Miracle-dz/UniFAR-FTUs huggingface dataset:
The Recognition of Handwritten Numbers by Extracting New Features Using Water Filling Method

In this paper, a new method is proposed to extract the features of a one-number Persian image in which for the final verification of the extracted features, a three-layer neural network (mesh) of Perceptron has been utilized. The method, which is called Water Filling method, is capable of extracting some ideal features from a one-number image that are stable against rotation, movement, size change and noise. The method is examined on a database of 60000 discretized numbers, from which 40000 numbers were used in the training stage and 20000 ones were used for the experiment. The recognition percentage of 92.7% shows the great efficiency of the proposed method.
A Visualization Interface for Document Searching and Browsing

We present a text document search engine with several new visualization front-ends that aid navigation through the set of documents returned by a query ( hit documents ). Our methods are based on identifying and selecting keywords on the fly. The choice of keywords depends not only on the frequency of their occurrence within hit documents but also on the specificity of their occurrence just within these hit documents.
Neural evidence accumulation persists after choice to inform metacognitive judgments

The ability to revise one’s certainty or confidence in a preceding choice is a critical feature of adaptive decision-making but the neural mechanisms underpinning this metacognitive process have yet to be characterized. In the present study, we demonstrate that the same build-to-threshold decision variable signal that triggers an initial choice continues to evolve after commitment, and determines the timing and accuracy of self-initiated error detection reports by selectively representing accumulated evidence that the preceding choice was incorrect. We also show that a peri-choice signal generated in medial frontal cortex provides a source of input to this post-decision accumulation process, indicating that metacognitive judgments are not solely based on the accumulation of feedforward sensory evidence. These findings impart novel insights into the generative mechanisms of metacognition. DOI: http://dx.doi. org/10.7554/eLife.11946. 001
Predicting Flavonoid UGT Regioselectivity

Machine learning was applied to a challenging and biologically significant protein classification problem: the prediction of avonoid UGT acceptor regioselectivity from primary sequence. Novel indices characterizing graphical models of residues were proposed and found to be widely distributed among existing amino acid indices and to cluster residues appropriately. UGT subsequences biochemically linked to regioselectivity were modeled as sets of index sequences. Several learning techniques incorporating these UGT models were compared with classifications based on standard sequence alignment scores. These techniques included an application of time series distance functions to protein classification. Time series distances defined on the index sequences were used in nearest neighbor and support vector machine classifiers. Additionally, Bayesian neural network classifiers were applied to the index sequences. The experiments identified improvements over the nearest neighbor and support vector machine classifications relying on standard alignment similarity scores, as well as strong correlations between specific subsequences and regioselectivities.
Tâtonnement Mechanisms for Combinatorial Exchanges

Combinatorial exchanges are double sided marketplaces with multiple sellers and multiple buyers trading with the help of combinatorial bids. The allocation and other associated problems in such exchanges are known to be among the hardest to solve among all economic mechanisms. It has been shown that the problems of surplus maximization or volume maximization in combinatorial exchanges are inapproximable even with free disposal. In this paper, the surplus maximization problem is formulated as an integer linear programming problem and we propose a Lagrangian relaxation based heuristic to find a near optimal solution. We develop computationally efficient tˆatonnement mechanisms for clearing combinatorial exchanges where the Lagrangian multipliers can be interpreted as the prices of the items set by the exchange in each iteration. Our mechanisms satisfy Individual-rationality and Budget-nonnegativity properties. The computational experiments performed on representative data sets show that the proposed heuristic produces a feasible solution with negligible optimality gap.
Teacher’s Irregularity and Students Performance: A Causative Analysis

Teacher irregularity is a noteworthy issue confronting numerous educational institutes. A high rate of irregularity of teachers from their posts is a serious obstruction to conveyance of education in many developing countries, but hard proof on the issue has been rare. There are more than a few critical variables that are associated with increased irregularity of teachers: poor working conditions, for example, poorer groups and infrastructure; teachers with less binds to the school's communities; contract teaching, absence of unpredictable supervision and appraisal, allotting instructor's non-teaching obligations, instructors strike and absence of congruity amongst guardians and educators are key reasons for teachers irregularity. By contrast, delegations for more lively top-down and base up observing are not connected with lower absence. These outcomes, together with the moderately high general state funded teacher participation rates in a situation where monetary impetuses for execution are frail, propose that nonfinancial motivators are imperative determinants of teacher performance. Head instructors and Education Officers has involved teachers for coming to class late, leaving right on time from school, teachers being in school and not taking care of their lessons and teachers doing private business amid working hours. The primary goal of this study is to figure out what the key elements of teacher’s irregularity are and how these factors influence students’ performance in their academic career. The study prescribes that with a specific end goal to decrease teacher irregularity there is need to address personal, school and environmental factors prompting teacher irregularity successfully. ■
An effective hybrid model based on PSO-SVM algorithm with a new local search for feature selection

