Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementing a Functional Language for Flix

Static program analysis is a powerful technique for maintaining software, with applications such as compiler optimizations, code refactoring, and bug finding. Static analyzers are typically implemented in general-purpose programming languages, such as C++ and Java; however, these analyzers are complex and often difficult to understand and maintain. An alternate approach is to use Datalog, a declarative language. Implementors can express analysis constraints declaratively, which makes it easier to understand and ensure correctness of the analysis. Furthermore, the declarative nature of the analysis allows multiple, independent analyses to be easily combined. Flix is a programming language for static analysis, consisting of a logic language and a functional language. The logic language is inspired by Datalog, but supports user-defined lattices. The functional language allows implementors to write functions, something which is not supported in Datalog. These two extensions, user-defined lattices and functions, allow Flix to support analyses that cannot be expressed by Datalog, such as a constant propagation analysis. Datalog is limited to constraints on relations, and although it can simulate finite lattices, it cannot express lattices over an infinite domain. Finally, another advantage of Flix is that it supports interoperability with existing tools written in general-purpose programming languages. This thesis discusses the implementation of the Flix functional language, which involves abstract syntax tree transformations, an interpreter back-end, and a code generator backend. The implementation must support a number of interesting language features, such as pattern matching, first-class functions, and interoperability. The thesis also evaluates the implementation, comparing the interpreter and code generator back-ends in terms of correctness and performance. The performance benchmarks include purely functional programs (such as an N -body simulation), programs that involve both the logic and functional languages (such as matrix multiplication), and a real-world static analysis (the Strong Update analysis). Additionally, for the purely functional benchmarks, the performance of Flix is compared to C++, Java, Scala, and Ruby. In general, the performance of compiled Flix code is significantly faster than interpreted Flix code. This applies to all the purely functional benchmarks, as well as benchmarks that spend most of the time in the functional language, rather than the logic language. Furthermore, for purely functional code, the performance of compiled Flix is often comparable to Java and Scala.
Three Essays on Sex Differences in the Web Environment : An Information Processing Perspective

This exploratory research investigated sex differences in information processing and in psychological orientations when consumers perform goal-oriented search tasks on the Internet. Grounded in cognitive psychology, two theories of differential processing between the sexes--the Selectivity Model and item-specific/relational processing--were compared using concurrent verbal protocols (Ericsson & Simon, 1993; Wright, 1980). The Selectivity Model (Meyers-Levy, 1989) predicts differences in information processing related to comprehension effort while the Itemspecific/Relational Theory (Putrevu, 2001) predicts differences in processing style but not in effort. One hundred and nine subjects performed one of two directed search tasks in a laboratory setting. Women put forth more comprehension effort than men, and showed a greater usage of a relational processing style than men. However, men failed to verbalize more item-specific thoughts than women in processing the product information available on the site. Overall, results support the Selectivity Model Meyers-Levy, 1989), whereby women are comprehensive processors and men selective ones. The item-specific/relational processing model receives limited support. The communal orientation of women was also partially supported in that
BECKMANN FRAGMENTATION OF DIPHENYLCARBAMOYLATED N-ARYLDIPHENACYLAMINE DIOXIMES. NEW METHOD FOR THE SYNTHESIS OF IMIDAZOOXADIAZOLONES

Abstract N-Aryl-N,N-diphenacylamine dioximes were prepared by the reaction of corresponding arylamines with α-bromoacetophenone oxime in ethanol-water at room temperature. These compounds reacted with aryl isocyanates in acetonitrile to give imidazooxadiazolones 9. The probable mechanism of the reaction is discussed.
Problems In Higher Algebra

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Graphics For Statistics And Data Analysis With R

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AN ADAPTABLE SECURE SMART CARD ARCHITECTURE FOR INTERNET OF THINGS AND CLOUD COMPUTING

