Miracle-dz/UniFAR-FTUs huggingface dataset:
Web latency reduction with prefetching

Nowadays, the Internet is so widely used in business transactions, education, entertainment, and so on, that it is no exaggeration to say that the Internet has become an indispensable part of people's life as well as of the modern society. Today's Internet is not perfect. One of its major flaws is user-perceived delays, or the so-called Web latency. This latency often happens when users open Web pages—it takes some time for a page to be completely downloaded and displayed. Web latency reduces the efficiency of the Internet. In addition, it forces users to be idle, and, thus, to lose concentration and even to feel frustrated. Numerous research efforts have been devoted to reduce Web latency in order to improve the efficiency of the Internet and the user experiences. Web prefetching, which tries to predict future requests for Web pages and downloads them before they are requested, has been proven to be a very useful technique for Web latency reduction. This dissertation mainly focuses on designing prefetching techniques that are effective, cost little computing and memory resources, and can adapt to various working environments. We first study the problem of reducing the latency of the traditional Web (or “pre-Web 2.0” Web). Traditional Web pages are mostly static, i.e., their content does not change. We propose a novel history-based prefetching algorithm. Different from existing prefetching algorithms, our algorithm allows users to pre-set and dynamically adjust the amount of memory that it can use, so that prefetching can adapt to different and dynamic working environments. In addition, our algorithm is more efficient than other existing prefetching algorithms because of its higher prediction accuracy and lower bandwidth cost. We also propose a method to find upper bounds on the performances of any history-based prefetching algorithms, which enables us to estimate the potential benefit of using prefetching in specific scenarios and evaluate the performance of prefetching algorithms. Next, we apply prefetching techniques to reduce the latency of Web 2.0 applications. Unlike traditional Web pages, the information to be updated in these applications usually does not have its own URL. As a result, most existing prefetching techniques cannot be used here. Very few studies have been done on reducing the latency of Web 2.0 applications. In this dissertation we propose a prefetching algorithm for online mapping applications, a typical type of Web 2.0 application. This algorithm can prefetch new areas on a map according to the viewing patterns of users. With this algorithm, the latency of online mapping applications can be significantly reduced at little added cost. In addition, we propose a method to find the optimal size of map tiles for online map prefetching, which enables us to minimize bandwidth cost. Keywords: bandwidth cost, machine learning, online mapping applications, prediction accuracy, prefetching, Web 2.0, Web latency reduction
Model-Based Systems Engineering And Systems Modeling Language

As the system size and complexity continues to increase,the traditional document-based systems engineering will produce more and more sorts of different documents,find information,understanding and change is hard.Model places it is intuitive,unambiguous,mod ular,reusable etc.rapid coverage of software,electronics and other engineering fields.In order to support model-based systems engineering MBSE,and INCOSE International Systems Engineering in the Object Management Group OMG Unified Modeling Language(UML) soft ware engineering for reuse and expansion,based on the introduction of a standard system modeling language SysML,eliminating the differ ent models language expressions and the different terminology,standardized symbols and semantics.SysML will improve a wide range of ap plications between systems engineering and other disciplines as well as effective communication system will greatly promote the develop ment of engineering theory and practice.In this paper,systems engineering methods,model based system engineering and systems modeling language SysML provides a brief analyses.
Heart or Mind? Unexplained chest pain in patients with and without coronary disease

Patients with unexplained chest pain continue to present a difficult challenge for clinicians, especially for cardiologists. Approximately 20% of patients undergoing diagnostic coronary arteriography for acute or chronic cardiac ischemia have angiographically normal coronary arteries. In these cases, patients with chest pain are usually reassured by their physician and other causes of substernal chest pain are searched. However, apart from a sizeable proportion of patients with gastroesophageal disorders, the remaining patients remain symptomatic without apparent reasons. The mechanism behind this phenomenon is likely to be the result of a combination of functional or anatomical abnormalities in the coronary microcirculation, a metabolic disorder which affects the handling of energy substrates by the heart, insulin resistance and neuropsychological components affecting pain perception. These patients often exhibit an increase in sympathetic outflow to the cardiovascular system, which might account for the reduction in coronary flow reserve, changes in metabolic utilization and development of insulin resistance that are seen in some of these patients. Therapeutically, beta-blockers appear to be most effective in controlling the symptoms associated with this condition, although those calcium antagonists which do not affect the neurohormonal system may be of some utility in patients with primary microvascular angina, in which microvascular spasm is operating or in whom excessive constriction of the distal component of the coronary circulation limits the vasodilatory reserve. This article reviews the clinical presentation, differential diagnosis, and approach to evaluation and therapy of this complex group of patients.
Robust Video Streaming over MANET and VANET

