Miracle-dz/UniFAR-FTUs huggingface dataset:
Lean MapReduce: A B-tree Inspired MapReduce Framework

There is a deluge of unstructured data flowing out from numerous sources, including the devices which make up the Internet-of-Things. This data flow is characterised by sheer volume, variety and velocity, and is expected to double every two years. Organizations perceive hidden value in unstructured data, but are usually constrained by budget and access to the right kind of technology in their effort to extract value. MapReduce has been adopted widely in the big data community for large scale processing of workloads. Current implementations of MapReduce run on persistent compute clusters which feature an underlying distributed file system. The clusters typically process numerous jobs during their lifetime. During periods of low or no activity, the resources are unutilized. This thesis investigates how resources can be optimally and efficiently utilized through the use of adhocly provisioned MapReduce clusters, which are grown into place for each job based on workload dimensions while meeting results deadlines. In order to achieve this, two different designs are developed based on two distinct adaptations of the B-Tree abstract data structure: a flat tree structure, which grows horizontally; and a chain structure with hanging leaves, which grows vertically. The project results show that resources are optimally and efficiently utilized, with each design implementation demonstrating individual advantages and disadvantages, for different workload dimensions.
Outline of a New Feature Space Deformation Approach in Fuzzy Pattern Recognition

Pattern recognition ability is one of the most important features that characterize intelligent behavior of either biological or artificial systems. Mathematical pattern recognition is the way to solve this problem using transparent algorithms that are mostly based on conventional mathematics. In complex systems it shows inadequacy, primary due to the needs for extensive computation and insufficient robustness. Algorithms based on soft computing approach offer a good alternative, giving a room to design effective tools for real-time application, having in mind that relevance (significance) prevails precision in complex systems. In this article is modified and extended Subtractive Clustering Method, which is proven to be effective in real-time applications, when massive pattern sets is processed. The new understanding and new relations that connect parameters of the algorithm with the information underlying the pattern set are established, giving on this way the algorithm ability to be data driven to the maximum extent. Proposed algorithm is verified by a number of experiments and few of them are presented in this article.
Features for debugging in a visual dataflow environment for end-user programmers

approved: ______________________________________________________ Christopher Scaffidi Debugging, an integral part of software development, is difficult for end-user programmers, especially in the case of complex programs. The process of isolating errors is time consuming without the help of debugging support provided by the tool. For example, the visual programming tool LondonTube supports creation of custom mobile-cloud-web applications, but previous research indicated that the users of LondonTube had questions on usage of program components and run time status of a program even while creating a simple application. To address these issues, this project was focused on creating two features, ‘Analyze’ and ‘Trace’, improving the visual programming tool by providing static analysis and runtime status for helping users to find/fix errors. A laboratory experiment evaluated the effectiveness of the prototype in comparison to the baseline (without the new features). The results of this study revealed that the users of this prototype were more satisfied with the system, took less time to complete assigned tasks, and asked fewer questions about usage of program components. ©Copyright by Laxmi Ganesan December 9, 2016 All Rights Reserved Features for debugging in a visual dataflow environment for end-user programmers
Synthesis of Benzo[b]imidazo[1,5-d][1,5]-thiazepines. Derivatives of a Novel Ring System

Abstract Derivatives of a novel tricyclic system having a 1,5-benzothiazepine skeleton are described. A method for synthesis of the title compounds 5, consisted of an intramolecular cyclization of 1-(2-carboxymethylthiophenyl)-2H-imidazol-2-ones 4 in PPA, is presented. Compounds 4 were prepared by a reaction of 1-(2-mercaptophenyl)-2H-imidazol-2-ones 3 with chloroacetic acid. 2H-Imidazolones 3 were obtained by a ring transformation of substituted 2(3H)-benzothiazolones 2 in reactions with primary amines. The method is convenient because of its simplicity and good yields.
Trustworthy and resilient time synchronization in wireless sensor networks