Todays, feature selection is an active research in machine learning. The main idea of feature selection is to select a subset of available features, by eliminating features with little or no predictive information. This paper presents a hybrid model with a new local search technique based on reinforcement learning for feature selection. We combined the particle swarm optimization (PSO) with support vector machine (SVM) for improving classification accuracy and selecting a subset of salient feature. This optimization mechanism with combination of discrete PSO and continuous PSO simultaneously selects a subset of salient feature and tunes support vector machine parameters. In this algorithm, a new local search based on reinforcement learning is utilized for obtaining optimal feature subset. The numerical results and statistical analysis show that the proposed method performs significantly better than the other methods in terms of prediction accuracy with smaller subset of features on low and high dimensional datasets.
Application of MLQ in High School Students from Earthquake-stricken and Non-stricken Area

Objective: To examine the reliability and validity of Meaning in Life Questionnaire(MLQ) in Chinese high school students,and analyze the difference of meaning in life between high school students from earthquake-stricken and non-stricken area.Methods: Data were collected from 744 high school students from serious disaster area,mild disaster area and non-stricken area.Results: The results of exploratory factor analysis showed that MLQ consisted of two factors,the presence of meaning in life(MLQ-P) and the search for meaning in life(MLQ-S). Both MLQ-P and MLQ-S had satisfying consistency reliability(0.81 and 0.79),construct reliability(0.80 and 0.78),four-week test-retest reliability(0.65 and 0.50),and good structure validity.The presence of and the search for meaning in life of students from earthquake-stricken area were lower than those from non-stricken area.Conclusion: MLQ has good usability in Chinese high school students.The presence of and the search for meaning in life can be reduced by earthquake.
Mid-Infrared Observations of T Tauri Stars: Probing the Star-Disk Connection in Rotational Evolution

We present mid-IR N-band (λeff = 10.2 μm) photometry of a carefully selected sample of T Tauri stars thought to be single from the Taurus-Auriga molecular cloud. Infrared excesses in these stars are generally attributed to circumstellar dust disks. Combining observations at 2.16 (Ks band) and 10.2 μm (N band) we probe a region in the circumstellar dust disk from a few stellar radii through the terrestrial planet zone (0.02-1.0 AU). By analyzing the distribution of the (Ks - N) color index with respect to previously measured photometric rotation periods we investigate what role circumstellar disks play in the rotational evolution of the central star. The resulting positive correlation between these two variables is consistent with the notion that a star-disk interaction facilitates the regulation of angular momentum during the T Tauri stage. We also demonstrate how including nonsingle stars in such an analysis weakens any correlation in the relation between (Ks - N) color and period. To further understand disk properties we also present spectral energy distributions for a few objects with new ground-based M-band (λeff = 4.8 μm) and Q-band (λeff = 20 μm) data and compare them to a geometrically thin, optically thick disk model.
Carrying a heavy load: Mayan women’s understandings of reparation in the aftermath of genocide

ABSTRACT Drawing on extensive testimony from Ixil women survivors of sexual violence, the 10 May 2013 verdict in the genocide trial of former de facto Guatemalan head of state and army general Efraín Ríos Montt highlighted the perpetration of sexual violence as an integral component in the attempt to destroy the Maya Ixil as an ethnic group and thus evidence of genocide. Acknowledging that sexual violence was a weapon of genocide in Guatemala contributes to a critical analysis of how the racialized violence targeted against the country’s indigenous peoples was gendered, and enables the women and men who are survivors of these crimes to seek redress. However, narrating sexual harm within justice-seeking processes is not without complication, and trials alone cannot respond to survivors’ demands for justice and social repair. This article examines how fifty-four Maya Q’eqchi’, Kaqchikel, Mam and Chuj women who are survivors of sexual violence make meaning of the everyday struggles to rethread their lives in the aftermath of genocide. The article uses data from a four-year participatory action research (PAR) project conducted by the authors with this group of Mayan women, including a series of workshops that used creative techniques—drawing, collage, dramatization and body sculptures—to elicit more complex and contestational stories than those emergent from a more linear narrative approach to understanding harm suffered and efforts for redress. Analysis of these data confirms that these Mayan women survivors have woven their understanding of reparation from three main threads: their experiences of loss and harm; their recognition of the Guatemalan state’s duplicity; and their protagonism in justice-seeking processes. The article concludes by arguing that women survivors' desire for repair requires attention to the deep-seated impoverishment that they highlight as the heavy load of gendered violence they carry with them.
A Model of Enterprise Strategic Risk Assessment: Based on the Theory of Multi-Objective Fuzzy Optimization