Mobile Ad Hoc Networks (MANETs) are a further step towards wireless networks with no or limited infrastructure and Vehicular Ad Hoc Networks (VANETs) extend this concept, introducing diverse mobility patterns but removing the need for battery power conservation. Video streaming and multimedia applications in general have become an engine of growth in wireless networking and this Chapter shows how video streaming can take place in this challenging environment. Error resilience and path diversity are presented as the key to robust streaming. The Chapter shows that simplified forms of multiple description coding are a practical route to take, with redundant frames in the temporal domain or Flexible Macroblock Ordering in the spatial domain offering preferred solutions. As a form of management of streaming, distributed sourcing via peer-to-peer streaming is experimented within VANET simulations. Error resilience methods, peer-to-peer streaming, and multi-path routing are reviewed. The Chapter considers the exploitation of path diversity over a MANET and a VANET. Path diversity allows the merging of the peer-to-peer concept with ad hoc networks. Future research directions are reviewed.
Techniques and tools for model-based design and analysis of embedded real-time software

As Embedded Real-Time (ERT) systems become more complex and safety-critical, languages to models. We have developed a suite of techniques and tools to improve industry acceptance of model-driven development of ERT software. As a result of collaboration among multiple institutions, an end-to-end tool-chain has been developed for the design and analysis of ERT software, with Avionics Mission Computing (AMC) as the main target application. As part of the tool-chain, we have developed a tool called AIRES for model-level static analysis. Compared to traditional static analysis techniques that work at and provides valuable dependency and timing information to the engineer at an early stage of the design cycle. AIRES mainly focuses on the static structural aspects while largely ignoring the dynamic behavior of component interactions. We use model-checking to formalize the natural language description of the dynamic behavior of the AMC software, and verify safety and liveness properties. We also present several techniques to improve scalability of model-checking by exploiting application-level domain semantics. To bridge the gap between logical models and implementation on the physical execution platform, many UML tools come with automatic code generators that translate models into code in a programming language. However, current code generation technology generates functional code without considering non-functional and real-time issues. We have adapted the schedulability analysis algorithm by Harbour, Klein and Lehoczky to fit the native runtime model of UML-RT, a UML profile widely used in the telecom domain. This algorithm can be used during state-space exploration to synthesize an implementation architecture for a logical UML-RT model that satisfies timing constraints. In summary, the techniques and tools developed in this thesis address multiple aspects of model-driven development of ERT software, in order to shift the focus of the software development process from programming language-level to the model-level, and reduce the overall system development cost.
A dynamic framework for interoperability in large multidatabases

As the number of autonomous databases proliferate and the need for more information sharing becomes more pressing, new research is needed to first determine the new problems that arise and second, find adequate solutions to them. This thesis is an effort to contribute to this emerging field. We first describe some important problems and propose an extensible solution to some of them. In that respect, we describe a system where sharing information among a large network of autonomous heterogeneous databases is achieved with little loss of site autonomy. The idea is to partition the information space, using information interest as a main criterion. Using information types as units of data sharing lets users investigate information by using documentation about structure and behavior, without sacrificing site autonomy. The query resolution relies on an object-oriented architecture to educate users about the available domain of information and help them locate component databases where the information resides.
Sn58Bi Solder Interconnection for Low‐Temperature Flex‐on‐Flex Bonding

Integration technologies involving flexible substrates are receiving significant attention owing the appearance of new products regarding wearable and Internet of Things technologies. There has been a continuous demand from the industry for a reliable bonding method applicable to a low‐temperature process and flexible substrates. Up to now, however, an anisotropic conductive film (ACF) has been predominantly used in applications involving flexible substrates; we therefore suggest low‐temperature lead‐free soldering and bonding processes as a possible alternative for flex‐on‐flex applications. Test vehicles were designed on polyimide flexible substrates (FPCBs) to measure the contact resistances. Solder bumping was carried out using a solder‐on‐pad process with Solder Bump Maker based on Sn58Bi for low‐temperature applications. In addition, thermocompression bonding of FPCBs was successfully demonstrated within the temperature of 150 °C using a newly developed fluxing underfill material with fluxing and curing capabilities at low temperature. The same FPCBs were bonded using commercially available ACFs in order to compare the joint properties with those of a joint formed using solder and an underfill. Both of the interconnections formed with Sn58Bi and ACF were examined through a contact resistance measurement, an 85 °C and 85% reliability test, and an SEM cross‐sectional analysis.
Adding social annotations to electronic dokuments