Wireless sensor networks have received a lot of attention recently due to its wide applications. Accurate and synchronized time is crucial in many sensor network applications. A number of time synchronization schemes have been proposed recently to address the resource constraints in sensor networks. However, all these techniques cannot survive malicious attacks in hostile environments. This dissertation includes three techniques to achieve secure time synchronization in different scopes of sensor networks. First, we develop a secure single-hop pair-wise time synchronization technique that provides time difference between two neighbor nodes using hardware-assisted, authenticated medium access control (MAC) layer timestamping. This technique can effectively defeat external attacks that attempt to mislead single-hop pair-wise time synchronization. Second, we propose a fault-tolerant cluster-wise time synchronization scheme to provide a common clock among a cluster of nodes, where the nodes in the cluster can communicate through broadcast. This scheme guarantees an upper bound of time difference between normal nodes in a cluster, provided that the malicious nodes are no more than one third of the cluster. Unlike the traditional fault-tolerant time synchronization approaches, the proposed technique does not introduce collisions between synchronization messages, nor does it require costly digital signatures. Third, we propose two secure and resilient global time synchronization schemes: level-based time synchronization and diffusion-based time synchronization. The basic idea of both schemes is to provide redundant ways for one node to synchronize its clock with another far-away node, so that it can tolerate partially missing or false synchronization information provided by compromised nodes. The level-based scheme builds a level hierarchy in the sensor network, and then synchronizes the whole network level by level. The diffusion-based scheme allows each node to diffuse its clock to its neighbor nodes after it has been synchronized. Both schemes are secure against external attacks and resilient against compromised nodes. We implement a secure and resilient global time synchronization protocol, TinySeRSync, on MICAz motes running TinyOS. The experimental results indicate that TinySeRSync is a practical system for secure and resilient global time synchronization in wireless sensor networks.
Beautiful Trees on Unstable Ground

While lexicostatistics and glottochronology were believed to be dead for a long time, the integration of stochastic methods taken from genetics has initiated an unexpected revival of these scorned disciplines. The proponents of these ’new quantitative methods’ in historical linguistics claim that the procedures are relatively robust regarding errors in the data (wrong cognate judgments, undetected borrowings or wrong translations). In order to check this claim, we have investigated the differences and errors in two large lexicostatistical datasets and tested their influence on the topologies of computed family trees. Our results show clearly that the shortcomings of lexicostatistics and glottochronology have not been overcome by these new computation methods: the main problems of lexicostatistics and glottochronology, the translation of basic concepts into individual languages and the execution of cognate judgments, are still so grave that no reliable results can be drawn from this methods.
Adaptive variable structure control of input delay non-minimum phase hypersonic flight vehicles

An adaptive variable structure control strategy is proposed for the output tracking control of input delay non-minimum hypersonic flight vehicles. The problem is challenging because of the complex nonlinearity of hypersonic flight vehicles and the existence of input delay. The nonlinear model of hypersonic flight vehicles is partially linearized, and a state tracking model is constructed based on the ideal internal dynamics of hypersonic flight vehicles. A filtered tracking error is introduced to handle the input delay. A variable structure control strategy is proposed for the stability of filtered tracking error system, and an adaptive law is established for the unknown perturbations. Finally, the effectiveness of the proposed control method is shown by the simulation results.
Single image depth estimation based on convolutional neural network and sparse connected conditional random field

Abstract. Deep convolutional neural networks (DCNNs) have attracted significant interest in the computer vision community in the recent years and have exhibited high performance in resolving many computer vision problems, such as image classification. We address the pixel-level depth prediction from a single image by combining DCNN and sparse connected conditional random field (CRF). Owing to the invariance properties of DCNNs that make them suitable for high-level tasks, their outputs are generally not localized enough for detailed pixel-level regression. A multiscale DCNN and sparse connected CRF are combined to overcome this localization weakness. We have evaluated our framework using the well-known NYU V2 depth dataset, and the results show that the proposed method can improve the depth prediction accuracy both qualitatively and quantitatively, as compared to previous works. This finding shows the potential use of the proposed method in three-dimensional (3-D) modeling or 3-D video production from the given two-dimensional (2-D) images or 2-D videos.
Low-Fidelity Prototyping for Multi-Touch Surfaces

This paper presents two low-fidelity prototyping methods for multi-touch tabletop applications. The prototypes, only consisting of basic ‘physical’ materials such as paper, cardboard, markers, scissors, etc. were designed to evaluate distinct aspects of the same multi-touch platform. One prototype allowed observing tabletop interaction in general: how users interact with each other and the surface they work on. The second prototype allowed the design team to test how users react to new, innovative multi-touch controls. Introducing these prototyping methods, this paper describes a way to iterate and test designs at an early stage in the development cycle.
Dietary salt reduction for control of hypertension

