Miracle-dz/UniFAR-FTUs huggingface dataset:
Bag-of-visual words based automatic image annotation

Content-based Image retrieval systems extract and retrieve images using their low-level features, such as color, texture, and shape. Nevertheless, these visual contents do not allow a user to formulate semantically meaningful image query. Image annotation systems are a solution to solve the inadequacy of CBIR systems and allow text based image retrieval. There have been several studies on automatic image annotation utilizing machine learning techniques and images' representation with low level features extracted using either global or local methods. However, typically, these approaches suffer from low correlation between the globally assigned annotations and the visual features used to obtain annotations automatically. In this paper, we present an approach to enhance the effectiveness of CBIR using learning based automatic images annotation based on bag of visual word images representation that is created automatically using a set of manually annotated training images. The experimentation is performed with 4,000 annotated images for training, 1000 images for testing from ImageNet. The result has shown 77.5% of performance accuracy. The result of this work is believed to be one step towards enhancing the performance and effectiveness of existing CBIR and minimizing the semantic gap.
Concept segmentation and labeling for conversational speech

Spoken Language Understanding performs automatic concept labeling and segmentation of speech utterances. For this task, many approaches have been proposed based on both generative and discriminative models. While all these methods have shown remarkable accuracy on manual transcription of spoken utterances, robustness to noisy automatic transcription is still an open issue. In this paper we study algorithms for Spoken Language Understanding combining complementary learning models: Stochastic Finite State Transducers produce a list of hypotheses, which are re-ranked using a discriminative algorithm based on kernel methods. Our experiments on two different spoken dialog corpora, MEDIA and LUNA, show that the combined generative-discriminative model reaches the state-ofthe-art such as Conditional Random Fields (CRF) on manual transcriptions, and it is robust to noisy automatic transcriptions, outperforming, in some cases, the state-of-the-art.
A memristor emulator circuit based on operational transconductance amplifiers

This paper introduces a memristor emulator circuit based on operational transconductance amplifiers. This emulator circuit is imitated the behavior of a titanium dioxide memristor model using commercial available integrated circuit. Thus, the hardware of proposed emulator circuit can be built using devices that find in the market. Also the circuit can be tested in both simulations and experiments which can be used as a teaching aid and for real circuit applications. Simulation and experimental results are given as agree well with theory.
The Marsili Volcanic Seamount (Southern Tyrrhenian Sea): A Potential Offshore Geothermal Resource

Italy has a strong geothermal potential for power generation, although, at present, the only two geothermal fields being exploited are Larderello-Travale/Radicondoli and Mt. Amiata in the Tyrrhenian pre-Apennine volcanic district of Southern Tuscany. A new target for geothermal exploration and exploitation in Italy is represented by the Southern Tyrrhenian submarine volcanic district, a geologically young basin (Upper Pliocene-Pleistocene) characterised by tectonic extension where many seamounts have developed. Heat-flow data from that area show significant anomalies comparable to those of onshore geothermal fields. Fractured basaltic rocks facilitate seawater infiltration and circulation of hot water chemically altered by rock/water interactions, as shown by the widespread presence of hydrothermal deposits. The persistence of active hydrothermal activity is consistently shown by many different sources of evidence, including: heat-flow data, gravity and magnetic anomalies, widespread presence of hydrothermal-derived gases (CO 2 , CO, CH 4 ), 3 He/ 4 He isotopic ratios, as well as broadband OBS/H seismological information, which demonstrates persistence of volcano-tectonic events and High Frequency Tremor (HFT). The Marsili and Tyrrhenian seamounts are thus an important—and likely long-lasting-renewable energy resource. This raises the possibility of future development of the world’s first offshore geothermal power plant.
Implementation of symbolic model checking for probabilistic systems

In this thesis, we present efficient implementation techniques for probabilistic model checking, a method which can be used to analyse probabilistic systems such as randomised distributed algorithms, fault-tolerant processes and communication networks. A probabilistic model checker inputs a probabilistic model and a specification, such as "the message will be delivered with probability 1", "the probability of shutdown occurring is at most 0.02" or "the probability of a leader being elected within 5 rounds is at least 0.98", and can automatically verify if the specification is true in the model. Motivated by the success of symbolic approaches to non-probabilistic model checking, which are based on a data structure called binary decision diagrams (BDDs), we present an extension to the probabilistic case, using multi-terminal binary decision diagrams (MTBDDs). We demonstrate that MTBDDs can be used to perform probabilistic analysis of large, structured models with more than 7.5 billion states, way out of the reach of conventional, explicit techniques, based on sparse matrices. We also propose a novel, hybrid approach, combining features of both symbolic and explicit implementations and show, using results from a wide range of case studies, that this technique can almost match the speed of sparse matrix based implementations, but uses significantly less memory. This increases, by approximately an order of magnitude, the size of model which can be handled on a typical workstation.
Reducing Donor Site Morbidity When Reconstructing the Nipple Using a Composite Nipple Graft

