Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancing Access to Media Collections and Archives Using Computational Linguistic Tools

In this paper, we outline the strategies, methodology, and infrastructure needed to bring advanced computational linguistic tools to researchers and archivists in the humanities. We discuss three use cases involving the application of the Language Application Grid (LAPPS), an open, web-based infrastructure providing interoperable access to hundreds of computational linguistic (CL) component web services, together with facilities for multistep analyses via tools pipelining, performance evaluation, and resource delivery. These include: CL analysis of corpora restricted under copyright; the challenge posed by radio and television media collections; and the use of LAPPS for assisting archivists in their collection and cataloguing efforts. We believe that the adoption and use of CL platforms such as LAPPS by the digital humanities (DH) will help foster better communication, sharing, and research between the two communities.
Cost Estimation with Neural Network and Affect of Different Input parameters on Cost Estimation

1 M.E CSE, 2Assitant Professor, UIET, Panjab University , Chandigarh, INDIA __________________________________________________________________________________ ABSTRACT: Software estimation is a process of estimating the effort, size and cost of the software, before the actual development of the software. Realistic estimation helps the software developers and managers in finding the required resources, man-hours, time and cost required in developing software. There are various types of estimation methods such as algorithmic, regression, machine learning, function pts etc, but still there is need of an accurate and precise software estimation method. Some models don’t include all the parameters, some are extremely vulnerable to misclassification or error deviation is high. In this paper neural network training functions are used for software estimation as neural network is good at discovering relations (linear or non linear). Results of neural network are compared with cocomo 2 efforts as it is the most used model in the world for estimation. Input to neural network training functions is Cocomo 2 parameters which consist of 17 cost multipliers and 5 scale factors. After analyzing the results, a hybrid set of parameters are given to neural network training functions. Results from these hybrid parameters shows that neural networks techniques shows lowest mean error as compared to cocomo 2 parameters.
Mip1, an MEKK2-Interacting Protein, Controls MEKK2 Dimerization and Activation

ABSTRACT Mitogen-activated protein kinase (MAPK) cascades are central components of the intracellular signaling networks used by eukaryotic cells to respond to a wide spectrum of extracellular stimuli. An MAPK is activated by an MAPK kinase, which in turn is activated by an MAPK kinase kinase (MAP3K). However, little is known about the molecular aspects of the regulation and activation of large numbers of MAP3Ks that are crucial in relaying upstream receptor-mediated signals through the MAPK cascades to induce various physiological responses. In this study, we identified a novel MEKK2-interacting protein, Mip1, that regulates MEKK2 dimerization and activation by forming a complex with inactive and nonphosphorylated MEKK2. In particular, Mip1 prevented MEKK2 activation by blocking MEKK2 dimer formation, which in turn blocked JNKK2, c-Jun N-terminal kinase 1 (JNK1), extracellular signal-regulated kinase 5, and AP-1 reporter gene activation by MEKK2. Furthermore, we found that the endogenous Mip1-MEKK2 complex was dissociated transiently following epidermal growth factor stimulation. In contrast, the knockdown of Mip1 expression by siRNA augmented the MEKK2-mediated JNK and AP-1 reporter activation. Together, our data suggest a novel model for MEKK2 regulation and activation.
On Boosting Semantic Web Data Access

The Semantic Web can be viewed as a collection of RDF graphs serialized by RDF documents that distributed in the Web. Its utility depends on three issues: availability (existence of data), accessibility (users can retrieve the data they want), and quality (users can judge the quality of the retrieved data). While more data are available in the Semantic Web, the latter two issues are often ignored or circumscribed due to lacking of tools and mechanisms. This dissertation proposes an ontology-based approach to these two issues so as to boost the utility of the Semantic Web. For accessibility, we identified three critical challenges: i) there are few links to (and almost no description about) RDF documents; ii) it is hard to query the Semantic Web since users are not familiar with semantic web vocabulary (i.e. the URIrefs) with over 150,000 unique entries; and iii) it is unrealistic to query the entire Semantic Web without effective data access service. In order to address these challenges, we proposed the Web of Belief (WOB) ontology to model the Semantic Web and its context (i.e. the web and the agent world), and developed Swoogle system that digests and searches semantic web data using WOB ontology. In particular, Swoogle helps publishers by ranking properties of a given class, and supports information consumers by estimating query complexity and searching URLs of relevant RDF documents. For quality, we first clarified the dimensions of quality (e.g. consistency, completeness, precision, importance and trustworthiness) for different concepts in WOB (e.g. RDF graph, web page, and agent). We then proposed the quality extension to WOB ontology for representing users’ quality judgments (esp. trust judgments) explicitly. Finally, we proposes a series of semantic web navigation models and corresponding ranking algorithms for ontological terms and RDF documents, and a series of algorithms for evaluating trustworthiness of a given RDF graph according to the availability of background knowledge. The contributions of this dissertation are the following: i) WOB ontology, which is one of the first attempts that make the Semantic Web self-descriptive in OWL semantics; ii) Swoogle, which is one of the first web-scale data access services that digest and search the Semantic Web; iii) semantic web navigation models and ranking algorithms; and iv) RDF graph trustworthiness evaluation mechanisms. The WOB ontology and Swoogle like systems, we believe, will bring emergent properties to the Semantic Web: the utility of the web-scale Semantic Web will be reinforced when users are less hassled in finding useful data and are more aware of data quality.
A Supervised Statistical Data Quantization Method in Machine Learning