In developed as well as developing countries, the four main factors affecting blood pressure are high salt intake, low potassium intake, overweight, and low physical activity level. This is also true for the increase in blood pressure with advancing age, occurring in all societies. It is now accepted that excess dietary salt raises blood pressure levels, whereas dietary salt reduction reduces blood pressure and prevents vascular complications. (1) The effect of salt on blood pressure is presumably due to the inability of the kidneys to excrete large amounts of salt, as humans are evolutionary adapted to ingest and excrete less than 1 gram of salt per day. (2) In this connection it should be noted that the more important element in common salt (sodium chloride) is the sodium ion, and any restrictions applying to common salt also apply to all food items that contain sodium ions, such as sodium glutamate and baking soda.
Selection of Eciently Adaptable Clustering Algorithm upon Base Station Failure in Wireless Sensor Network

Wireless Sensor Network (WSN) applications have increased in recent times in fields such as environmental sensing, area monitoring, air pollution monitoring, forest fires detection, machine health monitoring, and landslide detection. In such applications, there is a high need of secure communication among sensor nodes. There are different techniques to secure network data transmissions, but due to power constraints of WSN, group key based mechanism is the most preferred one. Hence, to implement scalable energy efficient secure group communication, the best approach would be hierarchical based like Clustering. In most of the WSN designs based on clustering, Base Station (BS) is the central point of contact to the outside world and in case of its failure; it may lead to total disconnection in the communication. Critical applications like these cannot afford to have BS failure as it is a gateway from sensor networks to the outside world. In order to provide better fault tolerant immediate action, a new BS at some other physical location will have to take the charge. This may lead to a total change in the hierarchical network topology, which in turn leads to re-clustering the entire network and in turn formation of new security keys. Therefore, there is a need to find a suitable algorithm which clusters sensor nodes in such a way that when a BS fails and a new BS takes the charge, new group key gets established with minimum computation and less energy consumption. A survey on various clustering algorithms like Energy-Efficient Multi-level Clustering Algorithm for Large-scale WSNs (EEMC), Low Energy Adaptive Clustering Hierarchy Protocol for WSNs (LEACH), Power Efficient and Adaptive Clustering Hierarchy Protocol for WSNs (PEACH), Hybrid, Energy-Efficient, Distributed Clustering Approach for WSNs (HEED) and Energy Efficient Clustering Algorithm for Maximizing Lifetime of WSNs (EECML) has been done to analyze their performance with respect to various parameters like power consumption of sensor nodes, cluster overlapping, cluster stability, fault tolerance and node-cluster information based on the literature survey and on the basis of these parameters we came to know that HEED and EECML are probably the most suitable algorithms to achieve our aim. So HEED and EECML are implemented on TinyOS, an open source platform for building WSN applications and their performance is evaluated for required parameters. From evaluation it is found that EECML could be the most suitable algorithm that makes a WSN adaptable upon failure of BS.
Mitigating the Impact of Mutual Interference in IEEE 802.15.4-based Wireless Body Sensor Networks

Wireless Body Sensor Networks (WBSNs) are deployed in health-related applications and their usage has rapidly increased in the past decade. Collecting various human vital signals such as heart rate or blood pressure has been made possible with the usage of WBSNs that can be implanted in or attached on the human body for monitoring and other purposes. WBSN technology can be beneficial for patients in terms of being monitored almost at all times, and signalling professional care-givers if required. Although WBSNs have significant advantages in both medical and non-medical fields, their characteristics create quite a number of issues, including mutual interference, inefficient frequency spectrum utilisation, limited resources, communication capabilities and storage capacity, and energy constraints. One of the most challenging issues is the mutual interference caused by neighbouring WBSNs. As the number of co-located IEEE 802.15. 4-based WBSNs becomes larger in an operating frequency, their performance will be eventually influenced by the mutual interference. In fact, mutual interference eventually causes significant performance degradation due to inefficient channel utilisation. We have investigated the impact of mutual interference on WBSN performance and a number of effective solutions is proposed to enable IEEE 802.15. 4-based WBSNs to share and better utilise timeand frequency resources. More specifically, the adaptive resource allocation schemes considered in this thesis aim to improve the packet transmission reliability and to reduce the overall energy consumption in the presence of mutual interference. To accomplish this, a number of distributed schemes are proposed that step-by-step improved the packet transmission reliability and reduced the overall energy consumption. Thereafter, the sensitivity of the per-
The Design of Cellphone Indoor Positioning System Based Magnetic Assisted Inertial Navigation Technology

