Miracle-dz/UniFAR-FTUs huggingface dataset:
Affective-feature-based sentiment analysis using SVM classifier

Based on the methods of the traditional topic-based text classification, machine learning method was performed to the coarse-grained sentiment classification of reviews. Sentiment classification involved a lot of problems. In this paper, the sentiment Vector Space Model (s-VSM) was used for text representation to solve data sparseness. In addition, the critical issues of the sentiment classification, i.e. the selection of classification algorithms, the determination of feature selection method and the selection of feature dimension, are verified by experiments. Furthermore, in order to consider the entire corpus contribution of features and each category contribution of features, the feature selection method of Chi-square Difference between the Positive and Negative Categories (CDPNC) was proposed. It combined DF with CHI and had the better performance. Experiments showed that the Macro-F and Micro-F achieved 90.18% and 90.08% respectively.
Antispammer for Mobile Messages

SMS spams are dramatically increasing year by year due to the growth of mobile phone users around the world. Recent reports have clearly indicated the same. Mobile or SMS spam is a physical and thriving problem due to the fact that bulk pre-pay SMS packages are conveniently available these days and SMS is considered as a trusted and personal service, so it gathers more response rate from the customers. SMS spam filtering is a comparatively recent errand to deal such a problem. It inherits many concerns and quick fixes from Email spam filtering. However it fronts its own certain issues and problems. This paper inspires to work on the task of filtering mobile messages as Ham or Spam. The paper analyses different machine learning classifiers on large corpus of SMS spam.
A Case Study on Testing and Performance Evaluation of Electric Motorcycles to Discuss the Environmental Impact of Urban Area in Macao

The environmental impact and energy crisis of using motorcycles in urban area have urged the rapid development of electric motorcycles (EMs) in Macao in recent years. This work mainly presents the findings of a small-scale electric motorcycle test in Macao. The trial EMs are arranged for a series of tests, such as maximum thirty minutes speed, complete battery discharge, hill starting ability and so on, which are compared with the standard specification. Particularly, the normalized mains-to-wheel energy of EM consumes 1.5 kW¡Ph averagely. Expressed in greenhouse gas (GHG) emission, EM emits 388 g CO2 per kilometer, 66% less than gasoline motorcycle. Furthermore, EMs can achieve zero-urban-emission of carbon dioxide benefitting from the remote location of power station in Macao. This study shows that EM can effective be adopted at Macao for an effective emission control.
Application of BP Network for Travel Behavior Analysis: Complexity Recognition of Trip Chaining

The article develops a BP network for trip chaining pattern recognition based on the data obtained from Beijing Resident Trip Survey. First a set of socioeconomic and demographic factors related to traveller information which potentially influence trip-chaining patterns are pre-treated through principle components analysis, therefore seven variables are selected as input variables of neural network, and a categorical trip chaining pattern (simple and complex trip chaining) are used as output variables. In order to quantify prediction accuracy, two performance measures are applied to evaluate it. Besides, a logistic regression model is also introduced to make a comparison, and the conclusions indicate BP network performs much better; actually the generalization capability of the former is much better too.
Understanding and Addressing the California Latino Achievement Gap in Early Elementary School

One of the most pressing problems in California is improving student academic performance, especially the state’s burgeoning Latino student population. This study examined the extent of the achievement gap between Latino and White students over the first two years of elementary school and the characteristics of students and schools that contribute to it. The analysis revealed that Latino students begin kindergarten at a considerable educational disadvantage relative to White students and the disadvantage increases during the first two years of school. Yet schools do little to widen or narrow these differences. Instead achievement differences increase when students are not in school. Consequently, to reduce the achievement gap will require both effective education policies and policies that address the overall social welfare of Latinos outside of school.
Comprehensive analytic formulae for stellar evolution as a function of mass and metallicity

We present analytic formulae that approximate the evolution of stars for a wide range of mass M and metallicity Z. Stellar luminosity, radius and core mass are given as a function of age, M and Z, for all phases from the zero-age main sequence up to, and including, the remnant stages. For the most part we find continuous formulae accurate to within 5 per cent of detailed models. These formulae are useful for purposes such as population synthesis that require very rapid but accurate evaluation of stellar properties, and in particular for use in combination with N-body codes. We describe a mass-loss prescription that can be used with these formulae, and investigate the resulting stellar remnant distribution.
Objective Audio Quality Assessment Based on Spectro-Temporal Modulation Analysis

