Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement of Arabic Text Classification Using Semantic Relations with Part of Speech Tagger

When it comes to Arabic text documents, Text Categorization (TC) becomes a challenge. TC is needed for clustering purposes in order to complete Text Mining (TC). Based on the nature of Arabic language, extracting roots or stems from the breakdown of multiple Arabic words and phrases is important task before applying TC. The results obtained by applying the proposed algorithm are compared with the results of three popular algorithms. These algorithms are Khoja stemmer, Light stemmer, and Root extractor. The performance of these three techniques are evaluated and compared based on the accuracy of Naive Bayesian classifier. The obtained result demonstrates that these techniquesare not as promising as expected.Therefore, we decided to consider the position tagger and conceptual representation to answer the question, which approach enhances the Arabic TC performance? Arabic WordNet (AWN))is used as a lexical and semantic resource. The performance of new relation "Hashyponym",suggested in this work, iscompared with otheralready used relations like Synset, term+ Synset, all Synsets, and Bag of words representation to demonstrate its effectiveness. From the experimental results, it was found that the new suggested relationimproved the Arabic text classification, at which the macro average F1 is raised to 0.75437compared with the performance of the other approaches. Key-Words: -Arabic Text classification, Stemmer, Part of Speech, Conceptual features, Semantic relations.
Environmental Wireless Sensor Networks

This paper is concerned with the application of wireless sensor network (WSN) technology to long-duration and large-scale environmental monitoring. The holy grail is a system that can be deployed and operated by domain specialists not engineers, but this remains some distance into the future. We present our views as to why this field has progressed less quickly than many envisaged it would over a decade ago. We use real examples taken from our own work in this field to illustrate the technological difficulties and challenges that are entailed in meeting end-user requirements for information gathering systems. Reliability and productivity are key concerns and influence the design choices for system hardware and software. We conclude with a discussion of long-term challenges for WSN technology in environmental monitoring and outline our vision of the future.
Calorie Restriction with a High-Fat Diet Effectively Attenuated Inflammatory Response and Oxidative Stress-Related Markers in Obese Tissues of the High Diet Fed Rats

Obesity characterized by increased mass of adipose tissue leads to systemic inflammation. Calorie restriction (CR) improves parameters associated with immune response and antioxidant defense. We hypothesized that CR with a high fat diet (HFCR) regulates local and systemic inflammation and oxidative stress damage in a high fat diet induced obesity (HF group). We investigated effect of HFCR on inflammation and oxidative stress-related markers in liver and adipose tissues as well as adipokines in plasma. HFCR lowered liver triglyceride levels, total cholesterol levels, and the plasma leptin/adiponectin ratio to normal levels and improved glucose tolerance. HFCR also improved fatty liver and normalized adipocyte size and morphology. HFCR reduced lipid peroxidation and decreased the expression levels of inducible nitric oxide synthetase, cyclooxygenase-2, NF-E2-related factor, and heme oxygenase-1 in the liver. Moreover, HFCR suppressed the expression levels of C- reactive protein and manganese superoxide dismutase in the adipose tissue in the HF group. These results suggest that HFCR may have beneficial effects on inflammation and oxidative stress as well as lipid profiles in the HF diet induced obesity. Moreover, HFCR may be a good way to increase compliance in obese patients and to prevent obesity induced complications without changes in dietary pattern.
Combining parallel coordinates with the reorderable matrix

There is evidence that the highlighting of the same data items in different views will improve the proficiency of knowledge acquisition tasks. Making the connection for the user will reduce the cognitive load of switching from one view to another and allows making observations that would not be possible from the two separate views. However, making the connection between conceptually different views might not be so straightforward if the view types are complex and different enough. We compare two conceptually different information visualization techniques called parallel coordinate plots and the reorderable matrix, and consider how the two representations could be combined. We report an experiment where the participants performed tasks with an application having a reorderable matrix and parallel coordinate views to the same data, with linking and without linking. It was found out that although the view linking initially slows the task performance, it will accelerate learning and it is well received by users.
SSMT:A Machine Translation Evaluation View To Paragraph-to-Sentence Semantic Similarity

This paper presents the system SSMT measuring the semantic similarity between a paragraph and a sentence submitted to the SemEval 2014 task3: Cross-level Semantic Similarity. The special difficulty of this task is the length disparity between the two semantic comparison texts. We adapt several machine translation evaluation metrics for features to cope with this difficulty, then train a regression model for the semantic similarity prediction. This system is straightforward in intuition and easy in implementation. Our best run gets 0.808 in Pearson correlation. METEORderived features are the most effective ones in our experiment.
Principal Component Analysis for Hyperspectral Image Classification