Data quantization methods for continuous attributes play an extremely important role in artificial intelligence, data mining and machine learning because discrete values of attributes are required in most classification methods. In this paper, we present a supervised statistical data quantization method. It defines a quantization criterion based on the chi-square statistic to discover accurate merging intervals. In addition, a heuristic quantization algorithm is proposed to achieve a satisfying quantization result with the aim to improve the performance of inductive learning algorithms. Empirical experiments on UCI real data sets show that our proposed algorithm generates a better quantization scheme that improves the classification accuracy of C4.5 decision tree than existing algorithms
A Hybrid Rule-Based Machine Translation System from English to Telugu

This paper deals with adaptive rule based machine translation from English to Telugu. This is a proposed approach and it is based on rule-based methodologies. Various approaches like If-then based methods to select the appropriate rule for target language, Rough sets to classify a given sentence into a particular class and probability to decide whether the word selected for Telugu is appropriate for the given sentence or not are used in this method. Set of production rules, training set for English sentences and English to telugu dictionary are developed for this purpose.
UvA-DARE ( Digital Academic Repository ) Children ’ s competence to consent to medical treatment or research

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Power Allocation for Amplify-and-Forward Opportunistic Relaying Systems

In this letter, power allocation methods are devised for Amplify-and-Forward (AF) opportunistic relaying systems aiming at minimizing the outage probability. First, we extend the result on outage probability in [8] and develop an approximate expression to simplify the power allocation problem. A corresponding optimization problem is constructed and proved to be convex. Then an iterative numerical method is proposed to find the optimal power allocation factor. We also propose a near-optimal method which can directly calculate the power allocation factor to reduce computational complexity. Numerical results show that the proposed methods have a similar performance with the ideal one, and outperform equal power allocation significantly with little overhead.
A comparative study of major service providers for cloud computing

Cloud computing enables aggregation of a pool of resources that are provided dynamically as a utility to users over the internet. The users assume that infinite resources are available and pay only for the resources they consume. Different cloud computing providers have emerged during recent years, providing various services and features to endusers. Due to the diversity of the features provided by these providers, it becomes very difficult for an ordinary user to select a platform based on their requirements. This paper provides reflections on different cloud computing providers currently available in market. It analyzes the features provided by existing providers and summarizes the current options available to an ordinary user. Based on the comparative study, conclusions have been drawn and research avenues have been highlighted. Keywordscloud computing, high performance computing, cloud service provider, provider comparison
An Investigation of Universal Background Sparse Coding Based Speaker Verification on Clean Speech

In this letter, we propose a universal background model, named universal background sparse coding (UBSC), for speaker verification. The proposed method trains an ensemble of clusterings by data resampling, and produces sparse codes from the clusterings by one-nearest-neighbor optimization plus binarization. The main advantage of UBSC is that it does not suffer from local minima and does not make Gaussian assumptions on data distributions. Because the denoising frontend of UBSC has not been developed yet, we evaluated UBSC on a clean speech corpus—TIMIT. We found empirically that, when cosine similarity measurement is used as the scoring method of a trial, UBSC is comparable to Gaussian mixture model. Moreover, UBSC performs better with inner product similarity than with cosine similarity.
The Carlisle Indian Industrial School.

