Miracle-dz/UniFAR-FTUs huggingface dataset:
A heuristic to predict the optimal pattern-growth direction for the pattern growth-based sequential pattern mining approach

Sequential pattern mining is an efficient technique for discovering recurring structures or patterns from very large datasets, with a very large field of applications. It aims at extracting a set of attributes, shared across time among a large number of objects in a given database. Previous studies have developed two major classes of sequential pattern mining methods, namely, the candidate generation-and-test approach based on either vertical or horizontal data formats represented respectively by GSP and SPADE, and the pattern-growth approach represented by FreeSpan, PrefixSpan and their further extensions. The performances of these algorithms depend on how patterns grow. Because of this, we introduce a heuristic to predict the optimal pattern-growth direction, i.e. the pattern-growth direction leading to the best performance in terms of runtime and memory usage. Then, we perform a number of experimentations on both real-life and synthetic datasets to test the heuristic. The performance analysis of these experimentations show that the heuristic prediction is reliable in general.
Improved color flow mapping using the wideband maximum likelihood estimator

The use of the wideband maximum likelihood estimator (MLE) is shown to improve the color flow mapping of the blood velocity profile. Using a transmitted signal with a significant fractional bandwidth, it is shown that the wideband MLE improves the quality of the color flow estimate and simultaneously allows an increase in the frame rate. It is the improved global accuracy of the estimate, with the use of a new signaling scheme, that allows an increase in the frame rate of the display. The signaling scheme interleaves transmission of pulses in two or three directories. This interleaving, with nonperiodic transmission, reduces the total number of pulses required to achieve a fixed level of local and global accuracy. To study the overall estimator performance, the authors evaluate the expected estimator output and demonstrate that the width of the mainlobe and height of subsidiary peaks are reduced through the use of this estimator and signaling scheme. <<ETX>>
Identifying content of low profile target in cluttered environment using the BioSonar

In the mine counter measures context image based automatic target recognition has two main limitations: in a cluttered and heavily cluttered environment recognition algorithms observe a drastic increase in the PFa (probability of false alarm) making the output results impractical at the best. The second main limitation is unknown threats such as IEDs (Improvised explosive devices) or simply unknown types of mines. In this paper we present classification results from a trial done in Portland harbour, Weymouth, UK in October 2012. The test targets were 9 identical gas cylinders (65 cm height, 30 cm diameter) filled with three different contents: 3 cylinders were filled with seawater, 3 with sand and 3 with gravel. The targets were placed on two different highly reflective seabed types (mud with broken shells). The estimated SNR of these low profile targets in these difficult environments is negative making them almost undetectable in traditional sidescan images. We demonstrate here the capability of the wideband BioSonar to identify and distinguish between the 3 types of cylinders. We present in situ learning using spiral reacquisition pattern and show that the wideband classification is robust to environment variations.
A Novel Fusion Method for Dynamic Updating in Inverted Index Establishment

The establishment of inverted index is the main content of index system. In this paper, we propose a novel fusion method using dynamic updating. Firstly, the establishing process of the inverted index is introduced. Secondly, the regimes of the dynamic updating for hash lists based index are discussed in details. Experimental results show that this method can save index merging time as well as spare some memory space.
Evanescent Mentation: An Amelioative Conceptual Foundation for Research and Theory on Message Production

Current theorizing about message encoding can be seen to reflect a characterization of the phenomenon that is overly static, coherent, and uniplanar, including some variations on the uniplanar theme–too verbal, too propositional, and too mentalistic. This paper examines each of these points of received understanding and suggests the need for approaches that are better able to capture the fluid, disjointed, and multiplanar character of messages and message production. The dominant approach to message production, the goals-plans-action framework, is then examined in light of this alternative characterization, with the result being the emergence of a rich set of new conceptual issues and questions. Finally, the potential of a particular theory of message production, second-generation action assembly theory (Greene, 1997), for addressing these issues is examined.
Segmentation of Retail Mobile Market Using HMS Algorithm

