Miracle-dz/UniFAR-FTUs huggingface dataset:
A case study of technology transfer in the field of knowledge-based systems: the 9-ingredients approach

This paper presents a case study of a successful technology transfer project in the field of knowledge-based systems. The project concerns the development of a knowledge-based system application, called ASTRA, for state assessment, preventive diagnosis, and intervention planning of power transformers. Generalizing from this experience, a new approach, called the 9-ingredients approach, to KBS technology transfer is then defined, and a collection of general rules for its practical and successful implementation are provided. The 9-ingredients approach is claimed to guarantee effective project management and reasonable risk control.
A Probabilistic Approach for Object Recognition in a Real 3-D Office Environment

The scenario used focuses on object recognition in an office environment scene with the goal of classifying office equipment that is located on a table. The recognition system operates on three-dimensional point-clouds of objects on a loosely covered table where no previous information about the precise position of the table is given. As the point-clouds do not cover the complete objects and the data is noisy, especially for smaller objects a robust detection of special features is difficult. The workflow employed is a three step process: In a first step the table plane is detected and the point clouds of the objects are extracted from the surface. In the second step an object-oriented bounding-box is calculated to get the geometric dimensions, i.e. the properties measured. During a learning phase these simple features are used to calculate the parameters of Bayesian networks. The trained networks are used in the third step, i.e. the classification step. The dimensions of an unknown object form the input for a Bayesian network that yields the most probable object type.
Application of MPC with multiple objective for a solar refrigeration plant

This paper presents a multiobjective model predictive control algorithm which allows the specification of different control goals at different operating points. The algorithm is illustrated with an application to a very detailed model of a solar cooling plant. The plant is used to cool down an office building with varying operating conditions. The plant is a nonlinear multivariable process subject to high disturbances and changing operating loads.
Automatic detection of large object features from SAR data

The feature extraction operation, from SAR images, encounters many difficulties due to the high level of noise and the poor quality of the object contour. This paper aims towards an implementation of an automated algorithm for extracting features of large objects such as rivers, lakes, or highways from SAR imagery. The method uses a watershed algorithm to detect homogeneous areas. The above areas are then grouped into probable objects using heuristic reasoning about the similarity between neighbouring regions and the difference between larger background areas. Results from SAR images of the Ottawa area are given. <<ETX>>
An approach to a fuzzy color detection method, robust with regard to variable illuminant level conditions

We propose a method for characterizing a set of training colors, based on sample color pixels captured with a camera. The system tries to detect the training colors on test pixels within images captured under illumination level conditions different from those used in the training process. The training color characterization is based on Smith's perceptual color model, but using only hue and saturation components. Both, training and classification processes make use of fuzzy techniques to assume vagueness involved within training and test data.
Role of Mobile Technology in Promoting Campus-Wide Learning Environment

The present study examines the role of mobile technology in promoting campus-wide learning environment. Its main objectives were to a) evaluate the role of mobile technology in higher education in terms of its i). appropriateness ii). flexibility iii). Interactivity, & iv). availability & usefulness and to b). identify the problems of students with mobile technology. The population of the study consisted of all the Ph.D scholars (N= 83) and faculty (N=10) of the Department of Education, International Islamic University, Islamabad. The samples of the study comprised of 100% of both the populations. The study was descriptive in nature, therefore survey approach was considered appropriate. The researchers used questionnaires as research tools developed on five point rating (likert) scale to collect the data from the respondents (students and faculty respectively). The researchers personally administered the finalized research tools (through pilot testing) to the respondents to collect the data. The data collected was analyzed through applying the mean score and percentage and main findings were (i) 92% of the respondents (students and faculty) were of the opinion that mobile technology is appropriate for effective communication and interaction (ii). 94% of the respondents ((students and faculty) were of the view that mobile technology promotes flexible interactive learning environment (iii). 93% of the respondents (students) supported that they do face problems with technology.
Using Data Envelopment Analysis to benchmark logistic performance in Belgian manufacturing companies

Performance evaluation and benchmarking are widely used methods to identify and adopt best practices as a means to improve performance and increase productivity, and are particularly valuable when no objective or engineered standard is available to define efficient and effective performance. DEA is a linear programming technique used to evaluate the efficiency of decision-making units (DMUs) where multiple inputs and outputs are involved. The main idea of DEA is to extend the traditional concept of productivity or efficiency (ratio of output and input) to make it suitable for the multiple-input multiple-output case. We will use the Excel implementations introduced in Zhu, J., (2003), to study a set of data from 80 Belgium companies that includes some logistic measurements, general information and best practices adoption rates. The aim of this work is to discuss the ways DEA can be useful in benchmarking, and, in an effort to address the unique needs of SMEs to develop easy-to-understand, graphical ways to display the analyses' results.
Can epidemic models describe the diffusion of research topics across disciplines