Internet of Things (IoT) and Cloud Computing paradigm is a next wave in the era of computing and it has been identified as one of the emerging technologies in the field of Computer Science and Information Technology. It has been understood from the literature that integration of IoT and cloud computing is in its infantile phase, that has not been extended to all application domains due to its inadequate security architecture. Hence, in this paper a novel adaptable secure smart card architecture for internet of things and cloud computing is proposed. This is a unique architecture adoptable for the public to have a secure access over diversified smart applications and services distributed in the cloud, anywhere, anytime, any device and any network irrespective of the underlying technologies in a smart environment with one IoT enabled User Adaptable Intelligent Smart Card (UAISC) through mobile devices. The cloud services are integrated and connected through an IP/MPLS (Internet Protocol / Multi-Protocol Label Switching) core System. Elliptic Curve Cryptography (ECC) is used to ensure complete protection against the security risks such as confidentiality, integrity, privacy and unique authentication. This model eliminates ambiguity, ensures security with enhanced performance and realizes the vision of “one secure smart card for any applications and transactions”. The performance of the proposed architecture is tested by establishing a test bed in a simulated environment and the results are reported.
Routing strategies and buffer management in delay tolerant networks

The Delay Tolerant Networks (DTN) is an intermittently connected network that enables communication among wireless nodes in the heterogeneous environments where end-to-end connectivity between nodes does not exist. These networks are characterized by a long delay, asymmetric data rate and low data rate. DTN uses store and forward mechanism to transmit messages from the source node to destination node. Routing in DTN is challenging because of frequent and long duration periods of disconnectivity. Therefore, the selection of routing protocol in DTN depends on the application environment in which it is used. This study presents a comprehensive survey on DTN and brief outlines of popular routing protocols in DTN. This paper also highlights the buffer management technologies that are used in DTN. Where an efficient buffer management scheme is required to choose at each step which of the messages should be transmitted in case of limited bandwidth and which of the messages should be dropped when the buffer is full. Regardless of which routing algorithms used.
Torminator: A Tor fingerprinting suite.

The king of high-secure, low-latency anonymity1 Tor is a very popular anonymisation software and network. For which we created Torminator, a fingerprinting suite written in the Java programming language. Fingerprinting is an attack type applicable to Tor utilising side-channel information from the network packets. With side-channel data, we can analytically access information that purportedly been hidden by design by Tor. Because Tor is a low-latency, low-overhead by design, it will leak communication patterns with intermediate (thus total) communication size. In our case this may able us figure out to which site/service the Tor user is using. This means that anyone with access to user’s traffic can use the fingerprinting attack to partly compromise the provided anonymity. By investigating such attacks, it may help us to better understand how to withstand and resist attacks from powerful adversaries such as state agencies. Torminator automatises the process for gathering fingerprints. It uses the official Tor Browser through its GUI to enter websites to recreate the real world scenario. This gives us real and reliable fingerprints without having to employ a human to do anything, as Torminator simulates user interaction on Tor Browser for us. We can also give Torminator a list of websites to fingerprint, making it easy to generate lots of fingerprints for a great number given sites. A contribution of Torminator, is that we improved on the previous de facto standard of the fingerprints collected from the available tools from previous works. We have gathered fingerprints and have now a dataset of 65792 fingerprints. Fingerprints like these can be used with machine learning techniques to teach a machine to recognise web-pages by reading the packet size and directions saved in the fingerprint files. 1 Quote from a leaked top-secret NSA document, acknowledging the fundamental importance of Tor as a software.
International Trends in Biology Education Research from 1997 to 2014: A Content Analysis of Papers in Selected Journals

This paper provides a descriptive content analysis of biology education research papers published in eight major academic journals indexed in Social Science Citation Index [SSCI] of Thomson Reuters® from 1997 to 2014. Total of 1376 biology education research [BER] papers were examined. The findings indicated that most of the papers were published in the JBE and IJSE, and frequently studied topics were environment and ecology, genetics and biotechnology, and animal form and function. The findings were also indicated that learning, teaching and attitudes was in the forefront as the frequently investigated subject matters, undergraduate and secondary school students were mostly preferred as sample group and sample size mostly varies between 31-100 and 101-300. In addition, it was found out that interactive qualitative research designs were mostly preferred. Besides, that single data collection tool was generally used and this data collection tool included questionnaires, interviews and documents. Finally, frequency/percentage tables, central tendency measures, statistical analysis such as t-test and ANOVA/ANCOVA and content analysis were commonly used as data analysis.
Neural Networks In The Analysis And Design Of Structures

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Automatic text ontological representation and classification via fundamental to specific conceptual elements (tor-fuse)