In the modern world of marketing, analyzing the trends in market is a key point towards to scope of improvement of any company. Considering the analysis of a retail market where market trends change very frequently based on customer needs and interest is highly challenging. Market segmentation is one of the approaches included in analysis of market trends which gives a diverse view of the market. The research here concentrates, especially on a case study based on fast moving consumable goods market and identifying market change patterns by applying a novel data mining approach. Data mining includes a wide variety of techniques and algorithm which can be effectively used in the process of market analysis. The research work carried out coins a new algorithm which combines various association rules and techniques, the HMS (Hybrid market segmentation) algorithm with some specialized criteria is used to support the market segmentation. The primary data needed for the analysis and operation are collected through a questionnaire based survey conducted on people from various demographic regions as well as various age groups. Used a quota based sampling approach for the research, The data mining approach here helps to study the large dataset collected and also to extract the useful information required to model the system. The system here is a learning system which improves the market segmentation functionality as data set improves, The paper implements a hybrid data mining approach which effectively segments the retail mobile market in to various customer and product groups and also provides a prediction and suggestion system for company as well as customer.
A sliding-box approach to detecting people in images of indoor environments using wide-baseline stereo camera systems

Over the past two decades, there has been a massive increase in the use of digital cameras in surveillance systems and monitoring activities. This has made it difficult for human operators to provide 100% coverage at all times. The ability to detect human forms in video images has generated a great deal of interest among researchers in the computer vision community who are working on the design of automatic visual surveillance tools. Previous studies of this topic were strongly based on image processing techniques, which in spite off their speed and simplicity are sensitive to changes in lighting, occlusion and the variability of human poses. Recently, machine learning approaches based on sliding windows have proven to be successful in people detection. This significant success is due in part to the use of powerful machine learning models, new and more informative visual features, and part-based models which cope with object variability. A common denominator of these techniques is that they rely mainly on statistical learning methods that exploit image-intensity information to capture object appearance features. An important limitation of appearance-based approaches is that they do not incorporate relevant geometric information that can provide important, useful spatial cues such as the real size of the object to be detected, depth, and likely spatial appearance location in the scene. A few recent works consider the benefits of including information from various viewpoints. Detection using one camera is suitable when there is mild occlusion, however, if there is heavy occlusion multiple views help to improve final detection. Despite the existence of techniques for linking information across multiple views, significant challenges remain. In this thesis, we propose a multiple view detection approach in order to bridge
An Approach for Protecting the OpenFlow Switch from the Saturation Attack

Security is always a serious issue influencing the development of Software-Defined Network (SDN). The central control mechanism makes the SDN controller a bottleneck of the network which is vulnerable to network saturation attack. In this paper, we propose an approach to defense this kind attack. Firstly, we add a miss matched packet cache module in the OpenFlow switch which can temporarily cache the packets that don't match in the flow table. Besides, we apply the mechanism of separating the header and payload of packets in the cache queue once the switch detects the volume of cache queue exceeding the threshold of the cache size. In addition, the switch can classify the packets headers and send it in an alert message to the SDN controller for further processing. At last in the paper, we evaluate the effort of our proposed approach in Mininet. With our approach, the SDN network can effectively defend the network saturation attack.
Technical Note: Influence of the Frame on the Sound Reduction Index of a Window

The Sound Reduction Index of 1.2 × 1.2 m windows composed of two identical pivoting leaves of three different thickness of laminated glass and inserted in two different frames (aluminium and PVC) had been measured using pressure and sound intensity techniques. The Sound Reduction Index has been also predicted. These results have allowed us to compare the relative importance of the frame acoustic isolation effectiveness against the glass pane effectiveness in order to achieve an overall window Sound Reduction Index.
From Words to Numbers: A Set Theory Framework for the Collection, Organization, and Analysis of Narrative Data

Recent advances in computer-assisted content analysis have made available to historians and social scientists much richer and more flexible historical records from textual sources (e.g., archives, newspapers). These records are organized in complex data structures (namely, separate but interconnectedfiles). Furthermore, the content of these records is mostly words. This paper shows how narrative data organized in complex data structures can be analyzed statistically. It also shows that powerful linguistic schemes for content analysis (namely, semantic grammars) can be reexpressed as set theoretical problems. Set theory also provides the mathematical foundation for the relational data model that can be used to store text data collected via a semantic grammar. Finally, set theory provides the basic tools (namely, the cardinal number) necessary to go "from words to numbers. " It is this basic transformation that allows researchers to perform general kinds of quantitative analyses on such qualitative data as words.
Modeling Multiple Granularities of Spatial Objects