The Carlisle Indian Industrial School was the first offreservation boarding school and began the social experiment of assimilation of Native Americans into American culture. For almost 40 years, from 1879 to 1918, the school sought to civilize "savage" Indian children. Richard H. Pratt, founder of the school, believed that the school was the solution to the "Indian problem. " To successfully carry out the mission to assimilate and rehabilitate, Pratt believed that the school must "Kill the Indian, save the man. " To achieve this goal, totalitarian methods were employed, and all aspects of life were controlled. All traces of Indian culture were removed from the view and memory of students. Students were given new Anglo names, and they were forbidden to speak their native language. Boys' hair was cut in the Anglo fashion, and Indian dress was replaced with military uniforms. Girls were given Victorian-style uniform dresses and shoes. During summers, students were placed with Anglo families instead of returning home. Students spent half the day at scholastic study, primarily learning English, and the other half pursuing vocational training. By total immersion in Anglo-American culture, students internalized the belief that Whites were culturally superior. Pratt believed that the goal of the Carlisle Indian Industrial School, to assimilate and rehabilitate Indian students, was successful. However, in retrospect, the U.S. government's goal of eradicating Native American cultures failed miserably. (TD) Reproductions supplied by EDRS are the best that can be made from the original document. The Carlisle Indian Industrial School 1
Development of a Multicommutated Flow System with Chemiluminometric Detection for Quantification of Gentamicin in Pharmaceuticals

A new flow procedure based on multicommutation with chemiluminometric detection was developed to quantify gentamicin sulphate in pharmaceutical formulations. This approach is based on gentamicin's ability to inhibit the chemiluminometric reaction between luminol and hypochlorite in alkaline medium, causing a decrease in the analytical signal. The inhibition of the analytical signal is proportional to the concentration of gentamicin sulphate, within a linear range of 1 to 4 μg mL−1 with a coefficient variation <3%. A sample throughput of 55 samples h−1 was obtained. The developed method is sensitive, simple, with low reagent consumption, reproducible, and inexpensive, and when applied to the analysis of pharmaceutical formulations (eye drops and injections) it gave results with RSD between 1.10 and 4.40%.
Dynamic Pricing of Electricity: A Survey of Related Research

In this paper, we survey 82 papers related to revenue management and dynamic pricing of electricity and lists future research avenues in this field. Dynamic pricing has the potential to modify electric load profiles by charging different prices at different demand levels and hence can act as an effective demand side management tool. There are different forms of dynamic prices that can be offered to different markets and customers. Forecasting of demand, and demand price relationship play an important role in determining prices and helps in scheduling load in dynamic pricing environments. Consumers’ willingness-to-pay for electricity services is also necessary in setting price limits. Elasticity of demand is an indication of the demand response to changing prices. Market segmentation can enhance the effects of such pricing schemes. Appropriate scheduling of electrical load enhances the consumer response to dynamic tariffs.
Automated corpus callosum extraction via Laplace-Beltrami nodal parcellation and intrinsic geodesic curvature flows on surfaces

Corpus callosum (CC) is an important structure in human brain anatomy. In this work, we propose a fully automated and robust approach to extract corpus callosum from T1-weighted structural MR images. The novelty of our method is composed of two key steps. In the first step, we find an initial guess for the curve representation of CC by using the zero level set of the first nontrivial Laplace-Beltrami (LB) eigenfunction on the white matter surface. In the second step, the initial curve is deformed toward the final solution with a geodesic curvature flow on the white matter surface. For numerical solution of the geodesic curvature flow on surfaces, we represent the contour implicitly on a triangular mesh and develop efficient numerical schemes based on finite element method. Because our method depends only on the intrinsic geometry of the white matter surface, it is robust to orientation differences of the brain across population. In our experiments, we validate the proposed algorithm on 32 brains from a clinical study of multiple sclerosis disease and demonstrate that the accuracy of our results.
Exploring the Dynamics of International Trade by Combining the Comparative Advantages of Multivariate Statistics and Network Visualization

This paper contributes to an ongoing debate in International Political Economy about the appropriateness of globalization, regionalization and macroeconomic imbalance theory by identifying quantitative estimates for all three tendencies from world trade data. This is achieved with a series of gravity models enhanced stepwise by the mapping of the estimation errors of a given model on representations of the overall structure of trade. This not only allows the identification of imperfections in a given model but also permits the further improvement of the models since any systematic regional organization in the error-terms can be identified. The results of the most elaborated model indicate that single factor explanations of global economic integration are presumably misleading. Instead, each of three explanations captures only part of the ongoing changes, as they can be identified under a comparative static perspective from world trade data.
Object Oriented Implementation of Combinational Controllability and Observability Algorithms

: Testability measures are controllability and observability. Controllability guides the test generation algorithms while setting a value to primary input (PI) in line justification problem. When more than one path are available for error propagation Observability finds which path is to be select. This paper describes controllability functions (CC0, CC1) and observability function (ob) for combinational circuits. The controllability functions and observability function are implemented using object oriented language C++ for circuit having 2-fanin-fanout, 8-fanin-fanout, and 10-fanin-fanout combinational circuit. ISCAS netlist format is used. The ISCAS 85 benchmark c17 circuits are used for analysis. The flow charts and results for various combinational circuits are given in this paper.
Classification of high spatial resolution images using semantic allocation level-probabilistic topic model

