Miracle-dz/UniFAR-FTUs huggingface dataset:
A Framework for Arabic Handwritten Recognition Based on Segmentation

Automatic off-line Arabic handwriting recognition still faces a big challenges. Due to the cursive nature of the Arabic language, most of published works are based on recognition of a whole word without segmentation. This paper presents a new framework for the recognition of handwritten Arabic words based on segmentation. This framework involves two phases (training phase and testing phase). In the training phase, Arabic handwritten characters were trained to be recognized, while in the testing phase, words were segmented into characters for recognition. Classification is achieved in two steps (classification of the segmented characters and classification of the word). A dictionary is constructed and used to correct any errors occurring during the previous stages of the recognition process. This work has been tested with IFN/ENIT database and a comparison made against some existing methods and promising results have been obtained.
Strength Reduction for Loop-Invariant Types

Types are fundamental for enforcing levels of abstraction in modern high-level programming languages and their lower-level representations. However, some type-related features such as dynamic method calls and dynamic type casts can contribute substantially to the performance of a program. Loop-invariant type is a concept relating to an object whose dynamic type never changes inside a loop. In this case, operations on the type of the object may be redundant in the loop. As these operations often cause exceptions, existing redundancy elimination techniques usually fail to optimise them. This paper proposes a new approach to reducing the cost of two important operations on loop-invariant types: method tests and dynamic type checking. We demonstrate its usefulness and benefit in IBM's Jikes RVM, a dynamic compilation system for Java.
Scaling Down Inequality: Rating Scales, Gender Bias, and the Architecture of Evaluation

Quantitative performance ratings are ubiquitous in modern organizations—from businesses to universities—yet there is substantial evidence of bias against women in such ratings. This study examines how gender inequalities in evaluations depend on the design of the tools used to judge merit. Exploiting a quasi-natural experiment at a large North American university, we found that the number of scale points used in faculty teaching evaluations—whether instructors were rated on a scale of 6 versus a scale of 10—significantly affected the size of the gender gap in evaluations in the most male-dominated fields. A survey experiment, which presented all participants with an identical lecture transcript but randomly varied instructor gender and the number of scale points, replicated this finding and suggested that the number of scale points affects the extent to which gender stereotypes of brilliance are expressed in quantitative ratings. These results highlight how seemingly minor technical aspects of performance ratings can have a major effect on the evaluation of men and women. Our findings thus contribute to a growing body of work on organizational practices that reduce workplace inequalities and the sociological literature on how rating systems—rather than being neutral instruments—shape the distribution of rewards in organizations.
The Machine Learning Algorithm as Creative Musical Tool

Machine learning is the capacity of a computational system to learn structures from datasets in order to make prediction in front of newly seen datasets. Such approach offers a significant advantage in music scenarios in which musicians can teach the system to learn an idiosyncratic style, or can break the rules to explore the system capacity in unexpected ways. In this chapter we draw on music, machine learning, and human-computer interaction to elucidate an understanding of machine learning algorithms as creative tools for music and the sonic arts. We motivate a new understanding of learning algorithms as human-computer interfaces. We show that, like other interfaces, learning algorithms can be characterised by the ways their affordances intersect with goals of human users. We also argue that the nature of interaction between users and algorithms impacts the usability and usefulness of those algorithms in profound ways. This human-centred view of machine learning motivates our concluding discussion of what it means to employ machine learning as a creative tool.
Quiver topology and RG dynamics

Renormalization group flows of quiver gauge theories play a central role in determining the low-energy properties of string vacua. We demonstrate that useful predictions about the RG dynamics of a quiver gauge theory may be extracted from the global structure of its quiver diagram. For quiver theories of a certain type, we develop an efficient and practical method for determining which superpotential deformations generate a flow to an interacting conformal fixed point.
Research Article Cloning, Expression, and Characterization of Prophenoloxidases from Asian Corn Borer, Ostrinia Furnacalis (gunée)

Insect phenoloxidase (PO) belongs to the type 3 copper protein family and possesses oxidoreductase activities. PO is typically synthesized as a zymogen called prophenoloxidase (PPO) and requires the proteolytic activation to function. We here cloned full-length cDNA for 3 previously unidentified PPOs, which we named OfPPO1a, OfPPO1b, and OfPPO3, from Asian corn borer, Ostrinia furnacalis (Gunée), in addition to the previously known OfPPO2. These conceptual PPOs and OfPPO2 all contain two common copper-binding regions, two potential proteolytic activation sites, a plausible thiol-ester site, and a conserved C-terminal region but lack a secretion signal peptide sequence at the N-terminus. O. furnacalis PPOs were highly similar to other insect PPOs (42% to 79% identity) and clustered well with other lepidopteran PPOs. RT-PCR assay showed the transcripts of the 4 OfPPOs were all detected at the highest level in hemocytes and at the increased amounts after exposure to infection by bacteria and fungi. Additionally, we established an Escherichia coli (E. coli) expression system to produce recombinant O. furnacalis PPO proteins for future use in investigating their functions. These insights could provide valuable information for better understanding the activation and functioning mechanisms of O. furnacalis PPOs.
Analisis musim penangkapan ikan cakalang di perairan Kepala Burung

