Miracle-dz/UniFAR-FTUs huggingface dataset:
Proceeding Series of the Brazilian Society of Computational and Applied Mathematics A New Approach To Software Effort Estimation Using Linear Genetic Programming

Accurate estimation of effort in software projects is one of the most desired capabilities in software development organizations and is a big challenge for projects manager. Effort is measured in terms of person months and is evaluated with the help of different methods. COCOMO is the most popular non-proprietary software estimation model in literature as well as in industry [1] [2]. Both underestimated and overestimated effort are harmful for projects and caused development schedule delay and poor software quality. There are many software cost estimation models have been developed over the last decades. The computing intelligence techniques have been grown. Some researches showed a promising research direction in software cost estimation with genetic programming [4]. Linear Genetic Programming (LGP) is a genetic programming variant that evolves sequences of instructions from an imperative programming language. This paper presents a LGP approach to evaluates and compares the potential on software project data set. The objective in this paper is the finding of new models from historical data using LPG that evolves sequences of instructions from an imperative programming language, like C, C++, Java, etc [3]. In this work, programs are represented as sequences of instructions that accept a minimum number of variables, called registers. The instruction form may be the following: r0 = r1 + r5 where ri is a register. This type of representation includes an operator on operand registers. In this work implementation, each instruction of a program includes four values (an operation, a destination and two operands). For instance, a 4-tuple as < +, 0, 2, 9 > represents the instruction r[0] = r[2] + r[9]. New populations are produced by selecting with larger probability good ranking solutions and performing genetic operations to produce offspring solutions that let the population evolve. This process is repeated over many generations until some stop condition is reached. Experiments in this paper have been conducted from COCOMO database [1], a data set publicly available which consists of 63 projects. Data were divided into training and validation periods. The objective
Estimation of Radiation Risk Due to Exposure to Terrestrial Radiation

An in-situ measurement of background radiation level of Ewoi community in Ogbia Local Government Area of Bayelsa State Nigeria was done using two radiation meters (Digilert-200 and Radalert-100). Exposure rate measured ranged from 0.010 to 0.028 mRh-1 with mean value of 0.016 mRh-1. The mean value of absorbed dose was 128.67 nGyh-1 while the mean indoor and outdoor annual effective dose equivalents were 0.40 mSvy-1 and 0.13 mSvy-1 respectively. The mean excess lifetime cancer risk estimated from both indoor and outdoor effective doses were 1.0 × 10-3 and 0.33 × 10-3 respectively. It was observed that all the radiological parameters estimated from the radiation exposure rate were higher than their world average values except the annual effective doses. The result of this study shows that Ewoi community recorded slightly high background radiation level and this may be attributed to oil and gas exploration activity in the area. The result of this work serves as radiological baseline data of the area for future studies. Considering the values obtained in this research work and the likely health impact, it is important that further studies be carried out on activity concentration of radionuclide on the soil, water (drinking water resources of the community), and food crops from Ewoi community for identification of radionuclides present and their concentrations in order to quantify their radiological health implication and ways to control it.
Probabilistic Hazard Information (PHI): Highlighting the Benefits via New Verification Techniques for FACETs

National Weather Service (NWS) severe thunderstorm and tornado warnings are issued today as deterministic polygons that cover a two-dimensional area of expected threat of severe weather within a time period usually from 30 to 60 minutes. As part of the Forecasting A Continuum of Environmental Threats (FACETs) initiative, new methods of delivering severe thunderstorm and tornado forecast and warning information are being investigated. A concept known as Probabilistic Hazard Information (PHI) has been under development and testing at the National Oceanic and Atmospheric Administration (NOAA) Hazardous Weather Testbed (HWT) since 2008. Within the PHI concept, customized severe weather warning products can be derived from rapidly-updating probability grids that are produced by forecasters.
Computational investigation of feature extraction and image organization

This dissertation investigates computational issues of feature extraction and image organization at different levels. Boundary detection and segmentation are studied extensively for range, intensity, and texture images. We developed a range image segmentation system using a LEGION network based on a similarity measure consisting of estimated surface properties. We propose a nonlinear smoothing algorithm through local coupling structures, which exhibits distinctive temporal properties such as quick convergence. We propose spectral histograms, consisting of marginal distributions of a chosen bank of filters, as a generic feature vector based on that early steps of human visual processing can be modeled using local spatial/frequency representations. Spectral histograms are studied extensively in texture modeling, classification, and segmentation. Experiments in texture synthesis and classification demonstrate that spectral histograms provide a sufficient and unified feature in capturing perceptual appearance of textures. Spectral histograms improve significantly the classification performance for challenging texture images. We also propose a model for texture discrimination based on spectral histograms which matches existing psychophysical data. A new energy functional for image segmentation is proposed. With given regional features, an iterative and deterministic algorithm for segmentation is derived. Satisfactory results
Numerical Models for Hydraulic Refracturing on Vertical Oil Wells