Objective audio quality assessment is an interdisciplinary research area that incorporates audiology and machine learning. Although much work has been made on the machine learning aspect, the audiology aspect also deserves investigation. This thesis proposes a non-intrusive audio quality assessment algorithm, which is based on an auditory model that simulates human auditory system. The auditory model is based on spectro-temporal modulation analysis of spectrogram, which has been proven to be effective in predicting the neural activities of human auditory cortex. The performance of an implementation of the algorithm shows the effectiveness of the spectro-temporal modulation analysis in audio quality assessment.
Plasmodium falciparum Hep1 Is Required to Prevent the Self Aggregation of PfHsp70-3

The majority of mitochondrial proteins are encoded in the nucleus and need to be imported from the cytosol into the mitochondria, and molecular chaperones play a key role in the efficient translocation and proper folding of these proteins in the matrix. One such molecular chaperone is the eukaryotic mitochondrial heat shock protein 70 (Hsp70); however, it is prone to self-aggregation and requires the presence of an essential zinc-finger protein, Hsp70-escort protein 1 (Hep1), to maintain its structure and function. PfHsp70-3, the only Hsp70 predicted to localize in the mitochondria of P. falciparum, may also rely on a Hep1 orthologue to prevent self-aggregation. In this study, we identified a putative Hep1 orthologue in P. falciparum and co-expression of PfHsp70-3 and PfHep1 enhanced the solubility of PfHsp70-3. PfHep1 suppressed the thermally induced aggregation of PfHsp70-3 but not the aggregation of malate dehydrogenase or citrate synthase, thus showing specificity for PfHsp70-3. Zinc ions were indeed essential for maintaining the function of PfHep1, as EDTA chelation abrogated its abilities to suppress the aggregation of PfHsp70-3. Soluble and functional PfHsp70-3, acquired by co-expression with PfHep-1, will facilitate the biochemical characterisation of this particular Hsp70 protein and its evaluation as a drug target for the treatment of malaria.
Adapting a knowledge-based schema matching system for ontology mapping

In recent years, a large number of entities (ontology classes and properties) are found in different datasets over the Semantic Web. Due to the open and distributed nature of the Web, it is necessary to manage the heterogeneity problem between entities. In this context, the mapping of ontology entities from different datasets is important for data integration, data exchange and data warehousing. Existing semi-automatic ontology matching systems need some parameters such as thresholds and weights, and send the results to the users for adding correct and removing incorrect mapping manually. However, there is no existing solution for correcting these mappings automatically. The main goal of our research work is to do ontology mapping by adapting our Knowledge-based Schema Matching System (KSMS) that allows users to correct and validate the matching results automatically. Our approach is based on Hybrid Ripple-Down Rules (RDR) that combines machine learning and knowledge acquisition approaches. In the hybrid approach, first a machine learning algorithm is used for classifying entities, and then rules are added by incremental knowledge acquisition for solving matching errors such as false positives and false negatives at the element level. The system also computes structure level matching considering hierarchical structure of a full graph. In this research, we perform experiments on the conference track of the ontology alignment contest OAEI 2014. Experimental results demonstrate that our system improves performance in terms of precision, recall and F-measure.
Tracking Language Mobility in the Twitter Landscape

The unprecedented data explosion has drastically changed the data science landscape. At the same time, Big Data analytics have reshaped the design and implementation of the applications that analyse the data. In this paper, we explore the use of Big Data tools for extracting value from Twitter data. We acquire a large set of Twitter data (10TB in size) and process it by relying on Spark DataFrame. The purpose of our analytics pipeline is to study the mobility of languages as captured by the Twitter signal. We study the evolution of languages from both a temporal and a spatial perspective, by applying density-based clustering and Self-Organising Maps techniques. The analysis enabled the detection of tourism trends and real-world events, as perceived through the Twitter lens.
On function field

We give a reduction of the function field Mordell-Lang conjecture to the function field Manin-Mumford conjecture, for abelian varieties, in all characteristics, via model theory, but avoiding recourse to the dichotomy theorems for (generalized) Zariski geometries. Additional ingredients include the “Theorem of the kernel”, and a result of Wagner on commutative groups of finite Morley rank without proper infinite definable subgroups. In characteristic 0 the methods also yield another account of the local modularity of A] for A a traceless simple abelian variety. In positive characteristic, where the main interest lies, we require another result to make the strategy work: so-called quantifier-elimination for the corresponding A] = p∞A(U) where U is a saturated separably closed field, which we prove in the last section. ∗Partially supported by ANR MODIG (ANR-09-BLAN-0047) Model theory and Interactions with Geometry and ANR ValCoMo (ANR-13-BS01-0006) Valuations, Combinatorics and Model Theory †Partially supported by EPSRC grants
A novel method and fast algorithm for MR image reconstruction with significantly under-sampled data