This paper introduces a new approach to describe the spread of research topics across disciplines using epidemic models. The approach is based on applying individual-based models from mathematical epidemiology to the diffusion of a research topic over a contact network that represents knowledge flows over the map of science –as obtained from citations between ISI Subject Categories. Using research publications on the protein class kinesin as a case study, we report a better fit between model and empirical data when using the citation-based contact network. Incubation periods on the order of 4 to 15.5 years support the view that, whilst research topics may grow very quickly, they face difficulties to overcome disciplinary boundaries.
The Seamstress Library

Copyright c ⃝ 2007 Hayden Walles. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the section entitled “GNU Free Documentation License”.
Machine learning algorithms with applications in finance

Online decision making and learning occur in a great variety of scenarios. The decisions involved may consist of stock trading, ad placement, route planning, picking a heuristic, or making a move in a game. Such scenarios vary also in the complexity of the environment or the opponent, the available feedback, and the nature of possible decisions. Remarkably, in the last few decades, the theory of online learning has produced algorithms that can cope with this rich set of problems. These algorithms have two very desirable properties. First, they make minimal and often worst-case assumptions on the nature of the learning scenario, making them robust. Second, their success is guaranteed to converge to that of the best strategy in a benchmark set, a property referred to as regret minimization. This work deals both with the general theory of regret minimization, and with its implications for pricing financial derivatives. One contribution to the theory of regret minimization is a trade-off result, which shows that some of the most important regret minimization algorithms are also guaranteed to have non-negative and even positive levels of regret for any sequence of plays by the environment. Another contribution provides improved regret minimization algorithms for scenarios in which the benchmark set of strategies has a high level of redundancy; these scenarios are captured in a model of dynamically branching strategies. The contributions to derivative pricing build on a reduction from the problem of pricing derivatives to the problem of bounding the regret of trading algorithms. They comprise regret minimization-based price bounds for a variety of financial derivatives, obtained both by means of existing algorithms and specially designed ones. Moreover, a direct method for converting the performance guarantees of general-purpose regret minimization algorithms into performance guarantees in a trading scenario is developed and used to derive novel lower and upper bounds on derivative prices.
Semantic Analysis and Structured Language Models

An integrated semantic analysis system is presented, and the structured language models are proposed based on it. The semantic analysis system can automatically tag semantic class for each word and analyze the semantic dependency structure between words with the precision of 90.85% and 75.84% respectively. In order to describe sentence structure and long-distance dependency, two kinds of structured language models are examined and analyzed. Finally, these two language models are evaluated on the task of Chinese speech recognition. Experiments show that the best semantic structured language modelheadword trigram modelachieves 0.8%
A Criteria based Decision Tree for Classification

A Decision Tree algorithm (DTA) from data is created using construction criteria and considering also the don’t care attributes, for each level of the tree. The DTA goal is to create on-demand a short and accurate decision tree from either data or a stable (or dynamically changing) set of rules . A set of steps provides a decision tree with definite number of nodes and leaves. The pruning of decision rules case is also examined with the consequences on the accuracy. An improved version of DTA (IDTA) provides smaller DT and eliminates branches by using the criterion of less length (CLL).
Distributed adaptive consensus control of heterogeneous multi-agent chaotic systems with unknown time delays

In this study, the distributed consensus tracking problem for heterogeneous multi-agent chaotic delayed non-linear systems is addressed. Each follower is modelled as a chaotic system with non-identical and unknown time delays, non-linear dynamics, and disturbances. The general case of directed communication graphs is considered and the graph structure is taken into account in the control protocol design. Distributed controllers with an adaptive learning feature based on neural network approximations are designed to guarantee that all agents synchronise to the leader's state with bounded synchronisation error. A graph-dependent Lyapunov proof provides error bounds that depend on the maximum time delays, the graph topology, and the agent dynamics properties. Special cases of the dynamics considered here cover many examples in the literature, including master–slave synchronisation of two chaotic delayed systems. A simulation with multiple non-identical chaotic agents is given to verify the effectiveness of the proposed method.
A block shifting method for reduction of blocking effects in subband/wavelet image coding