The paper presents an approach for simulation of refracturing treatment on vertical oil wells. The model is accounting for filtration of hydraulic fracturing fluid through the proppant packed inside the crack formed during previous hydraulic fracturing treatments. The simulations provide a possibility to estimate history of stress intensity factor appearing at the tip of the existing crack once the time profile of pressure within the wellbore is given. Introducing critical value of the stress intensity factor for the fractured media, time-to-fracture initiation (after pressure increase start) can be estimated and compared to instance of fracture event registered in real conditions. Also, the possibility of fracture reorientation through formation of new fractures at the region adjacent to the wellbore is studied.
REST SOA Orchestration and BPM Platforms

BPM initiatives and SOA approaches emerged and developed as distinct efforts although they could be perceived as the two sides of the same coin in the context of online business and information systems. Thus, inherently, their modeling perspectives met (or collided) and generated some frameworks and languages like SOAML or SOAMF. Starting from these standards and initiatives we will propose a methodological approach that could lead to a feasible implementation of web service integration in a formal Business Process context.
Hypoxia-based habitat compression of tropical pelagic fishes

Large areas of cold hypoxic water occur as distinct strata in the eastern tropical Pacific (ETP) and Atlantic oceans as a result of high productivity initiated by intense nutrient upwelling. We show that this stratum restricts the depth distribution of tropical pelagic marlins, sailfish, and tunas by compressing the acceptable physical habitat into a narrow surface layer. This layer extends downward to a variable boundary defined by a shallow thermocline, often at 25 m, above a barrier of cold hypoxic water. The depth distributions of marlin and sailfish monitored with electronic tags and average dissolved oxygen (DO) and temperature profiles show that this cold hypoxic environment constitutes a lower habitat boundary in the ETP, but not in the western North Atlantic (WNA), where DO is not limiting. Eastern Pacific and eastern Atlantic sailfish are larger than those in WNA, where the hypoxic zone is much deeper or absent. Larger sizes may reflect enhanced foraging opportunities afforded by the closer proximity of predator and prey in compressed habitat, as well as by the higher productivity. The shallow band of acceptable habitat restricts these fishes to a very narrow surface layer and makes them more vulnerable to over-exploitation by surface gears. Predictably, the long-term landings of tropical pelagic tunas from areas of habitat compression have been far greater than in surrounding areas. Many tropical pelagic species in the Atlantic Ocean are currently either fully exploited or overfished and their future status could be quite sensitive to increased fishing pressures, particularly in areas of habitat compression.
Proposed Collaborative Filtering Recommender System Based on Implicit and Explicit User's Preferences

The expansion of web applications like e-commerce and other services yields an exponential increase in offers and choices in the web. From these needs, the recommender system applications have arisen. This research proposed a recommender system that uses user's reviews as implicit feedback to extract user preferences from their reviews to enhance personalization in addition to the explicit ratings. Diversity also improved by using k-furthest neighbor algorithm upon user's clusters. The system tested using Douban movie standard dataset from Kaggle, and show good performance.
Art curation and critique in the age of digital humanities

ABSTRACT Research and selection of information, as well as its display and analysis, have become central to our daily lives. They are interestingly close to the cultural techniques that preside to the production of knowledge and to art-related practices, such as curating, exhibiting and critique, which seems to bring testimony to the triumph of the so-called ‘knowledge society’ and ‘creative economy’. But, while both scientific knowledge and cultural industry are rapidly relying on algorithms, automated analysis and data visualisation to deal with the exponential growth of data, cultural institutions and humanities are still struggling to decide in what terms are they going to enter the digital age, although they are already being transformed through the infrastructure of digital archives and digital media. This proposal will reflect upon the meaning of activities such as curating, displaying and critique, that have shaped the worlds of art and culture over the last century and upon their redefinition by the mathematics and geometry that are developping the new information geographies of the cultural economy.
Research report Representation of orientation in the somatosensory system

