Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification Algorithm based analysis of Breast cancer data

- The classification algorithms are very frequently used algorithms for analyzing various kinds of data available in different repositories which have real world applications. The main objective of this research work is to find the performance of classification algorithms in analyzing Breast Cancer data via analyzing the mammogram images based its characteristics. Different attribute values of cancer affected mammogram images are considered for analysis in this work. The Patients food habits, age of the patients, their life styles, occupation, their problem about the diseases and other information are taken into account for classification. Finally, performance of classification algorithms J48, CART and ADTree are given with its accuracy. The accuracy of taken algorithms is measured by various measures like specificity, sensitivity and kappa statistics (Errors).
Intrusion Detection Based on Rule Extraction from Dynamic Cell Structure Neural Networks

Knowledge embedded within artificial neural networks (ANNs) is distributed over the connections and weights of neurons. So, the user considers ANN as a black box system. There are many researches investigating the area of rule extraction by ANNs. In this paper, a dynamic cell structure (DCS) neural network and a modified version of LERX algorithm are used for rule extraction. On the other hand, intrusion detection system (IDS) is known as a critical technology to secure computer networks. So, the proposed algorithm is used to develop IDS and classify the patterns of intrusion. To compare the performance of the proposed system with other machine learning algorithms, multi-layer perceptron (MLP) with output weight optimization-hidden weight optimization (OWO-HWO) training algorithm is employed with selected inputs based on the results of a feature relevance analysis. Empirical results show the superior performance of the IDS based on rule extraction from DCS, in recognizing hard-detectable attack categories, e.g. userto-root (U2R) and also offering competitive false alarm rate (FAR). Although, MLP with 25 selected input features, instead of 41 standard features introduced by knowledge discovery and data mining group (KDD), performs better in terms of detection rate (DR) and cost per example (CPE) when compared with some other machine learning methods, as well.
Team-based coverage control of moving sensor networks

The coverage optimization problem for mobile sensing networks is examined from a team-based perspective. The objective is to arrange robots in a given environment to minimize the serving cost based on a given density function, which defines the probability of events in the environment. A team concept is introduced here to allow adjusting to the changes in the environment or the assigned task(s) in a collaborative way as a team. Firstly, an optimization problem is solved in the team level in order to assign a sub-region to each team. The region assigned to each team is then divided among its members by minimizing a locational cost. A distributed communication algorithm is also given, in which each team member, e.g., robot can access the information of its neighbors in order to compute the associated region. Finally, numerical simulation results are given to demonstrate the effectiveness of the presented approach. The results indicate that agents in both teams- and agents-level reach the optimal configuration considering the given density function.
Measuring issue salience: Using supervised machine learning to generate data from free responses to the \most important problem"question

Future political communication research will depend on measuring issue-salience in massive data sets largely consisting of unstructured text. We examine a set of supervised machine-learning approaches that can categorize short segments of unstructured text data based on human judgments of content, with an attractive cost structure. We achieve levels of accuracy that are generally acceptable in human-coder content analysis, and which has perfect replicability once human coders produce appropriate training data. We use this approach to classify free responses to the \most important problem" question from the 2000 NAES rolling cross section, and show that our results generally correspond to exogenous real-world events that are widely thought to have shifted the attention of the public.
An analysis of nonverbal communication in an online chat group.

The Open Polytechnic Working Papers are a series of peer-reviewed academic and professional papers published in order to stimulate discussion and comment. Many Papers are works in progress and feedback is therefore welcomed. Abstract In face-to-face communication, meaning is carried by blending two components: the verbal (with words) and the nonverbal (without words). Nonverbal communication adds nuance or richness of meaning that cannot be communicated by verbal elements alone. Unfortunately, nonverbal elements are generally absent in online discourse. This paper argues that, given time and experience, some of the same richness of face-to-face communication can occur in a virtual text-based medium. This paper describes the results of research on the various methods students in an online chat group employ to overcome the absence of nonverbal elements. The paper reviews academic literature on online interactions, examines the various techniques students new to an online environment use, and discusses the implications of the findings for online educators for group building and student support. Transcripts of the postings of students in an online chat group at the Open Polytechnic of New Zealand were recorded, collected, collated and analysed to determine the extent and intent of nonverbal communication used by students in their weekly chats. Our research into students' use of nonverbal communication in online chat points to the need for e-educators to develop effective teaching strategies to encourage student participation in online discussions. iv © The Open Polytechnic of New Zealand Working Papers (2-03)
Users' Sentiment Analysis in Social Media Context using Natural Language Processing