People conceptualize objects in an information space over different levels of details or grantrkarities and shift among these granularities as necessary for the task at hand. Shifting among granularities is f~~ndamental for understanding and reasoning about an information space. In general, shifting to a coarser granularity can improve one's understanding of a complex information space, whereas shifting to a more detailed granularity reveals information that is otherwise unknown. To arrive at a coarser granularity. objects must be generalized. There are multiple ways to perform generalization. Several generalization methods have been adopted from the abstraction processes that are intuitively carried out by people. Although, people seem to be able to carry out abstractions and generalize objects with ease. formalizing these generalization and shifts between them in an information system, such as geographic inforniation system, still offers many challenges. A set of rules capturing multiple granularities of objects and the use of these granularities for enhanced reasoning and browsing is yet to be well researched. This thesis pursues an approach for arriving at multiple granularities of spatial objects based on the concept of coarsening. Coarsening refers to the process of transforming a representation of objects into a less detailed representation. The focus of this thesis is to develop a set of cocrrsening opernkws that are based on the objects' attributes, attribute values and relations with other objccts, such as containment, connectivity, and nearness. for arriving at coarser or amalgamated objects. As a result. a set of four coarsening operators-group, C O I ~ ~ O S C , coexis\, and jiller are defined. A framework, called a g~~unzilcrr.i\y grcrph. is presented for modeling the application of coarsening operators iteratively to form amalgamated objects. A granularity graph can be used to browse through objects at different granularities, to retrieve objects that are at different granularities, and to examine how the granularities are related to each other. There can occur long sequenccs of operators between objects in the graph, which need to be simplified. Con~positions of coarsening operators are deril~ed to collapse or simplify the chain of operators. The semantics associated with objects amalgamations enable to determine correct results of the con~positions of coarsening operators. The composition of operators enables to determine all the possible ways for arriving at a coarser granularity of objects from a set of objects. Capturing these different ways facilitates enhanced reasoning of how objects at multiple granularities are related …
Second-Generation UMTRI Coding Scheme for Classifying Driver Tasks in Distraction Studies and Application to the ACAS FOT Video Clips SAfety VEhicles using adaptive Interface Technology (SAVE-IT Project) Task 3C: Performance

This report describes the development of a new coding scheme to classify potentially distracting secondary tasks performed while driving, such as eating and using a cell phone. Compared with prior schemes (Stutts et al., first-generation UMTRI scheme), the new scheme has more distinctive endpoints for tasks and subtasks, is less subjective (e.g., no “high involvement” eating), includes codes for activities absent from prior schemes (e.g., chewing gum), and more closely links subtasks to visual, auditory, cognitive, and psychomotor task demands. The scheme has codes for 12 tasks (use a cell phone, eat/drink, smoke, chew gum, chew tobacco, groom, read, write, type, use an in-car system, internal distraction, and converse) plus codes for drowsiness. The scheme takes several factors into account, such as where the driver is looking, where the driver’s head is pointed, what the driver’s hands are doing, the weather, and the road surface condition. Each main task was divided into 3 to 17 subtasks (e.g., groom using tool, reach and get phone). This scheme was used to code video clips of drivers’ faces from the ACAS field operational test. In the first pass, 2,914 video clips were coded (for task, drowsiness, weather, and road) using custom UMTRI software. In the second pass, a sample of 403 distracted and 416 nondistracted clips were coded frame by frame (15,965 frames) for the subtasks performed, gaze direction, and where the head was pointed.
ADVANCED EMBEDDING OF INFORMATION BY SECURE KEY EXCHANGE VIA TRUSTED THIRD PARTY USING STEGANOGRAPHY

The proposed paper is about the way of hiding the data in a more secure way by the steganographic algorithms along with key exchange using the trusted third party.If steganography is detected, the system will fail but data security depends on the robustness of the applied algorithm. In this paper, we compress the secret message and encrypt it by the receiver's public key along with the stego key and embed both messages in a carrier using an embedding algorithm. The stego - image is the result we get by running the algorithm you select on the message (file to hide) and cover (image). It can be saved into BMP or PNG format. The reason that it can only be saved in these formats is because they are lossless - there is no information lost as part of the file formatting.
Understanding librarians’ experiences of copyright: Findings from a phenomenographic study of UK information professionals

Purpose Librarians and information professionals increasingly need to deal with copyright issues in their work, however evidence suggests that they can lack confidence and often refer queries to a dedicated copyright specialist. The purpose of this paper is to explore the professional experiences of copyright of a group of academic librarians in the UK, with a view to devising appropriate copyright education strategies. Design/methodology/approach The research was conducted using phenomenography, which is a qualitative approach from education. Data were collected though group interviews to explore the variation of experience. Four categories of description were devised which are placed in an outcome space. Findings There were four qualitatively distinct ways that librarians experience copyright in their professional lives, including viewing it as a problem, as complicated, as a known entity and as an opportunity. The variations in experience relate to a variety of factors such as the librarians’ role, ideology, level of experience, context and with whom they might be dealing. Originality/value This is the first study of this nature, building on quantitative findings from a multinational survey. It concludes that librarians need to be taught about copyright in a way that acknowledges and addresses the challenges so that they can view it as empowering and as part of wider information literacy initiatives.
Infrared pyroelectric sensor for detection of vehicular traffic using digital signal processing techniques

