Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Portable Middleware Services in POSIX Environments

POSIX is a robust, function rich, open standard, supporting multithreading and real-time extensions. This paper presents first results of our investigation on the suitability of POSIX environments as building blocks for middleware services. We examine the service quality of certain key POSIX functions of Solaris 2.5 and IRIX 6.2 {\sc Unix} operating systems. Using user-level time probing, with sub-microsecond resolution, we evaluate experimentally the overhead and accuracy in POSIX timer mechanisms, thread management, scheduling and synchronization services, and then, we examine temporal portability issues. Different timer functions have different accuracy on the same system, or even behave irregularly. Applications with moderate number of threads (32), suffered high response and barrier synchronization times, which depended on the pthread''s priority and the current workload. Both systems could not share the processor fairly among the threads. We noticed different timing behavior in timer and alarm services, minima scheduling quanta, logical time progress and clock drifting. Temporal portability is not automatic but knowledge of the subtle differences between systems is the first step towards this goal. We believe that enhancing the temporal portability of POSIX, where programs with timing constraints maintain their time semantics across platforms, is a far more viable alternative than yet additional, ad-hoc extensions to operating systems.
Distributional Semantics for Robust Automatic Summarization

Distributional Semantics for Robust Automatic Summarization Jackie Chi Kit Cheung Doctor of Philosophy Graduate Department of Computer Science University of Toronto 2014 Large text collections are an important resource of information about the world, containing everything from movie reviews and research papers to news articles about current events. Yet the sheer size of such collections presents a challenge for applications to make sense of this data and present it to users. Automatic summarization is one potential solution which aims to shorten one or more source documents while retaining the important information. Summarization is a complex task that requires inferences about the form and content of the summary using a semantic model. This dissertation examines the feasibility of distributional semantics as the core semantic representation for automatic summarization. In distributional semantics, the meanings of words and phrases are modelled by the contexts in which they appear. These models are easy to train and have found successful applications, but they have until recently not been seriously considered as contenders to support semantic inference for complex NLP tasks such as summarization because of a lack of evaluation methods that would demonstrate their benefit. I argue that current automatic summarization systems avoid relying on semantic analysis by focusing instead on replicating the source text to be summarized, but that substantial progress will not be possible without semantic analysis and domain knowledge acquisition. To overcome these problems, I propose an evaluation framework for distributional semantics based on first principles about the role of a semantic formalism in supporting inference. My experiments show that current distributional semantic approaches can support semantic inference at
Global sensitivity metrics from active subspaces with applications

Predictions from science and engineering models depend on input parameters. Global sensitivity metrics quantify the importance of input parameters, which can lead to model insight and reduced computational cost. Active subspaces are an emerging set of tools for identifying important directions in a model’s input parameter space; these directions can be exploited to reduce the model’s dimension enabling otherwise infeasible parameter studies. We develop global sensitivity metrics called activity scores from the estimated active subspace and analytically compare the active subspace-based metrics to established sensitivity metrics. These commonly used metrics include Sobol’ indices derived from a variance-based decomposition and derivative-based metrics. Additionally, we outline practical computational methods to estimate the activity scores. We then consider three numerical examples with algebraic scalar valued functions from engineering and biological models. In each case, the models admit reduced dimensional active subspaces. For each of the models, a variety of sensitivity metrics are compared to the activity scores.
The Spray/Load and Dative Alternations: Aligning VP Structure and Contextual Effects.

The theoretical and experimental work presented in this thesis investigates the spray/load and dative alternations. The purpose is to provide a comprehensive analysis of the alternations in terms of their syntactic structures and to account for how contextual information drives differences in the linear order of their VP arguments. This analysis shows that the syntactic structures of the spray/load and dative alternations are identical; each variant in an alternation is characterised by one of two available structures proposed in Janke and Neeleman (2012). Each structure is shown to respect a novel thematic hierarchy that is based on the value of binary feature clusters (Reinhart, 2000) rather than by direct reference to semantic labels. The choice of a particular structure is demonstrated to be affected by the non-semantic context in which the spray/load or dative sentence is generated. This is a consequence of the limited processing capacity of Working Memory and the allocation of attentional resources to a stimulus. Experimental data from an as yet untested variable of the visual context – the egocentric perception of distance – is found to interact with word order preferences of the alternations. I conclude that non-semantic contextual information interacts with the encoding of an event which ultimately has consequences for syntactic choices.
Air Compressor Fault Diagnosis Through Vibration Signals using Statistical Features and J48 Algorithms

