Miracle-dz/UniFAR-FTUs huggingface dataset:
WEPSAM: Weakly Pre-Learnt Saliency Model

Visual saliency detection tries to mimic human vision psychology which concentrates on sparse, important areas in natural image. Saliency prediction research has been traditionally based on low level features such as contrast, edge, etc. Recent thrust in saliency prediction research is to learn high level semantics using ground truth eye fixation datasets. In this paper we present, WEPSAM : Weakly Pre-Learnt Saliency Model as a pioneering effort of using domain specific pre-learing on ImageNet for saliency prediction using a light weight CNN architecture. The paper proposes a two step hierarchical learning, in which the first step is to develop a framework for weakly pre-training on a large scale dataset such as ImageNet which is void of human eye fixation maps. The second step refines the pre-trained model on a limited set of ground truth fixations. Analysis of loss on iSUN and SALICON datasets reveal that pre-trained network converges much faster compared to randomly initialized network. WEPSAM also outperforms some recent state-of-the-art saliency prediction models on the challenging MIT300 dataset.
Experimental modeling of controlled suspension vehicles from onboard sensors

In this article, identification of vertical dynamics of vehicles with controlled suspensions is considered. Identification is performed from experimental data measured on a four-poster bench test of a segment C car, equipped with a CDC-Skyhook dampers control system. The measurements are obtained from the onboard accelerometers needed by the control system. A nonlinear model in regression form is identified, having the road profile and damper control currents as inputs and chassis accelerations as outputs. The model is identified by means of a set membership structured identification method, which takes advantage of physical information on the structure of the system, decomposing the system into three subsystems: one represents the chassis and engine and the other two represent the overall behavior of front and rear suspensions, wheels and tires. This decomposition allows us to avoid the complexity accuracy problems derived from the high dimension of required regression space. Indeed, the overall high-dimensional identification problem is reduced to the identification of lower dimensional subsystems and to the estimation of their interactions. An iterative scheme is used for solving the decomposed identification problem. As the chassis pitch is small for the usual road profiles, the chassis-engine block is considered linear and standard linear methods are used for its identification. The other two subsystems are the main sources of nonlinearities in the system, mainly due to the significant nonlinearities of controlled dampers and of tires. Owing to the complexity/accuracy problems of a physical modeling of these subsystems, an input–output approach is taken. In particular, a nonlinear set membership method that does not require the search of the functional form of involved nonlinearities is used for the identification of these subsystems. The iterative algorithm converged in two iterations to a model providing a quite satisfactory simulation accuracy for all the considered road profiles and CDC-Skyhook settings.
A Comparison of System Modelling for Distributed Applications: RM-ODP vs MDA *

Constructing a business application is a big task as issues always rise starting from how to model business requirements to how the system should be deployed. This process involves making decisions on the design, architecture, and several management aspects for the system. As part of our research project to study the design and construction of distributed enterprise applications, we survey trends in computer technology that relate to building of such applications. This paper reports our survey on system modelling for distributed applications. We cover two key standards: the Reference Model of Distributed Processing (RM-ODP) by ITU-T/ISO and the Model Driven Architecture (MDA) by OMG. Under the two standards, we discuss system modelling from various viewpoints as well as modelling languages. The paper concludes with a comparison between these standards and their status of use at present for constructing distributed systems.
Fast algorithm for skew detection

Document image processing has become an increasingly important technology in the automation of office documentation tasks. Automatic document scanners such as text readers and OCR (optical character recognition) systems are an essential component of systems capable of those tasks. One of the problems in this field is that the document to be read is not always placed correctly on a flat-bed scanner. This means that the document may be skewed on the scanner bed, resulting in a skewed image. This skew has a detrimental effect on document analysis, document understanding, and character segmentation and recognition. Consequently, detecting the skew of a document image and correcting it are important issues in realizing a practical document reader. In this paper we describe new algorithms for skew detection and skew correction. We then compare the performance and results of this skew detection algorithm to other published methods from O'Gorman, Hinds, Le, Baird, and Postl. Finally, we discuss the theory of skew detection and the different approaches taken to solve the problem of skew in documents. The skew correction algorithm we propose has been shown to be extremely fast, with run times averaging under 0.25 CPU seconds to calculate the angle on a DEC 5000/20 workstation.
Downlink beamforming design for cognitive cellular systems