The aim of this work is to improve the accuracy, robustness and efficiency of the compressed sensing reconstruction technique in magnetic resonance imaging. We propose a novel variational model that enforces the sparsity of the underlying image in terms of its spatial finite differences and representation with respect to a dictionary. The dictionary is trained using prior information to improve accuracy in reconstruction. In the meantime the proposed model enforces the consistency of the underlying image with acquired data by using the maximum likelihood estimator of the reconstruction error in partial $k$-space to improve the robustness to parameter selection. Moreover, a simple and fast numerical scheme is provided to solve this model. The experimental results on both synthetic and in vivo data indicate the improvement of the proposed model in preservation of fine structures, flexibility of parameter decision, and reduction of computational cost.
An Attribute-Based Encryption Scheme Secure against Malicious KGC

Different from identity-based encryption scheme, an attribute-based encryption scheme is a scheme in which each user is identified by a set of attributes, and some function of those attributes is used to determine decryption ability for each ciphertext. But key escrow problem is also the inherent problem in attribute-based encryption scheme. To avoid the problem of key escrow, this paper presents an attribute-based encryption scheme by use of another secret key that the KGC cannot obtain. Following, based on a concrete attribute-based encryption scheme insecure against malicious KGC, we give a scheme secure against malicious KGC. Furthermore, compared with the original scheme, our scheme doesn't increase the length of the public key and the ciphertext. The security of our scheme is obtained directly from the security of the original scheme.
Traffic Signal Optimization in “La Almozara” District in Saragossa Under Congestion Conditions, Using Genetic Algorithms, Traffic Microsimulation, and Cluster Computing

Urban traffic congestion is a pandemic illness affecting many cities around the world. We have developed and tested a new model for traffic signal optimization based on the combination of three key techniques: 1) genetic algorithms (GAs) for the optimization task; 2) cellular-automata-based microsimulators for evaluating every possible solution for traffic-light programming times; and 3) a Beowulf Cluster, which is a multiple-instruction-multiple-data (MIMD) multicomputer of excellent price/performance ratio. This paper presents the results of applying this architecture to a large-scale real-world test case in a congestion situation, using four different variables as fitness function of the GA. We have simulated a set of congested scenarios for ¿La Almozara¿ in Saragossa, Spain. Our results in this extreme case are encouraging: As we increase the incoming volume of vehicles entering the traffic network - from 36 up to 3600 vehicles per hour - we get better performance from our architecture. Finally, we present new research directions in this area.
Detection of an oscillatory phenomenon in optical transient counterpart of GRB090522C from observations on Peak Terskol

Sep 2005 Swift-BAT triggered and located GRB050922C. The light curve shows the intense broad peak with T90 of (5 ± 1) s. The Nordic Optical Telescope has obtained spectra of the afterglow with several absorption features corresponding to a redshift of z = 2.17 ± 0.03. Observation of optical transient of GRB050922C was carried out in the R-band with the 60-cm telescope equipped with a CCD on Peak Terskol (North Caucasus). The OT magnitude was fading from R � 16 to � 17.5. Detection of an oscillatory phenomenon in the R post-burst light curve is described in this work. Analysis of the R data reveals coherent harmonic with a period of 0.0050 ±0.0003 days (7.2 min) during observing run of about 0.05 days (� 70 min). Amplitude of oscillations is about 0.05 magnitude. The simplest model suggests that GRB050922C may result from tidal disruption of a white dwarf star by a black hole of about one thousand solar mass. The periodicity in the light curve can be identified with relativistic precession of an accretion disc.
Responsible research and innovation: Critical reflection into the potential social consequences of ICT

The potential social consequences that may arise as a result of the development and widespread use of ICT are of increasing interest to the general public, policymakers and researchers. Prominent examples include transformations of our concept of privacy when using social networking and other websites, ownership and control of personal data, and the ways crowd-sourced information transform how events are coordinated and how they unfold in real-time. Although there is broad acceptance that questions of professional responsibility are relevant to ICT it is often unclear how this could be achieved or how responsibilities should be defined and managed when considering the potential social consequences of ICT. `Responsible Research and Innovation' (RRI) has emerged in Europe proposing approaches for researchers to identify and consider the potential social consequences and impact of their research outputs within the entire research and innovation lifecycle. First, it asks researchers to take on a practice of critical reflection considering the potential societal impacts of their research outputs, and second to include the general public in a dialogue around the development of research goals and strategy. We discuss findings from interviews conducted with a broad range of stakeholders regarding challenges to identifying, debating and resolving social and ethical concerns associated with ICT research and practice. We also consider how we might extend existing or develop new approaches that facilitate both critical reflection and wider participation within the entire research and innovation lifecycle.
The Enigma of Capital and the Crisis of Capitalism

