Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of an Enhanced EfficientParallel Opinion Mining for Predicting thePerformance of Various Products

with the help of Web 2.0, the centers around user participation, posting online reviews has become an increasingly popular way for people to share their views with other user’s opinions and sentiments toward products and services. It becomes a common practice for e-commerce websites to provide the facilities for people to communicate and publish their reviews between them. These online reviews present a wealth of information on the Services and Products, which will facilitate the improvement of their business. Hence a growing number of recent studies have been focused on the Opinion Mining. A few Opinion Mining based methods have been studied and analyzed. From our study, it is observed that a few opinion mining based TSCAN algorithm had not produced good results due to referring the users and customers opinions features with similar meaning as different. To overcome this issue, the Modified TSCAN Algorithm is proposed here. It is mainly focusing on the experts opinions which overcomes the existing system drawback in terms of referring genuine opinions, so that the readers could understand the content easily. By using this model, the more information can be extracted and associated through their temporal closeness, which will give comprehensible content. This model is involving vital role in the Opinion Mining because users can share their opinions about the products. From our implementation, it is observed that this scheme provides the best suitable solution for the user’s interests and demands. Thus our research work is proposed and implemented an efficient method for Opinion Mining called an Enhanced Efficient Parallel Opinion Mining (EEPOM) based TSCAN Algorithm. It is focusing more websites and it is extracting more information in parallel manner, so that we can get optimized fruitful result with the expert’s opinions. From our results, it is noted that it provides the best suitable solution for the user’s interests and demands and it i s improving the performance of existing technique in terms of Quality of Information, Prediction and obtaining of genuine opinions.
Enabling efficient OS paging for main-memory OLTP databases

Even though main memory is becoming large enough to fit most OLTP databases, it may not always be the best option. OLTP workloads typically exhibit skewed access patterns where some records are hot (frequently accessed) but many records are cold (infrequently or never accessed). Therefore, it is more economical to store the coldest records on a fast secondary storage device such as a solid-state disk. However, main-memory DBMS have no knowledge of secondary storage, while traditional disk-based databases, designed for workloads where data resides on HDD, introduce too much overhead for the common case where the working set is memory resident. In this paper, we propose a simple and low-overhead technique that enables main-memory databases to efficiently migrate cold data to secondary storage by relying on the OS's virtual memory paging mechanism. We propose to log accesses at the tuple level, process the access traces offline to identify relevant access patterns, and then transparently re-organize the in-memory data structures to reduce paging I/O and improve hit rates. The hot/cold data separation is performed on demand and incrementally through careful memory management, without any change to the underlying data structures. We validate experimentally the data re-organization proposal and show that OS paging can be efficient: a TPC-C database can grow two orders of magnitude larger than the available memory size without a noticeable impact on performance.
A Device-Similarity-Based Recommendation System in Mobile Terminals

Smart Mobile device are becoming popular platforms for information accessing, especially when coupled with recommendation system technologies. They are also treated as key tools for mobile users both for leisure and business applications. Recommendation techniques can increase the usability of mobile systems by providing more personalized and interested content. In this paper, a novel personalized recommender system is proposed, focusing on Mobile Terminal (MT) similarities, such as brands, versions and types of Operating Systems. These similarities play a key role in filtering original recommendation data sets at the preprocessing stage. By calculating and comparing the Mean Absolute Error (MAE) values through 5-fold cross validation of the Slope One algorithm with/without optimizing data sets by device-similarity, the overall effectiveness and accuracy of the recommendation results are at least 20% improved in our experiment.
Fairy Tale Corpus Organization Using Latent Semantic Mapping and an Item-to-item Top-n Recommendation Algorithm

In this paper we present a fairy tale corpus that was semantically organized and tagged. The proposed method uses latent semantic mapping to represent the stories and a top-n item-to-item recommendation algorithm to define clusters of similar stories. Each story can be placed in more than one cluster and stories in the same cluster are related to the same concepts. The results were manually evaluated regarding the groupings as perceived by human judges. The evaluation resulted in a precision of 0.81, a recall of 0.69, and an f-measure of 0.75 when using tf*idf for word frequency. Our method is topic- and language-independent, and, contrary to traditional clustering methods, automatically defines the number of clusters based on the set of documents. This method can be used as a setup for traditional clustering or classification. The resulting corpus will be used for recommendation purposes, although it can also be used for emotion extraction, semantic role extraction, meaning extraction, text classification, among others.
Kinematic Evidence for Different Planetary Nebula Populations in the Elliptical Galaxy NGC 4697

