12 research outputs found
Tarfia Faizullah, 37th Annual ODU Literary Festival
No full textTARFIA FAIZULLAH is the author of Seam (Southern Illinois University Press, 2014), winner of the Crab Orchard Series in Poetry First Book Award. Her poems appear in American Poetry Review, Ploughshares, The Missouri Review, The Southern Review, New England Review, and elsewhere. A Kundiman fellow, she received her MFA from Virginia Commonwealth University. Honors include a Ploughshares Cohen Award, a Fulbright fellowship and a Copper Nickel Poetry Prize. In fall 2014, she joins the University of Michigan as the Nicholas Delbanco Visiting Professor in Poetry
Registers of Illuminated Villages
No full textTarfia Faizullah’s highly anticipated second collection, Registers of Illuminated Villages, extends and transforms her powerful accounts of violence, war, and loss into poems of many forms and voices—elegies, outcries, self-portraits, and larger-scale confrontations with discrimination, family, and memory. One poem steps down the page like a Slinky; another poem responds to makeup homework completed in the summer of a childhood accident; other poems punctuate the collection with dark meditations on dissociation, discipline, defiance, and destiny; and the near-title poem, “Register of Eliminated Villages,” suggests illuminated texts, one a Qur’an in which the speaker’s name might be found, and the other a register of 397 villages destroyed in northern Iraq. Faizullah, the author of the award-winning collection Seam, is an essential poet, whose work only grows more urgent, beautiful, and—even in its unsparing brutality—full of love.
Tarfia Faizullah is the author of Seam, winner of a VIDA Award and a Great Lakes Colleges Association New Writers Award. She teaches at the University of Michigan and lives in Detroit
Considerations for Regression Testing Process in Agile Development Environments
No full textTesting is central piece in agile development methodology, that fast is taking roots and at the same time it is becoming challenging, in particular due to short time periods between incremental updates of the software, as such the time for overall testing including regression testing is becoming limited. In regression testing, the changed code as well as certain portion of existing code that is impacted is to be retested. Intuitively, it seems that the best option is to automate the regression testing. However, precisely due to time constraints the automation approach is proving to be challenging for regression testing in agile development environments. As the time to create and update these automation scripts will become limiting factor in case of overnight/weekly releases. Regression testing in agile development environments should be using some prioritization of tests for next cycle to enable timely regression testing. We can use the information of future release plans, defined by the specifications for each release, and prioritize the test cases as we write them. As there can be information available for future release cycles (as there are releases in planned for short intervals) we can have some methods of using this information and make the process of regression testing process in future more effective and efficient. In this paper, we present a technique of using future builds (new enhancements, features, or fixes) to select test cases on a class of software which is used for interconnecting diverse systems in near real time fashion. The results are encouraging and the technique can be used to guide regression testing process in agile development environment.</jats:p
Analysis of Recent Deep Learning Techniques for Arabic Handwritten-Text OCR and Post-OCR Correction
No full textArabic handwritten-text recognition applies an OCR technique and then a text-correction technique to extract the text within an image correctly. Deep learning is a current paradigm utilized in OCR techniques. However, no study investigated or critically analyzed recent deep-learning techniques used for Arabic handwritten OCR and text correction during the period of 2020–2023. This analysis fills this noticeable gap in the literature, uncovering recent developments and their limitations for researchers, practitioners, and interested readers. The results reveal that CNN-LSTM-CTC is the most suitable architecture among Transformer and GANs for OCR because it is less complex and can hold long textual dependencies. For OCR text correction, applying DL models to generated errors in datasets improved accuracy in many works. In conclusion, Arabic OCR has the potential to further apply several text-embedding models to correct the resultant text from the OCR, and there is a significant gap in studies investigating this problem. In addition, there is a need for more high-quality and domain-specific OCR Arabic handwritten datasets. Moreover, we recommend the practical development of a space for future trends in Arabic OCR applications, derived from current limitations in Arabic OCR works and from applications in other languages; this will involve a plethora of possibilities that have not been effectively researched at the time of writing
A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
No full textOptical character recognition (OCR) is the process of extracting handwritten or printed text from a scanned or printed image and converting it to a machine-readable form for further data processing, such as searching or editing. Automatic text extraction using OCR helps to digitize documents for improved productivity and accessibility and for preservation of historical documents. This paper provides a survey of the current state-of-the-art applications, techniques, and challenges in Arabic OCR. We present the existing methods for each step of the complete OCR process to identify the best-performing approach for improved results. This paper follows the keyword-search method for reviewing the articles related to Arabic OCR, including the backward and forward citations of the article. In addition to state-of-art techniques, this paper identifies research gaps and presents future directions for Arabic OCR
SsAG: Summarization and sparsification of Attributed Graphs
Get PDFWe present SsAG, an efficient and scalable lossy graph summarization method
that retains the essential structure of the original graph. SsAG computes a
sparse representation (summary) of the input graph and also caters to graphs
with node attributes. The summary of a graph is stored as a graph on
supernodes (subsets of vertices of ), and a weighted superedge connects two
supernodes. The proposed method constructs a summary graph on supernodes
that minimize the reconstruction error (difference between the original graph
and the graph reconstructed from the summary) and maximum homogeneity with
respect to attributes. We construct the summary by iteratively merging a pair
of nodes. We derive a closed-form expression to efficiently compute the
reconstruction error after merging a pair and approximate this score in
constant time. To reduce the search space for selecting the best pair for
merging, we assign a weight to each supernode that closely quantifies the
contribution of the node in the score of the pairs containing it. We choose the
best pair for merging from a random sample of supernodes selected with
probability proportional to their weights. A logarithmic-sized sample yields a
comparable summary based on various quality measures with weighted sampling. We
propose a sparsification step for the constructed summary to reduce the storage
cost to a given target size with a marginal increase in reconstruction error.
Empirical evaluation on several real-world graphs and comparison with
state-of-the-art methods shows that SsAG is up to faster and
generates summaries of comparable quality
Live multimedia streaming multicast in local area social networking environment of MANETs using SLIM+ Protocol
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