Pengetahuan mengenai pola musim penangkapan merupakan salah satu faktor penting dalam kegiatan eksploitasi sumberdaya cakalang, terutama dalam menetapkan waktu yang tepat untuk meningkatkan intensitas penangkapan sekaligus menentukan waktu yang tepat pula untuk mengurangi intensitas penangkapan. Penelitian ini bertujuan untuk: (1) mengetahui kecenderungan hasil tangkapan per satuan upaya penangkapan (CPUE) ikan cakalang setiap bulan, dan (2) menentukan pola musim penangkapan ikan cakalang di perairan Raja Ampat dengan pendekatan CPUE. Analisis pola musim penangkapan ikan menggunakan Metode Persentase Rata-rata ( t he Average Percentage Methods ) yang didasarkan pada Analisis Runtun Waktu ( Times Series Analysis ). Jumlah kapal pole and line yang beroperasi di perairan Kepala Burung dan berpangkalan di Kota Sorong sampai dengan tahun 2014 berjumlah 31 buah. Dari hasil tangkapan dan jumlah trip setiap bulan, kemudian dihitung CPUE dan selanjutnya dilakukan analisis musim penangkapan. Indeks Musim (IM) yang lebih besar dari 100 terdapat pada bulan-bulan April, Mei dan September sampai dengan Desember. Jadi dalam satu tahun terjadi dua kali musim penangkapan dengan puncak pada bulan April dan pada bulan November, sedangkan puncak musim paceklik terjadi pada bulan Januari dan bulan Juli.
RIMM: A novel map matching model with rotational invariance

The Map Matching Problem (MMP) aims to find a real optimal region from a map repository, which is the most similar map to the ideal sample. Though there is a significant difference between MMP and the general Image Matching Problem. The former prefers approximate match and ignores details of edge. Both of them must solve variances of scale, translation and rotation. However, the Scale Invariant Feature Transform (SIFT) is not perfect to match the rotated maps because the number of feature points selected by SIFT is really variant while the image is rotated. This paper presents a simple novel Rotational Invariant Map Matching (RIMM) model that is really invariant to scale, translation and rotation. The RIMM model achieves these invariance abilities based on the inherent, stable, and unique attributes of a map, which are mass center and Primary Gradient Direction. Because these two features are irrelevant to translation, rotation and scale. Our test results show that the RIMM model accurately eliminates variance of translation and rotation. Moreover, The RIMM model can quantitatively evaluate the matched maps by the cosine similarity so that the candidate maps can be ranked and compared objectively.
Exploring Text Semantics to Extract Key-Fragments for Model Answers

In context with the recent developments in the understanding of text semantics at machine level, this paper is an attempt to extract some of the most crucial fragments that play a key role as semantic units in natural language text. The context is intuitively extracted from typed dependency structures basically depicting dependency relations using the relevant Part-Of-Speech tagged representation of the text. These relations imply deep, fine grained, labeled dependencies that encode long-distance relations and passive information. The present work focuses on extracting the key noun phrases participating both in subject and object roles that are intended to be subsequently used in framing sentential components for model answers in any selected working domain.
Vision based square matrix design and detection for robotic routing

Robot vision is essential to aid in performing robotic tasks. Existing robotic path tracking methods used in the industry such as line following are simple but not capable to cater for continuous floor plan changes. This paper proposes a new vision based pattern design and detection algorithm that can be implemented on an existing mobile robot structure to provide path tracking and routing. A unique pattern that is capable to embed information is successfully designed. The detection algorithm is tested at different lighting conditions, pattern size and orientation. A larger pattern size and good lighting conditions has produced favorable high detection of the pattern. Subsequently, after pattern detection, a unique identifier of the pattern is generated in the form of a binary matrix. The generated binary matrix is proposed to be used as a location identifier to aid in mobile robot routing.
Optimization for Speculative Execution of Multiple Jobs in a MapReduce-like Cluster