The availability of hyperspectral images expands the capability of using image classification to study detailed characteristics of objects, but at a cost of having to deal with huge data sets. This work studies the use of the principal component analysis as a preprocessing technique for the classification of hyperspectral images. Two hyperspectral data sets, HYDICE and AVIRIS, were used for the study. A brief presentation of the principal component analysis approach is followed by an examination of the infor- mation contents of the principal component image bands, which revealed that only the first few bands contain significant information. The use of the first few principal component images can yield about 70 percent correct classification rate. This study suggests the benefit and efficiency of using the principal component analysis technique as a preprocessing step for the classification of hyperspectral images.
Reciprocity in Social Networks: Measurements, Predictions, and Implications

Understanding and modeling the mechanisms by which directed social networks evolve are active areas of research. One related emerging topic is to understand and predict the formation of reciprocal edges, which has many potential applications such as directed social network modeling, friend recommendation, information propagation and targeted spamming. However, there have been few studies providing a systematic understanding of reciprocity at scale. In this paper, we bridge this gap using a novel large-scale Google+ dataset crawled by ourselves as well as one publicly available social network dataset. First, we investigate the structure and evolution of reciprocal and unreciprocal edges. Specifically, we find that reciprocal edges mainly connect users with similar degrees while unreciprocal edges mainly link ordinary users (e.g., users with low degrees) and popular users (e.g., celebrities). However, the impacts of reciprocal edges linking ordinary and popular users on the network structures become increasingly significant as the social networks evolve. Second, we observe that factors including user behaviors, node attributes and edge attributes all have significant impacts on the formation of reciprocal edges. Third, in contrast to previous studies that treat reciprocity prediction as either a supervised or a semi-supervised learning problem, we identify that reciprocity prediction is better modeled as an outlier detection problem. Finally, we perform extensive evaluations with the two datasets, and show that our proposal outperforms previous reciprocity prediction approaches.
Visual scanpath representation

Eye tracking scanpaths contain information about how people see, but traditional tangled, overlapping scanpath representations provide little insight about scanning strategies. The present work describes and extends several compact visual scanpath representations that can provide additional insight about individual and aggregate/multiple scanning strategies. Three categories of representations are introduced: (1) Scaled traces are small images of scanpaths as connected saccades, allowing the comparison of relative fixation densities and distributions of saccades. (2) Time expansions, substituting ordinal position for either the scanpath's x or y-coordinates, can uncover otherwise subtle horizontal or vertical reversals in visual scanning. (3) Radial plots represent scanpaths as a set of radial arms about an origin, with each arm representing saccade counts or lengths within a binned set of absolute or relative angles. Radial plots can convey useful shape characteristics of scanpaths, and can provide a basis for new metrics. Nine different prototype scanning strategies were represented by these plots, then heuristics were developed to classify the major strategies. The heuristics were subsequently applied to real scanpath data, to identify strategy trends. Future work will further automate the identification of scanning strategies to provide researchers with a tool to uncover and diagnose scanning-related challenges.
DESIGN AND IMPLEMENTATION OF A CACHE HIERARCHY -AWARE TASK SCHEDULING FOR PARALLEL LOOPS ON MULTICORE ARCHITECTURES

Effective cache utilization is critical to performance in chip-multiprocessor systems (CMP). Modern CMP architectures are based on hierarchical cache topology with varying private and shared caches configurations at different levels. Cache-aware scheduling has become a great design challenge. Many scheduling strategies have been designed to target specific cache configuration. In this paper we introduce a cache hierarchy-aware task scheduling (CHATS) algorithm which adapt to the underlying architecture and its cache topology. The proposed scheduling policy aims to improve cache performance by optimizing spatial and temporal data locality and reducing communication overhead without neglecting load balancing. CHATS has been implemented in the parallel loop construct of XPU framework introduced in previous works [1,7]. We compared CHATS to several popular scheduling policies including dynamic and static scheduling and task-stealing. Experimental results on synthetic and real workloads shows that our scheduling policy achieves up to 25% execution speed up compared to OpenMP, TBB and Cilk++ parallel loop implementations. We use our parallel loop implementation in two popular applications from the PARSEC benchmark suite and we compare it to the provided OpenMP, TBB and PThreads version on different architectures.
Communication styles in children and young people with profound mental handicap : a developmental perspective