The main goal of this research work is to model a natural language processing toolkit to infer users' sentiment analysis for study of feedback about a product, person or thing using the means of social media interactions. A complete discussion on the mechanism behind the data gathering using social networking channels has been made in this research as well as a small account on the design of natural language processing toolkit has been also given. To demonstrate the validation and applicability of the end application, the proposed methodology will be implemented with input sets obtained from live users' interactions on social media and feeding them to a natural language model for observation. In this research, we will also check the validity of the result obtained comparing the results obtained by polls survey and by natural language model.
Syntactic control for compositional vector space models

The framework of Compositional Distributional Semantics unifies vector space models for lexical meanings with a compositional account of how these meanings combine into phrases and larger units. The syntactic engines that have been used to drive the interpretation process (Lambek grammars, pregroups) are problematic in two respects (overgeneration and undergeneration) compromising the accuracy of the quantitative values associated with a derivation. We address these problems by moving to a non-symmetric, non-associative, non-unital type logic with a tree-building tensor operation, generating phrases rather than strings. Composition (tensor) and decomposition of phrases (cotensor) are treated on a par. Reordering and restructuring are controlled by adjoint pairs of modalities, the grammatical analogues of Linear Logic’s ’ ! ’. We discuss the categorical structures for this model of syntax and the associated graphical language. We identify some empirical areas where the model leads to improved performance. In the field of natural language semantics, the compositional distributional framework of [3] and subsequent work (see [9] for an overview of results obtained so far) has achieved remarkable progress by unifying vector space models for lexical meanings with a compositional account of how these meanings combine into phrases and larger units. Interpretation takes the form of a functorial transition from Form to Meaning: a structure-preserving map that associates the operations for building syntactic structure with vector composition operations, thus assigning quantitative values to these structures. The quality of the quantitative values thus obtained is determined by the accuracy of the syntactic engine driving the interpretation process. Compositional Distributional Semantics has used type logics for that purpose: Lambek’s original Syntactic Calculus (L), and its more recent Pregroup incarnation (PG). Categorically, these are systems with a (non-symmetric) monoidal bi-closed or compact closed structure, respectively. As models of natural language syntax, these calculi are lacking in two respects: overgeneration and undergeneration. Both L and PG model the composition of phrases with an associative multiplicative tensor operation, claiming in fact that no aspect of grammatical organization beyond linear order can a↵ect
Combined speech and channel coding for mobile radio applications

The authors present the combined speech and channel coding scheme for half rate (8 kbps/user) digital cellular application. The speech coding algorithm is code-excited linear prediction (CELP) and the channel codes used are punctured Reed-Solomon codes. A preliminary study on combining CELP and rate-compatible convolutional codes was also conducted. Unequal error protection of different speech elements is employed. The error-protected digits are sent through the digital cellular channel via pi /4-QPSK modulation. The channel is assumed to introduce Rayleigh flat fading and additive Gaussian noise. The received signal is demodulated through differential detection. The performance of the codec has been studied for different received SNR, fade rates, and interleaving delays. <<ETX>>
Online semantic mapping of logistic environments using RGB-D cameras

Automated guided vehicles require spatial representations of their working spaces in order to ensure safe navigation and carry out high-level tasks. Typically, these models are given by geometric maps. Even though these enable basic robotic navigation, they off-the-shelf lack the availability of task-dependent information required to provide services. This article presents a semantic mapping approach augmenting existing geometric representations. Our approach demonstrates the automatic annotation of map subspaces on the example of warehouse environments. The proposals of an object recognition system are integrated in a graph-based simultaneous localization and mapping framework and eventually propagated into a global map representation. Our system is experimentally evaluated in a typical warehouse consisting of common object classes expected for this type of environment. We discuss the novel achievements and motivate the contribution of semantic maps toward the operation of automated guided vehicles in the context of Industry 4.0.
Multiple intelligences E-portfolio system for higher learning