In this paper, we propose a new method to reduce blocking effects in subband/wavelet image coding. Unlike the traditional postprocessing approaches, by using the block shifting method, the blocks in different bands are coded with different shifting positions. After subband/wavelet reconstruction, the block edges of different bands can be separated to reduce blocking effects. The simulation results are demonstrated to verify the proposed method.
Smart antenna testbed for DS-CDMA systems

In CDMA communication systems, smart antenna can be used to suppress multipath fading with antenna diversity and to increase the system capacity by supporting multiple co-channel users. Although many existing and new array signal processing techniques are being proposed for the smart antenna scheme, most of the performance evaluation to date relied on computer simulations or theoretical analysis based on typical model or assumptions. The paper shows a smart antennas testbed adopting a scheme using DOA (direction of arrival) estimation and beam tracking technology. The conformation and system parameters of the testbed is presented, and the new algorithms for DOA estimation and pattern synthesis are proposed at the same time. The practical experimental results show the good performance of the smart antenna testbed.
A universal 0.03-mm2 one-pin crystal oscillator in CMOS

A one-pin crystal oscillator with an integrated load capacitance of 15 pF has been realized in a standard 0.35-/spl mu/m CMOS technology. Due to the structure of the oscillator and the use of MOS gate capacitance for the load capacitors, the chip area can be very small. The total active area including load capacitors is less than 0.03 mm/sup 2/. The design can be operated with supply voltages in the range of 1.4-3.6 V and allows crystal frequencies in the range of 3-30 MHz. The current consumption of the oscillator core is 180 /spl mu/A at 10 MHz with 3.3-V power supply. It produces a rail-to-rail output swing, regulated by an amplitude control loop, and has the same flexibility and ease of frequency tuning as a common Pierce oscillator. As no special IC process options are required, the design is very suitable for clock generation in digital very-large-scale-integration chips.
Can You Summarize This? Identifying Correlates of Input Difficulty for Multi-Document Summarization

Different summarization requirements could make the writing of a good summary more difficult, or easier. Summary length and the characteristics of the input are such constraints influencing the quality of a potential summary. In this paper we report the results of a quantitative analysis on data from large-scale evaluations of multi-document summarization, empirically confirming this hypothesis. We further show that features measuring the cohesiveness of the input are highly correlated with eventual summary quality and that it is possible to use these as features to predict the difficulty of new, unseen, summarization inputs.
Creating a Concurrent In-Memory B-Tree Optimized for NUMA Systems

The size of main memory is becoming larger. With the number of Central Processing Unit (CPU) cores ever increasing in modern systems, with each of them being able to access memory, the organization of memory becomes more important. In multicore systems, there are two main architectures for memory organization with respect to the cores Symmetric Multi-Processor (SMP) and Non-Uniform Memory Architecture (NUMA). Prior work has focused on the improvement of the performance of B-Trees in highly concurrent and distributed environments, as well as in memory, for shared-memory multiprocessors. However, little focus has been given to the performance of main memory B-Trees for NUMA systems. This work focuses on improving the performance of B-Trees contained in main memory of NUMA systems by introducing modifications that consider its storage in the physically distributed main memory of the NUMA system. The work in this thesis makes the following contributions to the development of a distributed B-Tree, specifically in a NUMA environment, modified from a B-Tree originally designed for high concurrency: • It introduces replication of internal nodes of the tree and shows how this can improve its overall performance in a NUMA environment. • It introduces NUMA-aware locking procedures with the aim of managing contention and exploiting locality of lock requests with reference to previous client operation request locations. • It introduces changes in the granularity of locking, starting from the original locking of every node to the locking of certain levels of nodes, showing the tradeoff between the granularity of locking and the performance of the tree based on the workload. • It considers the combination of the different techniques, with the aim of finding the combination which performs well overall for varying read-heavy workloads and number of client threads.
Bus Functional Model (BFM) Simulation of Processor Intellectual Property

This note provides the flow for simulating Processor Intellectual Property (PIP) cores using Bus Functional Models (BFMs). The PIP cores used as examples include a new core, existing cores, and a sample system core. The simulation results of the OPB GPIO core are given to illustrate the capability of a BFM simulation. An introduction to writing stimuli for BFM simulation using a Bus Functional Language (BFL) in conjunction with a VHDL test bench is given. The example designs are intended to aid in the understanding the functionality of PIP cores. The comments in the code indicate the function being verified. The user can learn techniques for writing test benches/BFLs by reading and re-using the example code. The references provide information for users interested in writing complex or exhaustive test bench/BFLs. Included Files This application note includes BFM simulation files: www.xilinx. com/bvdocs/appnotes/xapp516.zip
Predictive Modeling for Navigating Social Media