To implement indoor positioning,sufficient signal nodes fast attenuated by the interference of the con-struction and consequently cause the low positional accuracy,are mostly required. In order to avoid the existingproblem,by further studying the technologies of indoor positioning,a positioning algorithm with inertial navigationtechnology assisted geomagnetic matching that suits for cellphone terminal indoor positioning is presented. Differentfrom the traditional outdoor positioning system,by taking advantage of differentiated information of Earth's magnet-ic field at different points and selecting an appropriate geomagnetic matching algorithm,the indoor positioning ofcellphone devices without depending on external devices can be achieved,simultaneously applying the combined lo-calization method of inertial navigation-assisted geomagnetic positioning improve the indoor positioning accuracy.Finally,a cellphone indoor positioning software which is based on Android platform is novelly and successfully de-signed with mobile built-in sensors,Simulations and experiments show that this localization method is effective.
Multiple Purpose Annotation using SLAT — Segment and Link-based Annotation Tool —

In recent years, the use of large scale corpora in NLP applications, such as statistical parsing, has become prominent. As their use gained credibility, naturally so did the types of information they provided. There exist today many groups that create corpora: ANC, SFB at the University of Potsdam, just to name a few. In many cases these groups also provide specialized annotation tools for their corpora. However, these tools are just that: specialized, i.e. designed to work with a very specific annotation definition, without flexibility in mind. In the early stages of a project, often times the specification for annotating changes. This makes it difficult to use a tool with such rigid boundaries. In this paper, we propose a browser-based annotation tool SLAT, which allows for easily adding and customizing annotations. We also explain the steps involved in customizing SLAT to meet a user’s project needs.
Iterative and Incremental development

In my last column I took a satirical look at the waterfall process, trying to expose some of its faults. This month we are going to look at an alternative that tries to deal with those faults. But before we begin I’d like to say: Thank goodness that waterfall showed up in the early seventies! Back in those days, there was very little thought put into the structure of a software application prior to coding it. If waterfall did nothing else, it got us to accept the need to think before we code.
A study on the use of conditional random fields for automatic speech recognition

Current state of the art systems for Automatic Speech Recognition (ASR) use statistical modeling techniques such as Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) to recognize spoken language. These techniques make use of statistics derived from the acoustic frequencies of the speech signal. In recent years, interest has been rising in the use of phonological features derived from these acoustic frequency features in addition to, or in place of, the acoustic frequency features themselves. These phonological features are derived from the manner that speech is physically produced in the vocal tract of the speaker, rather than models of how speech is heard by the listener. Integrating phonological features into ASR models presents new challenges. The mathematical assumptions made to build current models may work well for features derived from acoustic frequencies, but do not necessarily fit phonological features as nicely. Explorations into how to alter the mathematical models to allow for this new type of input feature is an ongoing area of ASR research. This dissertation examines the use of the statistical model known as a Conditional Random Field (CRF) for ASR using phonological features. CRFs are statistical models of sequences that are similar to HMMs, but CRF models do not make any assumptions about the independence or interdependence of the data being modeled. This dissertation provides (1) a CRF-based pilot system is able to achieve superior performance in a phonetic recognition task to a comparably configured HMM model, and achieve this performance with many fewer parameters, (2) an extension of this model to create new features for an HMM-based system for word recognition, and (3) a fully developed system for word recognition using CRFs.
Multi-objective Decision-making Method of Green Product in Manufacturing Based on Entropy-TOPSIS Method

The green products in manufacture industry still differ from each other in terms of economics, technology, environmental friendliness, though they all reach the criteria of limitation. This paper expounds the necessity for product choice optimization and provides a model based on information entropy to judge different advantages of product groups. According to Entropy Weight Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the basic theory of green product evaluation model is established. The index system includes selected evaluation indicators of green products, considering about the evaluation index of the critical values to construct a hierarchy criterion, which uses entropy to objectively determine the weight vector of each evaluation index. Then, combined with TOPSIS analysis approach and calculates the pros and cons of green products. It is concluded that the evaluation results based on the Entropy-TOPSIS evaluation model are consistent with the actual results, and the results are consistent with the evaluation results of fuzzy mathematics. The method comprehensively considered many influential factors of the green products, to avoid the limitation of single criterion, and the importance of various factors are analyzed and compared, the prediction result is more scientific, as the theoretical basis of green products, and provide a reliable guidance method for product improvement. This is objective to empower the law, which also fully embodies the idea of variable weights and overcome the past green products that exist in the evaluation process homogeneity empowering. That means fixed weights lead to the limitation of a lack of flexible evaluation.