Online learning involves students with widest spectrum of learning with no limits of boundaries in digital world. Therefore, online learning requires wider range of learning approaches that needs creativities and innovative skills in order to internalize and externalize the learning. Bloomâ€Ÿs Taxonomy is a famous learning philosophy that adopted at higher learning institutions to develop higher order thinking skills among students. Another educational philosophy that has potential in developing higher order thinking skills is Multiple Intelligences (MI) theory. Literatures show that MI learning is not getting enough attentions among researchers and educators in higher learning context especially in online learning environment. The success story of MI theory in traditional classroom and online learning environment in primary education is reported in published articles, journals and books around the world. This indicates that MI theory also has its own potential to be applied at higher learning context, especially in online learning environment with the availability of various online learning tools and techniques. This research proposes the integration of Bloomâ€Ÿs Taxonomy and MI theory as cognitive thinking skills in online learning environment for higher learning. The matrix of Bloomâ€Ÿs Digital Taxonomy, Multiple Intelligences Theory, online assessment techniques and learning evidence-based outcomes lead to the construction of Cognitive Multiple Intelligences Online (MIOL) Assessment Model. As a result, an e-Portfolio system is proposed as an online assessment system in evaluating MI evidence-based outcomes to indicate significant learning from Low Order Thinking Skils (LOTS) to Higher Order Thinking Skills (HOTS). The system which is called Multiple Intelligences e-Portfolio System (MIePS) should be able to create a centre of attention in higher learning as effective online assessment systems in assessing performance-based learning to enhance the learning process and outcomes.
Video Image Retrieval Using Data Mining Techniques

Data mining technique can be applied in various documents. In this paper concentration on the application of video data, called video data mining, because acquisition and storage of video data is an easy task but retrieval of information from video data is challenging task. So video data mining plays an important role in efficient video data management for information retrieval. This paper describes a proposed framework for video data mining to extract the information from video data. This includes developing the technique for shot detection then key frame analysis is considered to compare the frames of each shot to each others to define the relationship between shots. After all hierarchical clustering technique is adopted to make a group of similar shots to detect the particular event on some requirement as per user demand.
Problem-Solving in Deterministic Factor Analysis

The article is concerned with author's methods of factor analysis which allow one to draw conclusions about changes in financial position of a company in the most accessible and less time-consuming way and to estimate the impact of factors on index changes within the economic system and index change trends. The article contains numerical data based on traditional methods of factor analysis. The main task of the author's methods of factor analysis is to identify the factors which determine changes in economic index value in relation to the main factors being its components. The author's methods are aimed at obtaining key (more informative) parameters to have a comprehensive idea of changes in sales revenues.
Integrating Eye-Tracking and Wireless Electroencephalogram (EEG) in Consumer Neuroscience

Consumer neuroscience addresses marketing relevant problems through the integration and application of neuroscientific theories, concepts, findings and methods to the research discipline of consumer behavior. The key contribution of this paper is to complement the advancement of traditional consumer research through the investigation of the patterns of interdependency between the Electroencephalogram (EEG) signals from the different brain regions while participants undertook a choice task designed to elicit preferences for a marketing product (crackers). Specifically, the task required participants to choose their preferred crackers described by shape (square, triangle, round), flavor (wheat, dark rye, plain) and topping (salt, poppy, no topping). We analyze the Electroencephalogram (EEG) signals collected from the different brain regions using the commercially available 14 channel Emotiv EPOC wireless EEG headset and relate the EEG data to the specific choice options with a Tobii X60 eye tracker. Fifteen participants were recruited for this experiment and were shown 57 choice sets; each choice set described three choice options. The patterns of cortical activity were obtained in the five principal frequency bands, Delta (0 4 Hz), Theta (3 7 Hz), Alpha (8 12 Hz), Beta (13 30 Hz), and Gamma (30 40 Hz). Our results indicate significant phase synchronization between the left and right frontal and occipital regions indicating interhemispheric communications during the choice task. Our experimental results also show that participants spent more time looking at the non-preferred items in each choice set at the beginning of the experiment (exploration mode), while reducing that time progressively to indicate significant amount of cognitive processing assigned to preferred items (exploitation mode).
NORM Technique based PAPR Reduction in MC-CDMA Systems

Multicarrier code division multiple access (MC-CDMA) is one of the promising technologies for future-generation wireless networks. It offers high data rates, protection against frequency-selective fading and efficient utilization of the spectrum. The peak to average power ratio (PAPR) is very high in MCCDMA systems. The partial transmit sequence technique (PTS) and the selective mapping technique (SLM) reduce the PAPR with more computational complexity. In this study, the NORM technique was used for PAPR reduction in MC-CDMA systems. The performance of NORM was analyzed with PTS and SLM in terms of cumulative complementary distribution, power saving gain, amplifier efficiency, computational complexity and bit error rate. Simulation results showed that NORM has better PAPR reduction with less computational complexity.
Variable structure control of an overhead crane