Background Numerous procedures are available for nipple reconstruction without a single gold standard. This study presents a method for reducing donor-site morbidity in nipple reconstruction using a composite nipple graft after transverse rectus abdominis musculocutaneous flap breast reconstruction. Methods Thirty-five patients who underwent nipple reconstruction using a composite nipple graft technique between July of 2001 and December of 2009 were enrolled in this study. To reduce the donor site morbidity, the superior or superior-medial half dome harvesting technique was applied preserving the lateral cutaneous branch of the fourth intercostal nerves. The patients were asked to complete a previously validated survey to rate the color and projection of both nipples, along with the sensation and contractility of the donor nipple; and whether, in retrospect, they would undergo the procedure again. To compare projection, we performed a retrospective chart review of all the identifiable patients who underwent nipple reconstruction using the modified top hat flap technique by the same surgeon and during the same period. Results Thirty-five patients were identified who underwent nipple reconstruction using a composite nipple graft. Of those, 29 patients (82.9%) responded to the survey. Overall, we received favorable responses to the donor site morbidity. Projection at postoperative 6 months and 1 year was compared with the immediate postoperative results, as well as with the results of nipples reconstructed using the modified top hat flap. Conclusions The technique used to harvest donor tissue is important. Preserving innervation of the nipple while harvesting can reduce donor site morbidity.
Superficial Zone Extracellular Matrix Extracts Enhance Boundary Lubrication of Self-Assembled Articular Cartilage

Objective Previous work has shown that increasing the production of boundary lubricant, superficial zone protein (SZP), did not reduce the friction coefficient of self-assembled articular cartilage constructs and was possibly due to poor retention of the lubricant. The aim of this investigation was to reduce the friction coefficient of self-assembled articular cartilage constructs through enhancing SZP retention by the exogenous addition of extracellular matrix (ECM) extracted from the superficial zone of native articular cartilage. Design Superficial zone cartilage was shaved from juvenile bovine femoral condyles using a dermatome, minced finely with razor blades, extracted with 4 M guanidine-hydrochloride, buffer exchanged with culture medium, and added directly to the culture medium of self-assembled articular cartilage constructs at low (10 µg/mL) and high (100 µg/mL) concentrations for 4 weeks. Biochemical and biomechanical properties were determined at the conclusion of 4 weeks culture. Results ECM treatment increased compressive and tensile stiffness of self-assembled articular cartilage constructs and decreased the friction coefficient. Glycosaminoglycan content decreased and collagen content increased significantly in self-assembled constructs by the ECM treatment. Conclusions Friction coefficients of self-assembled articular cartilage constructs were reduced by adding extracted superficial zone ECM into the culture medium of self-assembled articular cartilage constructs.
Sports Activity after Arthroscopic Superior Labral Repair using Suture Anchors in Overhead-Throwing Athletes

Purpose To evaluate the results of arthroscopic repair of type II superior labral anterior posterior lesions of the shoulder in overhead athletes. Hypothesis Such repair is useful for overhead athletes in terms of postoperative sports activity. Study Design Case series; Level of evidence, 4. Methods The study group was composed of 40 patients with a mean age of 24 years (range, 15-38 years); mean follow-up was 41 months (range, 24-58 months). They were divided into an overuse (n = 22) and a trauma group (n = 18). The authors used 2 suture anchors loaded with a nonabsorbable suture at the 11-o'clock and 1-o'clock positions through the anterosuperior and lateral trans-rotator cuff portal. A modified Rowe score and postoperative athletic activities were evaluated. Results After arthroscopic repair, mean modified Rowe scores improved from 27.5 to 92.1 points (P <. 0001). Rated on this scale, the results were excellent in 30 (75%), good in 6 (15%), and fair in 4 (10%) athletes; there were no poor results. Satisfactory outcomes were achieved in 36 (90%) of these patients; 30 (75%) experienced a return to the preinjury level. The complete return rate of baseball players in the overuse group was lower than that of other overhead athletes in the trauma group. Conclusion Arthroscopic superior labral repair is a safe and reliable procedure in overhead athletes.
Progress in Imidazolium Ionic Liquids Assisted Fabrication of Carbon Nanotube and Graphene Polymer Composites