In this dissertation, we introduce a novel text representation method mainly used for text classification purpose. The presented representation method is initially based on a variety of closeness relationships between pairs of words in text passages within the entire corpus. This representation is then used as the basis for our multi-level lightweight ontological representation method (TOR-FUSE), in which documents are represented based on their contexts and the goal of the learning task. The method is unlike the traditional representation methods, in which all the documents are represented solely based on the constituent words of the documents, and are totally isolated from the goal that they are represented for. We believe choosing the correct granularity of representation features is an important aspect of text classification. Interpreting data in a more general dimensional space, with fewer dimensions, can convey more discriminative knowledge and decrease the level of learning perplexity. The multi-level model allows data interpretation in a more conceptual space, rather than only containing scattered words occurring in texts. It aims to perform the extraction of the knowledge tailored for the classification task by automatic creation of a lightweight ontological hierarchy of representations . In the last step, we will train a tailored ensemble learner over a stack of representations at different conceptual granularities. The final result is a mapping and a weighting of the targeted concept of the original learning task, over a stack of representations and granular conceptual elements of its different levels (hierarchical mapping instead of linear mapping over a vector). Finally the entire algorithm is applied to a variety of general text classification tasks, and the performance is evaluated in comparison with well-known algorithms.
A Full-Reference Image Quality Assessment for Multiply Distorted Image based on Visual Mutual Information

Abstract A full-reference image quality assessment (FR-IQA) method for multi-distortion based on visual mutual information (MD-IQA) is proposed to solve the problem that the existing FR-IQA methods are mostly applicable to single-distorted images, but the assessment result for multiply distorted images is not ideal. First, the reference image and the distorted image are preprocessed by steerable pyramid decomposition and contrast sensitivity function (CSF). Next, a Gaussian scale mixture (GSM) model and an image distorted model are respectively constructed for the reference images and the distorted images. Then, visual distorted models are constructed both for the reference images and the distorted images. Finally, the mutual information between the processed reference image and the distorted image is calculated to obtain the full-reference quality assessment index for multiply distorted images. The experimental results show that the proposed method has higher accuracy and better performance for multiply distorted images.
Tamil Text detection in videos

Detecting text in videos with complex background is challenging. Text appearing in videos contains useful information and it is required for easy understanding of videos. We propose a novel method that improves the performance of detecting Tamil text in videos. In this paper, we propose a different method for detecting Tamil text in videos using Localization method. Initially, the input video is divided into 'n' number of frames and key frame is selected from the frames by avoiding temporal redundancy. Then, edge is detected on the key frame by using Sobel edge detector, then the image is filtered to remove any noise present by bounding box algorithm and text region is separated from non text region by localization process finally the text is detected.
Optimization of Electroencephalograph-Based Classification for Imaginary Motion Brain Computer Interface Study

Using Electroencephalography (EEG) to detect imaginary motions from brain waves, is a very nascent and challenging field that started developing rapidly in the past few decades. EEG involves having some electrodes attached on the scalp of the patient to capture the brain signals generated through the patients thought process and record them in a computer. This technique of human and computer interfacing is called Brain Computer Interface (BCI). Disability is a serious problem of our nation and hence BCI can play an important role in facilitating the lives of people who are incapable of communicating due to spinal cord injuries. This technique uses the brain signals to make decisions, control objects and communicate with the world using brain integration with peripheral devices and systems. This requires some intelligence to classify these motions. Neural network have been used as a mean to classify motions, however, the accuracy of classification for certain motions was limited. The novelty of the proposed approach is in using a majority vote system for a network of artificial neural networks (ANNs) that is used to optimally classify imaginary motions performed by individual or multiple subjects. Three kinds of imaginary motions were classified which are imaginary left hand movement, imaginary right hand movement, and imagination of words starting with the same letter. Using an optimized set of electrodes, classification accuracy was optimized for the three users as a group and also individually. The optimization procedure was based on the ranking of the electrodes according to their 2 classification accuracy, electrodes with the lowest accuracies were eliminated to achieve the optimal accuracy. The group optimization of 3 subjects altogether resulted in an electrode structure consisting of 15 electrodes with a relatively high classification accuracy of almost 80%. The individual optimization for each subject resulted in structure of 20 electrodes for subject 1 and subject 3 with classification accuracies of 63.63% and 84.33%, respectively, and a single electrode structure for subject 2 with an accuracy of 94.01%. The overall average classification accuracy of all the users with the individual optimization of electrodes was as high as 82.32%. Index Words: Artificial Neural Network, Brain Computer Interface, Majority Vote, Electroencephalography OPTIMIZATION OF ELECTROENCEPHALOGRAPH-BASED CLASSIFICATION FOR IMAGINARY MOTION BRAIN COMPUTER INTERFACE STUDY by SYLVIA BHATTACHARYA B.S, West Bengal University Of Technology, India, 2013 A Thesis Submitted to the Graduate Faculty of Georgia Southern University in Partial Fulfillment of the Requirement for the Degree
A New GPS Aided Initial Alignment Algorithm for SINS on Moving Base