An experimental passive infrared system was designed and developed to detect and monitor vehicular road traffic. The system uses infrared pyroelectric sensors to sense vehicles passing through its field of view. In conjunction with computerized signal processing and correlation techniques the sensor information can then be used to count the number of vehicles passing and compute their speed and length. Laboratory and field tests results indicate that this technology should be cost effective, weather resistant and have the potential for a variety of sophisticated traffic monitoring applications. >
Image Processing for Surveillance and Security

Security is a fundamental issue in today's world. In this chapter we discuss various aspects of security in daily life that can be solved using image processing techniques by grouping in three main categories: visual tracking, biometrics and digital media security. Visual tracking refers to computer vision techniques that analyses the scene to extract features representing objects (e.g., pedestrian) and track them to provide input to analyse any anomalous behaviour. Biometrics is the technology of detecting, extracting and analysing human's physical or behavioural features for identification purposes. Digital media security typically includes multimedia signal processing techniques that can protect copyright by embedding information within the media content using watermarking approaches. Individual topics are discussed referring recent literature.
Audit opinion of listed companies: A Takagi-Sugeno fuzzy neural network based study

Abstract Financial indexes of listed companies are closely related to audit opinion. With financial indexes used as variable and data of listed companies as research sample, this paper constructed a model for the prediction of audit opinion basing on the Takagi-Sugeno Fuzzy Neural Network and carried out the validity test. Research results showed that the prediction results of the model are accurate and of great theoretical and practical significance.
Routing techniques for massively parallel communication

A survey of some packet-switched routing methods for massively parallel computers is presented. Some of the techniques are applicable to both shared-memory and message-passing architectures. These routing methods are compared in terms of their efficiency in supporting programming models, efficiency in mapping to parallel machines, and practicality. Among the outlined methods, three nonadaptive techniques and some adaptive routing algorithms are discussed. >
Research on Assessment of Impact of Big Data Attributes to Disaster Response Decision-Making Process

This research is to assess the relationship Big Data attributes and disaster response process. The hypothesis are designed to form decision making between situation awareness and disaster response by defining major attribute of Big Data(Volume, Variety, Velocity, Complexity). It is proved whether there is a moderating effect in cause-and-effect relationship by visualizing Big Data. To test the hypotheses, it was conducted a questionnaire survey of civil servants in charge of disaster-related government employees, and collected 320 data(without 12 undependable responses). The research findings are suggested the attributes of accumulation, expandability, flexibility, real-time, analytical, combination of Big Data have a strong effect on disaster manager`s situation awareness.
A Three-Level Adaptive Delta Modulator

A three-level adaptive delta modulator for image encoding is presented. Its advantage is better tracking of signal levels. The characteristic constants of this modulator are designed to satisfy certain convergence and error properties. Behavioral curves of the modulator for different choices of the characteristic constants are included as well as the reconstructed versions of pictures that were encoded by this modulator.
Real‐time reconstruction of endocardial potential maps in non‐contact cardiac mapping

Purpose – The purpose of this paper is to describe a numerical inversion technology developed to reconstruct endocardial electric potential maps on the internal surface of heart chambers utilizing intracavitary multi‐electrode catheter measurements. The objective is to perform the reconstruction real time with high accuracy, thereby allowing the incorporation of the technology into medical imaging systems.Design/methodology/approach – Electrode potential points from several beats are merged in order to maximize the information extracted from the catheter measurements. To solve the ill‐posed inverse problem fast, numerically stable solution algorithms based on generalized Tikhonov regularization and bidiagonalization are developed. The latter algorithm also provides an efficient framework for choosing the regularization parameter optimally.Findings – Results of three examples are presented to thoroughly illustrate the performance of the algorithm: one with synthetic data generated in a computational electr.
Synthesizing optimal family of linear systolic arrays for matrix computations

A method is proposed for designing a family of linear systolic arrays for matrix-oriented problems for which two-dimensional arrays have been designed. The design exhibits a tradeoff between local storage, s, and number of processing elements, n. The arrays are linear, with each processor having storage O(s),1<or=s<or=n, for n*n matrix problems. The input matrices are fed as two-speed data streams using fast and slow channels to satisfy the dependencies in the algorithm. The technique leads to simpler designs with fewer number of processors and improved delay from input to output, compared to a previous family of linear arrays. <<ETX>>