Children with profound mental handicap pose particularly difficult challenges for parents, teachers and therapists because of their extremely low level of measurable achievement in all areas of their development. They have no formal system of communication and will remain at a pre-symbolic level of development throughout life. The absence of this developmental milestone is perhaps the most devastating feature of their handicap. Nevertheless, people with profound mental handicap do communicate with others and appear able to form emotional relationships, but little is understood of the underlying processes which make this possible. The aim of this study was to examine and describe the nature and content of communication in profound mental handicap. The developmental level of people with profound mental handicap and their limited behavioural repertoire suggests that the normal development of emotional and social communication in infancy offers an appropriate model for the processes underlying their communication. By revealing what is shared in the two populations insight might be also be gained into the robust elements of normal communication, i.e. those elements operating in both populations. The investigation was divided into two studies. In Study 1 interactions between 12 children (aged 8-18 years) with profound mental and multiple handicaps and their caregivers (mothers and nurses) were described. Study 2 compared the communicative involvement of six young people with profound mental handicap (aged 14-26 years) when with three groups of adults who were differentiated by their personal knowledge of the young person and their previous experience in the field of profound mental handicap. The two studies revealed that, beneath the apparent chaos and disorder, an ordered system of emotional communication is operating in children and young people with profound mental handicap, in common with similar observations in normal early development. The implications of these findings for the development of personal communication and relationships in children and young people with profound handicaps and the contribution of this knowledge to the understanding of the normal development of early communication are discussed. Possible therapeutic applications are also considered, proposing that close attention should be paid to the fundamental aspects of human behaviour which are shared in the general and profoundly mentally handicapped populations, influencing the practices which help children with profound mental handicap to move forward and out into the community of other human beings.
Translating and the Computer 29 , November 2007 ] Removing the Distinction Between a Translation Memory , a Bilingual Dictionary and a Parallel Corpus

This paper presents a prototype MT system which does not make the distinction between a dictionary, a sub-sentential aligned parallel corpus, and post-edited information (translators output) like a translation memory. The system is based on the METIS-approach (Vandeghinste et al, 2006), and uses an XML-based dictionary format in which not only simple word-to-word translations can be included, but which also contains complex dictionary entries, including discontinuous entries, like idioms and proverbs. The presented prototype is a system that automatically adapts its dictionary and target language corpus depending on the post-edited output as made by the users of the system, and will therefore have a learning curve in its performance.
An evaluation of geo-ontology representation languages for supporting web retrieval of geographical information

The internet is the single largest information resource in the world. It is, however, not being used to is full potential. Currently most the information is written using syntactical machine readable languages such as HTML. These languages are limited in that they are only intended for human consumption. To fully unlock the potential of such a vast resource of information, we need to make the information not only machine readable but machine-understandable. In order to gain machine understanding we need semantic languages which are able to define meaning to the information being stored. Agents (human or machine) could then use this information in variety of different ways. A large amount of geographical information is currently being stored and delivered over the internet. Internet providers such as the Ordnance Survey are realizing the potential and are currently offering their data in GML format. Geographic digital libraries, such as the ADL, are being established. There is, however, the need to realize the potential of semantically enriching the geographic information to provide more automated and intelligent ways of managing and retrieving the data over the web.
Vacant Properties and Violence in Neighborhoods.

OBJECTIVES Violence remains a significant public health issue in the United States. To determine if urban vacant properties were associated with an increased risk of assaultive violence and if this association was modified by important neighborhood institutions (e.g., schools, parks/playgrounds, police stations, and alcohol outlets). METHODS Longitudinal ecologic study of all 1816 block groups in Philadelphia. Aggravated assault and vacant property data were compiled yearly from 2002 to 2006 and linked to block groups. A mixed effects negative binomial regression model examined the association of vacant properties and assaults between and within block groups. RESULTS Among all block groups, 84% experienced at least one vacant property, 89% at least one aggravated assault, and 64% at least one gun assault. Between block groups, the risk of aggravated assault increased 18% for every category shift of vacant properties (IRR 1.18, 95% CI: 1.12, 1.25, P < 0.001). Parks/playgrounds and alcohol outlets potentially modified the association between vacant properties and aggravated assaults but only at low levels of vacancy. CONCLUSIONS Increasing levels of vacancy were associated with increased risk of assaultive violence in urban block groups.
The Structure of Spatial Networks and Communities in Bicycle Sharing Systems