On the moving base,a new single-antenna GPS aided initial precise alignment algorithm for strapdown inertial navigation system(SINS)is proposed and the classical coarse alignment stage is unnecessary in the algorithm.Two special inertial frames are defined and are selected as the computing reference frames,then,the calculation of initial alignment direction cosine matrix(DCM)is expanded into three parts which are given in detail.Moveover,the alignment errors are analyzed and the conclusion is drawn that the zero velocity of vehicular stopping can be used to improve alignment accuracy.In the end,some vehicular tests were carried out on moving base and the accuracy of azimuth reaches about 0.081°(1σ).
Joint precoding vector and modulation and coding scheme recalculation for LTE-A multi-user MIMO

Multi-user MIMO techniques were born due to the urge of high data rates and spectral efficiency in 4G systems. For scenarios with a large number of users to be served in one cell, high capacity gains can be achieved by transmitting independent data streams to different users sharing the same time-frequency resources through the use of MIMO precoding. With enough channel state information (CSI) at the transmitter, MIMO precoding allows to increase multi-user diversity gain. However, without a correct precoding vector selection, the interference between users can seriously degrade the overall network data rate. In a close-loop configuration, the base station (BS) receives from each user the preferred precoding vector and modulation and coding scheme (MCS). To achieve the highest multi-user diversity gains and avoid users' interference, the BS needs to recalculate the precoding vector and MCS for each user. The goal of this paper is to investigate the performance and complexity of state-of-the-art methods for recalculation of precoding vectors such as zero-forcing (ZF) and minimum mean square error (MMSE) for LTE-A scenarios. Additionally, we propose a low-complexity method that jointly recalculates the precoding vector and MCS with a codebook-based approach. We then evaluate our method in terms of throughput and with an LTE simulator. Keywords— LTE, MU-MIMO, feedback recalculation, ZF, MMSE
Knowledge-Driven Implicit Information Extraction

Perera, Sujan. PhD. , Department of Computer Science and Engineering, Wright State University, 2016. Knowledge-driven Implicit Information Extraction. Implicit information in unstructured text can be efficiently extracted by bridging syntactic and semantic gaps in natural language usage and by augmenting information extraction techniques with relevant domain and contextual knowledge. Natural language is a powerful tool developed by humans over hundreds of thousands of years. The extensive usage, flexibility of the language, creativity of the human beings, and social, cultural, and economic changes that have taken place in daily life have added new constructs, styles, and features to the language. One such feature of the language is its ability to express ideas, opinions, and facts in an implicit manner. This is a feature that is used extensively in day to day communications in situations such as: 1) expressing sarcasm, 2) when trying to recall forgotten things, 3) when required to convey descriptive information, 4) when emphasizing the features of an entity, and 5) when communicating a common understanding. Consider the tweet ‘New Sandra Bullock astronaut lost in space movie looks absolutely terrifying’ and the text snippet extracted from a clinical narrative ‘He is suffering from nausea and severe headaches. Dolasteron was prescribed’. The tweet has an implicit mention of the entity Gravity and the clinical text snippet has implicit mention of the relationship between medication Dolasteron and clinical condition nausea. Such implicit references of the entities and the relationships are common occurrences in daily communication and they add unique value to conversations. However, extracting implicit constructs has not received enough attention in the information extraction literature. This dissertation focuses on extracting implicit entities and relationships from clinical narratives and extracting implicit entities from Tweets. When people use implicit constructs in their daily communication, they assume the existence of a shared knowledge with the audience about the subject being discussed. This shared knowledge helps to decode implicitly conveyed information. For example, the above Twitter user assumed that his/her audience knows that the actress Sandra Bullock starred in the movie Gravity and it is a movie about space exploration. The clinical professional who wrote the clinical narrative above assumed that the reader knows that Dolasteron is an anti-nausea drug. The audience without such domain knowledge may not have correctly decoded the information conveyed in the above examples.