We propose a novel scheme to design downlink beamforming vectors in a cognitive cellular network which shares radio spectrum with a primary communication system. We formulate an optimization problem which minimizes the cognitive base station transmit power and the induced interference on the primary users and keeps them below predefined system thresholds. This is subject to providing a certain level of signal-to-interference-plus-noise ratio to the secondary users. We propose an iterative algorithm to solve the optimization problem. The convergence of the proposed iterative algorithm is analytically proved. Simulation results verify that the proposed algorithm quickly converges to the optimal solution. Results also indicate that the proposed algorithm guarantees the transmit power and interference constraints. We then compare the proposed scheme with a benchmark system based on the previous methods. Comparisons show significant reduction in the total interference at reasonable cost of the cognitive base station transmit power.
Performance analysis of a decentralized network simulator based on IMUNES

IMUNES, a fast integrated network emulator/simulator, presents an excellent replacement for the general purpose network test-bed since it increases scalability while providing support for real applications and protocols, as well as decreasing the cost of equipment and management. However, working on one machine only the scalability of IMUNES is limited, leading to inefficient and complex management of large-scale network simulations. In this paper we present a distributed version of IMUNES, designed to circumvent this problem. Guided by the principles of scalability, transparency and security, the resulting system is built on top of a decentralized structure. We evaluate the resulting system performance in terms of scalability, responsiveness, latency and throughput. Based on the observed values, it can be concluded that the simulation capacity increase is proportional to the increase in the number of machines available in the distributed system.
A study on image features using intensity profile for cortical dysplasia degree estimation

Cortical dysplasia is a congenital anomaly of cerebral cortex. It accompanies considerable number of pediatric intractable epilepsy patients. Therefore, it is very effective to detect cortical dysplasia lesions and to estimate dysplasia degree quantitatively in pediatric brain MR images for planning treatment. However, image features on the pediatric brain MR images of cortical dysplasia aren't clarified. This paper proposed the estimation method using intensity profile by detecting cortical dysplasia in pediatric brain MR images. The experimental results for three subjects with cortical dysplasia showed that the proposed method detected cortical dysplasia lesions with a mean sensitivity of 90.9 %, a mean specificity of 93.1 %, and a mean efficiency of 92.0%.
Sparking Student Creativity: Examining the Relationship between Knowledge Sharing, Emotional Intelligence, Intrinsic Motivation, Self-Efficacy and Creativity

This paper examines personal factors that influence students’ creativity. We conducted a study using quantitative approaches involving 75 students from a Slovenian business school. The results show that knowledge sharing, emotional intelligence, self-efficacy, and intrinsic motivation are all positively related to creativity. We discuss the implications for theory, practice, and future research.
Quantifying spatial and seasonal variability in atmospheric ammonia with in situ and space‐based observations

Ammonia plays an important role in many biogeochemical processes, yet atmospheric mixing ratios are not well known. Recently, methods have been developed for retrieving NH3 from space‐based observations, but they have not been compared to in situ measurements. We have conducted a field campaign combining co‐located surface measurements and satellite special observations from the Tropospheric Emission Spectrometer (TES). Our study includes 25 surface monitoring sites spanning 350 km across eastern North Carolina, a region with large seasonal and spatial variability in NH3. From the TES spectra, we retrieve a NH3 representative volume mixing ratio (RVMR), and we restrict our analysis to times when the region of the atmosphere observed by TES is representative of the surface measurement. We find that the TES NH3 RVMR qualitatively captures the seasonal and spatial variability found in eastern North Carolina. Both surface measurements and TES NH3 show a strong correspondence with the number of livestock facilities within 10 km of the observation. Furthermore, we find that TES NH3 RVMR captures the month‐to‐month variability present in the surface observations. The high correspondence with in situ measurements and vast spatial coverage make TES NH3 RVMR a valuable tool for understanding regional and global NH3 fluxes.
Evaluating Deadlock Detection Methods for Concurrent Software

Static analysis of concurrent programs has been hindered by the well-known state explosion problem. Although many different techniques have been proposed to combat this state explosion, there is little empirical data comparing the performance of the methods. This information is essential for assessing the practical value of a technique and for choosing the best method for a particular problem. In this paper, we carry out an evaluation of three techniques for combating the state explosion problem in deadlock detection: reachability searching with a partial-order state-space reduction, symbolic model checking and inequality-necessary conditions. We justify the method used for the comparison, and carefully analyze several sources of potential bias. The results of our evaluation provide valuable data on the kinds of programs to which each technique might best be applied. Furthermore, we believe that the methodological issues we discuss are of general significance in comparison of analysis techniques.
CHeM: A System for the Automatic Analysis of e-mails in the Restoration and Conservation Domain