Objectives: The fault diagnosis in reciprocating air compressor system was done through this article using vibration signals from accelerometer for both healthy and faulty conditions. Methods/Analysis: This article presents a condition monitoring strategy for compressor through vibration signals using accelerometer data in identifying five common faults of air compressor these were simulated manually. These vibration signals were processed through machine learning technique, where statistical features were extracted and the features contributing to the maximum classification accuracy were selected. The J48 decision tree algorithm is used in predicting the compressor faults in early stages. Findings: High classification accuracy of 98.33% was obtained for fault detection in compressor system. Application/Improvements: The proposed model can be used for regular monitoring of air compressor.
A Study on Chinese Prosodic Hierarchy Prediction Based on Dependency Grammar Analysis

Different prosodic hierarchy could divide texts into several prosodic chunks for better speaking and understanding.Currently,many shallow features such as part-of-speech,length of word are used to predict the prosodic hierarchy.But these features are not powerful for some prosodic unit prediction such as prosodic phrase.In fact,syntactic structure is in close touch with prosody structure.They influence and restrict each other.In this paper,based on dependency grammar,some deep features which are related with prosody hierarchy are extracted.Compared to the shallow features,the deep features such as inner-arc span and inner-arc type are more effective on the prediction of the middle level such as prosodic phrase.The F-score increases about 11%.
A Survey on: Personalized Geo-Specific Tag Recommendation for Photos on Social Websites

Now a day's social tagging is an important in social websites to provide a good tagging for photos uploaded to the websites to access high quality social tags. Tag recommendation by automatically assigning related tags to photos to find out particular interesting area. In this paper we concentrate on the personalized recommendation work and try to choose user preferred geo-location specific as well as relevant tags for photos on social website. For users and geo-locations we assume they have various preferred tags assigned to a photo and purpose a subspace learning method to individually uncover the user preference and geo-location preference. The goal of our work is to combine a visual and textual space into a unified subspace. According to unified subspace is mapped from the intermediate subspace and textual subspace respectively. We create formula for above learning problems into united form and present the minimization with its convergence rule. For a given an untagged photo with its geo-location to a user we used the nearest neighbor search in the relating unified space. The user preferred and geo-location specified tags. Experiments on big scaled data sets collected from flicker examine the effectively of the proposed system.
Encoding scala programs for the Boogie verifier

Proving the correctness of programs has been a major field of computer science research for the last decades. Over the years, many approaches have emerged that try to solve this problem. One of the main approaches is static verification of programs, which is particularly difficult for object-oriented programs. Since object-oriented programs are usually more complicated to verify, we designed an encoding for the object-oriented programming language Scala in the imperative programming language BoogiePL, which can be statically verified by the existing Boogie verifier. One reason for choosing Scala is that it supports a lot of interesting and helpful features, that are challenging to verify and are not supported in programming languages that can already be verified by existing static verifiers. We developed a tool that can translate a usable subset of Scala to BoogiePL. Our main focus was on coming up with an encoding for two of Scala’s most interesting features, namely closures and traits.
Anaphora Resolution in Bangla Language

In this paper we have discussed about our work of Anaphora resolution in Bangla. For solving this problem pronouns and verbs are considered as anaphor and their antecedent nouns. After trying several methods, first attempt was to find out if there is any relation among the classifications of pronouns and verb with those of nouns. That indicates to find out which types of pronouns refer to which type of nouns. But that process does not work as expected. This shows that, anaphor and antecedent in Bangla matches with each other in some factors that are also considered in other languages. Those are Number, Gender and Person. In case of personal pronouns in Bangla status of person can be differentiated such as Honorable, Normal and Negotiable. Experimenting with these factors proved that this method works well and give accuracy of around 80%. Though there are errors as, because of some nouns can have the same factors like the anaphor, they are not the antecedent in real. General Terms Natural language processing, Anaphora resolution, Reference resolution, Bangla Language Processing.
Ant Clustering Algorithm for Data Sets with Mixed Numeric and Categorical Values

This paper put forward an improved ant clustering algorithm by introducing strategies such as formula improvement,radius increase,short-term memory,space partition etc.,at the same time adopted a new distance measure function to combine numeric and categorical values together,and carried out cluster analysis of mixed data sets.The simulation experiment result illustrates that the ant-based new algorithm has the stronger robustness and it is quite feasible for data sets with mixed numeric and categorical values.
XLIFF: Multilingual Translation Memory Management among Divergent Language Families