Nowadays, a computing cluster in a typical data center can easily consist of hundreds of thousands of commodity servers, making component/ machine failures the norm rather than exception. A parallel processing job can be delayed substantially as long as one of its many tasks is being assigned to a failing machine. To tackle this so-called straggler problem, most parallel processing frameworks such as MapReduce have adopted various strategies under which the system may speculatively launch additional copies of the same task if its progress is abnormally slow or simply because extra idling resource is available. In this paper, we focus on the design of speculative execution schemes for a parallel processing cluster under different loading conditions. For the lightly loaded case, we analyze and propose two optimization-based schemes, namely, the Smart Cloning Algorithm (SCA) which is based on maximizing the job utility and the Straggler Detection Algorithm (SDA) which minimizes the overall resource consumption of a job. We also derive the workload threshold under which SCA or SDA should be used for speculative execution. Our simulation results show both SCA and SDA can reduce the job flowtime by nearly 60% comparing to the speculative execution strategy of Microsoft Mantri. For the heavily loaded case, we propose the Enhanced Speculative Execution (ESE) algorithm which is an extension of the Microsoft Mantri scheme. We show that the ESE algorithm can beat the Mantri baseline scheme by 18% in terms of job flowtime while consuming the same amount of resource.
IgE-Dependent Mast Cell Activation Potentiates Airway Responses in Murine Asthma Models1

We have studied murine models of asthma using FcεRIα-chain-deficient (FcεRIα−/−) mice to investigate the role of IgE-dependent mast cell activation in these models. When mice were either 1) immunized once with OVA in alum i.p. and then challenged with OVA intranasally, or 2) repeatedly immunized with OVA in the absence of adjuvant and subsequently challenged with nebulized OVA, FcεRα−/− mice had significantly fewer eosinophils and lower IL-4 levels in their bronchoalveolar lavage fluid compared with wild-type mice. When mice were given anti-IL-5 antibody before OVA challenge in protocol 1, eosinophilic infiltration into the airways was significantly suppressed in both genotypes, but only FcεRIα−/− mice showed significantly reduced airway hyperresponsiveness (AHR). In addition, when mice immunized and challenged with OVA also received a late OVA provocation at a higher concentration and were then exposed to methacholine, only wild-type mice developed a substantial increase in AHR. Since FcεRI is expressed mainly on mast cells in mouse airways, we conclude that IgE-dependent activation of this cell type plays an important role in the development of allergic airway inflammation and AHR in mice. The models used may be of value for testing inhibitors of IgE or mast cells for development of therapeutic agents for human asthma.
Increasing vector processor pipeline efficiency with a thread-interleaved controller

Vector processors are a fast and energy-efficient way of executing code with large amounts of data parallelism. Programmability has however been a difficult topic and has limited vector processors to a few niche applications. Recent advances in compiler auto-vectorization promise to make vector processors relevant for general-purpose computing. However, compiler-generated code is inefficient and makes poor use of vector resources, which are often the most area and power-consuming devices within a processing system. We propose adapting interleaved multi-threading, a proven technique for increasing pipeline efficiency of scalar processors, to improve utilization of vector resources, thereby providing gains in speed as well as potential reduction in energy consumption.
Simulations of Subtropical Cyclones in a Baroclinic Channel Model

Abstract The present study considers a variety of cyclone developments that occur in an idealized, baroclinic channel model featuring full condensation heating effects over an ocean with prescribed sea surface temperature variation. The geostrophic basic-state jet is specified by the tropopause shape, and horizontal shear is included by specifying the meridional variation of zonal wind on the lower boundary. The horizontal shear induces anticyclonic wave breaking of baroclinic waves. Normal mode perturbations are computed using a “fake-dry” version of the model but integrated forward using full physics. Low-latitude moist convection is particularly strong in simulations with strong surface easterlies that destabilize the troposphere through water vapor fluxes from the ocean surface. Deep convection produces a locally elevated dynamic tropopause and an associated anticyclone. This modified zonal flow supports moist baroclinic instability. The resulting cyclones, identified as subtropical cyclones, occur in.
Nonlinear ultrasound system modeling based on the Volterra filter

We introduce a second Volterra filter (SVF) as a mathematical model to separate harmonic components of a radiofrequency (RF) line from a simulated nonlinear pulse-echo system (NPS). Filter coefficients of both the linear impulse response and the quadratic kernel of the SVF can be determined by processing the input and output data in the frequency domain and solving a system of linear equations. Results from the validation of the approach show that while the linear impulse response correctly predicts the linear output in the fundamental energy band, the quadratic kernel appropriately captures energy primarily from the 2nd harmonic and low frequency bands. In addition, it is shown that the system identification based on the SVF is capable of separating the second order nonlinearity embedded under the level of noise signal. This is the significant advantage of the SVF approach over other static models such as linear bandpass filters.
Structural Insights into Triglyceride Storage Mediated by Fat Storage-Inducing Transmembrane (FIT) Protein 2