In this paper, we present the CHeM system, Cultural Heritage e-mail Manager, a support system for the analysis of e-mails of the Restoration and Conservation newsgroup, hosted by the Yahoo portal from December 2000 to January 2003. The complexity of the domain as well as the specificity of the e-mails, prompted us to build the first system prototype based on a client-side architecture, to help less expert users in classifying information contained in e-mails. The system goal is therefore to provide an instrument capable of classifying the received messages, downloaded onto the users’ desktops, into standard categories, based on their content, using the well-known techniques of Data Mining and Information Retrieval. The categories thus obtained are then used to label the messages in order to provide valuable information on the domain and therefore support specific information retrieval and produce new user groups by an automatic generation of mailing lists. The methodology presented and the first test results are encouraging with a view to porting the system in other similar domains.
Morphology Based Automatic Disease Analysis through Evaluation of Red Blood Cells

Cell morphology has been an active area in the field of bio-medical research. When applied for blood microscopic images, one can study blood cell characteristics and detect abnormalities. In this paper, we introduce an automatic, cost effective and accurate way of red blood cell analysis and evaluation through Blob detection, Morphology operations and Hough circle transformation techniques for identification of four common types of anemia. Our research has filled the gaps in the existing literature by developing an integrated system to Count RBC, Diagnose Elliptocytes, Microcytic, Macrocyte and Spherocytes Anemia, Detect abnormalities and Separate overlapped cells, automatically, accurately and efficiently. The result shows an insight in the manually processed results with 99.545% accuracy of RBC count. Each sub method is closely running in the range 91%-97% of accuracy. The achievements are highlighted as efficiency through automation, cost effective, elimination of human error and easy to manipulate.
Revaluating aging in place: from traditional definitions to the continuum of care

Purpose Traditional definitions of aging in place often define aging in place specifically as the ability to remain in one’s own home or community setting in later life. The purpose of this paper is to reframe aging in place and show how narrowly defined aging in place models can be potentially negative constructs that limit options for older adults. The authors propose a paradigm shift, or a re-framing of, the popularized idea of aging in place. The authors challenge mainstream and literature-based beliefs that are deeply rooted to the idea that aging in place ideally happens in the home in which a person has lived for many years. Design/methodology/approach The paper reviews common concepts and constructs associated with aging in place as well as gaps or exclusions, and US-based aging in place policy initiatives favoring the aging in place model. Findings An expanded definition of aging in place embraces heterogeneity in residence types and living options. A realistic assessment of person-environment fit, matching an older person’s capabilities to his or her environmental demands, allows for the development of additional aging in place options for those living across the continuum of care. Social implications Aging in place should be moved from the personal “success” or “failure” of an older individual to include the role of society and societal views and policies in facilitating or hindering aging in place options. The authors demonstrate that these options, in facilities within the continuum of care, can be thought of as appealing for older persons of all levels of physical and cognitive functioning. Originality/value Research about aging in place tends to stress the value of one place (one’s home) over other living settings. This limits the ability of people to age in place and curtails discussion of all the items necessary to maintain place.
Development of a Fetal Weight Chart Using Serial Trans-Abdominal Ultrasound in an East African Population: A Longitudinal Observational Study

Objective To produce a fetal weight chart representative of a Tanzanian population, and compare it to weight charts from Sub-Saharan Africa and the developed world. Methods A longitudinal observational study in Northeastern Tanzania. Pregnant women were followed throughout pregnancy with serial trans-abdominal ultrasound. All pregnancies with pathology were excluded and a chart representing the optimal growth potential was developed using fetal weights and birth weights. The weight chart was compared to a chart from Congo, a chart representing a white population, and a chart representing a white population but adapted to the study population. The prevalence of SGA was assessed using all four charts. Results A total of 2193 weight measurements from 583 fetuses/newborns were included in the fetal weight chart. Our chart had lower percentiles than all the other charts. Most importantly, in the end of pregnancy, the 10th percentiles deviated substantially causing an overestimation of the true prevalence of SGA newborns if our chart had not been used. Conclusions We developed a weight chart representative for a Tanzanian population and provide evidence for the necessity of developing regional specific weight charts for correct identification of SGA. Our weight chart is an important tool that can be used for clinical risk assessments of newborns and for evaluating the effect of intrauterine exposures on fetal and newborn weight.
Universities Conducting STEM Outreach: A Conceptual Framework.