For the purpose of localization, only textual output is not sufficing the need of Machine Translation unless until it is in a usable format. In Indian scenario, localization as an industry has not been recognized yet which had led to lack of Language Standards leading to varied translation quality. Localization is the process of adapting a product or service to a particular language, culture, and desired local "look-and-feel" Machine Translation is one of the most important activities under localization but it is not complete unless until it is adapted by end user in a desired manner. In this paper, we are introducing format retention utility using "XLIFF" as an important tool to English to Indian Language Machine Translation. Machine translation (MT) is the technique of translating source text of input language into the target language text. This process uses bilingual data set along with other language assets to frame language and phrase models which are used while translating text in machine translation. Here, Machine Translation System (MTS) that uses the Tree Adjoining Grammar (TAG) is considered. Here, uniqueness and complexity of task has been discussed. In this paper, we are proposing a design and architecture to support the system along with experiments, results and future aspects. It is closely related to the long-term vision of enabling code to support local, regional, language, or culturally related preferences.
Face detection based on multiple kernel learning algorithm

Face detection is important for face localization in face or facial expression recognition, etc. The basic idea is to determine whether there is a face in an image or not, and also its location, size. It can be seen as a binary classification problem, which can be well solved by support vector machine (SVM). Though SVM has strong model generalization ability, it has some limitations, which will be deeply analyzed in the paper. To access them, we study the principle and characteristics of the Multiple Kernel Learning (MKL) and propose a MKL-based face detection algorithm. In the paper, we describe the proposed algorithm in the interdisciplinary research perspective of machine learning and image processing. After analyzing the limitation of describing a face with a single feature, we apply several ones. To fuse them well, we try different kernel functions on different feature. By MKL method, the weight of each single function is determined. Thus, we obtain the face detection model, which is the kernel of the proposed method. Experiments on the public data set and real life face images are performed. We compare the performance of the proposed algorithm with the single kernel-single feature based algorithm and multiple kernels-single feature based algorithm. The effectiveness of the proposed algorithm is illustrated. Keywords: face detection, feature fusion, SVM, MKL
WAN-Based Distributed Web Crawling

There are three core issues recognized for WAN-based distributed Web crawling systems: Web Partition, Agent collaboration and Agent deployment. Centering around these issues, this paper presents a comprehensive overview of the current strategies adopted by academic and business communities. The experiences, problems and challenges encountered by the WAN-based distributed Web crawlers are classified and discussed in depth. A summary of the current evaluation indicators is also given. Finally, conclusion and some suggestions for future research are put forward.
A simulation framework for the design and evaluation of computational cameras

In the emerging field of computational imaging, rapid prototyping of new camera concepts becomes increasingly difficult since the signal processing is intertwined with the physical design of a camera. As novel computational cameras capture information other than the traditional two-dimensional information, ground truth data, which can be used to thoroughly benchmark a new system design, is also hard to acquire. We propose to bridge this gap by using simulation. In this article, we present a raytracing framework tailored for the design and evaluation of computational imaging systems. We show that, depending on the application, the image formation on a sensor and phenomena like image noise have to be simulated accurately in order to achieve meaningful results while other aspects, such as photorealistic scene modeling, can be omitted. Therefore, we focus on accurately simulating the mandatory components of computational cameras, namely apertures, lenses, spectral filters and sensors. Besides the simulation of the imaging process, the framework is capable of generating various ground truth data, which can be used to evaluate and optimize the performance of a particular imaging system. Due to its modularity, it is easy to further extend the framework to the needs of other fields of application. We make the source code of our simulation framework publicly available and encourage other researchers to use it to design and evaluate their own camera designs.1
Comparison of Database Management Systems. A comparison of common Database Management Systems

Data can be referred to as objects or events stored on digital media. The evolution of IT solutions has sprung an interest in developing systems to store this type of data more efficiently. The modern method of storing general data is by utilizing a database, which is an organized collection of logically related data. Different types of databases suit various needs, therefore the purpose of this project is to compare the leading database solutions with each other. The databases chosen for this project are: MySQL, Neo4j and MongoDB. Furthermore we collaborate with two companies in order to acquire hands-on information of how companies work with data. This is done by developing modules for their web applications. The insight and data obtained through these companies is used to create test cases which is then benchmarked on the different database management systems. The test results indicates that MongoDB has overall the best performance. However, during a few complex test cases MongoDB lacks in performance. Neo4j performs worse on more test cases than MongoDB, and MySQL is by far the most consistent of these three technologies. The outcome is similar to our expectations and confirms parts of the related work that has been researched.
Data transmission in Hybrid Distributed Environment