Carbon nanotubes (CNTs) and graphene sheets are the most promising fillers for polymer nanocomposites due to their superior mechanical, electrical, thermal optical and gas barrier properties, as well as high flame-retardant efficiency. The critical challenge, however, is how to uniformly disperse them into the polymer matrix to achieve a strong interface for good load transfer between the two. This problem is not new but more acute in CNTs and graphene, both because they are intrinsically insoluble and tend to aggregate into bundles and because their surfaces are atomically smooth. Over the past decade, imidazolium ionic liquids (Imi-ILs) have played a multifunctional role (e.g., as solvents, dispersants, stabilizers, compatibilizers, modifiers and additives) in the fabrication of polymer composites containing CNTs or graphene. In this review, we first summarize the liquid-phase exfoliation, stabilization, dispersion of CNTs and graphene in Imi-ILs, as well as the chemical and/or thermal reduction of graphene oxide to graphene with the aid of Imi-ILs. We then present a full survey of the literature on the Imi-ILs assisted fabrication of CNTs and graphene-based nanocomposites with a variety of polymers, including fluoropolymers, hydrocarbon polymers, polyacrylates, cellulose and polymeric ionic liquids. Finally, we give a future outlook in hopes of facilitating progress in this emerging area.
Aligning Web Services with the Semantic Web to Create a Global Read-Write Graph of Data

Despite significant research and development efforts, the vision of the Semantic Web yielding to a Web of Data has not yet become reality. Even though initiatives such as Linking Open Data gained traction recently, the Web of Data is still clearly outpaced by the growth of the traditional, document-based Web. Instead of releasing data in the form of RDF, many publishers choose to publish their data in the form of Web services. The reasons for this are manifold. Given that RESTful Web services closely resemble the document-based Web, they are not only perceived as less complex and disruptive, but also provide read-write interfaces to the underlying data. In contrast, the current Semantic Web is essentially read-only which clearly inhibits networking effects and engagement of the crowd. On the other hand, the prevalent use of proprietary schemas to represent the data published by Web services inhibits generic browsers or crawlers to access and understand this data, the consequence are islands of data instead of a global graph of data forming the envisioned Semantic Web. We thus propose a novel approach to integrate Web services into the Web of Data by introducing an algorithm to translate SPARQL queries to HTTP requests. The aim is to create a global read-write graph of data and to standardize the mashup development process. We try to keep the approach as familiar and simple as possible to lower the entry barrier and foster the adoption of our approach. Thus, we based our proposal on SEREDASj, a semantic description language for RESTful data services, for making proprietary JSON service schemas accessible.
Restricted Recurrent Neural Tensor Networks: Exploiting Word Frequency and Compositionality

Increasing the capacity of recurrent neural networks (RNN) usually involves augmenting the size of the hidden layer, with significant increase of computational cost. Recurrent neural tensor networks (RNTN) increase capacity using distinct hidden layer weights for each word, but with greater costs in memory usage. In this paper, we introduce restricted recurrent neural tensor networks (r-RNTN) which reserve distinct hidden layer weights for frequent vocabulary words while sharing a single set of weights for infrequent words. Perplexity evaluations show that for fixed hidden layer sizes, r-RNTNs improve language model performance over RNNs using only a small fraction of the parameters of unrestricted RNTNs. These results hold for r-RNTNs using Gated Recurrent Units and Long Short-Term Memory.
The molecular signal for the adaptation to cold temperature during early life on Earth

Several lines of evidence such as the basal location of thermophilic lineages in large-scale phylogenetic trees and the ancestral sequence reconstruction of single enzymes or large protein concatenations support the conclusion that the ancestors of the bacterial and archaeal domains were thermophilic organisms which were adapted to hot environments during the early stages of the Earth. A parsimonious reasoning would therefore suggest that the last universal common ancestor (LUCA) was also thermophilic. Various authors have used branch-wise non-homogeneous evolutionary models that better capture the variation of molecular compositions among lineages to accurately reconstruct the ancestral G + C contents of ribosomal RNAs and the ancestral amino acid composition of highly conserved proteins. They confirmed the thermophilic nature of the ancestors of Bacteria and Archaea but concluded that LUCA, their last common ancestor, was a mesophilic organism having a moderate optimal growth temperature. In this letter, we investigate the unknown nature of the phylogenetic signal that informs ancestral sequence reconstruction to support this non-parsimonious scenario. We find that rate variation across sites of molecular sequences provides information at different time scales by recording the oldest adaptation to temperature in slow-evolving regions and subsequent adaptations in fast-evolving ones.
Software Defined Network Support for Real Distributed Systems

Software defined network is an emerging technique that allow users to reconfigure queuing, routing and forwarding of the entire network it runs on. Such technology makes it possible for applications to program the network as desired. In this work, we took ZooKeeper and Hadoop as examples to explore how SDN optimization may improve the performance of distributed systems at run-time. Our analysis has indicated that such instrumentation has great potential of improving the performance.
A Bayesian predictive assistance system for resource optimization — A case study in industrial cleaning process