Social media changes the way people use the Web. It has transformed ordinary Web users from information consumers to content contributors. One popular form of content contribution is social tagging, in which users assign tags to Web resources. By the collective efforts of the social tagging community, a new information space has been created for information navigation. Navigation allows serendipitous discovery of information by examining the information objects linked to one another in the social tagging space. In this dissertation, we study prediction tasks that facilitate navigation in social tagging systems. For social tagging systems to meet complex navigation needs of users, two issues are fundamental, namely link sparseness and object selection. Link sparseness is observed for many resources that are untagged or inadequately tagged, hindering navigation to the resources. Object selection is concerned when there are a large number of information objects that are linked to the current object, requiring to select the more interesting or relevant ones for guiding navigation effectively. This dissertation focuses on three dimensions, namely the semantic, social and temporal dimensions, to address link sparseness and object selection. To address link sparseness, we study the task of tag prediction. This task aims to enrich tags for the untagged or inadequately tagged resources, such that the predicted tags can serve as navigable links to these resources. For this task, we take a topic modeling approach to exploit the latent semantic relationships between resource content and tags. To address object selection, we study the task of personalized tag recommendation and trend discovery using social annotations. Personalized tag recommendation leverages the collective wisdom from the social tagging community to recommend tags that are semantically relevant to the target resource, while being tailored to the tagging preferences of individual users. For this task, we propose a probabilistic framework which leverages the implicit social links between like-minded users, i.e. who show similar tagging preferences, to recommend suitable tags. Social tags capture the interest of the users in the annotated resources at different times. These social annotations allow us to construct temporal profiles for the annotated resources. By analyzing these temporal profiles, we unveil the non-trivial temporal trends of the annotated resources, which provide novel metrics for selecting relevant and interesting resources for guiding navigation. For trend discovery using social annotations, we propose a trend discovery process which enables us to analyze trends for a multitude of semantics encapsulated in the temporal profiles of the annotated resources.
A Study Of Machine Learning Classifiers for Anomaly-Based Mobile Botnet Detection

In recent years, mobile devices are ubiquitous. They are employed for purposes beyond merely making phone calls. Among the mobile operating systems, Android is the most popular due to its availability as an open source operating system. Due to the proliferation of Android malwares, it is crucial to study the best classifiers that can detect these malwares effectively and accurately through selecting the most suitable network traffic features as well as comprehensive comparison with related works. This study evaluates five machine learning classifiers, namely Naive Bayes, k-nearest neighbour, decision tree, multi-layer perceptron, and support vector machine. The evaluation was validated using malware data samples from the Android Malware Genome Project. The data sample is a collection of malwares gathered between August 2010 and October 2011 by the University of North Carolina. Among various network traffic characteristics, three network features were selected: connection duration, TCP size and number of GET/POST parameters. From the experiment, it is found that knearest neighbour provides the optimum results in terms of performance among the classifiers. The experimental results also indicate a true positive rate as high as 99.94% and false positive of 0.06% for the knearest neighbour classifier.
Peer-to-Peer Human Computation & “Help Me Decide”: Enabling search users to help other users make purchase decisions

In this paper, we propose the notion of Peer-to-Peer Human Computation, where users help each other, mediated by a platform that connects people who request help to people who offer help, acts as a repository of acquired information, and learns from users‟ questions and advice. We compare this idea to related ideas such as human computation and social search. As an example, we describe a peer-to-peer human computation system named Help Me Decide, designed to assist search users make complex purchase decisions. We list design goals for such a system. We describe the architecture of this system, and detail a prototype of a subset of the Help Me Decide system that was developed and deployed to several hundred users in Microsoft. We discuss the incentive system we developed. We summarize the statistics collected on the usage of the Help Me Decide prototype and the results from a survey of the users. We contrast this system with other related systems. We conclude with some thoughts about next steps.
A DP-BASED CHECKPOINTING SCHEME IN REAL-TIME APPLICATIONS

In this paper, we consider computation algorithms for checkpoint placement in real-time applications. Under the condition that the processing time is bounded by a time limit, we derive sequentially the optimal checkpoint time based on the dynamic programming. In numerical examples, we examine the dependence of optimal checkpoint sequence on both failure time and processing time distributions, and investigate the effectiveness of sequential checkpoint placement.