Fat storage-Inducing Transmembrane proteins 1 & 2 (FIT1/FITM1 and FIT2/FITM2) belong to a unique family of evolutionarily conserved proteins localized to the endoplasmic reticulum that are involved in triglyceride lipid droplet formation. FIT proteins have been shown to mediate the partitioning of cellular triglyceride into lipid droplets, but not triglyceride biosynthesis. FIT proteins do not share primary sequence homology with known proteins and no structural information is available to inform on the mechanism by which FIT proteins function. Here, we present the experimentally-solved topological models for FIT1 and FIT2 using N-glycosylation site mapping and indirect immunofluorescence techniques. These methods indicate that both proteins have six-transmembrane-domains with both N- and C-termini localized to the cytosol. Utilizing this model for structure-function analysis, we identified and characterized a gain-of-function mutant of FIT2 (FLL(157-9)AAA) in transmembrane domain 4 that markedly augmented the total number and mean size of lipid droplets. Using limited-trypsin proteolysis we determined that the FLL(157-9)AAA mutant has enhanced trypsin cleavage at K86 relative to wild-type FIT2, indicating a conformational change. Taken together, these studies indicate that FIT2 is a 6 transmembrane domain-containing protein whose conformation likely regulates its activity in mediating lipid droplet formation.
Systems Biology Perspectives for Studying Neurodevelopmental Events

Brain development follows a complex process orchestrated by diverse molecular and cellular events for which a perturbation can cause pathologies. In fact, multiple neuronal cell fate decisions driven by complex gene regulatory programs are involved in neurogenesis and neurodevelopment, and their characterization are part of the current challenges on neurobiology. In this chapter, we provide an overview of the various genomic strategies in use to explore the spatiotemporally defined gene regulatory wires implicated in brain development. Finally, we will discuss the intake of these approaches for understanding the multifactorial events implicated in neurodevelopment and the future requirements for further expanding our understanding of the brain.
Human Apolipoprotein A-I Is Associated with Dengue Virus and Enhances Virus Infection through SR-BI

Diseases caused by dengue virus (DV) infection vary in severity, with symptoms ranging from mild fever to life threatening dengue hemorrhage fever (DHF) and dengue shock syndrome (DSS). Clinical studies have shown that significant decrease in the level of lipoproteins is correlated with severe illness in DHF/DSS patients. Available evidence also indicates that lipoproteins including high-density lipoprotein (HDL) and low-density lipoprotein (LDL) are able to facilitate cell entry of HCV or other flaviviruses via corresponding lipoprotein receptors. In this study, we found that pre-incubation of DV with human serum leads to an enhanced DV infectivity in various types of cells. Such enhancement could be due to interactions between serum components and DV particles. Through co-immunoprecipitation we revealed that apolipoprotein A-I (ApoA-I), the major protein component in HDL, is associated with DV particles and is able to promote DV infection. Based on that observation, we further found that siRNA knockdown of the scavenger receptor class B type I (SR-BI), the cell receptor of ApoA-I, abolished the activity of ApoA-I in enhancement of DV infection. This suggests that ApoA-I bridges DV particles and cell receptor SR-BI and facilitates entry of DV into cells. FACS analysis of cell surface dengue antigen after virus absorption further confirmed that ApoA-I enhances DV infection via promoting initial attachment of the virus to cells. These findings illustrate a novel entry route of DV into cells, which may provide insights into the functional importance of lipoproteins in dengue pathogenesis.
Collaborative Competitive filtering II: Optimal Recommendation and Collaborative Games

Recommender systems have emerged as a new weapon to help online firms to realize many of their strategic goals (e.g., to improve sales, revenue, customer experience etc.). However, many existing techniques commonly approach these goals by seeking to recover preference (e.g., estimating ratings) in a matrix completion framework. This paper aims to bridge this significant gap between the clearly-defined strategic objectives and the not-so-well-justified proxy. We show it is advantageous to think of a recommender system as an analogy to a monopoly economic market with the system as the sole seller, users as the buyers and items as the goods. This new perspective motivates a game-theoretic formulation for recommendation that enables us to identify the optimal recommendation policy by explicit optimizing certain strategic goals. In this paper, we revisit and extend our prior work, the Collaborative-Competitive Filtering preference model, towards a game-theoretic framework. The proposed framework consists of two components. First, a conditional preference model that characterizes how a user would respond to a recommendation action; Second, knowing in advance how the user would respond, how a recommender system should act (i.e., recommend) strategically to maximize its goals. We show how objectives such as click-through rate, sales revenue and consumption diversity can be optimized explicitly in this framework. Experiments are conducted on a commercial recommender system and demonstrate promising results.
Modeling and Analysis of Multi-Party Fair Exchange Protocols

Fair multi-party exchange protocol is a new focus of research. We have made some researches for the modeling and analysis of multi-party fair exchange protocols recently. We use alternating transition systems, a game based model, to model protocols and alternating temporal logic, a game based logic, to express requirements that the protocols must ensure. This method is automated by using the model-checker MOCHA, a model-checker that supports the alternating transition systems and the alternating temporal logic. Fairness of multi-party exchange protocols are analyzed by using MOCHA.