While annotations have been common in normal books for a long time it has never been a big hit in electronic documents. In normal books it has been proven that it can be useful to make annotations and to read other peoples old annotations. As electronic learning material is becoming more and more common a positive effect of adding such shared annotations should also be possible there. This thesis explores what attributes such a system would need to maximize the utility readers get from an annotated text and the technical challenges with implementing such a system. As previous studies have not found any conclusive results about the effect that sharing annotations have on readers this is also investigated by trying an implemented system on users. The fact that annotations change the structure of a documents based on how users annotate it is also discussed and suggestions are made to potential useful applications of this new structure. Based on a structured analysis of previous research and other implementations a set of attributes important for a shared annotation system are presented. A prototype system that attempts to implement a set of these attributes was also created to get insight into the technical challenges encountered. A set of common challenges were found and solved, with a solution for asynchronously updating annotations being the most novel solution found, but the whole system is a good example of a mash-up of different solutions. Users were also interviewed about the use of the system and they are all positive about the implications of a sharing annotations and think it will make it easier to effectively find additional material about a subject. As previous studies this indicates that students have at least a positive view on using such a system.
Prediction of drug targets using metabolic modelling

Cancer, as one of the leading causes of death worldwide, is a disease characterized by the abnormal and uncontrolled proliferation of cells. Currently available anti-cancer drugs come with a variety of different side effects reducing the quality of life of cancer patients. Due to these severe side effects in anti-cancer therapy it is important to find a compromise between killing the cancer cells (efficiency) and not affecting the healthy cells (toxicity) to improve the quality of life of those patients. There exist different methods of finding new drug targets in cancer such as the in vitro development of new drugs which is very time consuming and expensive. The in silico prediction of targets, on the other hand, is fast and cost effective and allows to make a pre-selection of drug targets based on candidate genes. In this work, I propose a new workflow which implements metabolic modelling for finding metabolic drug targets in cancer. Therefore, context-specific models for cancer (including primary and metastatic melanoma) and healthy controls were reconstructed from Recon 2 (a genome scale metabolic model) using FASTCORMICS and two different expression datasets. In silico single gene deletion was performed in the models to search for potential candidate genes which are essential in cancer (reduce biomass production by 50%) but not in healthy (do not affect ATP production). In a second step, (approved) drugs targeting metabolic genes and their side effects, were extracted from the DrugBank, STITCH and SIDER through data mining and mapped to the metabolic network. A total of 65 possible drug targets have been found. These targets include genes which are known targets for chemotherapeutic agents such as the thymidylate synthase (TYMS), the fatty acid synthase (FASN) or dihydrofolate reductase (DHFR). Furthermore, two anti-cancer agents have been predicted for FASN which have already been proposed for the treatment of cancer.
Compressive System Identification (CSI): Theory and applications of exploiting sparsity in the analysis of high-dimensional dynamical systems

The information content of many phenomena of practical interest is often much less than what is suggested by their actual size. As an inspiring example, one active research area in biology is to understand the relations between the genes. While the number of genes in a so-called gene network can be large, the number of contributing genes to each given gene in the network is usually small compared to the size of the network. In other words, the behavior of each gene can be expressed as a sparse combination of other genes. The purpose of this thesis is to develop new theory and algorithms for exploiting this type of simplicity in the analysis of high-dimensional dynamical systems with a particular focus on system identification and estimation. In particular, we consider systems with a high-dimensional but sparse impulse response, large-scale interconnected dynamical systems when the associated graph has a sparse flow, linear time-varying systems with few piecewise-constant parameter changes, and systems with a high-dimensional but sparse initial state. We categorize all of these problems under the common theme of Compressive System Identification (CSI) in which one aims at identifying some facts (e.g., the impulse response of the system, the underlying topology of the interconnected graph, or the initial state of the system) about the system under study from the smallest possible number of observations. Our work is inspired by the field of Compressive Sensing (CS) which is a recent paradigm in signal processing for sparse signal recovery. The CS recovery problem states that a sparse signal can be recovered from a small number of random linear measurements. Compared to the standard CS setup, however, we deal with structured sparse signals (e.g., block-sparse signals) and structured measurement matrices (e.g., Toeplitz matrices) where the structure is implied by the system under study.
A Content-Based Approach to Email Triage Action Prediction: Exploration and Evaluation