Scene classification is treated to be a philosophical and the powerful method for HSR (High spatial resolution) images. Many academicians and researchers paid their attention towards the agglomeration of numerous features. Initially, a survey on PTM (Probabilistic Topic Model) was done in a finest manner and they have concluded that, a single feature (i.e., a spectral feature) was not best suited for HSR images. The next investigation was performed on CAT-PTM and their basic theory behind this method was that, the words of visual dictionary are highly correlated. Due to these inadequacies, the above methods are not valid for HSR images. Thus, the paper proposes SAL-PTM (Semantic Allocation Level - Probabilistic Topic Model) method through which three features (i.e., Texture, Scale Invariant Feature Transform and Spectral) are extracted for better performance. The semantic description of low level descriptors is generated by means of K-means clustering. Finally, the features obtained from the latent semantic allocations are isolated by means of PTM and their performance was evaluated and compared by using LDA (Latent Dirichlet Allocation) and PLSA (Probabilistic Topic Model). A U.S geological survey dataset and UC Merced Dataset was tested on SAL-PTM (Semantic Allocation Level-Probabilistic Topic Model). In response to that, a precise outcome was obtained suggesting that our proposed SAL-PTM method was confined to prove its effectiveness.
Porcine Reproductive and Respiratory Syndrome Virus Induces IL-1β Production Depending on TLR4/MyD88 Pathway and NLRP3 Inflammasome in Primary Porcine Alveolar Macrophages

Porcine reproductive and respiratory syndrome virus (PRRSV) is an Arterivirus that has been devastating the swine industry worldwide since the late 1980s. Previous studies have reported that PRRSV infection induced the production of IL-1β. However, the cellular sensors and signaling pathways involved in this process have not been elucidated yet. Here, we studied the mechanisms responsible for the production of IL-1β in response to highly pathogenic PRRSV. Upon PRRSV infection of primary porcine alveolar macrophages, both mRNA expression and secretion of IL-1β were significantly increased in a time- and dose-dependent manner. We also investigated the role of several pattern-recognition receptors and adaptor molecules in this response and showed that the TLR4/MyD88 pathway and its downstream signaling molecules, NF-κB, ERK1/2, and p38 MAPKs, were involved in IL-1β production during PRRSV infection. Treatment with specific inhibitors or siRNA knockdown assays demonstrated that components of the NLRP3 inflammasome were crucial for IL-1β secretion but not for IL-1β mRNA expression. Furthermore, TLR4/MyD88/NF-κB signaling pathway was involved in PRRSV-induced expression of NLRP3 inflammasome components. Together, our results deciphered the pathways leading from recognition of PRRSV to the production and release of IL-1β, providing a deeper knowledge of the mechanisms of PRRSV-induced inflammation responses.
A Novel Discourse Parser Based on Support Vector Machine Classification

This paper introduces a new algorithm to parse discourse within the framework of Rhetorical Structure Theory (RST). Our method is based on recent advances in the field of statistical machine learning (multivariate capabilities of Support Vector Machines) and a rich feature space. RST offers a formal framework for hierarchical text organization with strong applications in discourse analysis and text generation. We demonstrate automated annotation of a text with RST hierarchically organised relations, with results comparable to those achieved by specially trained human annotators. Using a rich set of shallow lexical, syntactic and structural features from the input text, our parser achieves, in linear time, 73.9% of professional annotators' human agreement F-score. The parser is 5% to 12% more accurate than current state-of-the-art parsers.
A causality based time management mechanism for federated simulation

One of the six categories of management services provided in the Run Time Infrastructure (RTI) to federated simulations is time management. Currently, it provides only two message ordering policies, that is, time stamp ordering and receipt ordering. Temporal anomalies occurring during the execution of federation due to the heterogeneous latencies in the communication network are not handled in receipt ordering. While time stamp ordering eliminates the temporal anomalies entirely, it incurs great communication latency, and huge bandwidth requirement. The paper presents a novel time management mechanism which provides a less costly message ordering service, namely causal ordering, to federates. It does not require the specification of lookahead and allows federates that do not require stringent message ordering properties to achieve much more efficient execution. A series of experiments has been carried out to benchmark the performance of this new time management mechanism and the results show that it includes a slight overhead compared to the receipt ordering mechanism but achieves significant performance improvement over the time stamp ordering mechanism.