We have analyzed the magnitudes, kinematics, and positions of a complete sample of 320 planetary nebulae (PNs) in the elliptical galaxy NGC 4697. We show the following. (1) The PNs in NGC 4697 do not constitute a single population that is a fair tracer of the distribution of all stars. The radial velocity distributions, mean velocities, and dispersions of bright and faint subsamples differ with high statistical confidence. (2) Using the combined data for PNs brighter than 26.2, we have identified a subpopulation of PNs that is azimuthally unmixed and kinematically peculiar, and thus neither traces the distribution of all stars nor can be in dynamical equilibrium with the galaxy potential. (3) The planetary nebula luminosity functions (PNLFs) of two kinematic subsamples in NGC 4697 differ with 99.7% confidence, ruling out a universal PNLF. We estimate that the inferred secondary PN population introduces an uncertainty in the bright cutoff magnitude of ∼0.15 mag for this galaxy. We argue that this secondary PN distribution may be associated with a younger, ≳1 Gyr old stellar population, perhaps formed in tidal structures that have now fallen back onto the galaxy, as has previously been suggested for the X-ray point sources in this galaxy, or coming from a more recent merger/accretion with a red galaxy. The use of PNs for extragalactic distance determinations is not necessarily compromised, but their use as dynamical tracers of dark halos will require deep observations and careful analysis of large PN samples.
Self-Transfer Learning for Fully Weakly Supervised Object Localization

Recent advances of deep learning have achieved remarkable performances in various challenging computer vision tasks. Especially in object localization, deep convolutional neural networks outperform traditional approaches based on extraction of data/task-driven features instead of hand-crafted features. Although location information of region-of-interests (ROIs) gives good prior for object localization, it requires heavy annotation efforts from human resources. Thus a weakly supervised framework for object localization is introduced. The term "weakly" means that this framework only uses image-level labeled datasets to train a network. With the help of transfer learning which adopts weight parameters of a pre-trained network, the weakly supervised learning framework for object localization performs well because the pre-trained network already has well-trained class-specific features. However, those approaches cannot be used for some applications which do not have pre-trained networks or well-localized large scale images. Medical image analysis is a representative among those applications because it is impossible to obtain such pre-trained networks. In this work, we present a "fully" weakly supervised framework for object localization ("semi"-weakly is the counterpart which uses pre-trained filters for weakly supervised localization) named as self-transfer learning (STL). It jointly optimizes both classification and localization networks simultaneously. By controlling a supervision level of the localization network, STL helps the localization network focus on correct ROIs without any types of priors. We evaluate the proposed STL framework using two medical image datasets, chest X-rays and mammograms, and achieve signiticantly better localization performance compared to previous weakly supervised approaches.
Mythology Converges With Technology To Combat Biomaterials Associated Infections

The integration of nanotechnology, biotechnology, biology, physical science, chemistry, biomedical science and medicine; for the best scope, development and application in science and technology is being studied for decades. But the intimation of the immense potentiality of mythology converging with technology is scarce. For the first time, we report here the combo power of the mythological Peepal tree, Ficus religiosa, in convergence with nanotechnology to combat biomaterials associated infections. Infections associated with biomaterials such as medical devices, implants and prosthetic materials, are due to bacterial adhesion and subsequent biofilm formation at the implantation site. Although much research has been focused on sterilization, aseptic procedures and on developing polymers that resist biofilm formation; bacterial infection still remains a major impediment in the utility of such biomaterials. The emergence of multidrug resistant ‘super bugs’, like the ones recently discovered, ‘NDM-l’ (New Delhi Metallobeta-lactamase), have made this global problem rather alarming. For reducing this incidence of biomaterials associated infections we have developed self-sterilizing silver/chitosan bionanocomposite via biosynthesis for application as coating over biomedical implants and devices. This material has been characterized through various techniques and antimicrobial studies have
A taxonomy of privilege escalation attacks in Android applications

Google's Android is one of the most popular mobile operating system platforms today, being deployed on a wide range of mobile devices from various manufacturers. It is termed as a privilege-separated operating system which implements some novel security mechanisms. Recent research and security attacks on the platform, however, have shown that the security model of Android is flawed and is vulnerable to transitive usage of privileges among applications. Privilege escalation attacks have been shown to be malicious and with the wide spread and growing use of the system, the platform for these attacks is also growing wider. This provides a motivation to design and implement better security frameworks and mechanisms to mitigate these attacks. This paper discusses; 1 the security features currently provided by the Android platform; 2 a definition, few working examples and classifications of privilege escalation attacks in Android applications; 3 a classification and comparison of different frameworks and security extensions proposed in recent research.
The Lennard-Jones Potential Function