This article discusses a hybrid distributed environment including two levels of nodes: the first level which has high quality channels between the nodes, and the second one in which the nodes are directly involved in communication and the communication is performed through the first-level nodes. For the indicated distribution scheme, the questions of selection of the best path between the first-level nodes are solved based on such communication channel characteristics as delay in packet transmission, jitter in transmission, packet loss in transmission, and choosing a method for data transmission through the selected best path: protocols of transport and network layers have been compared, and the use of network layer has been preferred. Full Text: PDF DOI: http://dx.doi. org/10.11591/ijece.v6i6. 12129
Clonal Selection Algorithm Based on Anti-Idiotype

Based on the antibody clonal selection theory of immunology,an artificial immune system algorithm,clonal selection algorithm based on anti-idiotype(AICSA),is proposed to deal with complex multi-modal optimization problems by introducing the anti-idiotype.This algorithm evolves and improves the antibody population through clonal proliferation,anti-idiotype mutation,anti-idiotype recombination and clonal selection operation,which can perform global search and local search in many directions rather than one direction around the identical antibody simultaneously.Theoretical analysis proves that AICSA can converge to the global optimum.By introducing the anti-idiotype,AICSA can make the most of the structure information of antibodies,accelerate the convergence,and obtain the global optimization quickly.In experiments,AICSA is tested on four different types of functions and compared with the clonal selection algorithm and other optimization methods.Theoretical analysis and experimental results indicate that AICSA achieves a good performance,and is also an effective and robust technique for optimization.
Providing Message Authentication and Integrity Based on Elliptic Curves in Wireless Sensor Networks

Today, when communicating over the network, to send a message from one location to another the security is of utmost importance, because every user is concerned that the message reaches the desired destination without being modified enroute. To provide authentication, a scheme was developed which was based on polynomials. However, this scheme had problems i.e., the messages transferred to a particular node or to a node in a group suffered from threshold limit, only a limited number of messages can be transferred. Once the messages transferred are greater than the threshold the system can be easily corrupted by calculating the coefficients. To overcome this limitation and to provide better security, authenticity and integrity, in this scheme we transfer an anonymous message based on hash function and encrypting the message using elliptic curve cryptography. The use of hash function provides message integrity by using secure hashing algorithm and the recent progress in ECC provides a secure way to transfer the message. In this scheme each and every node verifies the authenticity and integrity of the message. It protects the location of the source; in addition to that it overcomes the limitation of polynomial scheme. The analysis of both the systems shows that the proposed scheme is more efficient.
Automation for Robotics

In order to enable a better understanding of the key concepts of automation, this book develops the fundamental aspects of the field while also proposing numerous concrete exercises and their solutions. The theoretical approach that it presents fundamentally uses the state space and makes it possible to process general and complex systems in a simple way, involving several switches and sensors of different types. This approach requires the use of developed theoretical tools such as linear algebra, analysis and physics, generally taught in preparatory classes for specialist engineering courses.
Multimedia Transmission in Food Network System Based on LD/R Path Algorithm

The characteristic of multi-media Transmission in food network system is analyzed and the technology requirements are studied. Parameters of performance are put forward to scaling QoS. The influencing factors are analyzed and solving methods are proposed. This study proposes LD/RPath algorithm LD/RPath estimates the dynamic data volume on service nodes and links through reasonable data volume approximation. And the data splitting technique is imported to convert the node delay into the edge delay. In the mean time, the reliability of nodes is considered so that the multimedia delivery problem is transformed into a conventional shortest path problem. The experimental simulation results show that the algorithm is a kind of high efficient and reliable algorithm spending smaller complexity achieving good path selection performance.
Leveraging Similar Regions to Improve Genome Data Processing

Though DNA sequencing has improved dramatically over the past decade, variant calling, which is the process of reconstructing a patient’s genome from the reads that the sequencers produce, remains a difficult problem, largely due to the genome’s redundant structure. In this thesis, we describe SiRen, our algorithm for characterizing the genome’s structure in a way that makes sense from the perspective of the reads themselves. We use the term similar regions to refer to the areas of redundancy that we have identified. We then confirm that the similar regions are characterized by low variant calling accuracy. We show that the structure of the similar regions provides a platform for repairing alignment errors, thus leading to significantly improved variant calling accuracy.