Optimizing the resource consumption by the products (machines) and making them environment friendly is the aim of almost all producers today. May it be due to cost of resources, their limited availability, their affect on the environment or consumer awareness. Ample research is being carried out at national and international level for resource optimization. Adding intelligence and learning capability is being increasingly used as an approach for resource optimization. Different methods and models for machine learning are available in the literature. Bayesian network is one of the widely used learning model for resource optimization in wide range of applications [1], [2]. In this paper, we present the use of Bayesian network for resource optimization and decision support system in an industrial cleaning process. The proposed Bayesian predictive assistance system assists the cleaner in choosing the optimal parameters and would be a self-learning system that stores the successful cleaning results in a global database for future cleaning cycle.
Enhancing Contents-Link Coupled Web Page Clustering and Its Evaluation

Web page clustering is a fundamental technique to offer a solution for data management, information locating and its interpretation of Web data and to facilitate users for navigation, discrimination and understanding. Most existing clustering algorithms cannot adapt well to Web clustering directly in terms of efficiency and effectiveness. Combining contents analysis and hyperlink structure analysis has been proven a better approach. However, how to effectively combine the two features with different nature in clustering to get satisfactory results remains an open problem and there is still little work on it. In this paper, we present an experimental study on enhancing coupling of links and contents analysis of Web pages for robust clustering. In particular, we introduce two techniques: in-link reinforcement and anchor window analysis to improve the adaptability of contents-link coupled clustering. Our detailed evaluation indicates those techniques can effectively improve the quality of Web pages clustering for a wide range of topics. 1. Introduction there are more than 2 billion pages on the web without counting those so-called hidden Web pages that can be generated from the underneath databases. At the same time more than 100 million pages become obsolete every month. Locating truly needed Web pages and interpreting them appropriately is a big challenge faced by researchers in the fields of database, Information Retrieval (IR) and data mining. So, correctly clustering both the source Web pages and results of search engines is very important to help end users in navigation, discrimination, summarization and interpretation of the Web. Most existing and well-cited topic directories such as Yahoo! (www.yahoo.com) and open directory (www.dmoz.com) are mainly created and maintained manually by domain experts. Therefore those topic directories cover only a very small portion of the whole Web due to extremely low scalability of manual creating and maintenance. They are also more often outdated as the Web changes all the time. Some topics also have no corresponding sub-categories in Yahoo or open directory. Such unsatisfactory performance calls for the needs of semi-automatic or automatic clustering of Web pages that is expected to scale well and be able to follow the evolution of the Web well. Document clustering has been well studied in the field of tradition IR. The most commonly used techniques are developed under the vector-space model. Under this
Secured mutual authentication between two entities

Phishing is one of the most common attacks on the networks today and a primary enabler for fraud and identity theft, and the increasing sophistication of the phishers makes it very difficult for users to distinguish between genuine and fake. Thus, it is important to mutually authenticate each other. The mutual authentication between two entities is essential to establish a secure link over public/closed insecure networks. This paper gives an overview of proposed secure authentication system which includes the authentication server, one-time password generator, and database server. The system achieves mutual authentication by exchanging two one-time password (OTP) where OTP is a security mechanism, will be expired after single use/ some period of time and provides several advantages with respect to most of the available solutions at the state of the art. First, it enables transparent mutual authentication between two entities. Moreover, it guarantees authenticity of both entities within the same session. Finally, the proposed system ensures secure data transmission and protected access between two entities as well as to prevent from known attacks.
Homogeneity and heterogeneity in mild cognitive impairment and Alzheimer's disease: a cross-sectional and longitudinal study of 55 cases.

This study investigated cross-sectional and longitudinal neuropsychological data from 55 patients: 38 with Alzheimer's disease and 18 with mild cognitive impairment (MCI). The analyses were designed to investigate two issues: the relationship of MCI to Alzheimer's disease, and that of atypical to typical Alzheimer's disease. When longitudinal data were averaged across individual patients, a consistent staging of neuropsychological deficits emerged: the selective amnesia characteristic of the MCI phase was joined next by semantic and other linguistic impairments plus emerging difficulties with demanding visuospatial tasks. A two-stage statistical procedure was used to extract underlying factors that corresponded to the severity-governed decline in neuropsychological test scores and then to the consistent deviations away from this typical longitudinal profile; i.e. identifying patterns of atypical Alzheimer's disease. The severity-based factor accounted for nearly 60% of the variance in this MCI-Alzheimer's disease longitudinal and cross-sectional database. This suggests that there is a fairly high degree of homogeneity within this group of patients, and that most of their longitudinal progression can be predicted by dementia severity alone. There were also two main patterns of atypical variation corresponding to patients with exaggerated semantic or visuospatial deficits. Although such cases may mimic more focal lobar degenerative conditions, patients with atypical Alzheimer's disease have pronounced episodic memory impairments, suggesting amnesia as a critical diagnostic feature.