All results presented here use the scaled Lennard-Jones potential such that the pair well depth and equilibrium distance are both unity V LJ (r) = 1 r 12 − 2 r 6. (1) Thus all energies are given in units of the pair well depth, , and lengths are given in terms of the equilibrium distance σ. This means that for a dimer the potential and the separation are both one. Clusters of this type exhibit magic numbers cor-Most of the structures that are not magic numbers are built on variations of these icosahedra, but some (e.g. N = 38, 75 ≤ N ≤ 77) are non-icosahedral, and as such are much harder to find. A good test of an unbiased algorithm is if it can find these structures [19, 2]. A lot of the early work in this area (e.g. [4]) used a seeding technique, as well as guessing an approximate structure and then feeding this in as the original cluster configuration. This method quickly begins to fail, especially as the minimum structures may be very different when an extra atom is added. Simulated An-nealing has been successful for small to medium sized clusters; Genetic Algorithms have proved to be much more robust and consistent in terms of their ability to find the global minimum structures.
Strategies for development of competencies for implementation of urban public private partnership projects in India

Recent governmental programs in India envisage the delivery of urban services in partnership with the private sector through public private partnerships (PPP). As per the Indian constitution, urban local bodies (ULBs) are primarily responsible for the provision of infrastructural services in urban areas. While these urban local bodies are competent in procuring projects through the traditional design-bid-build model, PPPs are a relatively new concept. The lack of competencies in urban local bodies to select, structure and bid out projects via public private partnership has emerged as a critical bottleneck to the adoption of the PPP model in the delivery of urban services. State governments are attempting to address this competency gap by adopting a variety of strategies for the development of competencies in urban local bodies for the implementation of PPP projects. As the need for urban infrastructure and services heightens across Indian cities, it is imperative that current practices in the development of competencies be studied to ensure effective implementation of urban PPP projects. This research paper reports the findings of a research study conducted to identify appropriate strategies for the development of competencies for the implementation of urban PPP projects. Semi structured interviews were conducted with urban experts and empirical evidence was gathered through case studies of urban PPP projects in order to evaluate the quality and outcomes of current capacity building measures in the urban Indian environment. The areas of improvement in the prevailing policy & institutional framework have been suggested. This study enriches the current body of knowledge on competency development and gives recommendations to policy makers for devising appropriate strategies for streamlining PPPs in the delivery of urban services by bridging the competency gap.
Using GPS data to detect critical events in motorcycle rider behaviour

A relevant research issue in naturalistic studies is related to analysis and evaluation of the huge data recorded during the experiment. A procedure to identify critical events of PTW is presented and tested using parameters measured or derived by GPS. Raw measures of speed, position and time are copied directly from the data string stored by the GPS in National Marine Electronics Association (NMEA) protocol. From these other measures useful in explaining the rider behaviour were derived. Smoothing cubic spline and Butterworth filters were used to improve the estimation of measures effected by GPS inaccuracy. The Mahalanobis distance was adopted to identify multivariable outliers for each combination of raw and derived measures. The events classified by Mahalanobis distance as outliers from the 'normal' rider behaviour were compared with the critical traffic conflicts self-reported by the rider during the experimental test in order to evaluate recall and precision of the method.
A dual-network health state estimator and decision policy for unmanned combat teams

We propose a one-step lookahead rollout policy in closed-loop with a health state estimator to ensure effective cooperation among unmanned combat teams despite intermittent wireless communications breakdowns. To ensure effective cooperation despite network faults, the proposed scheme relies on dual networks. On the one hand, a sensory information management network (SIM-Net) provides the most probable distribution on the location and classification of the adversarial ground units by fusing mobile sensor measurements obtained by a team of surveillance vehicles. On the other hand, a routing and munitions management network (RMM-Net) enables unmanned combat vehicle (UCV) communications, which are required for their effective path planning and for the distribution of the rollout decision policy over the formations. Simulation results demonstrate the effectiveness of the proposed health state estimator and decision policy.
The demise of finance-dominated capitalism : explaining the financial and economic crises