This paper addresses the positioning of science, technology, engineering and mathematics (STEM) outreach programmes within universities’ operations. Though universities in many respects form a rather homogenous international community, there is wide diversity in regard to the provision of STEM outreach by different institutions. To explain this diversity, a conceptual framework was developed by using the Theory of Legitimacy as an organising concept. The framework considers two opposing scenarios that form a continuum between them, namely: ‘top-down’ initiative by the university governance versus ‘bottom-up’ grass-roots initiative. Using the Theory of Legitimacy, internal and external outreach relationships are characterised and explained under the two scenarios. The framework was applied to two settings chosen due to their essential differences in outreach provision, namely, Australia and Israel. The differences that were found in internal and external legitimacy provide a useful lens for examining the low-performing, fragile system in Australia as compared with the high-performing, thriving system in Israel.
Semi-Dense 3D Semantic Mapping from Monocular SLAM

The bundle of geometry and appearance in computer vision has proven to be a promising solution for robots across a wide variety of applications. Stereo cameras and RGB-D sensors are widely used to realise fast 3D reconstruction and trajectory tracking in a dense way. However, they lack flexibility of seamless switch between different scaled environments, i.e., indoor and outdoor scenes. In addition, semantic information are still hard to acquire in a 3D mapping. We address this challenge by combining the state-of-art deep learning method and semi-dense Simultaneous Localisation and Mapping (SLAM) based on video stream from a monocular camera. In our approach, 2D semantic information are transferred to 3D mapping via correspondence between connective Keyframes with spatial consistency. There is no need to obtain a semantic segmentation for each frame in a sequence, so that it could achieve a reasonable computation time. We evaluate our method on indoor/outdoor datasets and lead to an improvement in the 2D semantic labelling over baseline single frame predictions.
Energy Use Patterns and Firm Performance: Evidence from Indian Industries

This paper is an attempt to understand the relationship between firm performances based on energy use patterns of Indian manufacturing industries. Determinates of firm performances are estimated for the full sample and for the sample of firms using similar energy sources. Econometric analysis of the data collected from the CMIE PROWESS at firm level from 2005-2013 reveals that the determinants of profitability vary across groups. Energy intensity is positively related to profitability for three models except for the firms using natural gas as primary source of energy. R and D intensity is positively related to profitability for the full sample and for the firms using petroleum. For the firms using coal as primary source of energy, less R and D intensive firms are found to be profitable. For all the cases, firm size is found to be nonlinearly related to profitability. In the policy front, shifting primary energy source from coal and petroleum to natural gas; firms can become energy efficient and profitable.
Twitter Relevance Filtering via Joint Bayes Classifiers from User Clustering

The task of classifying feed item data, such as email or RSS items, has been a subject of proposed learning algorithms since the mid 1990s. Unlike spam classifiers, relevance classification cannot use a universal training set, but rather must be trained by each individual user, however most relevance classifiers use an approach similar to spam classifiers. Most research in relevance classification has employed a classifier over an individualized training set, usually one of a näıve Bayesian classifier, support vector machines, or neural-network, case-based, or knowledge-based approaches [2] [3]. However, in practice, few of these algorithms have been implemented, and those that have enjoy only limited success. In large part, this is due to common user expectations with regard to classification accuracy. Users tolerate few errors and expect immediate results [1]. The past few years have seen a radical shift in the way users consume data. In particular, services like Twitter have dramatically increased social feed consumption. This poses the opportunity to build implicit social graphs to improve the quality of relevance classification over smaller training sets. In this paper, we examine the use of Hierarchical Clustering on Twitter users to build these implicit social graphs and to then use the multinomial Bayesian classification approach over the augmented training sets to do Tweet relevance classification. In this way, we leverage the power of social feeds to improve the quality and training speed of individualized relevance classifiers.
Fine-grained application-specific instruction set processor design for the K-best list sphere detector algorithm

Very high spectral efficiency and data rates are among the goals of future wireless communication systems. A strong candidate for meeting the requirements is the use of multiple antennas at both the transmitter and the receiver, known as multiple-input multiple-output (MIMO) communications. Sphere detectors have been proposed to be used in MIMO reception to achieve or approximate the optimal maximum likelihood detection with reduced computational complexity. Furthermore, list sphere detectors (LSDs) can be used to approximate the maximum a posteriori detection in channel coded systems. The K-best LSD is a particularly interesting LSD variant with predetermined computational complexity and fixed throughput. In this paper, an application-specific instruction set processor is designed for the K-best LSD using transport triggered architecture. 2 × 2 64-level quadrature amplitude modulation transmission scheme with 16-bit arithmetic and a list size of 16 is used as a baseline design target. List size and word length simulations are presented to justify the choices. The designed processor has a significant amount of general-purpose properties, and it reaches a detection throughput of 7.6 Mbps with a hardware complexity of only 25 000 gate equivalents.