In this paper we discuss how orientation is represented and transformed in the somatosensory system. Information about stimulus orientation plays an important role in sensory processing. In touch it provides critical information about how stimuli are positioned on the hand, which is important for grasping and lifting objects. It also provides important information about tactile shape. Psychophysical studies show that humans have a high capacity to discriminate the orientation of shapes and gratings indented into the finger pad. Further, these studies demonstrate that orientation discrimination is a reliable and stable method for assessing tactile spatial acuity. Neurophysiological studies suggest that orientation information is processed by the slowly adapting type 1 (SA1) afferent system. While orientation is poorly represented in the responses of individual afferent fibers, it is well represented in the population response properties of peripheral SA1 afferents and in the responses of central neurons in the primary (S1) and secondary (S2) somatosensory cortex. In S2, neurons with orientation selective and orientation non-selective responses tend to have large receptive fields that span multiple pads on multiple digits. Neurons in S2 that are orientation selectiv eh ave similar tuning functions on different finger pads. These neurons may provide position-invariant responses or may be responsible for integrating features across hands, which is important for haptic object recognition of large shapes from the hand. Neurophysiological studies in trained animals show that the responses of about 85% of the neurons in S2 are affected by the animals focus of attention and that attention to the orientation of a bar modifies both the mean firing rate (i.e. gain) of neurons encoding orientation information and the degree of synchronous firing between pairs of neurons. # 2002 Elsevier Science B.V. All rights reserved.
Evaluation of Safety in Cesarean Myomectomy

Objectives: Cesarean myomectomy (removal of fibroids during cesarean section) prevents an additional surgery, the cost, associated complications, and the physical and psychological trauma. However, there is risk of increased bleeding. Nevertheless, many centers practice it taking optimum precautions. The aim of our study was to compare cesarean myomectomy patients in those women who underwent cesarean section without fibroid removal for (a) blood loss, fall in hemoglobin, blood transfusion requirement; (b) intraoperative and postoperative complications; and (c) duration of hospitalization. Materials and methods: We retrospectively studied medical records of cesarean sections done in our institution for women within the past 5 years. A total of 76 patients had documented fibroids, of whom 37 underwent concurrent myomectomy and 39 did not. Data were analyzed using Pearson’s Chi-square test for categorical variables and student t test for continuous variables with threshold of significance p < 0.05. Results: No significant difference was found in blood loss and intraoperative complications. A small increase was seen in febrile morbidity in myomectomy group, however, with no resultant increase in hospitalization. Conclusion: Cesarean myomectomy does not cause increased morbidity. Moreover, it saves the mother an additional surgery of interval myomectomy provided proper selection of cases, adequate preoperative and intraoperative preparedness, and caution are taken into consideration.
Detection of Intrusions in KDDCup Datasetusing GA by Enumeration Technique

In the last decades, there has been a massive growth in network connectivity between computer system which has achieved boundless potential outcomes and opportunities. Sadly, security related issues have likewise expanded at the same rate. Computer systems become victims of such attacks. These attacks or intrusions are modified information that cause harm to the working system program or application programs, typically read or alter private data or render the system futile. Several techniques are used to prevent and detect such attacks or intrusions. This paper presents an efficient GA based methodology to produce the classification rules for Network intrusion detection system. The chromosome structure has been selected by applying enumeration technique in which the computational time required to produce the population is significantly reduced and near optimal rules are generated. These classification rules are used to find networking attacks or intrusions. The proposed system is applied on KDDCup99 Dataset to yield more efficient and effective classification rules.
A dual process in memory: how to make an evaluation from complex and complete information? — An experimental study

In this paper, we will put forward an original experiment to reveal empirical “anomalies” in the process of acquisition, elaboration and retrieval of information in the context of reading economic related content. Our results support the existence of the memory dual process suggested in the Fuzzy Trace Theory: acquisition of information leads to the formation of a gist representation which may be incompatible with the exact verbatim information stored in memory. We give to subjects complex and complete information and evaluate their cognitive ability. To answer some specific questions, individuals used this gist representation rather than processing verbatim information appropriately.
1A1-F14 Estimation of Camera Motion with Feature Flow Model for Construction of 3D Environment