Es posible pensar la crisis económica actual como el resultado de una matriz multidimensional de causas sistémicas que operan de acuerdo con un número finito de dinámicas estructurales y de variables geoeconómicas y geopolíticas cuyas lógicas definen la reproducción de la estructura social de acuerdo con las pautas de comportamiento generadas por el capitalismo histórico o tan solo debemos interpretarla como el desajuste parcial de un sistema económico que carece de historia y racionalidad sistémica y cuya lógica únicamente puede reconstruirse de acuerdo con el concepto neoclásico y neoliberal de mercado y con el modelo de elección racional del homo economicus? ¿Es la crisis que arranca de 2008-2009 una crisis antropológica y ética, una crisis del mercado y de sus modos de organización de la competencia, una crisis de los modelos de regulación y de governance inspirados por el frame neoliberal impuesto durante las últimas décadas o estamos ante una crisis del capitalismo concebido como sistema de reproducción social y de los equilibrios de su último ciclo sistémico de acumulación estadounidense así como de las relaciones de fuerza existentes entre los sujetos colectivos que se involucran en los procesos de producción y de reproducción social y en la traducción política de sus necesidades en los ámbitos nacionales, regionales y globales? ¿Es posible ofrecer una interpretación de la crisis de acuerdo con el concepto estándar de racionalidad económica que inspira los paradigmas interpretativos predominantes en las ciencias sociales y humanas contemporáneas o es preciso recurrir simultáneamente a una teoría coherente del capitalismo como sistema social e histórico y a una teoría del poder inherente al mismo que nos ofrezcan herramientas para comprender el ciclo integral de la dominación y la explotación social, política, económica y cultural que definen los contornos de un momento histórico determinado?
Using Environmental Models to Optimize Sensor Placement

Sensors used in protective applications are conventionally placed on perimeters or over areas in an evenly distributed pattern. However, such patterns may actually be suboptimal, since environmental factors may make some forms of attack more or less likely than others. We describe a protective application of sensors for detecting underwater threats in an urban estuary environment. We demonstrate that environmental information, derived from a computational river current model, can be utilized to optimize sensor placement, increasing detection rates and decreasing the number of required sensors. Simulation results show a significant improvement in detection likelihood for a given number of sensors; alternatively, fewer sensors can be used while still maintaining the detection rate of a conventional approach
A Testbed for Localizing Wireless LAN Devices Using Received Signal Strength

This paper elaborates on the development of a wireless network testbed to measure the received signal strength indicator (RSSI) in different environments, as the first step for the application of fingerprinting-type localization algorithms of wireless LAN devices. Specifically, in the localization algorithm to the closest previously mapped sets of locations, the RSSI data collected first at known positions are then used to localize the mobile devices at random points. The localization algorithm tested is the minimum-distance algorithm in the RSSI feature space corresponding to the actual geographical points. This paper shows how the environment for RSSI measurement is built and what network configurations yield the most reliable measurements. In the first phase of building a testbed, configurations of off-the-shelf-equipment and the corresponding applications are explained. The second phase is to measure the RSSI in different propagation and physical environments. In this phase, different environments that have already been built in the first phase are examined. Firstly, RSSI is measured from access points' perspective. Secondly, RSSI measurements are taken from laptops' perspective. The third phase is to apply a localization algorithm using the collected data to verify the accuracy of the localization method and examine the characteristics of the collected data.
Multistatic sar satellite formations: potentials and challenges

This paper discusses the capabilities of multistatic SAR satellite configurations for different applications like high resolution DEM generation using multibaseline single-pass cross- track interferometry, 3-D vegetation mapping and layover solution with spaceborne SAR tomography, high resolution wide swath SAR imaging with sparse satellite arrays, multibaseline velocity estimation of moving objects and scatterer fields, spatial and radiometric resolution enhancement in SAR images, and multistatic imaging for improved detection and classification. Furthermore, some major challenges like phase and time synchronisation, multistatic SAR processing, satellite orbit selection, and relative position sensing will be addressed.
Why Filipino Migrants Remit? Evidence from a Home-Host Country Matched Sample

This paper uses a unique matched survey dataset to directly test remittance models using information from both remittance-sending and remittance-receiving households. Our results show that the decision to remit is strongly affected by both home and host country factors with altruism motive as an ubiquitous underlying element. We find that men’s behaviour is mainly influenced by their capacity to remit, not so for women. Further we show that job-to-skill mismatching is widespread among Filipinos overseas and depresses women’s remittances by over fifty per cent. Clearly, the economic and social losses associated with this kind of labour market outcome are significant.