The purpose of this paper is to use the Analytic Hierarchy Process to build up a model on identifying the strategic risk. The new model is based on Baird and Thomas' Contingency model which is in accord with the strategic management of the traditional mode of thinking. At the same time, on the basis of systematology we use the multi-objective fuzzy optimization theory as strategic risk measurement methods, to enable the enterprises from the perspective of the overall strategy to identify and measure strategic risk, to determine or measure the quantity of risk of strategic management.
Formal Model of Data Reuse Analysis for Hierarchical Memory Organizations

In real-time data-dominated communication and multimedia processing applications, due to the manipulation of large sets of data, a multi-layer memory hierarchy is used to enhance the system performance and also to reduce the energy consumption. Savings of dynamic energy can be obtained by accessing frequently used data from smaller memories rather than from large background memories. The optimization of the hierarchical memory architecture implies the addition of layers of smaller memories to which heavily used data can be copied. This paper presents a formal model for data reuse analysis which identifies those parts of arrays more intensely accessed, taking also into account the relative lifetimes of the signals. Tested on a two-layer memory hierarchy, this model led to savings in the dynamic energy from 40% to over 70% relative to the energy used in the case of a flat memory design
Maintenance Effort Estimation for Open Source Software: A Systematic Literature Review

Open Source Software (OSS) is distributed and maintained collaboratively by developers all over the world. However, frequent personnel turnover and lack of organizational management makes it difficult to capture the actual development effort. Various OSS maintenance effort estimation approaches have been developed to provide a way to understand and estimate development effort. The goal of this study is to identify the current state of art of the existing maintenance effort estimation approaches for OSS. We performed a systematic literature review on the relevant studies published in the period between 2000-2015 by both automatic and manual searches from different sources. We derived a set of keywords from the research questions and established selection criteria to carefully choose the papers to evaluate. 29 out of 3,312 papers were selected based on a well designed selection process. Our results show that the commonly used OSS maintenance effort estimation methods are actual effort estimation and maintenance activity time prediction, the most commonly used metrics and factors for actual effort estimation are source code measurements and people related metrics, the most commonly mentioned activity for maintenance activity time prediction is bug fixing. Accuracy measures and cross validation is used for validating the estimation models. Based on the above findings, we identified the issues in evaluation methods for actual maintenance effort estimations and the needs for quantitative OSS maintenance effort inference from size-related metrics. Meanwhile, we highlighted individual contribution and performance measurement as a novel and promising research area.
Synthesizer: Expediting synthesis studies from context-free data with natural language processing

Today’s low cost digital data provides unprecedented opportunities for scientific discovery from synthesis studies. For example, the medical field is revolutionizing patient care by creating personalized treatment plans based upon mining electronic medical records, imaging, and genomics data. Standardized annotations are essential to subsequent analyses for synthesis studies. However, accurately combining records from diverse studies requires tedious and error-prone human curation, posing a significant barrier to synthesis studies. We propose a novel natural language processing (NLP) algorithm, Synthesize, to merge data annotations automatically. Application to patient characteristics for diverse human cancers and ecological datasets demonstrates the accuracy of Synthesize in diverse scientific disciplines. This NLP approach is implemented in an open-source software package, Synthesizer. Synthesizer is a generalized, user-friendly system for error-free data merging.
Evidence based similarity for document categorization

The failure of effective information management over the web does not influence only on by the speed of information retrieval; but also influenced by individual and organizational activities. Thus, the search engine of information retrieval plays a pivotal role in retrieving results that are relevant to users' query. Categorization is an optimist alternative to improve the accuracy and speed of information retrieval. This paper proposes a method using evidential reasoning based on similarity for text categorization. Term and frequency of term has influenced the process of deciding the document category. In order to define a new category for new document, our proposed method has taking degree similarities into consideration.
Automated catena‐based discretization of landscapes for the derivation of hydrological modelling units