Map information is important for path planning and self-localization when mobile robots accomplish autonomous tasks. In unknown environments, mobile robots should generate an environment map by themselves. Then, we propose a method for construction of the 3D environment model by a mobile robot. Our proposed method is based on structure from motion. We use a single omni-directional camera to measure the objects in the environment. Our method estimates the camera motion with feature flow model. The feature flow model makes it possible to estimate the camera motion precisely by taking advantage of the wide field of view of the omni-directional camera. Experimental results showed the effectiveness of our proposed method.
An Empirical Evaluation of various Discrimination Measures for Discrimination Prevention

Discrimination prevention in Data mining has been studied by researchers. Several methods have been devised to take care of both direct and indirect discrimination prevention. In order to prevent discrimination, each of these methods tries to minimize the impact of discriminating attributes by modifying certain discriminating rules. The discriminating rules are identified using certain threshold and discrimination measure such as elift for direct discrimination and elb for indirect discrimination. Performance of these methods are measured and compared in terms discrimination removal using DDPD, DDPP for direct discrimination and IDPD, IDPP for indirect discrimination as well as resultant data quality using MC and GC for both kinds of discrimination.This paper deals with study of use of discrimination measures other than elift such as slift, clift and olift. The empirical evaluation presented here shows that slift provides best overall performance.
Cognitive and technological aspects of e-learning in context of robotization

The development of e-learning education led to the emergence of two main problems of such form of education, which are the user-system interaction from the cognitive prospective, and the analysis of massive data received out of students’ activity. The development of artificial intelligence concepts and robotic process automation (RPA) tools, both problems might be solved in a more efficient way. The article presents the intelligent system of feedbacks, realized as JavaScript extension to Moodle platform, which intends to strength cognitive output of the performed learning activity, creating an illusion of the trainer’s presence and, therefore, contribute to the resolution of the first problem. The resolution of the second problem is proposed using artificial intelligence worker built in Blue Prism RPA platform, which independently collects test results and runs an Excel macro, which performs validation of test questions upon strict criteria of selection. Such validation process allows to select the questions which are coherent with the index of complexity and the index of differentiation capacity.
Ordered Sequence Detection and Robust Pulse Interval Modulation for IM/DD Based Optical Wireless Communications

Digital pulse interval modulation (DPIM) is an advanced energy-efficient modulation techniques for optical wireless communications (OWC) based on intensity modulation with direct detection (IM/DD). DPIM requires no accurate symbol-level synchronization. It is capable of leveraging less slots to carry the same information as pulse position modulation (PPM) and thus more efficient than PPM. However, error propagation hampers the application of DPIM. Motivated by controlling error propagation, this paper proposes an ordered sequence detection (OSD) for DPIM. To detect a packet consisting of $L$-chips, the computational complexity of OSD is low, of the order $\mathcal{O}(L^2)$. Moreover, this paper also proposes a robust pulse interval modulation (RPIM) scheme with OSD. In RPIM, the last of every $K$ symbols is with more power to transmit information and simultaneously to provide a built-in synchronization signal. In this way, error propagation is bounded in a slot of $K$ symbols. Together with interleaver and forward error correction (FEC) code, the bit error rate (BER) can be greatly reduced. We derive the approximate uncoded BER performance of conventional DPIM with OSD and the newly proposed RPIM with OSD based on order statistic theory. We calculate the optimal power allocation in every $K$ symbol slots minimizing the uncoded BER by using a bisection search algorithm. Simulations are conducted to collaborate on theoretical analysis. Optimal parameter settings are also investigated in uncoded and coded systems by simulations. Simulation results show that RPIM with OSD considerably outperforms existing DPIM with optimal threshold detection in either uncoded or coded systems over various channels.
A Genetic Algorithm Based Feature Selection for Classification of Brain MRI Scan Images Using Random Forest Classifier

A brain tumour is a mass of tissue that is formed by a gradual addition of anomalous cells and it is important to classify brain tumours from the magnetic resonance imaging (MRI) for treatment. Magnetic Resonance Imaging is a useful imaging technique that is widely used by physicians to investigate different pathologies. After a long clinical research, it is proved to be harmless. Improvement in computing power has introduced Computer Aided Diagnosis (CAD) which can efficiently work in an automated environment. Diagnosis or classification accuracy of such a CAD system is associated with the selection of features. This paper proposes an enhanced brain MRI image classifier targeting two main objectives, the first is to achieve maximum classification accuracy and second is to minimize the number of features for classification. Feature selection is performed using Genetic Algorithm (GA) while classifiers used are Random forest Classifier.