Addresses the control problem of an overhead crane. A variable structure control scheme is used to regulate the trolley position and the hoisting motion towards their desired values. However, when this control law is used, the load swing angle exhibits small oscillations around the equilibrium point. To eliminate these oscillations, a state feedback controller is used in conjunction with the variable structure controller. Simulation results are given to verify the theoretical developments.
Defining Cloud Computing in Business Perspective: A Review of Research

The main purpose of the present paper is to analyse the various definitions of cloud computing proposed by different researchers and, using content analysis methodology, and establish areas of “agreement” and to construct such a “general” definition of cloud computing in business perspective. The paper attempts to compare the definitions of cloud computing by academicians and industy experts. This paper is divided into three sections. In the first section, content analysis methodology used on cloud computing definitions is presented. The second section highlights the key words in 36 definitions of cloud computing and tabulates the definitions through a categorization of substantive terms. The last section discusses the results of research methodology followed by conclusion and limitations.
StreamWorks: An Energy-efficient Embedded Co-processor for Stream Computing

Stream processing has emerged as an important model of computation especially in the context of multimedia and communication sub-systems of embedded System-onChip (SoC) architectures. The dataflow nature of streaming applications allows them to be most naturally expressed as a set of kernels iteratively operating on continuous streams of data. The kernels are computationally intensive and are mainly characterized by real-time constraints that demand high throughput and data bandwidth with limited global data reuse. Conventional architectures fail to meet these demands due to their poorly matched execution models and the overheads associated with instruction and data movements. This work presents StreamWorks, a multi-core embedded architecture for energyefficient stream computing. The basic processing element in the StreamWorks architecture is the StreamEngine (SE) which is responsible for iteratively executing a stream kernel. SE introduces an instruction locking mechanism that exploits the iterative nature of the kernels and enables fine-grain instruction reuse. Each instruction in a SE is locked to a Reservation Station (RS) and revitalizes itself after execution; thus never retiring from the RS. The entire kernel is hosted in RS Banks (RSBs) close to functional units for energy-efficient instruction delivery. The dataflow semantics of stream kernels are captured by a context-aware dataflow execution mode that efficiently exploits the Instruction Level Parallelism (ILP) and Data-level parallelism (DLP) within stream kernels. Multiple SEs are grouped together to form a StreamCluster (SC) that communicate via a local interconnect. A novel software FIFO virtualization technique with split-join functionality is proposed for efficient and scalable stream communication across SEs. The proposed communication mechanism exploits the Task-level parallelism (TLP) of the stream application. The performance and scalability of the
Multigroup cross section library for GFR2400

In this paper the development and optimization of the SBJ_E71 multigroup cross section library for GFR2400 applications is discussed. A cross section processing scheme, merging Monte Carlo and deterministic codes, was developed. Several fine and coarse group structures and two weighting flux options were analysed through 18 benchmark experiments selected from the handbook of ICSBEP and based on performed similarity assessments. The performance of the collapsed version of the SBJ_E71 library was compared with MCNP5 CE ENDF/B VII.1 and the Korean KAFAX-E70 library. The comparison was made based on integral parameters of calculations performed on full core homogenous models.
Comparing the process approach with the product approach inteaching academic writing to first-year undergraduates

The objective of this study is to determine whether the process approach or the product/ traditional approach is better in teaching first-year undergraduates to write academic texts. Fifty-one students of the University of Selangor had participated in the study. They were divided into Group A and Group B. A pre-test was given prior to the treatment and a post-test after it. For seven weeks, the participants in Group A were taught to write academic texts using the product approach while those in Group B were taught to write academic texts using the process approach. The data collected from the tests was analyzed using the Statistical Package for Social Sciences (SPSS) version 19.0. The data from the post-test was also triangulated with the data from the participants’ composing behaviors and the data from the participants’ folders. The results indicate that both the groups A and B show improvements in content, organization, vocabulary, language use, mechanics and overall results. However, the participants of group B show better improvement than those of Group A in terms of content, organization, mechanics and overall results. This shows that the process approach had not only significantly increased students’ level of proficiency in writing in general but also improved their ability to write academic text because it closely resembles the natural writing process. In conclusion, these findings imply that teachers of academic writing need to address procedural know-how explicitly and disclose what goes on in the process of writing academic texts to learners to help them write well.