In hydrological and soil erosion modelling at large spatial scales, semi‐distributed approaches may use representative hillslope profiles to reproduce landscape variability. Until now, the process of delineating landscape units as homogeneous parts of the landscape with regard to their terrain, vegetation, and soil properties required expert knowledge and familiarity with the study area. In addition, the delineation procedure was often highly time‐consuming and included a high degree of subjectivity. This paper presents a novel, semi‐automated approach for the delineation of landscape units, the derivation of representative toposequences, and their partitioning into terrain components. It incorporates an algorithm to retrieve representative catenas and their attributes for elementary hillslope areas based on elevation and other key spatial data frequently required as environmental model input, e.g. vegetation and soil data. An example application for the Ésera catchment in Spain illustrates that with the presented approach, upscaling of hillslope properties becomes feasible for environmental modelling at large scales while ensuring reproducible results.
Software structure evolution and relation to system defectiveness

We still do not have clear figure about how software systems evolve and how we may control its evolution process. Software structure has been identified that may have the biggest impact, especially because it may be represented from numerous perspectives. Novelty introduced in this paper is the way how we define the structure of evolving complex software systems. The structure is represented with help of graph representations, and subgraph frequencies, the concept reused from the network analysis theory. The graph structure of a software system and its evolution over the system versions, as well as its relation to defectiveness, is empirically studied in terms of subgraph frequencies and motifs for more than 30 releases of three large open source software systems. We identified that the same set of subgraphs of software system is present across the system version, but different sets, although overlapping, are present in different software systems. Furthermore, we confirmed the continuous system evolution in terms of continuous structure change and we find some evidence for its relation to system defectiveness.
Mathematical models and circuit implementations of memristive systems

In this paper we first present a novel, simple and general boundary condition-based model for nano-scale switching resistances with memory. The boundary conditions are embedded into a switching function modulating the rate of ionic transport, and, on the basis of the memristor under modeling, may be suitably chosen through an optimization procedure minimizing some reference parameter such as the mean squared error between observed and modeled data. The versatile nature of the switching function enables the model to detect complex dynamics from a number of memristive nano-structures, including the Hewlett-Packard memristor. In the second part of the manuscript, we explain how to use the switching dynamics of appropriate nonlinear two-ports to synthesize simple memristive electronic circuits employing purely-passive already-existing components.
Estimation of Human Orientation using Coaxial RGB-Depth Images

Estimation of human orientation contributes to improving the accuracy of human behavior recognition. However, estimation of human orientation is a challenging task because of the variable appearance of the human body. The wide variety of poses, sizes and clothes combined with a complicated background degrades the estimation accuracy. Therefore, we propose a method for estimating human orientation using coaxial RGBDepth images. This paper proposes Depth Weighted Histogram of Oriented Gradients (DWHOG) feature calculated from RGB and depth images. By using a depth image, the outline of a human body and the texture of a background can be easily distinguished. In the proposed method, a region having a large depth gradient is given a large weight. Therefore, features at the outline of the human body are enhanced, allowing robust estimation even with complex backgrounds. In order to combine RGB and depth images, we utilize a newly available single-chip RGB-ToF camera, which can capture both RGB and depth images taken along the same optical axis. We experimentally confirmed that the proposed method can estimate human orientation robustly to complex backgrounds, compared to a method using conventional HOG features.
Concurrency Control for Adaptive Indexing

Adaptive indexing initializes and optimizes indexes incrementally, as a side effect of query processing. The goal is to achieve the benefits of indexes while hiding or minimizing the costs of index creation. However, index-optimizing side effects seem to turn read-only queries into update transactions that might, for example, create lock contention. This paper studies concurrency control in the context of adaptive indexing. We show that the design and implementation of adaptive indexing rigorously separates index structures from index contents; this relaxes the constraints and requirements during adaptive indexing compared to those of traditional index updates. Our design adapts to the fact that an adaptive index is refined continuously, and exploits any concurrency opportunities in a dynamic way. A detailed experimental analysis demonstrates that (a) adaptive indexing maintains its adaptive properties even when running concurrent queries, (b) adaptive indexing can exploit the opportunity for parallelism due to concurrent queries, (c) the number of concurrency conflicts and any concurrency administration overheads follow an adaptive behavior, decreasing as the workload evolves and adapting to the workload needs.
Parallel weak learners, a novel ensemble method

Ensemble methods have proved to be an effective tool to increase the performance of pattern recognition applications. An ensemble method behaves like an expert committee in predicting the class to which a sample belongs. In this paper, we present a novel ensemble method with high classification accuracy and resistance to noisy data. In our proposed method, we exploit a type of bagging in which the bagging process is carried out on attributes instead of data samples. By testing this method on two well-known databases, we show that the proposed ensemble method is comparable in accuracy and efficiency to the state-of-the-art classifiers.