Bicycle sharing systems exist in hundreds of cities around the world, with the aim of providing a form of public transport with the associated health and environmental benefits of cycling without the burden of private ownership and maintenance. Five cities have provided research data on the journeys (start and end time and location) taking place in their bicycle sharing system. In this paper, we employ visualization, descriptive statistics and spatial and network analysis tools to explore system usage in these cities, using techniques to investigate features specific to the unique geographies of each, and uncovering similarities between different systems. Journey displacement analysis demonstrates similar journey distances across the cities sampled, and the (out)strength rank curve for the top 50 stands in each city displays a similar scaling law for each. Community detection in the derived network can identify local pockets of use, and spatial network corrections provide the opportunity for insight above and beyond proximity/popularity correlations predicted by simple spatial interaction models.
Reverse Engineering on the Mainframe: Lessons Learned from "In Vivo" Research

Despite growth in the popularity of desktop systems, Web applications, and mobile computing, mainframe systems remain the dominant force in large-scale enterprise computing. Although they're sometimes referred to as "the dinosaurs of computing," even mainframe systems must adapt to changing circumstances to survive. Although reverse-engineering and reengineering techniques can help identify and achieve these adaptations, current techniques are mainly geared mainly toward more modern environments, languages, and platforms. It remains to be seen whether successful techniques can be easily transferred to a mainframe context. This article reports on the application of two proven reverse-engineering techniques (software visualization and feature location) in the context of mainframe systems. The authors conclude that these techniques remain viable but become very labor intensive when implemented on a per-project basis.
Intratumor Genetic Heterogeneity of Breast Carcinomas as Determined by Fine Needle Aspiration and TaqMan Low Density Array

Background: Gene expression profiling is thought to be an important tool in determining treatment strategies for breast cancer patients. Tissues for such analysis may at a preoperative stage be obtained, by fine needle aspiration (FNA) allowing initiation of neoadjuvant treatment. To evaluate the extent of the genetic heterogeneity within primary breast carcinomas, we examined whether a gene expression profile obtained by FNA was representative of the tumor. Methods: Tumors from 12 consecutive cases of early predominantly estrogen receptor positive (ER+) breast cancer patients undergoing primary surgery were split in halves and FNAs were obtained from each half. A tissue biopsy of the tumors was also snap-frozen for comparison. Non-amplified RNA was investigated by the novel qRT-PCR-based technique, Low Density Array (LDA) using 4 reference genes and 44 target genes. Results: Comparison of gene expression at the single gene level in the two FNA samples from each tumor demonstrated various degrees of heterogeneity. However, compared as gene expression profiles, intratumor correlations for 9/12 patients were high and these pairs could in a theoretical blinding of all the FNAs be correctly matched by statistical analysis. High correlations between the gene profiles of tumor FNAs and tissue biopsies from the same patient were observed for all patients. A cluster analysis identified clustering of both the two FNAs and the tissue biopsy of the same 9 patients. Conclusion: The overall genetic heterogeneity of breast carcinomas, as sampled by FNA, does not prohibit generation of useful gene profiles for treatment decision making. However, sampling and analysis strategies should take heterogeneity within a tumor, and varying heterogeneity amongst the single genes, into account.
Deriving nondecoupling effects of heavy fields from the path integral: A heavy Higgs field in an SU(2) gauge theory.

We describe a method to remove nondecoupling heavy fields from a quantized field theory and to construct a low-energy one-loop effective Lagrangian by integrating out the heavy degrees of freedom in the path integral. We apply this method to the Higgs boson in a spontaneously broken SU(2) gauge theory (gauged linear \ensuremath{\sigma} model). In this context, the background-field method is generalized to the nonlinear representation of the Higgs sector by applying (a generalization of) the Stueckelberg formalism. The (background) gauge-invariant renormalization is discussed. At one loop the ln${\mathit{M}}_{\mathit{H}}$ terms of the heavy-Higgs-boson limit of this model coincide with the UV-divergent terms of the corresponding gauged nonlinear \ensuremath{\sigma} model, but vertex functions differ in addition by finite (constant) terms in both models. These terms are also derived by our method. Diagrammatic calculations of some vertex functions are presented as a consistency check. \textcopyright{} 1995 The American Physical Society.
Exposure to foreign markets and plant-level innovation: evidence from Chile and Mexico

Unlike most studies that calculate productivity as a residual, this study uses detailed plant-level data to examine the relationship between exposure to foreign markets and specific innovations including product design, investment in new tools (such as computers), research and development, and innovation in products and processes. The results suggest that exposure to foreign markets is positively related to most types of technology. The effects seem to be stronger in recently liberalized Mexico, which may suggest that the innovation gains from liberalization are greatest in the early stages of liberalization.