DAN PENILAIAN KINERJA TERHADAP KEPUASAN KERJA DAN KOMITMEN PEGAWAI NEGERI SIPIL (PNS) DI PEMERINTAH KABUPATEN

Tujuan dari penelitian ini adalah untuk menguji pengaruh reward intrinsik terhadap kepuasan kerja pegawai di Pemerintah Kabupaten Lumajang, menguji pengaruh reward ekstrinsik terhadap kepuasan kerja pegawai di Pemerintah Kabupaten Lumajang, menguji pengaruh penilaian kinerja terhadap kepuasan kerja pegawai di Pemerintah Kabupaten Lumajang, menguji pengaruh reward intrinsik terhadap komitmen pegawai di Pemerintah Kabupaten Lumajang, menguji pengaruh reward ekstrinsik terhadap komitmen pegawai di Pemerintah Kabupaten Lumajang, menguji pengaruh penilaian kinerja terhadap komitmen pegawai di Pemerintah Kabupaten Lumajang, dan menguji pengaruh kepuasan kerja pegawai terhadap komitmen pegawai di Pemerintah Kabupaten Lumajang. Teknik pengumpulan data dalam penelitian ini adalah kuesioner, wawancara dan observasi. Populasi dalam penelitian ini adalah semua pegawai Pemerintah Kabupaten Lumajang yang mempunyai golongan kepangkatan mulai golongan 1 (satu) sampai golongan 4 (empat) tahun 2012 yang berjumlah 6.487 orang.
Fingerprinting smartphones remotely via sensors data

Remote fingerprinting of physical devices has already been shown to be feasible, using tiny deviations in the clock frequencies of finger-printed machines. Both TCP and ICMP timestamps provided the basis for clock skew estimations. In the context of the emerging urban-sensing paradigm, we note the potential availability of logged sensors data, typically comprising timestamps, and probably labelled with external time reference information. This observation leads to the possible exploit of that new timestamps source to identify individual mobile nodes, and compromise participating users’ privacy. Our experiments verify this conclusion, confirming the earlier results published on the same line of research, and pull the attention to a privacy breach that could be mounted remotely, offline and in non-real time, on logs of sensors data with necessary time reference labels.
LibreKV: A Persistent in-Memory Key-Value Store

Emerging Non-Volatile Memory (NVM) possesses unique features including byte-addressability and high density which bring huge opportunities to combine DRAM and NVM in a unified main memory space. And key-value store (KVS) systems play an important role in many applications, such as large-scale websites. Several existing KVS have been proposed for NVMs. However, they have drawbacks such as extra write overhead leading, lower memory utilization and worse endurance. In this paper, we design and develop an NVM-based key-value store system named LibreKV. It specifically targets the hybrid DRAM and NVM memory architecture, leveraging the NVM as the eventual persistent storage medium. It uses both static hash table and dynamic hash tables to achieve a balance between system performance and memory utilization. It adopts a checksum based consistency mechanism to guarantee data consistency and persistent storage on NVM. LibreKV works independently without relying on an underlying file system and simplified the IO stack comparing to the traditional KVS system. Experimental results show that LibreKV outperforms the state-of-the-art KVS systems and achieves better scalability and consistency while with low overhead.
Explaining Teacher Effects on Achievement Using Commonly Found Teacher-Level Predictors

Teachers have large effects on students’ academic outcomes, but there is little understanding of which features of teachers and teaching best explain these effects. In this study, we collected 22 measures of teacher and teaching quality from three broad categories: classroom instruction (e.g., quality, content), teacher personal characteristics (e.g., knowledge, self-efficacy), and teacher background (e.g., education, experience). All three categories explained some unique variation in teacher effects, though measures of instruction explained the least, even after correcting for measurement error. Thus, although a diverse set of measures best distinguishes teachers’ effectiveness, a cost-effective starting point might be to focus on the less expensive but relatively information-rich measures of teacher personal characteristics and background.
An Effective Filter for Noise Removal in Magnetic Resonance Images Using Multilevel Fuzzy Reasoning Concept