Contents: List of contributors Acknowledgements Introduction Eckhard Hein, Daniel Detzer and Nina Dodig 1. The Transition Towards Finance-dominated Capitalism: French Regulation School, Social Structures of Accumulation and Post-Keynesian Approaches Compared Eckhard Hein, Nina Dodig and Natalia Budyldina 2. Finance-dominated Capitalism, Distribution, Growth and Crisis - Long-run Tendencies Eckhard Hein and Nina Dodig 3. Theories of Financial Crises as Cumulative Processes - An Overview Daniel Detzer and Hansjoerg Herr 4. Financial Crises Leading to Stagnation - Selected Historical Case Studies Nina Dodig and Hansjoerg Herr 5. Five Explanations for the International Financial Crisis Trevor Evans 6. Income Distribution and the Financial and Economic Crisis Jo Michell 7. Global and European Imbalances and the Crisis: A Critical Review Carlos A. Carrasco and Felipe Serrano 8. Financial Deregulation and the 2007-08 US Financial Crisis OEzgur Orhangazi 9. The Role of Incentives for the Great Recession: Securitization and Contagion Giampaolo Gabbi, Alesia Kalbaska and Alessandro Vercelli 10. Risk Management, the Subprime Crisis and Finance-dominated Capitalism: What Went Wrong? A Systematic Literature Review Sergio Lagoa, Emanuel Leao and Ricardo Barradas Index
Design of a flash-based 3D game to motive active learning and inspire teamwork on inter-campus competition

This research aims to design an innovative flash-based game for introducing traditional campus competition in Taiwan, called Mei-Chu Game. Most of the cultural and historical backgrounds were saved in different forms, such as images and documents. In this research, we converted those data and old forms into electronic forms, such as Three-Dimensional (3D) models and multimedia games. In addition, with the actual competitive environment of Mei-Chu Game, this research was combining the factor of competition with our educational game. Through the features of 3D instructional method and competition, we hope that can keep students' high motivation and promote students to do active learning even out of class in their lifelong.
Measuring Uncertainty in Monetary Policy Using Realized and Implied Volatility

Uncertainty surrounding the Bank of Canada’s future policy rates is measured using implied volatility computed from interest rate options and realized volatility computed from intraday prices of interest rate futures. Both volatility measures show that uncertainty decreased following major policy actions taken by the Bank in response to the 2007–09 financial crisis. Findings also indicate that, on average, uncertainty decreases following the Bank’s policy rate announcements.
Diffusive synchrotron radiation from extragalactic jets

Flattenings of non-thermal radiation spectra observed from knots and interknot locations of the jets of 3C 273 and M87 in the ultraviolet and X-ray bands are discussed within modern models of magnetic field generation in relativistic jets. Specifically, we explicitly take into account the effect of the small-scale random magnetic field, probably present in such jets, which gives rise to emission of diffusive synchrotron radiation, the spectrum of which deviates substantially from the standard synchrotron spectrum, especially at high frequencies. The calculated spectra agree well with the observed ones if the energy densities contained in small-scale and large-scale magnetic fields are comparable. The implications of this finding for magnetic field generation, particle acceleration and jet composition are discussed. Ke yw ords: acceleration of particles ‐ magnetic fields ‐ radiation mechanisms: non-thermal ‐ shock waves ‐ turbulence ‐ galaxies: jets.
On Shared Rate Time Series for Mobile Users in Poisson Networks

This paper focuses on modeling and analysis of the temporal performance variations experienced by a mobile user in a wireless network and its impact on system level design. We consider a simple stochastic geometry model: the infrastructure nodes are Poisson distributed while the user's motion is the simplest possible i.e., constant velocity on a straight line. We first characterize variations in the SNR process, and associated downlink Shannon rate, resulting from variations in the infrastructure geometry seen by the mobile. Specifically, by making a connection between stochastic geometry and queueing theory the level crossings of the SNR process are shown to form an alternating renewal process whose distribution can be completely characterized. For large or small SNR levels, and associated rare events, we further derive simple distributional (exponential) models. We then characterize the second major contributor to variation, associated with changes in the number of other users sharing infrastructure. Combining these two effects, we study what are the dominant factors (infrastructure geometry or sharing number) given mobile experiences a very high or low shared rate. These results are then used to evaluate and optimize the system-level Quality of Service (QoS) and system-level capacity to support mobile users sharing wireless infrastructure, including mobile devices streaming video which proactively buffer content to prevent rebuffering and mobiles which are downloading large files. Finally, we use simulation to assess the fidelity of this model and its robustness to factors which are presently taken into account.
Int-Haar: Improving Precision of the Haar Interval Wavelet Extension

This work describes the interval extension of the Haar Wavelet Transform (HWT), implemented with C-XSC, being the first step on the development of the Int-DWTs library, which will provide interval results for several Discrete Wavelet Transforms (DWTs). Optimizations for the normalized formulation of the 1D and 2D transforms in order to increase the accuracy of the results are also presented. In this work, 1D and 2D applications are addressed, as well as the error analysis of the different interval formulations studied for the transformations.