Email has remained a principal form of communication among people, both in enterprise and social settings. With a deluge of emails crowding our mailboxes daily, there is a dire need of smart email systems that can recover important emails and make personalized recommendations. In this work, we study the problem of predicting user triage actions to incoming emails where we take the reply prediction as a working example. Different from existing methods, we formulate the triage action prediction as a recommendation problem and focus on the content-based approach, where the users are represented using the content of current and past emails. We also introduce additional similarity features to further explore the affinities between users and emails. Experiments on the publicly available Avocado email collection demonstrate the advantages of our proposed recommendation framework and our method is able to achieve better performance compared to the state-of-the-art deep recommendation methods. More importantly, we provide valuable insight into the effectiveness of different textual and user representations and show that traditional bag-of-words approaches, with the help from the similarity features, compete favorably with the more advanced neural embedding methods.
The association between dietary sodium intake and the risk of cataract: data from Korean National Health and Nutrition Examination Survey 2012

Purpose: Sodium intake is known to be a critical dietary factor in several diseases including cataract. Earlier studies have reported that excess intake of sodium may elevate the risk of cataract. However, little is known about this in Koreans. Thus, the purpose of this study was to examine whether dietary intake of sodium and potassium might modify the risk of cataract. Methods: A total of 1,319 males (219 cases) and 1,966 females (369 cases) from Korean National Health and Nutrition Examination Survey 2012 were analyzed. Energy adjusted dietary intakes of sodium and potassium and their ratios were evaluated to ascertain their associations with the risk of cataract. Dietary intake levels were stratified into quartiles and their risk modifying effects were estimated with logistic regression models with or without subjects’ socio-economic characteristics and life styles for each sex. Results: Findings suggested that various descriptive factors were associated with the risk of cataract either in males or females. Males’ intake levels of sodium and potassium and their ratios did not differ between phenotypes. Higher intakes or higher ratio was not associated with the risk of cataract. In contrast, female controls had higher intakes of sodium and potassium. Higher intake of potassium reduced the risk of cataract in females. However, such association was not retained when subjects’ socioeconomic status and life styles were factored into the analysis. Conclusion: Dietary sodium and potassium intakes minimally affected the risk of cataract in Korean males and females. More studies are needed to ascertain the true pathological effect of sodium intake on cataract aetiology.
Discrete Wavelet Transform and Cross Bilateral Filter based Image Fusion

— The main objective of image fusion is to obtain an enhanced image with more relevant information by integrating complimentary information from two source images. In this paper, a novel image fusion algorithm based on discrete wavelet transform (DWT) and cross bilateral filter (CBF) is proposed. In the proposed framework, source images are decomposed into low and high frequency subbands using DWT. The low frequency subbands of the transformed images are combined using pixel averaging method. Meanwhile, the high frequency subbands of the transformed images are fused with weighted average fusion rule where, the weights are computed using CBF on both the images. Finally, to reconstruct the fused image inverse DWT is performed over the fused coefficients. The proposed method has been extensively tested on several pairs of multi-focus and multisensor images. To compare the results of proposed method with different existing methods, a variety of image fusion quality metrics are employed for the qualitative measurement. The analysis of comparison results demonstrates that the proposed method exhibits better results than many other fusion methods, qualitatively as well as quantitatively.
Effects of leaf conditions and flight activity on the behaviour of Platypus quercivorus (Murayama) (Coleoptera: Platypodidae)

The important role of semiochemicals in the interactions between plants and insects has been extensively investigated. The volatiles produced by oak trees are thought to attract the ambrosia beetle Platypus quercivorus (Murayama), which causes wilt disease in Quercus trees, resulting in widespread damage. In the present study, we hypothesized that (a) P. quercivorus is attracted to the leaf volatiles emitted by host trees and (b) the response of P. quercivorus to leaf volatiles is affected by flight. An experiment was performed to survey the preferences of both sexes of P. quercivorus for the leaf volatiles of Quercus crispula Blume at various stages of leaf deterioration, represented by different number of days after cutting. Additionally, the effect of flight on both sexes was evaluated by testing the beetle flight on a flight mill. The results showed that P. quercivorus was attracted to the volatiles emitted from fresh leaves and was not attracted to those emitted from dry leaves. This suggests that leaf volatiles from healthy hosts are primary attractants for P. quercivorus. Further, males exhibited increased olfactory responses to leaf volatiles as their flight duration increased, whereas the opposite pattern was observed in females. These results suggest that the different ecological roles of the sexes contribute to differences in olfactory responses.