In this study, a novel 2-stage noise adaptive multilevel fuzzy reasoning–based filter is proposed as a postprocessing filtering technique for noise removal from magnetic resonance images. The edge detection stage uses multilevel fuzzy reasoning to handle uncertainty present in the local information introduced by noise. The image-smoothing stage uses a low-pass filter to retain the low-frequency information of the noisy image. Finally, the output of the edge-detection stage, and the smoothed noisy image are added together to form the noise-free image. The effectiveness of the proposed method in noise removal from MR images is evaluated in terms of visual quality of the image and image quality assessment metrics such as peak signal-to-noise ratio, mean square error, structural similarity index measurement, and image enhancement factor. Experimental results show that the proposed method removes noise more effectively from MR images without destroying finer details and coarser structures as compared with the other tested state-of-the-art methods.
Real time ray tracing based on shader

Ray tracing is a rendering algorithm for generating an image through tracing lights into an image plane, it can simulate complicate optical phenomenon like refraction, depth of field and motion blur. Compared with rasterization, ray tracing can achieve more realistic rendering result, however with greater computational cost, simple scene rendering can consume tons of time. With the GPU’s performance improvement and the advent of programmable rendering pipeline, complicated algorithm can also be implemented directly on shader. So, this paper proposes a new method that implement ray tracing directly on fragment shader, mainly include: surface intersection, importance sampling and progressive rendering. With the help of GPU’s powerful throughput capability, it can implement real time rendering of simple scene.
The Role of Administrators in Professional Development: Considerations for Facilitating Learning Among Academic Librarians

ABSTRACT The role that administrators should take in enabling professional development is regularly discussed in the library literature. However, this article, seeking to provide information from a new perspective, stems from data gathered via qualitative interviews in order to explore experiences of academic librarians and to identify commonalities among the experiences that participants describe as meaningful. Themes that emerge relate to the balance between administrative suggestions versus mandates, librarians' desire to self-assess and self-select activities, the importance of job relevance, approaches to sharing information, and barriers to participation. They may offer insights to administrators who wish to facilitate continuing education of staff.
An exploratory study towards applying and demystifying deep learning classification on behavioral big data

The superior performance of deep learning algorithms in fields such as computer vision and natural language processing has fueled an increased interest towards these algorithms in both research and in practice. Ever since, many studies have applied these algorithms to other machine learning contexts with other types of data in the hope of achieving comparable superior performance. This study departs from the latter motivation and investigates the application of deep learning classification techniques on big behavioral data while comparing its predictive performance with 11 widely-used shallow classifiers. In addition to the application on a new type of data and a structured comparison of its performance with commonlyused classifiers, this study attempts to shed light onto when and why deep learning techniques perform better. Regarding the specific characteristics of applying deep learning on this unique class of data, we demonstrate that an unsupervised pretraining step does not improve classification performance and that a tanh nonlinearity achieves the best predictive performance. The results from applying deep learning on 15 big behavioral data sets demonstrate as good as or better results compared to traditionally-used, shallow classifiers. However, no significant performance improvement can be recorded. Investigating when deep learning performs better, we find that worse performance is obtained for data sets with low signal-from-noise separability. In order to gain insight into why deep learning generally performs well on this type of data, we investigate the value of the distributed, hierarchical characteristic of the learning process. The neurons in the distributed representation seem to identify more nuances in the many behavioral features as compared to shallow classifiers. We demonstrate these nuances in an intuitive manner and validate them through comparison with feature engineering techniques. This is the first study to apply and validate the use of nonlinear deep learning classification on fine-grained, human-generated data while proposing efficient conguration settings for its practical implementation. As deep learning classification is often characterized by being a black-box approach, we also provide a first attempt towards the disentanglement regarding when and why these techniques perform well.
Probabilistic Broadcast

We present a class of scalable and probabilisticly reliable communication protocols. The protocols are based on a probabilistic system model and thus their properties tend to be probabilistic in nature. The protocols are scalable in two senses. First, the message costs and latencies of the protocols grow slowly with the system size. Second, the reliability of the protocols, expressed in terms of the probability of a failed run of a protocol, approaches 0 exponentially fast as the number of processes is increased. This scalable reliability is achieved through a form of gossip protocol which is strongly self-stabilizing in a sense similar, although not identical to, the notion of self stabilizing systems proposed by Dijkstra.
Establish a Simulation Platform of DNS Root Server in Campus Network Laboratory

In the experiment teaching of computer network DNS course, domain names among students cannot communicate with each other, because the self-defined domain name is not authorized by the official ROOT DNS server of the Internet. The paper focuses on setting up a root domain simulation platform to simulate the root domain name and the top-level domain name server in the internal network of the campus. When students conduct DNS service experiments, they can achieve the same ‘real’ effect as the resolution of internet domain name.
Characterizing Task-Relevant Information in Natural Language Software Artifacts

To complete a software development task, a software developer often consults artifacts which mostly consist of natural language text, such as API documentation, bug reports, and Q&A forums. Not all information within these artifacts is relevant to a developer’s current task, forcing them to filter through large amounts of irrelevant information, a frustrating and time-consuming activity. Since failing to locate relevant information may lead developers to incorrect or incomplete solutions, many approaches attempt to automatically extract relevant information from natural language artifacts. However, existing approaches are able to identify relevant text only for certain types of tasks and artifacts. To explore how these limitations could be relaxed, we conducted a controlled experiment in which we asked 20 software developers to examine 20 natural language artifacts consisting of 1,874 sentences and highlight the text they considered relevant to six software development tasks. Although the 2,463 distinct highlights participants created indicate variability in the perceived relevance of the text, the information considered key to completing the tasks was consistent. We observe consistency in the text using frame semantics, an approach that captures the key meaning of sentences, suggesting that frame semantics can be used in the future to automatically identify task-relevant information in natural language artifacts.
A MODULE GENERATION ENVIRONMENT FOR MIXED-SIGNAL CIRCUITS

Mixed-signal systems have resulted from the integration of analog and digital systems on the same chip. In most mixed-signal integrated circuits, the analog component is used as the interface while the internal signal processing is done in the digital domain. In digital, analog and mixed-signal systems, the performance of the system depends on the quality of the layout. As the frequency of operation of systems increases, the layout influences on the system performance are higher than before. Layout parasites, variations in device sizes, symmetry and matching constraints affect performances, more so in analog systems than digital. Modules can be defined as devices or groups that have a certain functionality. Though the number of such basic building blocks is limited, a number of variants that differ greatly in their functionality and physical realizations exist. These variants are usually obtained by changing certain properties of the circuit/device. Each variant can also have a number of possible aspect ratios or shapes. The presence of good module generators, that allow quick development and easy specification of parameterized modules, considerably improves the quality of layouts. This thesis provides an environment for specification of highly parameterized modules through the Module Specification Language (MSL). The language has been adapted to module generation with an intuitive and easy-to-read structure. It also allows designers to easily incorporate their domain specific knowledge into the modules. The language allows modules to be hierarchically composed of other modules, thus promoting re-use of previously defined modules. Though rules for a particular technology must be obeyed while writing modules, use of aliases for physical layers provides some technology-independence to the modules. The language itself is independent of the module being specified, making it useful for the development of both analog and digital modules. Constructs specifically aimed at improving the quality of analog layouts are provided in the language. The module generation environment lends itself very well to layout generation as well, through the use of routing modules defined in the language or through external routers. On account of its design, the generation environment allows for integration with a variety of CAD tools. The tool currently works with both Magic and Cadence Virtuoso layout editors and can also be interfaced with other layout editors. To validate the tool and the language, an extensive library of parameterized modules was specified and tested through simulations and through use with other tools. To my dearest parents, loving brother and caring sister-in-law
Improving Channel Capacity in the LTE Downlink through Channel Prediction

xvii CHAPTER ONE .
Adaptive control of time delay teleoperation system with uncertain dynamics

A bilateral adaptive control method based on PEB control structure is designed for a class of time-delay force feedback teleoperation system without external interference and internal friction to study the uncertainty of dynamic parameters and time delay. The stability and tracking performances of the closed-loop constant time delay teleoperation system are analyzed by Lyapunov stability theory. Finally, the controller designed in this paper is successfully applied to the teleoperation system composed of a two-degree of freedom rotating manipulator as the master robot and the slave robot. The simulation is carried out in no operator and environment force or with operator and environment force. The adaptive bilateral control method's control performance is compared with that of the traditional time-delay teleoperation system. Finally, it is verified that the method has good control performance.